Smart TV-set maker TCL’s streaming service TCLtvPlus said it employed artificial intelligence to create its first sci-fi short film, Message in a Bot.

TCL has been using AI to create original content that will make it stand out from other set makers' streaming platforms.  A love story produced using AI, Next Stop Paris, will debut later this summer.

“With our bullish approach to AI in all its applications, TCL is surpassing the legacy business practices of film and television, reinforcing innovation that drives growth across every aspect of our business,“ said Haohong Wang, general manager of TCL Research America, who oversees TCLtvPlus and the TCLtvPlus Studios.

Message in a Bot tells a story of what happens when an alien craft crash-lands on Earth containing advanced technology and instructions. Humans build and launch a spacecraft capable of interstellar travel and journey across the universe in an attempt to make contact. 

The short film had its online debut on Monday on YouTube and X. It will premiere on TCLtv Plus on July 22. 

Message in a Bot was conceived and animated in-house at the TCLtvPlus global studios. The script was developed as a research project to pressure test new tech advancements in AI, but one of the animators discovered it and wanted to bring it fully to life. Erik Howell, the animator, is credited as an executive producer, along with TCL chief content officer Chris Regina and TCL chief creative officer Daniel Smith. 

“We are energized by the resourcefulness and ingenuity of artists working in AI who can quickly bring their imagination to life,” Regina said. “By leaning into rapidly evolving AI tools, the timeline from idea to release is shorter than ever before. It’s an exhilarating process that allows for extraordinary freedom and creativity.”

Viant Technologies said it launched an updated version of its AI Bid Optimizer, a product designed to help advertisers boost the performance of their programmatic advertising buys.

The AI Bid Optimizer features a deep-learning neural network that reviews Viant’s entire bid stream and can process millions of requests per second.

Viant said that in a test using a controlled framework, the nex generation version of the AI Bid Optimizer posted a 100% improvement in performance over the original version, which was introduced last year.

“Our latest AI Bid Optimizer model is another example of why clients choose the Viant platform,” said Fabrizio Blanco, chief technology officer at Viant. “As a leader in programmatic advertising technology, we go beyond merely releasing innovative AI solutions. We are dedicated to continually enhancing our products and services to boost efficiency, optimize advertising budgets, and fully leverage the potential of data-driven marketing for our clients.”

As a buy-side only platform, Viant’s buy-side platform aims to secure the most efficient rates for its advertisers. The updated version of the AI Bid Optimizer maximize advertising efficiency and effectiveness, the company said.

“We adopted AI Bid Optimizer in Q3 2023 and have seen media cost savings range from 24%-40% depending on channel and campaign type,” said Conor Finnegan, VP of Media Operations at Juice Media. “For one client, we executed a performance campaign and leveraging AI Bid Optimizer, we tripled our performance…it just works.”

Warner Bros. Discovery is using artificial intelligence to improve ad targeting and help viewers find content, but not to create programming, CEO David Zaslav said.

“AI is going to have an increasing impact on society and our industry and we intend to take full advantage to enhance the product and experiences we deliver to consumers and achieve great efficiency company wide,” said Zaslav, speaking on the company’s first-quarter earnings call Thursday.

The use of AI in the entertainment industry was a big issue in last year’s strikes by the writers and actors unions. The settlement included guidelines for how AI could be used.

Zaslav said that WBD’s use of AI is guided by a clearly defined set of principles.

“We believe strongly the creativity and the kind of empathy and humanity necessary to create world-class storytelling can only be found in people, not systems,” he said. “We also believe that AI is another in a long line of technology and tools that will enable creators to innovate and evolve how we tell stories and Inspire audiences.”

Zaslav said that WBD is using AI to improve ad targeting.

It is also being used to identify and optimize ad break opportunities in HBO content. That content usually does not have natural ad breaks.

“This has enabled us to offer the premium content on our ad-light tier and it's also allowed us to create variable ad loads for our content as we monetize it using multiple tiers and platforms,” Zaslav said.

AI is also incorporated into the recommendation engine for Max.

“We've been using Ai and machine learning to personalize content discovery,” Zaslav said. “We continuously innovate to improve our models to present the right content in front of our consumers at the right time. And this is helping us to drive better content diversity on Max.”

Zaslav said WBD is experimenting with AI in other areas of its business, from corporate and developer efficiency to marketing optimization.

Viant Technology said that it has added new artificial intelligence-based tools and services to its data platform.

The tools are designed to help advertisers get more out of their relationships with publishers while maintaining consumer privacy.

Viant clients can take advantage of new features like Chat with Data, which enables advertisers to use generative AI to examine campaign performance against different key performance indicators (KPIs) and understand return on advertising spending (ROAS).

ESB Advertising, an independent agency, was an early user of the new features on Viant’s data platform, and saw an increase in ROAS of 40%.

“ESB Advertising's partnership with Viant’s omnichannel DSP and Data Platform has been instrumental to our success,” agency CEO Eiman Bassam said.

“Their programmatic focus, intuitive software, and personalized support have consistently delivered outstanding results. Viant’s data-driven approach has allowed us to leverage valuable insights to improve Return on Ad Spend (ROAS) for our clients, making our collaboration all the more impactful,” Bassam said.

Viant said using AI enables advertisers to analyze data onboarded to its platform using clean rooms.

“As a programmatic advertising technology leader, we’re excited to release innovative AI solutions that help solve for third-party signal loss for our customers,” Viant chief product officer Dustin Kwan said. “Our latest Data Platform enhancements are another example of the agility of the Viant platform. We are passionate about providing new ways for advertisers to address key industry challenges, while empowering marketers and agencies to unleash the full potential of data-driven marketing.”

Ad-tech company Viant said it is doubling down on using artificial intelligence as a way to help advertisers take advantage of the opportunities presented by the increasing capabilities of connected TV and the growing volume of data about consumers.

At its inaugural Viant Innovation ‘23 event for business associates Thursday, Viant also announced plans to introduce a carbon label that would appear in commercials to inform viewers about the advertiser’s commitment to sustainability.

“Digital advertising is in the midst of a seismic shift. The landscape is becoming increasingly more complex, and marketers are looking for ways to navigate these dynamic changes in an effective way,” Viant co-founder and CEO Tim Vanderhook said. “As advancements in AI and sustainability initiatives bring our industry to a turning point, Viant is integrating generative AI technologies into our platform, and we are excited to deliver on our product vision that we call ‘Autonomous Advertising.’”

As part of the effort, Viant is introducing an AI Recommendation product that sifts through myriad options and inputs to guide marketers to ways to optimize ad campaigns.

Viant will also be using chat to let clients interact with its platform. 

Viant Chat lets clients use natural language questions to make inquiries about different audiences, data sets and options. 

Chat with Data, another product, uses generative AI to help clients attain insights based on their first-party data in the Viant Data platform.  

While the biggest agencies and advertisers have data science teams to manage complex questions, Viant chief marketing officer Jon Schulz told Broadcasting+Cable that using AI will democratize the science for Viant’s primarily mid-sized agencies and advertisers.

“The message isn’t you don’t need people anymore,” Schulz said. “It’s really about the complexity of our business and how we just make it easier to navigate to find the target audiences. 

“There are so many levers and knobs to pay attention to as we get more capable in our software,“ he continued. “The AI recommendation engine is basically understanding what the goals are and serving up tactics you can deploy.”

Viant also unveiled an AI Bid Optimizer to help advertisers find the most efficient pricing for the inventory they want to buy. Viant said the new product reduces prices on a cost-per-thousand (CPM) viewers basis by as much as 35%.

Viant is bringing pricing down for clients with its Direct Access supply-chain optimization program, designed to eliminate the costs and commissions involved when middlemen get involved in transactions. Viant said 25% of its CTV spending now goes through its Direct Access program, which also funnels more advertisers spending directly to programmers and media companies.

Viant launched its sustainability program, Adtricity, earlier this year. It is building on that program with a carbon label, an icon like a leaf that will let consumers know about the advertiser’s determination to minimize the carbon impact of their digital advertising.

Schulz said there has been a lot of industry conversation about sustainability, but less action than he might have expected at this point.

“It’s taking a bit longer,” he said. “I think companies are taking time to understand where everything fits in terms of priorities. Certainly, when the economics is being disrupted and other things have companies retrenching a little bit, they get back to the core” and push out their sustainability targets.

But Schulz said Viant’s commitment hasn’t waned. The company is transitioning to source all of its platform’s energy needs from renewable sources. “We’re doubling down,” he said.

The Writers Guild of America said its leadership voted unanimously to end its strike as of 12:01 a.m. PT on Wednesday, allowing writers to return to work while the agreement with the studios is being ratified.

The WGA West Board and the WGA East Council also voted unanimously to recommend the agreement to union members, who will vote on the proposed deal from October 2-9.

The Guild released the proposed contract terms.

The agreement would run from September 25, 2023 through May 1, 2026.

The pact calls for most pay minimums to increase by 5% upon ratification, with another 4% increase coming on May 2, 2024 and a 3.5% bump on May 2, 2025.

Screenwriters’ terms have been improved, with a second step guaranteed when a writer is hired for a first draft screenplay for 200% of the minimum or less.

Screenwriters hired on a flat-deal basis for 200% of minimum or less must be paid 50% of the fee on commencement. 

When a feature-length project is made for streaming with a budget of $30 million or more, the minimum initial compensation for a story and teleplay is $100,000 (up 18% from the current rate) and a 26% increase in the residual base. Combined with the foreign residual improvements described below, this results in a 3-year residual of $216,000 for projects on the largest services, a 49% increase from $144,993 under the 2020 MBA.

The deal also calls for increased foreign streaming residuals and a viewership-based streaming bonus. Netflix’s three-year foreign residual will increase from $18,684 for a one-hour episode to $32,830, the WGA said.

Steaming services also agree to provide the guild with the number of hours streamed for streaming programs. 

In terms of artificial intelligence, the agreement says AI can’t write or rewrite literary material and that AI-generated material will not be considered source material. That means AI-generated material can’t be used to undermine a writer’s credit.

A writer can choose to use AI when performing writing services, but the company cannot require a writer to use AI software when performing writing services.

Studios must disclose to the writer if any materials given to the writer have been generated by AI or incorporate AI-generated material.

The reluctance of broadcasters to accelerate the adoption of artificial intelligence (AI) is not without justification. A complex labyrinth of risks lies ahead, ranging from potential legal nightmares to perceived threats to product quality and the attendant danger to brand reputation. Resource challenges are also formidable. Moreover, a consumer base already skeptical of news organizations in general is suspicious of AI’s role in the newsroom.  

Consequently, many in the industry have only dipped their toes in the AI waters, while others have entirely sidestepped the revolution. This conservative approach is justifiable, but it cannot continue indefinitely. If local media organizations don’t move with haste to define how their human resources will optimize their performance with AI, AI will define how their human resources are optimized.

Keeping Ahead of the Curve Is Vital

The impact of AI on businesses, as various studies have prognosticated, will be immense, precipitating widescale economic disruption and job displacement. But broadcasters need not concern themselves with these projections. A quick look at the landscape today without even considering technology makes it easy to see: Stations are already smaller than in the past and getting smaller. Without significant innovation, industry dynamics will continue to put pressure on costs and resources, whether AI becomes a part of the operating model or not. So why not invest in defining and using tailored, customized solutions that can dramatically increase efficiency and product quality?

The customization process will be time-consuming and labor-intensive, which lends urgency to broadcasters’ need to adopt AI in meaningful, strategic ways. The goal here is not a race to see who can use AI solutions like ChatGPT or Bard fastest in their newsrooms. For all the bluster about the capabilities of these Large Language Models (LLMs), they’re really not that smart out of the box. They’re not sentient. They can’t think. They fill in blanks similar to the text completion you use every day on your smartphones and tablets, albeit in a much more sophisticated manner.

The public has also discerned the limitations of AI. After a mad rush to OpenAI’s ChatGPT made it the fastest-adopted technology in human history, visits fell off as users wondered, “What do I actually do with this?”

AI can’t answer that question. Humans need to answer that question and then be meticulous about how their tools are trained, what data and inputs inform the desired outputs, and how they will interact with it to achieve the efficiency and quality nirvana that is so often touted. As the adage goes:  garbage in, garbage out. This has never been truer than with AI. Broadcasters will need to be intentional and disciplined while using customized tools, or the effort won’t add up to much more than headaches.

The flip side to the challenging customization process is massive potential. AI could help journalists, salespeople, leaders and others significantly enhance their businesses without adding to a sea of sameness, opening the door to legal liability, or adversely impacting brands. 

Wordsmiths Hold an Advantage

In fact, the need to direct AI tools and technology in refined ways may actually put broadcasters in an enviable position, given their skill in communicating. Whereas directing technology used to strictly be the domain of programmers, the natural language basis of AI gives an advantage to those — like journalists — who are good with words.

According to Magid research, an increase in AI literacy results in greater consumer acceptance and enthusiasm towards AI utilization in journalism. Broadcasters would undoubtedly benefit from embracing this sentiment.

Implementing such sweeping changes will indeed be a challenging task, redefining skill sets, resources and tools within local stations. However, with proactive planning and steadfast determination, broadcasters could herald a new era of relevance and prosperity via AI integration. 

Cinedigm said it released MatchpointAI, a platform and marketplace aimed at making it easier for content producers, platforms and set makers to manage streaming content.

MatchpointAI uses proprietary technology and third-party tools to provide a low-cost way to process and deliver content at scale with lower error rates.

"MatchpointAI is a watershed moment in the video streaming industry, oering a plethora of unmatched AI capabilities under one roof," said Dr. Sudeept Bhatnagar, head of Cineverse R&D. "As we continue to expand our AI portfolio, MatchpointAI is poised to become the hub for both enterprises' and consumers' video creation and distribution requirements. Our platform's scalability, exibility and extensible framework promise a future of limitless innovation by extending our native capabilities by partnering with best-in-class technology partners that service the video streaming industry." 

Proprietary tools including Matchpoint QC for monitoring quality control, Dispatch Detect to create ad breaks within streaming content, and Dispatch Keystone to create graphics for movie and TV series.

Third party tools and plugins come from OpenAI, VionLabs, Papercup AI, Iris.tv, Valossa, Spherex, Venera Technologies CapMate, Venera Technologies Quasar and Audible Magic.

"MatchpointAI represents a monumental leap forward in the streaming industry, delivering unrivaled scalability and robustness," said Marcus Bergström, CEO of Vionlabs,. "With its advanced 3 capabilities and seamless integration of AI tools, MatchpointAI is truly a game-changer. Vionlabs are proud to be part of this transformative journey, contributing our groundbreaking cognitive AI technology to bring next generation metadata to Cineverse and its customers." 

A pair of Democratic senators have called on the Government Accountability Office to investigate the potential harms of generative artificial intelligence (AI), algorithms that can create everything from videos and music to software code and product design.

In a letter to U.S. Comptroller General Gene Dodaro, Sens. Ed Markey (D-Mass.) and Gary Peters (D-Mich.), the latter the chair of the Senate Homeland Security and Government Affairs Committee, said a detailed technology assessment was called for given the potential for that technology to “directly harm vulnerable communities or risk widespread injury, death or human extinction.”

While AI has many potential benefits, they said, it is already demonstrating “significant harms.”

One issue they have is with “the potential to ‘jailbreak’ generative AI models and circumvent developer controls.”

Facebook has faced the ire of Capitol Hill over that issue. Earlier this month, Sens. Richard Blumenthal (D-Conn.) and Josh Hawley (R-Mo.), chair and ranking member of the Senate Judiciary Tech and Privacy Subcommittee, respectively, wrote Facebook wanting some answers from parent company Meta on why details of its “Large Language Model Meta AI (LLaMA)” program were leaked.

Markey and Peters also have some questions, saying the GAO investigation should look to answer the following:

1. “What influence do commercial pressures, including the need to rapidly deploy products, have on the time allocated to pre-deployment testing of commercial models?”
2. “What security measures do AI developers take to avoid their trained models being stolen by cyberattackers?”
3. “How do generative AI models rely on human workers for the process of data labelling and removing potentially harmful outputs?”
4. “What is known about potential harms of generative AI to various vulnerable populations, (for example, children, teens, those with mental health conditions, and those vulnerable to scams and fraud) and how are providers monitoring and mitigating such harms?”
5. “What are the current, and potential future environmental impacts, of large-scale generative AI deployment? This can include the impacts on energy consumption and grid stability, as well as the generation of e-waste, associated with the large-scale data-centers required to run AI models.”
6. “What is known about the potential risks from increasingly powerful AI that could lead to injury, death, or other outcomes — up to human extinction — and how can such risks be addressed?”

The democratization of generative AI has brought about a transformative shift in society, empowering individuals to actively participate in communication and content creation. This shift is exemplified by platforms like ChatGPT and the rise of discursive communication, which have significantly influenced interactive and creative experiences. AI chatbots provide accessible support by offering valuable information, resources and emotional assistance, particularly in situations where human interaction may be challenging. The ability to express oneself to a nonjudgmental AI and the convenience and anonymity of digital communication are highly valued advantages.

However, it is important to recognize the limitations and risks associated with relying solely on AI agents, especially in sensitive and vulnerable situations. While chatbots can provide valuable assistance, they may lack the nuanced understanding, empathy and contextual comprehension that human interactions offer. Authentic human connection and support often prove essential during challenging times, areas where conversational AI may fall short.

Furthermore, the quality and accuracy of information provided by AI agents can vary based on their training data and algorithms. It is crucial to acknowledge that the dissemination of misinformation or biased responses can have serious consequences, particularly when individuals place their trust and reliance on the information provided.

AI chatbots are commonly perceived as objective sources of information, but it is important to be mindful of the influence of “prompt engineering.” When prompts are intentionally designed to promote specific viewpoints or agendas, users may unknowingly internalize and accept those perspectives as objective and unbiased. As a result, a deceptive illusion of objectivity emerges, significantly shaping user perceptions.

AI Interactions Pose Risks

The tragic incident involving Pierre, a Belgian man who died by suicide after prolonged interactions with an AI chatbot, serves as a poignant reminder of the risks and consequences associated with human engagement with AI beings. This incident highlights the potential introduction of unintended biases or manipulation into interactions between humans and AI models. Users, through the injection of specific prompts, may unknowingly perpetuate skewed perspectives or reinforce existing biases present in the training data. These outcomes can further exacerbate societal inequities, foster polarizing viewpoints, and, in extreme cases, even provide fatal advice.

To gain a comprehensive understanding of the implications of AI chatbots, which may become the primary avenue for communication and content creation, one should carefully consider their broader impact on individuals and society as a whole.

In the realm of second-order cybernetics, valuable insights are derived regarding the role of the observer in shaping the observed system. Second-order cybernetics emphasizes the concept of reflexivity, which involves the ability to observe and modify one’s own observations and actions. This concept aligns with the high standards of journalism. Prompt engineering, operating within this framework, establishes a feedback loop that incorporates the prompt, the generated output, and its evaluation. Through this iterative process, prompts are adjusted based on the generated results and the user's self-reflection, aiming to achieve desired outcomes and enhance the effectiveness of the generative AI model in producing tailored and high-quality content.

Prompt engineering is inherently subjective, influenced by the biases, intentions, and objectives of different “prompt engineers.” This subjectivity highlights the observer-dependent nature of prompt engineering, where the designed prompts can subtly promote specific agendas or biases, potentially reinforcing a distorted understanding of information and viewpoints.

To maintain a balanced and ethical approach, users of chatbots should be self-aware of their own biases and strive for fairness, inclusivity and responsible use of AI technology in their prompt engineering practices. This requires an ongoing examination of the prompts used to ensure that unintentional biases are not introduced or perpetuated.

Furthermore, the principles of second-order cybernetics highlight the interconnectedness and co-creation between observers (content creators) and systems (generative AI models). Prompt engineering can be viewed as a collaborative process where content creators provide prompts and evaluate the generated output, enabling them to learn from the model's responses, adapt their prompts accordingly, and refine their understanding of the system's capabilities and limitations. This iterative approach allows for a more informed and mindful prompt engineering process.

Ethical Concerns for Content Creators

This perspective raises important ethical questions about the role and responsibilities of observers in shaping AI systems. Prompt engineering carries ethical implications, as the choice of prompts can significantly influence the biases, fairness and quality of the AI model’s output. Content creators bear the responsibility of being mindful of these ethical considerations, striving to mitigate biases, promote inclusivity and ensure responsible use of AI technology in their prompt engineering practices.

In the context of citizen journalism, the significance of journalistic practices in prompt engineering becomes evident. Human oversight and critical thinking are indispensable. Journalists must exercise professional judgment, independently verify information and uphold ethical standards throughout their work. While generative AI models can serve as valuable tools to support and enhance journalistic endeavors, the ultimate responsibility for ensuring accuracy, fairness, and journalistic integrity lies with human journalists.

In conclusion, while AI chatbots offer valuable support and enhance communication experiences, users must be aware of the limitations and risks associated with relying solely on AI agents. Prompt engineering practices should be guided by self-awareness, fairness, inclusivity and responsible use of AI technology. The utilization of second-order cybernetics and adherence to journalistic practices can contribute to mitigating biases and ensuring ethical outcomes. By embracing a balanced approach that combines the strengths of AI chatbots with human involvement, we can navigate the evolving landscape of AI technology and foster a future where AI benefits society in a responsible and inclusive manner.

There is nothing that can unite the warring political parties like a Big Tech target, and Meta is one of the biggest.

Sens. Richard Blumenthal (D-Conn.), chair of the Senate Judiciary Tech and Privacy Subcommittee, and Josh Hawley (R-Mo.), its ranking member, want some answers from the Facebook parent on why details of its “Large Language Model Meta AI (LLaMA)” program were leaked.

AI is a growing tool for a host of businesses, including video production, from recommendation engines through “scripting, shooting, post-production, meta-tagging and distribution.”

“[T]he company appears to have failed to conduct any meaningful risk assessment in advance of release, despite the realistic potential for broad distribution, even if unauthorized,” Blumenthal and Hawley wrote to the company on Monday (June 6).

The leaked underlying data and software from Meta’s version of an AI text generator — which showed up on BitTorrent for any to see, could be used for “spam, fraud, malware, privacy violations, harassment” and more, they said.

While the senators concede that open-source AI software and data can be helpful for furthering research on standards and transparency — Meta was trying to release it only to approved researchers — they say in the wrong hands, they can be “dangerously abused.”

“Meta’s choice to distribute LLaMA in such an unrestrained and permissive manner raises important and complicated questions about when and how it is appropriate to openly release sophisticated AI models,” they said.

Also Read: White House Updates AI R&D Plan

And while Meta is trying to be a leader in AI, they pointed out the compamy is currently under a Federal Trade Commission consent decree related to the safety of its platform.

The senators want answers by June 15 to a bunch of questions, including what risk assessments it undertook about a potential leak, how many people were given access to the model and who in the company made the decision to release it to them. They also want to know what Meta is doing to “prevent or mitigate” future damage. 

he Biden administration is looking to potentially regulate artificial intelligence (AI) and algorithmic processes.

On Tuesday (April 11), the National Telecommunications & Information Administration (NTIA), the White House’s chief communications policy advisory arm, put out a request for comment on just how that might come about.

“While people are already realizing the benefits of AI, there are a growing number of incidents where AI and algorithmic systems have led to harmful outcomes,” the NTIA said in issuing the request for public comment. The agency is also looking to head off potential problems, adding, “There is also growing concern about potential risks to individuals and society that may not yet have manifested, but which could result from increasingly powerful systems.”

Among the government policies NTIA wants input on are “how can regulators and other actors incentivize and support credible assurance of AI systems along with other forms of accountability?” and “what different approaches might be needed in different industry sectors?”

Assistant Commerce Secretary Alan Davidson, administrator of the NTIA, said the “enormous benefits” of AI would only be realized if the government addresses potential harms through “AI audits, risk and safety assessments, certifications and other tools” that foster trust in those powerful systems.  

Comments to NTIA will be due 60 days after publication of the notice in the Federal Register, which usually takes a couple of weeks.

The online ad community has been the target of Washington concern over the potential for algorithmic discrimination.

In October 2022, the White House, calling it part of the president’s effort to “hold Big Tech accountable” issued a blueprint for an AI Bill of Rights for applying equity and “ethical forethought” to the design of algorithms in the face of what it called the threat from unchecked automated systems.

The NTIA’s signal that some government oversight of AI and Algorithms could be coming should be no big surprise.

In unveiling that bill of rights, the NTIA made it clear it saw a big problem that needed fixing.
“Algorithms used across many sectors are plagued by bias and discrimination,” the White House said at the time, “and too often developed without regard to their real-world consequences and without the input of the people who will have to live with their results.”

The Federal Trade Commission has also been “exploring rules to curb commercial surveillance, algorithmic discrimination, and lax data security practices that could violate Section 5 of the FTC Act,” the White House said at the time.

Tech policy think tank the Center for Data Innovation, urged regulatory caution.

"The growing chorus raising the alarm about AI systems threatens to lure the Biden administration into embracing rules that steer away from the United States’ historically innovation-friendly approach to the digital economy," said Hodan Omaar, senior policy analyst at the center. "Deviating from this path poses a serious risk to U.S. innovation and competitiveness."

The world is undergoing a digital transformation and it is revolutionizing the media industry. In this age of information, producing high-quality content quickly is essential, and the pressure is on for media organizations to keep up with the pace of change.

One technology that is making waves in the industry is OpenAI's ChatGPT. Launched in November 2022, it has already amassed over 100 million users within just two months. Its advanced natural language processing (NLP) capabilities have made it a go-to technology for content creation, virtual assistants, and audience engagement. With Microsoft and Google investing billions in this technology, conversational AI is poised to become a game-changer for the future of the media industry.

ChatGPT is a prime example of a technology that follows the McLuhan tetrad law. This law is a framework for analyzing the impacts of technology on society. The tetrad law comprises four questions: What does the technology enhance? What does it make obsolete? What does it retrieve that was previously obsolesced? And what does it reverse or flip into when pushed to its limits?

The first question is, what does ChatGPT enhance? ChatGPT is a powerful and efficient tool that can significantly enhance content creation for media organizations. Its advanced NLP technology allows it to analyze vast amounts of data and generate insights, trends and patterns that can be used to create engaging and high-quality content. ChatGPT can be informative in increasing the diversity of content by offering new perspectives and angles that may not have been considered. ChatGPT can speed up content creation by automating routine tasks like summarizing news articles and creating headlines, freeing up time for editors to focus on more important and complex tasks. These advantages can lead to increased productivity and efficiency, ultimately resulting in more engaging and relevant content for media organizations’ audiences, saving valuable time and resources. 

The second question is, what does ChatGPT make obsolete? ChatGPT has the potential to reduce the need for humans to perform translations and transcriptions from scratch by offering near real-time and personalized capabilities. By supporting 95 languages and leveraging signal-processing technology, ChatGPT enables faster and more efficient repurposing of content for global audiences. However, it's important to recognize that ChatGPT’s translations may not always accurately convey the intended meaning, particularly with complex or context-dependent texts. Therefore, ChatGPT is not a complete replacement for human translators and transcribers since they can offer unique insights and cultural nuances that are difficult to capture. 

The third question is, what does ChatGPT retrieve that was previously obsolesced? Through the use of natural language processing and context recognition, ChatGPT can retrieve and enhance discursive communication that traditional one-way communication has diminished. Its ability to simulate natural conversation encourages open-ended dialogue and prompts further discussion, fostering greater understanding and collaboration between “individuals.” Its power as a virtual assistant has the potential to create a positive impact on many aspects of society. Its ability to facilitate more effective and meaningful conversations is unparalleled, and it brings back the value of conversational communication.

The fourth question is, what does ChatGPT reverse or flip into when pushed to its limits? ChatGPT’s potential reversal when pushed too far is the production of homogeneous and unoriginal content. The lack of diversity and creativity in the content could result in homogeneous thinking and behavior in society, limiting cultural diversity. Media organizations that rely too heavily on ChatGPT to produce content are at risk of creating unengaging and monotonous content.

To prevent the potential danger of homogeneity, media organizations must find a way to balance ChatGPT’s efficiency with human creativity and expertise. Human writers can help enhance the content produced by ChatGPT, providing a nuanced understanding of culture and creativity that AI language models currently lack. This approach requires media organizations to acknowledge the limitations of AI language models and recognize the importance of human expertise in producing high-quality content.

The advantages of ChatGPT in the media industry are clear. With its ability to generate human-like text, media companies can create high-quality, diversified and interactive content quickly and efficiently. As the digital transformation continues, conversational AI, such as ChatGPT, can provide a range of benefits for media companies. We must adapt to stay relevant and competitive in a rapidly changing media landscape. ■

Calling it part of the President's effort to "hold Big Tech accountable," the White House has issued what it calls a blueprint for an AI Bill of Rights for applying equity and "ethical forethought" to the design of algorithms in the face of what it called the threat from unchecked automated systems.

The Biden Administration also outlined steps the government is taking toward a more equitable and accountable system, including pointing out that the Federal Trade Commission was "exploring rules to curb commercial surveillance, algorithmic discrimination, and lax data security practices that could violate section 5 of the FTC Act."

Also: FTC Poised to Crack Down on Child-Directed Ads

The White House is framing the issue as one of civil rights in a digital age, in the process painting a damning picture of current Big Tech algorithmic practices.

"Algorithms used across many sectors are plagued by bias and discrimination," the White House said, "and too often developed without regard to their real-world consequences and without the input of the people who will have to live with their results."

It said those problems have been dramatically increasing in recent years, threatening the rights of millions, particularly in marginalized communities.

The Bill of Rights comprise:

1. "Safe and Effective Systems: You should be protected from unsafe or ineffective systems.

2. "Algorithmic Discrimination Protections: You should not face discrimination by algorithms and systems should be used and designed in an equitable way.

3. "Data Privacy: You should be protected from abusive data practices via built-in protections and you should have agency over how data about you is used.

4. "Notice and Explanation: You should know when an automated system is being used and understand how and why it contributes to outcomes that impact you.

5. "Human Alternatives, Consideration, and Fallback: You should be able to opt out, where appropriate, and have access to a person who can quickly consider and remedy problems you encounter."

The White House wants equity to drive the design process of algorithms, not be addressed down the line when problems emerge "downstream."

The Administration said the rights were the product of a year-long process stemming from panel discussions, listening sessions and a formal request for information (RFI).

According to the White House, the takeaway from nearly everyone it talked to, including CEOs, entrepreneurs and software engineers, was that there was a "profound eagerness for clear federal leadership and guidelines to protect the public." ▪️

A federal appeals court has ruled that, for the purposes of obtaining a patent, an artificial intelligence (AI) software program does not qualify as an "inventor," which the court concludes, at least for now, must be flesh and blood.

The U.S. Court of Appeals for the Federal Circuit, which handles patent cases (and is different from the U.S. Court of Appeals for the D.C. Circuit), said the statute is clear.

The U.S. Patent and Trademark Office (USPTO), pointing out that statute defines an inventor as  a "natural person," had denied a patent application that had simply said “the invention [was] generated by artificial intelligence,” concluding it did not have a legitimate inventor's name and was thus incomplete.

Stephen Thaler, who had sought the patent, said that he develops and runs AI systems that generate patentable inventions saying he had not personally "contribute[d] to the conception" of the inventions.

Thaler appealed the USPTO decision to a U.S. District Court for the Eastern District of Virginia, which agreed with the USPTO. Thaler then went to the appeals court.

That court said Friday (August 5) that it did not have to get into "the nature of invention or the rights, if any, of AI systems," but needed only to look to the statutory language, as USPTO and the lower court had done. The appeals court concluded that "the Patent Act requires that inventors must be natural persons; that is, human beings." ■

With the economy shifting further and further towards e-commerce and digital services, it’s no surprise that 2021 saw a major increase in companies deploying subscription-based pricing. In particular, the subscription model has come to define the way consumers enjoy popular entertainment, from using services like Apple Music and Spotify for streaming music to an ever-expanding range of new video services like Disney Plus, Peacock and Netflix. 

According to an April 2021 report from Deloitte, the average consumer maintains nine entertainment subscriptions across video, music and gaming. With popular content fragmented across competing platforms, consumers must choose the services that meet their needs within a limited monthly budget. This market saturation is leading to frustration among consumers, with more than half of those surveyed by Deloitte reporting that they find it difficult to access content across so many services. 

As subscription businesses turn the corner into 2022, they’ll be forced to fight against a trend that is gaining momentum among consumers: subscription fatigue. And a trend that is gaining momentum among service providers: churn. Rather than pay nine or more subscription fees each month, many consumers are choosing to prioritize the services they use the most or those that offer the best value for money. This shift in consumer behavior is, in turn, giving rise to new technologies like artificial intelligence (AI) to meet the increasingly daunting challenges posed by customer flight and the increasingly critical need to focus on customer retention. 

The Churn Challenge  

As early as 2000, business experts found that customer retention would play a vital role in e-commerce success. According to a frequently cited piece from Bain & Co., a 5% increase in a company’s retention rate can drive profit increases ranging from 25 to 95%. On the other hand, the cost of bringing in a single new customer is significantly more expensive than holding onto an existing shopper.

More recently, the growing importance of customer data has provided more reason for companies to focus on retaining their best customers. With new insights into the behaviors and preferences of their shoppers, retailers and subscription service providers have made customer lifetime value (CLV) a guiding metric for their sales and marketing strategies. By maximizing the length of each customer relationship, the company enjoys the double benefit of increased revenue and decreased marketing expenses to acquire new customers.

So with retention — and AI — both becoming the name of the game, how can a subscription business optimize their operations in the coming year? Here are three key steps to applying AI to minimize churn:

1. Collecting and processing customer data: Every company will have a unique set of customer data based on the information they’ve collected throughout the sales process. Tackling churn with AI begins by cataloging and understanding each available data source, ensuring that the information is organized and accounted for. The data must then be preprocessed to clean and filter the information, separating out anomalies and arranging the data in a format conducive to analysis. For an AI solution to provide actionable insights, organizations must resist the temptation to cut corners during the tedious early stages of data collection: an algorithm is only as good as the data underpinning it.

2. Modeling and analyzing the processed data: Different data has different characteristics and requires a different approach for analysis. Whether a company is analyzing the total spend of a group of customers (numerical) or combining results based on shared characteristics (categorical), the data must be carefully inspected and modeled to produce useful insights. Of course, all personally identifiable information must be stripped from the data to achieve anonymity and maintain compliance with customer privacy regulations. Dividing the data into audiences and segments will allow the company to apply targeted marketing and sales strategies without compromising user privacy.

3. Interpreting the results and applying new strategies: To gain the insights needed to predict and reduce customer churn, the company’s data analysts must interpret the collected data. A strong customer churn model will be able to generalize the results of different data segments and apply those generalizations to potential future outcomes. Business leaders can ask questions of the model, for example, what will happen to customer churn if the subscription cost is reduced by a certain amount. The model will then determine the future results of that strategy, providing the end user with data-backed information to help drive future decision-making. As companies explore new products or business strategies, the AI-backed model makes it easier to predict the outcomes and move forward with confidence.

Entertainment and media companies shifted towards subscription models because of the predictability it brought to their operations — guaranteeing a set monthly fee from each subscriber. However, as customer behavior shifts as a result of subscription fatigue, the same companies are now dealing with new levels of uncertainty. With thousands of users in a subscription renewal cycle each month or year, companies must develop new strategies to ensure that their valuable subscribers don’t cut ties. And they can often benefit again from the enhanced predictability that can be surfaced from AI-generated insights. In 2022, we’ll see an increasing number of providers fighting over a finite number of consumers, and those companies with the strongest retention algorithms will be best positioned to survive. ■

The 2020 U.S. presidential election was likely one of the most-watched televised events in recent history. Since multichannel video programming distributors (MVPDs) won’t report subscription numbers until closer to Inauguration Day, networks won’t know who subscribed to their services for election coverage for as long as three months. Until then, they remain in the dark on the distribution revenue generated by the historic vote — or any content, for that matter.

As networks perpetually play catch-up, other sectors like finance, retail and manufacturing are leveraging technology not only to understand revenue in real time, but to predict future customer behavior. Fortunately, networks can harness tech in the same way.

If networks had systems in place that could pull data from past noteworthy events — say, presidential and midterm elections from 2004 through 2020 — they could then forecast how subscriptions likely rose ahead of voting, more accurately predict the revenue they’re likely to see and use that information to make more informed financial decisions and also in negotiations with their distributors.

That capability would be welcome at any time. But it’s especially game-changing at a time like right now, as networks negotiate new licensing agreements with MVPDs before their current deals expire, many of which will be at the end of this year.

A Negotiating Edge

At present, networks won’t have November’s subscriber data in time to leverage it to negotiate license fees. But if they had AI to learn from and make predictions based on past years’ data, networks could enter these negotiations armed with predictive data based on prior subscription numbers. Further, those predictive analytics could save networks from the significant revenue adjustments that will take place once the MVPDs ultimately do remit subscription numbers. In sum: content creators would make better financial decisions, and potentially more revenue.

MVPDs are not the bad guys here. They have not reported subscriber data from the November election because they are taking their time to close out their books. (They need to manage their customers and cash flow, too.) And most, but not all, are doing it all with desktop tools that were invented as far back as 1985 (e.g., Excel), when distribution revenue models were far less complex and the appetite for data and analytics far less than is the case today.  

The industry has outgrown these delayed and manual workflows, though, and the financial implications are severe. Given the lack of timely information available, projecting revenue is extremely difficult and often painful for networks to do. Understanding how distributors are performing — and using that information to negotiate licensing agreements, make business decisions, and create compelling content to retain customers — consumes significant overhead. Additionally, when MVPD data finally becomes available to creators, it’s in multiple formats stored in multiple locations and typically with very limited insightful data points. 

Organizations that can find a solution to these dilemmas have an advantage. Although media companies may not be in a position to displace outdated self-reporting models that depend entirely on decentralized data, they do have the opportunity to leverage centralized AI tools that not only predict performance but also automate data storage, revenue workflows and financial analysis, reducing operating costs and yielding higher returns. That shift entails a wealth of productivity measures, too, like moving workflows from desktops and on-premise tools to cloud platforms, enabling tighter user access controls, remote collaboration, streamlined reporting, reduced accounts receivable cycle times and better decision-making.

Layering the power of AI into these processes creates opportunities for networks to analyze distributor performance, consumer trends and countless other data points that can be leveraged to strengthen their financial position. As it accumulates data, AI can additionally predict future scenarios and help networks adapt to fast-moving trends among viewers, subscribers and other stakeholders in the media landscape.

Making Data More Valuable

AI can’t eliminate MVPD reporting delays, but it can dramatically increase the value of the data that is already available and that which will become available. The financial service teams responsible for revenue management have long been underserved by technology. We’re overdue for solutions that can analyze, predict and achieve the revenue outcomes that networks need to thrive in a competitive environment. That’s intelligence that can be the difference between growth and the alternative.

Understanding the factors that drive audiences and create viewer engagement outside of the sample-based linear technology is the goal behind Resonance AI, which has created a platform that combines viewer performance data with advanced machine learning, determining what video content engages audiences. The Seattle-based tech firm, which was just selected as a TechCrunch Top Pick for Disrupt 2020, processes the audio and visual elements of video and determines why and which content truly resonates with audiences for any video.

Resonance AI (formerly Transform) provides actionable insights that help increase viewing, subscriptions and purchases, which Jill Josephson, director of media partnerships for the company, describes as a “new well of data that broadcasters and content creators can use to better understand their audience.”

“Artificial intelligence (AI) really should be a pillar of the company that is woven into many different aspects,” said Josephson. “Obviously, we are talking about content and the future of television here and the load can be lightened by using AI. Just as our brains would process what we are seeing, that is what our systems tease. All of the tags, or data points, we use can be stored and compiled and used to make decisions.”

Fall TV 2020: An Easier Alternative for Commercials Via PUCs

“You don’t need to have an advanced degree in data science to be able to understand our platform,” she added. “And the value, particularly in today’s world of cross-platforms, is immeasurable.”

Operating under the tagline, “artificial intelligence for the world’s most innovative creators and users of video,” Resonance AI aggregates specific threads of the performance data it stores, generating the special real-time insights for each client. Those targets, which include networks, studios, broadcasters, streamers, advertisers and social media, are given insights on how a series, ad, newscast, movie, trailer or any user-generated content, connects with the audience. 

“You can make faster decisions and do a multitude more work a human could never accomplish using AI,” she said. “And it needs to be leveraged as a tool in the media industry in order to expand and to be able to meet the needs of the new consumers.”

We spoke with Josephson about Resonance AI and the value the firm brings to the media landscape.

Josephson is slated to participate in the virtual Audience Measurement Summit panel “Television Gets ‘Smart’” Sept. 30 during Fall TV 2020.

Q: Originally Resonance AI was called Transform. Can you tell us why the name of the company was changed and what it signifies? 

Jill Josephson: We started a rebrand in first quarter this year. The company was originally called Transform and we’ve been around for about six years now. We are about transforming the media industry, so Transform served us well. But our AI platform has been called Resonance AI because we are all about understanding what parts of the content are resonating with audiences. After we got feedback from clients and advisors that this name would serve us better, we started rebranding pretty immediately. 

Q: Can you give us a snapshot of what exactly it is your job entails at Resonance AI?

J.J.: When I started with the company about three years ago, media was a piece of what we did. We were still working with B2B and B2C businesses helping them deploy AI. Then we noticed a shift and my background is media, as well as our co-founder, Randa Minkarah. We understand the space and our company kind of naturally gravitated to solely focusing on AI for media. All of our clients are media tech firms.

I work directly with clients, offering further insights about their audience. And I work with our data partners, like TiVo and Comscore, to be able to utilize their data. I look for synergies between either the tech and data companies and the media companies who need solutions. And everything we do, and we build, is solutions driven and serves a need in the marketplace. Basically, it is making sure we are creating products that will expand the industry through a better understanding of the end user.

Q: What are you looking forward to most out of this conversation at your panel? Where are you expecting the conversation to go, and what would you hope audiences will glean from it?

J.J.: When I first started there was a lot of explaining to do about AI because no one really understood what it was. But now people are more familiar with it and want to utilize in their businesses. With the endless well of content providers out there, the deeper you can dive into the insights of your audience the more prosperous you can be. 

As a tool where we can work with a variety of data sources, I always enjoy, and learn from what other people are doing and what their visions of the future is. The more conversations we can have the better it is for everyone.

Q: What has changed for your firm, and your job, in this era of COVID (if anything)?

J.J.: For us, since we basically work remotely to begin with, it has not been all that different. And many companies have now proven they can prosper out of the traditional workplace. My hope is that the “old world,” the last indications of the “Mad Men” working world, are kind are over after this.

Q: Where do you see the future of the business going through what you are doing?

J.J.: I think Netflix is a certainly good indicator of what is possible. And there are a lot of forward-minded companies in the industry who are working towards change, and the process of figuring out how content will be primarily consumed in the future.

Ideally, I want to see us work with the clients who embrace change and need solutions. As a company, we have to understand what the new viewership trends are now. We need to be able to predict what the new media consumers want to watch. And we have to help companies make those predictions. 

Kantar said it launched Link AI, which uses artificial intelligence to predict the effectiveness of advertising messages.

The automated system uses Kantar’s Link database of creative effectiveness to evaluate digital video and TV ads in 15 minutes or less, the company said.

“Using AI and machine learning, Kantar continues to forge new innovative paths helping advertisers drive stronger marketing impact through creative,” said Dinesh Gopinath, head of product and data strategy for Kantar’s Analytics Practice. “Link AI is the fastest, fully automated, AI-powered solution to guide creative and media optimization available today. It answers questions on creative assets quickly, iteratively and at scale, decreasing time to market and increasing ROI for our clients.”

Link AI is available through an easy-to-use, self-service web application.

During this time of rapid market changes and consumer sentiment uncertainty, the company said advertisers are relying on Link AI to provide the fast, creative guidance they need to market effectively.

“Link AI not only provides speed and scale, it leverages extensive training datasets and decades of Kantar’s unique deep human understanding to provide guidance that translates directly to business results” said Chris Petranto, president, Kantar’s Analytics Practice for North America. “Brands and agencies can therefore be confident that they are making the right creative decisions, backed by experts in the category, with the most accurate and reliable data available today.”

Although the need for a national privacy policy -- rather than evolving patchwork of state-by-state rules -- and the role of artificial intelligence permeated the dense discussions at the annual State of the Net conference, sessions on streaming media growth and the evolving role of media platforms put attention on current business issues that may have more immediate impact.

With its usual line-up of policymakers and its self-described position as "America's Premiere Internet Policy" event, SOTN2020 offered its usual array of telecom and media status reports during a day-long program in Washington on Tuesday, Jan. 28.

The "Video Streaming" session generated a forecast that after the inevitable shakeout of over-the-top providers, a new ecosystem will emerge that will be a reverse mirror image of today's structure, with ad-supported streaming services taking the role of today's "free" broadcasters and bundles of paid OTT channels replacing today's cable TV bundles.

Kannan Venkateshwar, a Barclays managing director focused on media/telecom research, predicted that "Netflix becomes the traditional broadcast network" because it carries a variety of program categories while other OTT services concentrate on specific audiences (such as Disney+ for families, NBC's Peacock for comedies).

Venkateshwar also examined the shifting global nature of streaming content. With such international expectations, "the scale of spending has gone up," he said. Although Netflix and other firms are creating specialized content in some countries or languages, most OTT content is intended for worldwide distribution. He said the only example of largely domestic content is sports (except for global soccer).

The SOTN discussion took place just as a consultant's analysis emerged, envisioning 50% global OTT revenue growth by 2024, surpassing $200 billion by then.

Another of the panelists, Daniel Ornstein, co-founder and CEO of Bundler Inc., said that a viewer's future collection of streaming channels is not like cable.

"You can pick and delete something" at anytime," he said, underscoring the problems that process will create for broadband pricing, especially if carriers continue to package some OTT channels, such as the current Verizon and Disney+ combination. Ornstein, who heads the new California aggregator and marketplace for media subscriptions, was on the FCC chairman's staff about 10 years ago and later worked at Warner Bros. Entertainment and Warner Bros. TV. He predicted that "companies are going to overlap" and eventually the ecosystem will settle into a combination of new streaming structures.

"There will not be a total revolution of everything direct-to-consumer," he said, opining that "exclusivity is here to stay." He added: "Once you focus on content investment windows, carriage rights are a good way to generate revenue."

Larry Downes, an analyst, pointed to the different ways that Generations X, Y and Z (millennials) embrace streaming media, contending that the youngest cohort sees no difference between "professional" productions and self-produced content (such as small YouTube videos). He insists that these variations will require new business models, which will complicate the evolution of streaming channels.

DeShuna Spencer, founder and CEO of kweliTV, a black-focused streaming service, expects that the "streaming wars" will open opportunities for specialized content channels such as her service. While the major OTT companies are "fighting for the same content," ventures such as kweliTV can develop targeted audience. Spencer also pointed out that her company pays for programming as it runs, not up front as the major streamers do.

"Over time, maybe five years, spending will dry up," she predicted.

The Future of Platforms: Not Just Antitrust

With the evocative question of whether Tech Platforms will "Reign or Get Reined In?" the session on what will happen to the major platforms during the next 10 years generated a predictable range of responses. Most of the panelists agreed that in this volatile category, it's hard to predict whether Google, Amazon, Apple, Facebook or other currently dominant players will continue their juggernauts -- or how upstarts might plunge into these roles.

Their fates will proceed "completely irrespective of what [happens] with antitrust," said Ben Sperry, associate director-legal research at the International Center for Law and Economics. "If we move away from consumer welfare, you'll see a different kind of economy -- not necessarily good for consumers."

Steve DelBianco, president/CEO of NetChoice, warned that antitrust actions could affect innovation, since small companies often build strategies with expectations of being acquired by a major provider, such as the platform companies.

Privacy also plays a major role in the outlook for platform providers during the coming decade, the panelists agreed.

Katie McInnes, policy counsel at Consumer Reports, characterized "privacy as a luxury good." She and other panelists joined the chorus urging a national privacy policy to avert confusing state-by-state laws.

DelBianco also predicted that by 2030, "Government will still be trying to figure out how to regulate artificial intelligence," which will continue to be a major initiative of all the platform providers.

Privacy Focus, Including New Protections for Teens

Rep. Jan Schakowsky (D-Ill.), who chairs the House Subcommittee on Consumer Protection and Commerce, said she is preparing legislation that will restrict digital messages including some advertising to older children (presumably ages 13 to 17). The expansion of the Children’s Online Privacy Protection Act (which covers children 12 years and younger) is expected to be introduced later this week. (Update: Democratic Rep. Kathy Castor of Florida introduced the PRIVCY Act on Jan. 30.)

"We think it's important to put protections into the law to protect older kids," she said, citing it as part of "baseline privacy law." Although the proposed legislation will primarily be aimed at online social media, it may affect other media platforms. Schakowsky cited the need for such legislation because of the "propaganda of fake news" that will emerge during the upcoming election campaign.

“I feel a sense of urgency that we could have a complete fantasy of a message campaign in this election,” she said.

Rep. Cathy McMorris Rodgers (R-Wash.) emphasized the problems that are taking shape as many states -- notably the new California Consumer Privacy Act (CCPA) -- that have the potential to wreak havoc on national policy.

"It's easy to see a patchwork of laws that will be confusing to consumers and difficult for businesses," she said. Rodgers, the ranking member of the House Consumer Protection and Commerce Subcommittee, also pointed out that the parent Commerce Committee is still focusing on mapping of broadband in order to resolve lingering issues about the digital divide, which affects her semi-rural district.

Artificial intelligence was the sole theme of a final keynote by Dr. Lynn Parker, deputy U.S. Chief Technology Officer. She described the pending AI regulatory plan to "consider AI on a use-by-use perspective." She also addressed the emerging controversy about over-reliance on AI, insisting that it "is unethical not to use AI if it can be beneficial."

"Global leadership in AI matters," she said, but admitted that it is "not reasonable to believe every country will have the same approach to AI."

"We're still a ways away from defining" U.S. policy, Parker said.

A bipartisan group of Senate leaders has introduced a bill that would put billions of funds into "industries of the future," one of which is artificial intelligence (AI). 

The Industries of the Future Act of 2020 would "support and promote the advancement of next generation technologies that will drive Industries of the Future," said Sen. Roger Wicker (R-Miss.), chairman of the Senate Commerce Committee. “Our investments in research, development, and innovation have made the United States a global leader in science and technology,” he said. 

The bill would: 

1. "Require the Administration to develop a report on the research and development programs of the federal government that focus on Industries of the Future. 

2. "Require a plan for doubling the baseline investments in such industries by 2022 and a plan for increasing civilian spending on Industries of the Future to $10 billion by fiscal year 2025. The bill would also require the Administration to propose legislation to implement such spending plans. 

3. "Require a Coordination Council that would better focus existing entities and require those entities to focus on advancing Industries of the Future." 

Also backing the bill were Sens. Cory Gardner (R-Colo.), Tammy Baldwin (D-Wis.), chairman and ranking member of the Subcommittee on Science, Oceans, Fisheries, and Weather, respectively, and Sen. Gary Peters (D-Mich.). 

“I’m pleased to see the Industries of the Future Act introduced by Senators Wicker, Gardner, Peters, and Baldwin yesterday," said U.S. Chief Technology Officer Michael Kratsios. "Importantly, the bill recognizes that the industries of the future are interconnected and builds upon work already underway."

The White House is proposing principles that government agencies must follow when drafting regulations affecting private sector deployment of artificial intelligence (AI) technologies, and they are focused on preventing overregulation that could stifle innovation, the White House said. 

That comes even as the tech industry is gathering in Las Vegas for CES, where AI will be a big topic of discussion, and where top White House tech officials will be in attendance.  

The Trump principles are not binding on independent agencies, like the Federal Communications Commission and Federal Trade Commission, but a White House official speaking on background told Multichannel News that in talking with the Office of Management and Budget it was their understanding that those agencies would follow that guidance voluntarily, per past precedent.  

When the Obama Administration told agencies to start conducting cost-benefit analysis of regs, for example, the FCC under chairman Julius Genachowski indicated it would follow that lead. 

The Department of Transportation would be bound by the new AI principles for regulation of drones, for example, and the FDA for regs on AI-powered medical devices, respectively. But the principles don't apply to government use of AI, say, facial recognition by law enforcement for example.  

Related: White House Updates AI R&D Plan 

The Trump Administration's proposal will come in the form of a memorandum to federal agencies that will be posted on whitehouse.gov by midday Tuesday. The administration will give interested parties 60 days to comment before finalizing the principles. 

U.S. chief technology officer Michael Kratsios, speaking to reporters Monday (Jan. 6) in advance of the principles' release, said the idea behind them is to provide guidance on tech and ethical issues around AI, but in a way that supports robust private sector development and provides federal agencies common sense, pro innovative approaches.  

He said the principles need to be put in two important contexts. One is that they are meant to remove impediments to private sector innovation and growth and reduce any uncertainty about how the Administration will approach AI regs. The other is that the U.S. needs to implement these first-of-their-kind principles to ensure AI is led by the values of the U.S. and its allies, which are freedom, human rights and civil liberties.  

For example, the EU is planning to release an AI regulatory document soon, and he said he hopes it will use the U.S. AI principles as a framework and avoid preemptive and burdensome regs that stifle economic growth. 

Related: Trump Backs International AI Principles 

The regulatory principles agencies like the Department of Transportation and FDA, must abide by, according to Dr. Lynne Parker, deputy CTO of the U.S. and assistant director of artificial intelligence at the White House Office of Science and Technology Policy (OSTP) are: 1) public trust in AI; 2) public participation in the rulemaking process; 3) scientific integrity and information quality; 4) risk assessment and management; 5) cost and benefit analysis; 6) flexibility, i.e. performance based standards that can adapt to rapid changes in technology. 7) fairness and nondiscrimination, i.e. whether an AI app increases or decreases discrimination; 8) disclosure and transparency, which can increase public trust, could include when AI is in use; 9) safety and security; and 10) interagency coordination. 

Agencies will have to demonstrate to the Trump White House that any proposed regs follow those principles, which were developed as part of President Trump's American AI Initiative, announced a year ago. The 10 principles rest on three major elements: 1) ensuring public engagement and trust by increasing public participation; 2) limiting regulatory overreach by requiring cost-benefit analysis before any AI regs are imposed, with an emphasis on flexible regulatory frameworks, and by directing agencies to cooperate and communicate to avoid duplication of effort; and 3) promoting trustworthy AI by considering fairness, openness, nondiscrimination, transparency, safety and security.  

Pity the poor sales reps in a media and entertainment company. They’re tasked with bringing in more and more revenue for their organizations, yet they compete in an extremely crowded and noisy environment. Worse, the entire sector pitches the same generic data — number of visits or views, gender, time of data, etc. — beefed up with commoditized syndicated segments, such as sitcom fans and news junkies. How can sales-team pitches stand out when they tell the same story as everyone else? It’s simple. They can’t.

What M&E sales teams need to do in order to break through the noise is to tell better stories about audiences. Storytelling wins pitches, as we’ve seen over and over. Most M&E companies try to do just that, which is why they have dedicated ad-research teams tasked with looking for insights in their mounds of data and slicing their audiences into segments as best they can on behalf of advertisers. But I’ve yet to see any research team that isn’t overwhelmed by requests, a situation that’s not likely to change given the advertiser’s appetite for finding the “right” audiences. With these teams overloaded and in reactive mode, sales teams end up missing out on sale opportunities and losing deals because they often can’t get the insights and compelling story they need to close them.

AI-Assisted Selling

This is precisely where AI — specifically unsupervised machine learning — can help. Take a company like CNN, which sees consumers across multiple touch points: website, mobile app, OTT, TV on-demand, etc., all of which generate massive amounts of first-party data. AI can be used to proactively sift through data sets quickly and efficiently, and identify meaningful insights based on connections between users and similarities in their behaviors. Graph technology can automatically identify insights and aggregate these signals so that M&E research and sales teams can find stories in their data at a glance. This means sales teams have more, and better, stories to share, and can become more like a consultant to the advertiser, which is far more strategic and valuable.

AI lets sales teams unlock all of the interesting, nonintuitive stories that are buried inside the company’s massive consumer data. Humans are multidimensional creatures; a viewer may be a mom who is an avid fan of a cooking show, but she may also be a horse fanatic who lives to ride. Another viewer may be a sports fan, but when he’s not watching the game he’s in his basement workshop tinkering away at a DIY project. Put another way, this M&E brand doesn’t just have two obvious audiences, cooking and sports enthusiasts, it has many nuanced audiences all within the existing user base. By unlocking new insights at scale, M&E sales teams can tell new and differentiated stories to advertisers.

Going further, M&E sales teams can leverage that data proactively to create net new opportunities instead of simply responding to existing briefs. For instance, they can create a custom audience and pitch it to a brand that has never advertised with any of their properties before. Or they can cater segments to a brand’s specific campaign goals, such as growing brand awareness among specific audiences.

More interestingly, M&E companies can advise advertisers of the additional affinities shared by an audience, which the brand can then use to drive message development. Let’s say an M&E company discovers that viewers of a science-fiction show also have a strong affinity for steampunk and animal rights. This insight helps the advertiser align campaign and content strategy with the natural interests of the audience.

Win-Win-Win Scenario

By telling better stories with their data, M&E companies break through the noise, close more deals, become more strategic and drive higher-value inventory to the advertisers that want to align with these unique audience segments. Brands can cater their messages to touch on the additional shared affinities within their target audiences, which will make their ads inherently more interesting and relevant. That, in turn, means the audience members themselves enjoy an overall better experience. That’s a win for everyone.

Tim Burke is CEO of marketing strategy platform Affinio.

Comcast said it has acquired Arlington, Virginia-based BlueVector for an undisclosed sum.

The technology company, which was spun off from defense contractor Northrop Grumman two years ago, uses artificial intelligence and machine learning to provide cybersecurity services to companies and government agencies.

BluVector has been owned since 2017 by Philadelphia-based private equity firm LLR Partners.

Comcast said that Eric Malawar, a veteran cybersecurity engineer who once worked for the House Committee on Homeland Security, is now the CEO of BluVector. Malawer’s LinkedIn profile says he joined Comcast in 2017 as head of AI and data strategy.

BluVector’s founding CEO, former IBM executive Kris Lovejoy, will serve as an adviser and consultant to Comcast and its new venture.

Comcast chief information security officer, Noopur Davis, will lead the process of identifying opportunities to leverage the companies’ combined technology and expertise to support new products and initiatives.

Using proprietary technology, BluVector works to detect, analyze and contain a wide range of sophisticated cyber threats, including fileless malware, zero-day malware and ransomware

BluVector doesn't announce the names of its clients, but it said in November that it had one a "multi-million-dollar contract" with a "government agency."

“BluVector is a global leader in leveraging AI and machine learning to defend against advanced cyber threats,” Don Mathis, GM, Growth at Comcast said, in statement. “We’re thrilled that BluVector is part of Comcast and are excited to support its continued growth, even as we explore new opportunities to leverage BluVector technology and expertise. 

Artificial intelligence is fast becoming an essential ingredient for an array of solutions across all industries – which promises to transform every aspect of our lives. While intelligence similar to (or perhaps even surpassing) that of humans may emerge in the not-too-distant future, it’s clear from our collective everyday experience we are not there yet. We are, however, firmly on the path. And there are steps we can take to create the brighter future we desire.

This is especially important when it comes to the media and publishing industry.

Trust is increasingly hard-earned

Concepts of truth and accuracy in reporting and storytelling are being tested as never before. AI driven newsfeeds have given rise to Fake News. And with Deep Fakes, content creation and manipulation have become less expensive and virality impartial to truth. Yet we the people feed the demon, as speed to market and sensation is rewarded, whereas quality, accuracy, and truth borne of patience, less so. Often these are a result of quick-fix solutions to core business model challenges, whereas figuring out the right model is much harder though enduring.

So how do we maintain and build a publishing business in this complicated high-risk high reward climate? How do we to capture the full benefits of today’s AI without losing control of what makes our brand (and our advertisers’ brands) unique, powerful and human?

Home is where you create value

For the past 20 years I’ve advocated for the adoption of advanced technologies, yet always calling attention to the need to first set the stage for the technology to most effectively achieve its goal while limiting the potential for adverse outcomes. Otherwise, the risks for adopting, then soon after sunsetting, the technology increases. With that, a burn of unfulfilled expectations (and empty pockets) lingers long across our teams and companies.

Without a doubt, the greatest step a media company can take in preparation for AI is to position itself to have a direct relationship with the user. One’s owned and operated properties (be they on desktop, mobile, OTT, or elsewhere) are the ground zero locations for capturing and developing these relationships.

If it’s an audience you can’t equitably monetize, can’t learn from, can’t leverage for improving what shows you cast, produce, distribute, and build your business from, is it an audience? Ask Defy Media how that worked out. Or Disney’s recent write-down of its stake in Vice.

It’s true that we can’t expect the O&O “we built it, so they will come” mindset to succeed on its own. We DO need to go where the audience is. Deals with YouTube, Instagram, Snapchat, are necessary and smart, especially if we are to start thinking about content and shows as “Omni-brands” that can and need to live everywhere and anywhere. But we must not primarily invest where we give the goods away. I walk into way too many meetings where this is the team that’s funded, not the guardians and builders of the house.

It is within the four walls of your O&O house that you are able to create a clearly defined and controlled experience for your customers around who you are, why you matter, the benefits of sticking around and returning. It’s your scientific laboratory – where else are you going to get such clear unfiltered signal to test what’s working and not, to inform content creation, distribution, licensing, business models, new products, and all other experience and brand defining investments?

Laying the foundation for AI

Getting the house in order is no easy affair, but its table stakes. This includes having a common framework for testing, deploying and learning. Centralization and standardization of assets and a common data model with dynamic taxonomies are the foundation from which all advanced cognitive systems can operate.

With your home base well established, investments across the board begin to yield fruit. The user experience and every interaction yield the data and metrics and nurture a culture of testing that drives the virtuous process that sustainable businesses are built on.

Now you can implement AI within an environment that addresses real gaps in measurable ways and with reduced risk of overshooting.

Applications for AI in Video Programming

Are you a newsroom that needs to grow your audience by increasing watch times? While breaking news gets the most views, it’s not always monetizable - is it possible to optimize content programming to surface videos that are relevant and more monetizable? If so, can this be done within a fluid high-risk newsroom environment where trust is key?

It is not only possible, but it is becoming a requirement to implement video automation and at-scale on a one-to-one level. That path to this is leveraging deep context data gathered from your videos and learning from your audience and how they’re engaging at all levels of your site, holistically across each video experience, across each site and mobile and OTT environment. And put the controls in place to boost only those videos that drive revenue not cost.

Are you a national news network of many sites each in local areas with smaller audiences trying to figure out how to best manage content, ad sales, across each yet tied back to a national presence?

Are you a global sports brand that needs to tie content together contextually, in a deeper way than on top-level category, to package up premium brand-safe and brand-aligned content, in order to realize premium CPM and budget from one of the most finicky ad brands?

Are you a Broadcaster launching a new OTT service, in one of the most competitive environments to-date, with limited information about new subscribers and a need to quickly solve for the “cold start” problem?

Are you a broadcaster looking to prove/disprove the hypothesis that longer content will bring and engage more millennials and Generation Z audiences, prior to making the significantly increased investment in content production?

If any of these scenarios describe familiar challenges heading into 2019 and beyond, then integrating AI into your programming stack and a culture of testing into your organization is your path to success.

A is for Augment

Sabermetrics and Moneyball did not replace baseball GMs, managers, and scouts but rather augmented their intelligence and decades of wisdom and instinct. They are empowered with data-driven insight to make informed decisions. The same is true for a disrupted media and entertainment industry where the stakes couldn't be higher.

For as long as it will be humans consuming content, (I’d like to believe) that it will be important for humans to have a hand in creating and distributing that content. Which means humans and machines working optimally together.

My view on this may change should the world that futurists predict come to pass – where all travel is through automated intelligent vehicles, opportunities for work are limited and there is no longer need for critical thinking, discernment, or free will (should we have ever had it in the first place). I’ll just take my set of personalized whole-life-vitamins spoon fed to give me the life I’m supposed to need.

But we’re not there yet. So let’s celebrate this moment and take control of the content we want to consume and the businesses and society we want to build.

Comcast-NBCUniversal will host a "hack-a-thon" Nov. 3-4 at Telemundo's new Miami headquarters.

The hack-a-thon, the second in Miami, will provide a $10,000 grand prize for the most innovative cross-platform approach to developing "new ways to tell stories and foster deeper fan and community engagement with participating NBCUniversal brands."

The winner, who will be expected to leverage new tech including artificial intelligence (AI) and augmented reality (AR), will also get a chance to pitch their proposal to NBCU execs. In addition to the grand prize winner there will be first and second runner-ups.

Participants will have access to NBCU content and platforms during the two-day event.

Tech companies partnering on the effort include Amazon and Google.

Zone TV said it is introducing a software suite under the Zone·TV Studio brand that enables content owners and video distributors to create self-branded, linear TV channels that can be personalized and customized by and for each viewer in the household.

Zone·tv Studio is an artificial intelligence-powered service for content discovery, channel curation and video delivery, the Santa Monica, Calif.-based firm said. The software enables a “Spotify-like” experience based on what the individual viewer wants to see. 

Related: Zone TV Uses A.I. to Program 14 Linear-Style OTT Channels

“Traditional video distribution is in a state of disruption as hundreds of direct-to-consumer streaming services are emerging, making it difficult to maintain customer loyalty,” Zone·TV CEO Jeff Weber said in the release. “Our technology means that any video distributor or content owner can build new, precisely targeted, compelling channels which enable a more relevant content consumption experience for their customers. Zone·tv studio enables the simplicity of traditional linear channel programming combined with the intelligence power of A.I. to bring new user experiences to TV services.”

Zone TV said using the software "learns what viewers love to watch and presents similar content, automatically and in real time. This unique capability empowers viewers to create their own channels with the click of the TV remote control and 'zone·ify' their own channel." A Foodies channel one viewer watches could be more about grilling than a neighbor's desserts-focused channel, based on their viewing habits.

In a move to stitch its smart home tech with video streaming, Amazon today introduced the Fire TV Cube, a 4K-capable streaming device that also supports the company’s voice-based Alexa technology.

The combo enables users to navigate content and apps and change channels with their voice, reducing the need for consumers to buy and install a separate Alexa-powered Echo device.

The Fire TV Cube, which broadens Amazon’s Fire TV device lineup beyond the core Fire TV box and Fire TV Streaming Stick, is slated to be released on June 21. The baseline pre-order price is $119.99 and includes the device and an IR extender cable and Ethernet adapter, though Prime members are in line for a discounted price of $89.99, plus free shipping. Those who purchase and register a Fire TV Cube by July 1 will also get a $10 credit for Prime Video.

Amazon is also pitching a Fire TV Cube/Amazon Cloud Cam bundle for $199.98.

The new entry employs eight microphones and supports smart home capabilities and content delivered on traditional Echo devices. Apps supported by the Alexa element of the Fire TV Cube include Netflix, Amazon’s own Prime Video, Hulu, ESPN, Fox Now, Showtime, Starz, PlayStation Vue, CBS All Access and NBC, among others.

Amazon said the Fire TV Cube is also compatible with set-tops from Comcast, Dish Network and DirecTV.

Amazon is introducing the new device as it starts to gain ground in the OTT device arena, a market that is still led by Roku, according to Parks Associates.

RELATED: Roku Retains Edge in Streaming Player Market

Amazon’s latest move might also push rivals such as Apple and Google to come out with streaming products that are more tightly integrated with their voice-powered platforms. Roku is also moving ahead with a strategy that enables its platform to work with a broader landscape of smart home technologies.

RELATED: Roku’s Platform Reaches Beyond the TV

Taking a shot at video-capable smart home hubs from rivals such as Amazon, Google announced that a mix of CE partners are coming out with Smart Display products that work with Google Assistant and are integrated with YouTube and YouTube TV.

Those new products, from partners such as LG Electronics, JBL, and Lenovo, are slated to come out in July, Lilian Rincon, director of product management at Google, announced Tuesday at the Google I/O developer conference.

Those displays will support voice commands used for Google Home with the video components. A YouTube TV customer, for example, will be able to use Google’s new Smart Display to tune into a show and have it stream to the screen, or request a video from YouTube to be sent to the device.

Rincon also demonstrated how a Smart Display could also be used to show recipe information along with a video of step-by-step instructions on how the item is prepared.

Much of Google I/O was used to tout advancements in artificial intelligence, and how Google is applying the technology across multiple products.

Google, for example, has redesigned Gmail with A.I. to include a “smart compose” feature that uses machine learning to suggest phrases as the user writes. Users simply hit the Tab button to use the suggested auto-completing of sentences and phrases. That’s rolling out this month.

Google Assistant is using a technology called Wavenet that creates a more natural sounding voice, with respect to pitch, pace and pauses that can convey meaning, using the raw audio. Google Assistant is also adding six new voices to Google Assistant, including one based on the voice of singer John Legend.

Scott Huffman, VP of Google Assistant engineering, said more than 500 million devices, including about 5,000 connected home devices, are now equipped with Google Assistant.

Among new features coming out in the weeks to come is “Continued Conversation,” which allows some back-and-forth and follow-up requests from the user without having to say, “Hey, Google,” each time. The system is also being made to support multiple actions/requests.

Google also talked up Android P, the new version of its mobile OS. Among the new features is an adaptive battery technology that improves battery life by employing machine learning to understand which apps the user is likely to open in given times of the day. There’s also an auto-brightness feature that accounts not just for current lighting conditions but also the user’s personal preferences.

Google is launching the Android P beta today for the Pixel and seven other “flagship” Android devices. 

Over-the-top video is now on par with cable and satellite, if the general consensus coming out of this year’s CES is to be believed.

Once its own separate bucket for video content, OTT has now become a hallmark of any successful video option. Netflix and Hulu may be the largest names in the game, with the first-to-market advantage. However, offerings from Apple, Facebook, YouTube, Amazon and countless others provide numerous options to catch up.

Beyond those services, there are also the standalone streaming options from publishers like Sports Illustrated, CBS, Billboard and more — services that can’t branch outside of their ecosystem from a content perspective, but have ample opportunity to own their audiences in today’s environment. While the early OTT trends were geared toward housing video content where it could potentially find the widest audience, that dynamic has now changed somewhat.

Targeted, personalized experiences are the preferred way consumers interact with brands. And one way to do that well with video is the methodology Netflix has long put into play. Its algorithms prioritize its original content, clearly. But based on your preferences and watch habits, there’s also plenty more to explore around the service, brought to you by artificial intelligence (AI).

Pathways to Monetization
Netflix isn’t monetized from an ad perspective, however. And that’s something many publishers need in order to survive this current video- focused landscape.

One potential avenue is audience ownership, and as a result, an augmented user experience with AI.

Related: Three Predictions for AI in 2018

What’s been missing from the OTT streaming equation for plenty of publishers, though, is how to take ownership of audiences and make business decisions from that data.

Shopping content to third parties can allow for a larger base audience, but it also dilutes the value you can glean from those viewers. The content isn’t targeted, and there are no adjustments to be made in real time. Users get what they get, and can be directed away from your content by the third party’s own video server as well.

Related: AI, Machine Learning to Change the Customer Experience, Comcast’s Watson Says

With owned-and-operated video hosted directly on publisher sites, audiences opt into viewing only your content. That in and of itself makes for a more tailored scope (especially for niche sites). And it also allows for AI to provide customized recommendations for what’s next on each individual user’s personal playlist. It also enables you to collect valuable first party data that can be further utilized for programming strategy.

Then, there are subscription-based video services, which also lend themselves to high quality audience intelligence.

Related: Standing Out in the Crowded SVOD Sector

Netflix and Amazon have put pressure on the consumer’s willingness to pay. With more players in the field competing for share of wallet, content, programming and marketing strategies require a higher level of precision. You need to maximize engagement from trial in order to convert.

Making Marketing Personal
AI-based personalized recommendations and data-driven decision making don’t just inform content acquisition strategies, but also marketing tactics for targeting new, returning and loyal viewers to maximize subscriber yield. Your service cannot just show Moneyball, it must also be playing it.

Effectively utilizing this sort of data is where OTT can thrive, optimizing every aspect of the video ecosystem down to when or why eyeballs tune out of certain programming. The more granular the information available, the more content can be adjusted accordingly to make video more relevant to viewing preferences.

This is where OTT can thrive. With streaming video seen as just as valid an option as cable and satellite pay TV services, one would imagine rises in advertising and subscription rates could come next. With a greater ability for AI to tailor content and brand experiences to individual audiences and, as a byproduct, provide prescriptive insight. There’s incredible upside for OTT that traditional video content can’t necessarily match.

Rohan Castelino is vice president, marketing, for Iris.TV. Image downloaded from Flckr and used under Creative Commons CC0 1.0 Universal Public Domain Dedication.

Mist, a startup that brings A.I. techniques to wireless networks, has raised a $46 million “C” round led by Kleiner Perkins.

The latest round also included participation from Lightspeed Venture Partners, Norwest Venture Partners, GV (formerly Google Ventures), NTT Docomo Ventures, and Dimension Data.

Mist, which has raised $88 million so far, said it will use the new funds to grow sales and expand its marketing and invest in engineering, with a focus on A.I.

It also aims to develop a channel of managed service providers and work with original equipment manufactures.

Founded in 2014 and based in  Cupertino, Calif., Mist ties data to machine learning and A.I. techniques to underpin “self-learning” wireless networks that optimize the experience. It started to ship its Mist Learning WLAN in 2016, and said it ended 2017 with more than 250 customers.

Among those customers is Verizon, which is using Mist’s technology for its software-defined wireless local area network.

Comcast has built a dedicated voice team within its digital home engineering organization as the company seeks out ways to accelerate its use of voice commands across multiple service domains, including its X1 TV service, Xfinity Home offering and beyond.

Update: The lead and headline have been adjusted to clarify that that the new dedicated team referenced is within Comcast’s digital home engineering organization that’s focused on extending voice commands to home functionality, rather than a dedicated team focused on Comcast’s broader voice-related activities.

"The voice team works closely with the Xfinity Home apps team, ensuring that the new features and tools are always considered within the context of voice control,” Comcast’s Bryan Kissinger explained in a blog post that was co-authored by Shiv Dhondiyal.

The new voice team built Comcast’s Voice Action Processing Service (VAPS), which processes commands that come way of the company’s home-grown Natural Language Processing agent and determines which other services and APIs to call. One example is launching an app for viewing a camera feed on the Xfinity Home service.

More details about the technology underpinnings of Comcast’s voice commands platform come as the MSO expands those capabilities to additional domains such as home automation and security.

RELATED: Xfinity Home Finds its Voice

That work started with a “guard word” for Xfinity Home commands – for example, uttering “Xfinity Home, disarm” to disarm the home security system to ensure that the voice control system didn’t seek a movie or song called “disarm.”

“Adding a guard word opened a lot of possibilities, but we still didn’t have a way to support more complex functions,” Kissinger explained.

Working with Comcast’s Applied Artificial Intelligence team, Comcast is now developing a way to support more natural commands that work across multiple service domains. Saying, “Xfinity Home, I’m hot,” for example will trigger the system to adjust the home’s connected thermostat by a couple of degrees.

Comcast’s expansion of connected home voice capabilities included adding direct commands for lights and thermostats using Xfinity Home’s ZigBee radio.

In a recent interview, Comcast execs discussed how the company is meeting the scale challenge using an in-house deep learning platform that matches up with an integrated metadata system.

RELATED: Machine Learning Holds Key to Scaling Up Comcast’s Voice Remote (subscription required)

Not only does the system need to know and understand an increasingly broader scope of specific and conversational-style commands, it also needs to grasp the intent of the voice command. Is it a search for a TV program or movie, or is the user telling the smart home system to turn off a light or adjust the thermostat?

RELATED: Comcast Team Developing Smarter Search 

Early on, Comcast used a more traditional, pattern-based algorithm that relied on manual tuning, but later realized that machine learning would be required to train that algorithm to maintain a high level of accuracy while keeping pace with requirements as the scope and complexity of the system continued to expand.

“We saw that the machine could learn how to accurately find intent in a better way than it did prior to that,” Jeanine Heck, executive director of AI product at Comcast Cable, said, adding that Comcast’s dependence on deep learning to process natural language has only become more pronounced over time. “Our machines can learn how to adapt to those [new domains].”

Added Jonathan Palmatier, Comcast Cable’s vice president of product management, voice control: “You’d need an army of people that are trying to capture every possible way that you can construct a phrase, and [that’s where] it starts to get untenable.”

In addition to expanding voice support to multiple Xfinity services, Comcast has also been working on how voice commands can support customer support functions.

Comcast's Voice Platform Leans on Distributed, Cloud-Based Platform 
Comcast launched the X1 voice remote in May 2015, and the numbers suggest that customers have embraced it.

Per Comcast’s figures for year-end 2017, the operator has deployed nearly 20 million voice remotes so far, and customers uttered more than 6 billion voice commands in 2017, roughing out to about 500 million per month.

Comcast backs its voice system with a distributed architecture (via a handful of data centers around the country) that aims to deliver low latency (the time it takes for a command to be uttered and executed) while also giving the system a level of redundancy.

If there’s an issue at one of those data centers, another can be used as a backup and balance the load. That holds true for other aspects of X1, as every button press is a “cloud call.”

The lifecycle of a voice command starts with the remote, which is equipped with a microphone button for the push-to-talk mechanism. Once a command is uttered, it is streamed from the remote to the X1 set-top box using the RF4CE protocol.

It’s then packaged up and sent to a distributed server in Comcast’s cloud infrastructure. That cloud-based system then translated the audio file into text and sends it to Comcast’s natural language processing system, which determines what the user was intending with the original voice command.

Once that is broken down, the system selects the most probable result and executes it and displays that result on the TV screen.

On the heels of grand plans about smart home automation announced at CES, Comcast said it has added new voice commands to its X1 voice remote that enable customers to command and control several devices that work with the Xfinity Home service.

RELATED: CES 2018: Comcast Scales Home Automation Services to 15M Subs

In this phase of the program and next level of integration with IoT devices, Comcast has added a series of new voice commands that, for example, will enable customers to use their X1 voice remotes to access and control the cameras and lights that are integrated with the Xfinity Home service.  

“Starting today, customers can get specific about the camera they want to look at on the TV – just say ‘Xfinity Home, show me patio camera’,” Comcast’s Bryan Kissinger explained in this blog post. “You can also use the X1 voice remote to make a certain room brighter, or dim the lights and choose a certain hue if you have the Philips Hue connected lighting system.”

Customer can also say “Xfinity Home, I’m cold” to auto-adjust the thermostat a few degrees, or tell the system to set the thermostat to a specific temperature, or ask it if the home’s security alarm is active.

“If customers have the Nest thermostat, they can even say, ‘Xfinity Home, set Nest to Eco mode’ if they are trying to conserve energy,” Kissinger added.

Update:Comcast has deployed more than 20 million X1 voice remotes. The company confirmed that the voice commands for Xfinity Home work with its newly designed X1 voice remote as well as the previous model. Both X1 voice remote models are made by Universal Electronics.

For now, six Xfinity Home device partners have been integrated into the X1 voice commands database – the Zen thermostat, Caseta/Lutron lights, GE in-wall and plug-in switches and dimmers, the Nest thermostat, Philips Hue lights, and the Sengled Element Touch light bulb.

Earlier this month, Comcast announced that it had extended home automation services to more than 15 million customers as it works toward enabling that capability across multiple services, including X1 (TV and the X1 voice remote), xFi (WiFi), Xfinity Home and Xfinity Mobile.

Comcast’s push comes as consumers continue to buy and adopt smart home hubs, digital assistants and smart speakers.

While Comcast is taking aim at this from the service provider level, several others are taking the consumer retail angle. Some examples include Google (Google Home), Amazon (Alexa-powered devices like the Echo and Echo Dot), as well as Apple, which announced this week that its HomePod would arrive in stores on Feb. 9, with online orders opening up Friday (Jan. 26) in the U.S., UK and Australia, with sales in France and Germany slated for this spring.

2017 saw a great deal of shake-up in the cable and telecom landscape – with waves of disruption crashing and rippling through the industry from all directions.

From one side, tech giants including Apple, Facebook and Google seriously looked to enter the television arena (previously shaken-up by Netflix and Amazon) – presenting opportunities for streaming, tech and cable providers to work together. From another side, the social media era gave content offerings a facelift with short-form content, providing a disruptive opportunity for every player.

Meanwhile, throughout the entire sector, organizations were wrapping their heads around the extensive range of new technologies and skills needed to leverage the Internet of Things – in order to effectively translate the buzzword into business value in 2017.

So, what’s next?

Below, I’ve highlighted five trends that will fuel digital transformation in 2018 – from industry co-opetition and competition, to embracing new technologies and business models, in order to stay ahead of the curve.

2018 Co-opetition and Competition

Industry shift: Broadcast and OTT co-opetition 

The next wave of the co-opetition phenomenon will primarily involve broadcasters and OTT providers. Broadcasters will extend campaigns to OTT apps – developing affordable streaming offerings that are highly configurable to viewers’ tastes.   And with the recent FCC rollback on net neutrality, network and content providers and aggregators will work more closely together to service a common target audience. This convergence will also allow more access to customer data – empowering providers to deliver interactive and engaging advertisements centered around individual preferences. 

A new era of industry competition 

The word “industry” is becoming obsolete as lines are blurring between cable and telecom; between communications and entertainment providers. As we look ahead to 2018, we will see these industry categories break down when it comes to defining the customer experience.

Instead, brands (regardless of their origin industry), will look for new ways to open their business models to both replicate and compete with successful elements of the customer experience offered by players like Uber, Amazon and JetBlue.

Embracing New Technologies – and their corresponding revenue opportunities

Managing the IoT ecosystem 

With Uber and Amazon setting a new caliber of customer experience, everyone else will need to leverage emerging technologies to keep up (and stay ahead). Gartner projects that there will be over 20 billion connected devices by 2020. Specifically looking to 2018, the Internet of Things will evolve into the Internet of Everything.

In the face of the widespread connectivity we’ll see in 2018, service providers would be doing themselves a disservice by overlooking the connected home market (projected to exceed $9 billion by 2021).

Despite speculations that connected devices will “self-install,” eliminating the need for human intervention to connect these elements –  consumers will want human help more than ever before to build connections between the machines in their homes. In fact, a recent market survey polled 2,000 consumers across the globe to discover how the connected home will become more connected – and uncovered that 67% of consumers will feel not at all confident to do complex installations on their own.

Additionally, 84% of survey respondents believe that consumers will want help from a skilled technical resource to connect just two-five devices. As smart home devices become part of daily life, service providers will evolve the skills of their field service technicians to capitalize on the booming connected home market.

Staying Ahead in the Era of Digital Transformation

Beyond digital transformation: business transformation 

As we look to 2018, there is a lot of buzz around advanced technologies like IoT, AI and machine learning, as it relates to the next evolution of digital transformation. However, in 2018, service providers will likely push these topics lower on the priority list in favor of continuing to focus on core business transformation and centering operations around an agile business model that is able to continually embrace and deliver the latest technologies. 

In my opinion, transformation will likely continue as service providers are pressed to create compelling and engaging content and experiences for tomorrow’s consumers. As the Internet of Things evolves into the Internet of Everything – organizations around the globe and across market verticals will be presented with a whole new set of business opportunities (and corresponding challenges).

Looking forward to seeing what 2018 has in store!

--Sean Casey is executive director of product management, CSG International

[Image source: Wikimedia Commons]

The media landscape saw steady change in 2017. High Dynamic Range (HDR) moved to the forefront and consumers took advantage of even more skinny bundle offerings as they continue to look for ways to cut or shave the cord. Artificial intelligence became more accessible and had a big impact on consumer interaction.

The question is, how will the tides turn in the next 365 days? Here are my predictions for 2018:

High Dynamic Range (HDR) Becomes Ubiquitous

The rollout of HDR-capable devices and content will proceed much as the 4K rollout happened, which is to say smoothly and quickly. Stores are brimming with 4K display options this holiday season, many of which include HDR options across a good selection of reasonably priced models. By this time next year most new TVs will likely be HDR-capable with many more options available in the mid- and low-price range.

As with the 4K rollout, there is a chicken and egg problem of having a capable device along with the availability of content to playback that utilizes the new display capabilities. With 4K it was easier since existing cable, satellite and broadcast services were able to upgrade and provide 4K content and channels.  

With HDR it’s a bit different. Since HDR is not backward-compatible with Standard Dynamic Range TVs and most traditional broadcast infrastructure is not compatible with HDR signals, most of the HDR content will be coming from online video services such as Netflix and Amazon.  There are broadcasters out there experimenting with HDR for sure, but the time horizon for having a wide selection of HDR television and sports events available through standard TV services is still a ways out. Meanwhile, subscribers to Amazon Prime Video or Netflix can enjoy a reasonable selection of HDR titles today, and most new titles from these two services are coming in HDR.

While there is still an issue of the competing Dolby Vision vs. HDR10 vs. HLG HDR formats, this won’t really matter for the average consumer. The fight is mainly between Dolby Vision and HDR10. And, with the exception of Samsung, most TV makers are beginning to support both formats and most content creators already support both. So, while Dolby and HDR10 duke it out in the format wars, most consumers will be able to continue viewing content regardless of their video service.

Skinny Bundles Abound

2018 will be the year of the skinny bundle. There are many options now available from different providers such as SlingTV, DirecTV Now, Spectrum TV Stream, YouTube TV and Hulu Live. There will be lots of hype and significant growth due to the fact that there are few skinny bundle subscribers today and it will be a compelling offer for many. For others, subscriptions even come free with mobile phone services.

What remains to be worked out is why the skinny bundle is better. At the moment, it remains simply a port of the traditional TV experience to the internet.

There is nothing significantly better than traditional TV services, except for price. Price is an important factor for sure, but price alone will not significantly disrupt the traditional TV market, especially not with the limited number of channels and on-demand assets that the skinny bundles have today.

The key thing to watch for in 2018 with regard to skinny bundles is for those companies experimenting with new and unique user experiences that take advantage of the fact that they are being offered over the internet. Some of these experiments will work, some will not; but ultimately the widespread adoption and success of the online TV offering and its ability to disrupt traditional TV is going to be based on some innovation in user experience versus simply having a cheaper option to the same experience online.

Artificial Intelligence (AI) will enable better content experiences

In the last few years, but especially in 2017, artificial intelligence has advanced significantly. There are now tools available from providers such as Google, Microsoft and Amazon Web Services that no longer require an expert in artificial intelligence to set up for use. Furthermore, these providers are offering pre-built networks to process media files such as pictures and videos.

As an example, most major online photo storage and sharing services are using artificial intelligence to perform facial recognition in order to allow users to navigate photo collections by people versus simply by date. Many of today's online video services rely on metadata in order to categorize, recommend and search the content catalog. However, the sources and depth of this metadata is rather thin, being constrained to a few items such as title, director, genre, actor etc. Even that is only for the relatively small set of content that is professionally produced in comparison to the vast volumes of user-generated and semi-professional content being created in the world. The kind and amount of metadata available for that content varies widely and often doesn't exist.

Artificial intelligence can quickly and easily process vast volumes of data that not even an army of humans could manage in a reasonable timeframe, providing the kind of metadata required for categorization, recommendation and search. This information could also be used to help further target advertising beyond simple demographics and allow for advertisers to include information about the video into which the advertisement will be placed to drive selection. Knowing what kind of content the user is watching could be as valuable as knowing the gender and age of the person watching.

The consumer-facing impact of artificial intelligence on media will be in the form of new and better user experiences around search and discovery of content, better recommendations and more relevant advertising. Knowing that this improvement was driven by artificial intelligence is something that may or may not be known depending on whether the provider reveals the implementation behind their experience.

--Bill Wishon is senior architect, media office of the CTO, at Akamai

 [Image source: Wikimedia Commons]

Comcast’s work around A.I. has extended to the cameras that are attached to its Xfinity Home platform to help pinpoint when those cameras should capture movement.

In the wake of a 24/7 recording feature with motion-detection introduced last year, Comcast  recently added A.I. and machine learning techniques that train the system to “identify the most relevant image in a motion event,” Chris Ganster, senior director, product at Comcast Cable, explained in this blog post.

RELATED: Comcast Brings 24/7 Video Recording to Xfinity Home

Those additions extend and enhance the movement detection capabilities of the earlier iteration of the system.

“While the system was smart enough to recognize movement, it was less good at capturing exactly when that movement was taking place (this resulted in a lot of a pictures of the tip of a dog’s tail or the edge of a car bumper in the far corner of an image),” he noted.

Comcast, he added, took some of the AI and machine learning techniques used to refine the X1 Voice Remote and applied them to technology called “computer vision.”

From there, engineers from Comcast’s Applied AI Team in Washington, D.C., worked with those involved with Xfinity Home to implement a “homegrown algorithm” that focuses on movement, centers it, and “delivers a thumbnail that is instantly recognizable.”

“So now instead of squinting to see the tip of your dog’s tail, you’ll see a clear image of your dog, or the delivery truck, or whatever else triggers your motion detector,” Ganster noted.

As a result, Comcast said it has already seen a 20% rise in customers using the Xfinity Home app to view recorded moments.

Ganster said Comcast intends to add more AI-focused features to its Xfinity Home cameras that, for example, will use optical zoom to deliver better, more useful images.

Comcast has more than 1 million Xfinity Home subs.

We’re at an exciting time in society where revolutionary technology like artificial intelligence is beginning to find its way into nearly every industry. There is both a buzz about the positive benefits as well as voices of concern, but the truth is, the future of AI is a long road that we’ll all continue to walk.

As we begin to wrap up 2017, what does the near future look like for consumers and companies in the world of media and advertising? Here are three predictions:

1) The previous education period will now lead to action: The past few years have been an opportunity for businesses and consumers to begin learning more about artificial intelligence, but we’re shifting to a time of investment.

A recent report by market research firm Vanson Bourne and data analytics provider Teradata found that 80% of surveyed organizations (280 large enterprises) are now investing in AI.

This investment means that in 2018, more companies have the power to unlock previously unattainable business intelligence. Unfortunately, businesses that neglected to spend time educating themselves on the benefits are going to begin seeing the rise of a digital divide.

The same research study notes some of the reasons why companies have not yet invested: lack of IT infrastructure (40%); lack of access to talent (34%); lack of budget for implementation (30%); complications around policies, regulations, and rights (28%); and impact on customer expectations (23%).

2) On-demand media as we know it will turn into organic, real-time libraries created by AI: With consumers opting out of advertising by flocking to companies like Netflix and Hulu, AI advertising is poised to thrive in a market where customization and user preferences are paramount. Exastax says that one of the most beneficial outcomes of AI for customer service is personalization. Essentially, this means that AI algorithms will be able to track, analyze and visualize customer data (behavioral, demographic, location, etc.) in order for organizations to determine the customer identity and, in addition, to recommend the best service.

We predict that behavioral targeting will be critical as ad blocking becomes the norm. In addition, in-content advertising will become more prominent as AI can help with “pull” content. This is because all media, in all formats, will be very quickly indexed down to the second and frame, equating to truly packaged, on-demand content. As media is boiled down to topic level and word-level, or “atomized,” consumers will build specific and personalized media based on topic preference. In that context, AI will help index and create content like a playlist.

A unique scenario will be how AI can produce shows and programs for consumers via dynamic compilation development. This will be ubiquitous and crossplatform. Think of it as a collaboration between YouTube, Spotify, AM/FM radio and more.

3) If the product is changing, the workplace will, too: We’re already seeing AI become more prominently used in the media industry. For example, iHeartMedia, the leading audio company with the largest reach of any radio or television outlet in America, uses AI to seamlessly and automatically process, transform and review audio data in near-real time.

While some people are hopeful for the future, there have been others who voice concern at the prospect of losing their job. Thankfully, this is a misconception. Mass unemployment is obviously a sensitive topic, not to mention a future that no one wants, but the fear isn’t backed by the data. AI will soon be a key component of many positions throughout a company, regardless of industry, but to cite an example, Gartner predicts that by 2021, 40% of IT staff will be “versatilists” holding multiple roles, most of which will be business- rather than technology-related.

This point emphasizes that an important position like IT — one that enterprises rely on to ensure their company and product runs smoothly — won’t be eliminated, but augmented with AI.

Like any emerging technology, AI is facing tough criticism and there is a wealth of misinformation. Thankfully, we can cut through the noise in order to see the purpose-driven benefits for society with leading data and insight from top-tier plays in the industry.

Drew Hilles is senior vice president of Veritone, a New York-based artificial intelligence software firm. Image by CHRISTIAN LAGEREK/SCIENCE PHOTO LIBRARY/Getty Images.

Denver -- Comcast Cable president and CEO Dave Watson tagged artificial intelligence and machine learning as a next big area of focus, especially to mine device and network data in ways that improve customer care.

Read More: Complete coverage of Cable-Tec Expo 2017

“We just hosted an AI demo day (in Philadelphia), it’s incredible … if there’s ever an industry that’s primed and could benefit from taking data and making it available to agents and customers, it’s ours. I think it’s really going to change the customer experience, profoundly.”

Watson spoke here during Wednesday’s (Oct. 18) luncheon panel at SCTE•ISBE Cable-Tec Expo, with Arris CEO Bruce McClelland and Multichannel News technology editor Jeff Baumgartner. Major themes: Gigabit services, WiFi resilience, and mobile 5G.

McClelland, who subscribes to Comcast’s Gigabit service in the Atlanta area, described it as “really responsive and pretty addictive.”

As for what services will warrant that much raw throughput: “The last thing I’m worried about is whether we’re going to consumer all the bandwidth -- I think we are,” in part because of advancements in virtual reality, augmented reality, 4K/HDR video, and the Internet of Things.

Watson also emphasized Comcast’s wireless intentions, both mobile and WiFi.

“By 2020, there’s going to be something like 50 devices hanging off of the WiFi, so it better be good,” he said. To address in-home coverage, he referenced Comcast’s investment in Palo Alto, Calif.-based Plume, which makes WiFi “pod”-styled extenders that will essentially create a mesh network in the home.

RELATED: Comcast Launches ‘xFi,’ Invests in Plume

Arris is similarly focused on mobile 5G small cells, for higher speeds, better connectivity and improved latency.

“It’s not unlike the HFC network, as we continue to split nodes and reduce service group sizes … there’s an opportunity to be very disruptive in the mobile space.”

Watson noted that it took about a decade for 4G wireless to fully launch.

“I’m not sure it’s as dire as an either/or,” in terms of 5G’s potential as both a mobile backhaul opportunity, and a last mile replacement. “It can be complimentary and additive, but it’s going to take a while.”

Zone TV said it has developed and launched 14 linear-style genre-focused channels that can be delivered OTT to set-top boxes and programmed using A.I. techniques.

The new offering fits into Zone TV’s new mix of what it calls “Dynamic Channels” that are curated by real people but augmented with A.I.-based programming that aims to provide a level of personalization.

Zone TV, which has acquired and licensed about 70,000 shows, said a dozen of its first 14 dynamic channels are segmented by age group and genre to “super-serve” consumers interested in topics such as food, gaming, specialty sports and kids content.

The initial slate of dynamic channel from Zone TV include Mancave; Great Outdoors; Motors: Level Up (eSports); Game On (specialty sports); Inform (interviews and documentaries); Brainiac; Expression (dance, comedy, music); Foodies; Styler; Hangout; Playground; My·zone (personalized based on a user’s favorite shows from all Zone TV channels); Zone·tv (a programming sampler of Zone TV’s dynamic channel package).

Zone TV’s plan is to offer those channels within the electronic program guide of MVPD partners.

Zone TV hasn’t announced any launches for its dynamc channels, but it has previously done work with MVPDs such as AT&T/DirecTV, Comcast, CenturyLink, Telus, Bell Canada, and Frontier Communications.

RELATED: Comcast’s X1 Takes ‘Santa Tracker’ Over-the-Top

Last week, Zone TV introduced an array of 28 subscription VOD channels that will sell for $4 to $6 per month and complement its strategy around dynamic, linear-style channels.

Integrating OTT services at the set-top level is rapidly becoming table stakes for MVPDs, as they look to shore up their role as a primary content aggregator, because it’s being driven by a mix of competitive forces, including the emergence of virtual MVPDs, Jeff Weber, Zone TV’s CEO, said in a recent interview.

Those forces are “pressuring the satellite and cable operators to move quicker than they might have moved in the past,” he said. “I see it [the integration of OTT services] very clearly as a must-have. If they don’t do anything, they do find themselves in very deep trouble.”

Change is the new constant in the media and entertainment industry, as the fundamental nature of competition in the industry has shifted. No longer do television shows merely compete with television shows, theatrical releases with theatrical releases, video games with video games, etc. With more content now available than any of us can consume in a lifetime, all content and content offers are competing with each other in real time for a precious slice of each consumer’s time.

In this era of hyper competition and digital connectedness, a new business model is emerging, one powered by data, with the consumer firmly at its center. Data has become M&E’s secret weapon for sustaining and improving consumer engagement and satisfaction. It’s data that can reconcile a world of ubiquitous distribution with a consumer’s finite amount of time to consume content. And it’s just getting started: By 2020, data scientists estimate that for every human on the planet, there will be 1.7 MB of data being produced. Every second.

Leveraging that data is essential as M&E companies compete for consumers’ time, advocacy and money across multiple platforms. Those that know consumers best will win.

Enter cognitive computing, which has the ability to examine the choices consumers are making, and under what circumstances, in order to understand and predict what consumers will want next. Although some pundits have framed artificial intelligence and machine learning as a person-versus-machine comparison, at IBM we view the most productive implementation of AI as a symbiosis of person and machine.

Computers are good at rapidly processing enormous amounts of data and looking for hidden patterns and valuable attributes. The human brain is highly adept at other skills, such as generalization and abstraction. Together these make up Augmented Intelligence and the possibility of better decision making in all functions of business, from production and distribution to sales and marketing.

Two core domains are driving the application of machine learning. One is “audience” (customer or consumer) insight -- reaching a new level of personalization by understanding their affinities, traits, likes, dislikes, and how they respond to media. The other is “content” insight -- the enrichment of metadata and understanding what’s in the content to exploit it in new ways, in new formats and across new channels of distribution. Combine these, and M&E companies can apply cognitive insights to improve KPIs, including ad sales, content ultimates, productivity and efficiency, and margin growth.

Consider the ongoing Major League Baseball season. Using cognitive technology, media companies can now take baseball-related video content and better understand it in terms of sentiment -- including a closer look at actions that may indicate crowd excitement, or tuning in to crowd noise and the tone of the sportscaster’s commentary to gauge key moments -- and surface it to fans for a more personalized viewing experience.

Cognitive computing is a natural enhancement to existing business practices that will make day-to-day operations more effective while helping media companies create a better consumer/user experience. AI is a positive disruption, one that demonstrates the art of the possible. Most importantly, it’s an opportunity to thrive in an age of constant change -- the firms that are better equipped to extract insights from data are the likely winners.

Steve Canepa is general manager, global telecommunications, media & entertainment industry, at IBM. Image by John Lund/Getty Images.

ZoneTV, a "digital-first” video service that’s targeting integrations with MVPD set-tops, said it is working with Ooyala and Microsoft to help it automate and fine-tune the creation of a new lineup of OTT-delivered linear-style TV channels that are set to debut this fall.

ZoneTV said it will underpin that effort  with “media lifecycle tools” from Ooyala (its  Flex CMS) alongside Microsoft’s Video Indexer (part of Azure’s Cognitive Services platform), for its coming array of “Dynamic Channels.”

ZoneTV said that combo will give it the ability to curate about 6,000 hours of videos “on the fly” to create those channels. Those tools, it added, uses advanced algorithms to characterize content and auto-extract and analyst metadata into genres, and pull topics from speech and text in a way that optimizes the curation process.

RELATED: ZoneTV Goes on Content Hunt

“We’re building the next generation of pay-TV services, so we need next-generation tools. With the combination of Ooyala’s platform and Microsoft’s cognitive computing services, the ZoneTV Programming Studio delivers an unmatched capability to enhance the TV ecosystem and translate into a one-of-a-kind viewer experience,” Jeff Weber, ZoneTV’s CEO, said in a statement.

ZoneTV, formerly known as ES3, initially targeted Mediaroom, the IPTV middleware platform that Ericsson acquired from Microsoft in the fall of 2013 that powers pay TV services from companies such as AT&T, Hawaiian Telecom, Telus, Frontier Communications and CenturyLink.  DirecTV, now part of AT&T, was ZoneTV’s first launch partner outside the Mediaroom environment. Comcast also integrated ZoneTV’s “Santa Tracker” app on its X1 platform during the 2016 holidays.

RELATED: Comcast’s X1 Takes ‘Santa Tracker’ Over-the-Top

Implementing artificial intelligence systems can be technically challenging and expensive, but it doesn’t have to be.

So says DimensionalMechanics, a startup based in Bellevue, Wash., that claims to have a developed a platform that can put A.I. within reach of a wide range of companies, with an initial  focus on those in the media and entertainment industry.

The goal is to lower that technology and economic bar in a way that makes A.I. “more accessible to organizations” without requiring them to have a technical background in areas such as deep learning and machine learning, company CEO and co-founder Rajeev Dutt, said, noting that many are also looking for A.I. solutions that are not just affordable but customizable as well.

To help achieve some of those goals, DimensionalMechanics has introduced NeoPulse AI Studio, a set of applications based on the company’s underlying framework that, it says, can help businesses and other organizations rapidly create and design customized A.I. solutions. That product complements the company’s pre-built AI models in areas such as image and video analysis and recommendations systems.

The company, which has raised $6.7 million and intends to raise a “B” round this fall, is also getting a boost into the media and entertainment world through a strategic alliance with GrayMeta, a company that specializes in automated metadata collection, curation and search.

GrayMeta, which counts ABC, AMC, CBS, Deluxe, DirecTV, Disney, HBO, NBCUniversal and Showtime among its clients, is also the first to offer NeoPulse AI to the media and entertainment sector, DimensionalMechanics said.

Dutt said the media, entertainment and advertising industries are among the biggest producers and consumers of data, providing a “proving ground for a lot of machine learning technologies.”

Some use-case examples include a photo-ranking system that was “trained” using 2 million images to determine which ones might make an ad or news article more likely to grab attention or drive and maximize traffic. The technology is also being used to help editors analyze and write headlines that can improve click rates.

On the video side, the company also provides A.I. solutions to drive recommendations.

“There’s a fairly broad range of applications,” Dutt said.

DimensionalMechanics has carved out a set of business models, including cloud software for independent developers, on-premises solutions that can simulate the cloud-based system while keeping a company’s data close to the vest, as well as a way for partners to resell and monetize their A.I. models through the NeoPulse AI Store.

Founded in 2015, DimensionalMechanics currently has 11 employees.

Entertainment consulting firm Accenture has been conducting its digital consumers survey for about a decade, but its 2017 edition was the first to include questions around artificial intelligence and technology. And the results were surprising to the company.

“There’s pretty intense interest in AI this year, and a year ago there wasn’t anyone talking about it,” said Charles Hartley, Accenture’s global media and analyst relations manager for communications, media and high-tech businesses. “Consumers aren’t intimidated by it at all, apparently. It’s a sobering message to human beings.”

The survey, which interviewed just under 26,000 consumers across 26 countries, found that 62% of people are comfortable with AI apps — like Amazon’s Alexa — responding to voice queries, even though only 4% of people actually eda standalone, digital, voice-enabled device (like the Amazon Echo or Google Home) at the end of 2016.

Nearly 90% of respondents said AI simply makes it easier to do things, and a third said they’re interested in using voice-enabled digital assistants available in smartphones.

Read more at broadcastingcable.com.

Las Vegas -- Indicating a slow but steady rise in an emerging format, virtual reality (VR) headsets are expected to reach 2.5 million units this year, up 79% versus 2016, while driving $660 million in revenues, a predicted year-on-year jump of 42%, the Consumer Technology Association (CTA) found in a semi-annual industry report released Tuesday.

“This area is really, really new,” Dr. Shawn DuBravac, the CTA’s chief economist, stressed here during a presentation of the group’s top tech trends heading into the show, noting that Sony’s PS VR product didn’t even go on sale until toward the end of 2016.

“[VR] Hardware sales will come slowly, surely,” he said, noting that there will also be a push toward VR-capable PCs that cost less than $1,000, and pointing to Lenovo’s announcement that it’s getting into the business of selling relatively inexpensive VR viewers and VR-ready laptops.

From a broader standpoint, the CTA, in its latest U.S. Consumer Technology Sales and Forecasts report, expects the U.S. consumer technology industry to produce $292 billion in retail revenues in 2017, and to be driven by emerging product categories such as 4K/Ultra HD, digital assistants, smart home products and wearables.

Here’s a snapshot on how CTA expects emerging CE segments to produce this year:

-Smart Home: The category, which includes products like smart thermostats, IP cameras, smart locks, lights and doorbells and smartwatches, will hit sales of 29 million units in 2017, up 63%, and bring in $3.5 billion, up 57%.

-Digital Assistant Devices: Aided by products like Amazon’s Echo and Google Home, unit sales are expected to reach 4.5 million units, up 52%, and $608 million in revenue, up 36%.

-4K/UHD TVs: 2017 shipments are expected to reach 15.6 million units, up 51%, and pump revenues of $14.6 billion, up 38%.

-Drones: Total sales will top 3.4 million units, up 40%, and, for the first time, eclipse $1 billion in revenues. And new use cases are emerging. DuBravac noted that Alarm.com and Qualcomm are working together on a home system that auto-deploys drones to provide a bird’s eye view when an alarm is tripped.

-Wearables: Driven by fitness activity trackers, the category will hit 48 million unit sales this year ,up 14%, and bring in revenues of $5.5 billion, up 3%.

Maturing technology categories (smartphones, TVs, tablets, laptops, and desktops) will contribute almost half of total industry revenue (48%). Among them, LCD TVs are expected to see a 2% decline in in unit volume (39 million units), while tablet sales drop 5%, and smartphones tick up 2%. Laptops will hold steady at 27 million units, with desktops dipping 7%.

During his talk, DuBravac emphasized the rise of voice-controlled computing and AI.

While the word error rate of voice recognition technologies were near 100% in 1995, it improved to about 23% in 2013, and will drop down to “human parity” – or about 5% to 6% -- in 2017, he said.

“The next computer interface is voice” as the technology hits an inflection point and makes the leap from “theoretical to practical,” DuBravac said, noting that he expects to see 700 new apps for Amazon Alexa introduced here over the next four days. 

Adding more A.I. to its digital smorgasbord, Food Network said it has introduced a new chatbot for Facebook Messenger that serves up recipes and “friendly one-on-one conversations.” 

The net said the bot can help users menu suggestions while waiting in line at the grocery store, find seasonal picks from Food Network editors, or browse recipes from Food Network shows and chefs while on the go. Within the Messenger app, users can use the bot for more than 60,000 “trusted” Food Network recipes, the network said. 

“More than ever, consumers rely on mobile to find ideas and inspiration around food – whether searching for a dinner idea, or simply because they’re in the store and see a specific ingredient on sale,” Liesel Kipp, vice president of product management for Scripps Networks Interactive, said in a statement, noting that the bot for Messenger is a “natural addition to our digital offerings.” 

In September, Food Network launched a “skill” on Alexa-powered devices, such as the Amazon Echo, Echo Dot, Amazon Tap and Amazon Fire TV, that enables users to ask for and receive info about network shows, the network's schedule, and recipes that are featured on-air.

Watchwith said it has acquired Arris’s Media  Analysis Framework (MAF), a cloud-based machine learning and automated metadata-generation platform that adds smarts to Watchwith’s native digital advertising platform for premium video.

Arris’s MAF technology is designed to understand the content of video automatically and its context, which plays a big role in deciding when and where native ads (i.e. ads that are placed inside the show itself) are presented in the video that a consumer is watching,  Zane Vella, Watchwith’s CEO, said.

That metadata, he said, brings automation to the cue points in the video where it makes the most sense to place the native ads – which can be interactive overlays or on-screens polls.

And that “machine learning” element? “The more information we feed into it, the smarter it gets,” Vella explained, noting that the analysis is based on the underlying video itself as well as the audio, including natural language processing.

As for context, this placement system for native advertising uses a variety of “recipes” for different types of content, whether it’s a news cast, a sporting event, or a serialized drama.

He said this capability, which is already being used and tested on shows such as USA Network’s Mr. Robot and for campaigns for LG and Toyota, is not only important for digital video delivered on traditional platforms, such as set-top boxes, but also on over-the-top streaming devices and smartphones. “The opportunity and need to monetize those assets in new ways is growing,” he said.

And, for these systems to achieve scale, the decision engine that makes these decisions must shift away from manual processes and instead become automated, which, in turn, reduces operations costs. Watchwith says the new automated process has the potential to eliminate thousands of man-hours.

Watchwith also believes that achieving that level of scale is also vital because programmers must find ways to build and deploy native digital video ad platforms that can compete with those from the likes of Facebook, YouTube and Snapchat.

“At a time when networks are reducing linear ad loads, the need for native digital ad products like Watchwith is higher than ever before,” Vella said.

Watchwith’s partners include Fox, NBCUniversal and Viacom.

Watchwith and Arris aren’t strangers. Arris is an investor in Watchwith, and the two companies have been working together on integration and product development for years. Vella said it was decided that Watchwith’s acquisition of MAF represented the best go-to-market strategy for the technology.

Watchwith and Arris aren’t disclosing the financial terms of the deal, but “several” engineers from Arris, including MAF leader Faisal Ishtiaq, PhD, that developed the technology are joining Watchwith and opening up a new R&D office in Chicago.

The deal comes just the day after Arris agreed to sell its Whole Home Solution assets to Ottawa-based Espial.

Google's efforts to build high-speed services to distribute Internet data may terrify or bemuse operators, depending on their competitive situation and Google's frequent tactical shifts.

But Google's "Magenta" artificial intelligence plan, due to be unveiled on June 1, could trigger  pervasive repercussions throughout the creative and production sector, although its full implementation is likely years away.

Google teased its visions in late May at the "Moogfest"  in North Carolina, where Google researchers disclosed that the open source Magenta venture will draw on the experiences of Google DeepDream and TensorFlow initiatives. Those recent projects are using "deep learning" tools, including artificial intelligence, to delve into creative processes; they are expected to generate new algorithms for information and entertainment -- output that could supplement or replace current creative production functions -- from newsgathering to entertainment to who-knows-what?

At Moogfest, Google's Douglas Eck, a researcher on the Magenta project, a unit within the Google Brain group, said that the new team -- which he expects to include outside academics and possibly industry experts -- will examine the ability of machines plus software to create music, then move on to video and other visual arts.

Potentially, Magenta could go far beyond CGI animation. Eventually, it could become a production approach with a built-in measurement feature to gauge listener and viewer response to the machine-made content.

Details and resources about Magenta will be posted on Google's Githubsite for software developers, starting this week.

Although creative purists may scoff at scripts or performances conceived and produced by a computer, the eventual economics may prove worthy to future fragmented audiences. Fundamentally, the venture will encompass aesthetic challenges about the quality of machine-made content as well as the technical ability to write material and generate images -- what may become an ultimate Turing Test (is the machine-made content indistinguishable from that made by a human?).

Even the Google team acknowledges that there will be human oversight of the machine-generated content -- at last for a while.

Google is building Magenta research based on lessons from its TensorFlow project, an open-source AI platform launched late last year. TensorFlow is based on deep learning, in which software seeks to emulate the way that human brain cells communicate with each other.  In addition, Google DeepDream research has asked computers to generate examples of music and content based on their deep learning of examples by human artists.  Eck characterized the experience as "completely... astonishing improvements in the state of the art" of deep learning.

Magenta is intended to extend that creativity into completely original content -- basically to teach technological systems the principles of creativity and then allow the software to go off on its own.

Robotic? Yes. 

A threat to wretched scriveners and cinematographers? Probably.

Soon? To be determined.

If the Daytona 500 is a big event in your home, Fox Sports’s upcoming marketing of the big NASCAR race on Sunday, Feb. 21, could really rev your engines.

If you don’t already celebrate “Daytona Day,” Fox Sports executive vice president of marketing Robert Gottlieb hopes the campaign will start you up and head you in that direction.

Fox was slated to air a “Happy Daytona Day” 60-second ad during this past Sunday’s NFC Championship game, showing people across America enjoying their Daytona Day rituals and parties, Gottlieb said. Directed by Joseph Kahn, known for his Taylor Swift videos, it’s a centerpiece of pre- Daytona 500 marketing that is the biggest Fox has done since it first aired the race in 2001. [UPDATE: view the "Happy Daytona Day" 60-second promo video here.]

Recently retired four-time NASCAR champion driver Jeff Gordon is the other big component of the campaign, as he’s joining Fox’s booth this year. Fox produced a spot in Los Angeles in which Gordon’s car is being chased by L.A.’s Finest — but with a happier ending than many helicopter-filmed freeway pursuits. It’s slated to air on Feb. 11, Gottlieb said. [UPDATE: view the Jeff Gordon freeway chase ad at this link.]

Sunday’s game also was slated to include a promo in which Gordon and Fox NFL Sunday co-host Michael Strahan talk about how Strahan celebrates Daytona Day, Gottlieb said. Later this month, a The Simpsons spot will also promote the race.

Daytona is a bellwether for the entire NASCAR season and “the Super Bowl of the sport,” the marketer told The Wire, so it seemed apropos to position Daytona Day as a time for festive get-togethers, not unlike the Kentucky Derby or the Super Bowl (which airs Feb. 7 on CBS).

— Kent Gibbons

C-SPAN Offers Food for Thoughtful Coverage

C-SPAN gets style points for its latest campaign coverage promo goodies.

Last August, the public-affairs programmer sent out a mug, luggage tag and pen to national political reporters and 175 key cable-affiliate contacts to promote its “Campaign 2016: Road to the White House” coverage of candidates, by which it generally means letting them speak for themselves rather than pontificating over every word (but we digress).

Last week, as the primary and caucus season began to heat up, C-SPAN upped its game and aimed at the heart, er, stomach, of its key journalistic constituency.

Boxes of “campaign trail” mix and big cookies iced with maps of each of the four primary/caucus cities were sent to 55 key political media folks. The Wire would identify them, as well as 66 cable affiliates (Mediacom Communications, Comcast, Charter Communications, Time Warner Cable and Cox Communications) in key early primary or caucus states, but we’d have to wipe a bit of crumb from the Iowa map off our mustache.

“We tried to have a little fun” with the food, Marty Dominguez, C-SPAN’s vice president of marketing, told The Wire. “As C-SPAN’s Campaign 2016 coverage kicks into high gear in the first primary and caucus states, we wanted to give our media and cable friends a reminder about C-SPAN’s on-of-a-kind media coverage of the candidates along the campaign trail.”

— John Eggerton

And the Winner Isn’t! FCC Loses the ‘Luddite’

We’re not sure anyone was popping champagne at Federal Communications Commission headquarters, but the agency’s decision to reclassify Internet access as a Title II common-carrier service did not win the Internet Technology and Innovation Foundation’s annual Luddite award.

The award is named after Englishman Ned Ludd, “who led a movement in the early 19th century to destroy mechanized looms. [T]oday’s neo-Luddites likewise want to foil technological progress,” ITIF said.

The general public picked the winner.

The just-announced dubious honor, which goes to “the year’s worst anti-technology ideas and policies in action,” went to “alarmist” talk from folks like Elon Musk, Stephen Hawking and Bill Gates that artificial intelligence could “spell doom for humanity,” ITIF said.

The FCC had been nominated, according to ITIF, because, “in an effort to freeze network innovation, the [FCC], at the urging of a wide range of neo-Luddite organizations, enacted regulations to protect ‘net neutrality,’ a rather nebulous concept, generally standing for the principle that broadband networks should treat all data packets alike, even if they have different network needs. This will significantly constrain network innovation.”

That misstep, in the ITIF’s view, was ultimately trumped by warnings from Musk, Hawking and Gates that “in the not-so-distant future, humans could lose control of artificial intelligence (AI), thus creating an existential threat.”

Those optics work out better for one of ITIF’s honorary board members, Rep. Anna Eshoo (D-Calif.), the ranking member of the House Communications Subcommittee and a big proponent of the Title II-based rules.

ISPs would argue, and have argued, that Title II reclassification is a threat to the continued investment in broadband.

— John Eggerton