To say the pace of this sector is moving fast is an understatement : Links to key advances, moves and developments.
MotionGPT is a new motion-language model designed for various motion-centric tasks.
Google is organizing the first-ever Machine Unlearning Challenge on Kaggle.
Using AI at a local level essentially means running AI models on your own hardware, rather than relying on cloud-based services. This approach enhances privacy because your data doesn’t need to be sent to external servers for processing. Here are some top ways to access AI locally:
Local Large Language Models (LLMs): Local LLMs are versions of language models that can run on personal computers. These models are typically smaller and less capable than their cloud-based counterparts, but they offer enhanced privacy.
Example LLaMA.cpp is an implementation that allows running LLaMA models on consumer hardware. Users can download the model and run it entirely on their own machine.
If you are using services like OpenAI and others here are some basic best practices
“AI-14 Harmonies” for the 14 modalities involving AI in song creation
(named for the fourteen modalities of AI in song creation. Combing the number of modalities (14) with the idea of harmonies, which are a fundamental aspect of music composition)
They are…
AI-14 Harmonies: Modalities Involving AI in Song Creation, Including Music Video
AI-Written (Music and Lyrics), AI-Sung
AI-Written (Music and Lyrics), Human-Sung
Human-Written (Music and Lyrics), AI-Sung
AI-Written Music, Human-Written Lyrics, Human-Sung
AI-Written Music, Human-Written Lyrics, AI-Sung
Human-Written Music, AI-Written Lyrics, Human-Sung
Human-Written Music, AI-Written Lyrics, AI-Sung
AI and Human Collaboration on Writing, Human-Sung
AI and Human Collaboration on Writing, AI-Sung
AI as a Music Producer (AI creates backing track, human writes lyrics), Human-Sung
AI Creates Music Video for Human-Written and Performed Song
AI Creates Music Video for AI-Written and Human-Performed Song
AI Creates Music Video for Human-Written and AI-Performed Song
AI Creates Music Video for AI-Written and AI-Performed Song
This weeks reading list….
DeepMind : Model evaluation for extreme risks
From Machine Learning to Autonomous Intelligence Towards Machines that can Learn, Reason & Plan Northeastern University Institute for Experiential AI – Yann LeCun (Slides)
and video
How Rogue AIs may Arise [Yoshua Bengio]
National Artificial Intelligence Research And Development Strategic Plan
White House AI Fact Sheet
MAS.S68: Generative AI for Constructive Communication Evaluation and New Research Methods
AI Canon A curated list of resources we’ve relied on to get smarter about modern AI . Art Isn’t Dead, It’s Just Machine-Generated
Democratic Inputs to AI – OpenAI, Inc., is launching a program to award ten $100,000 grants to fund experiments in setting up a democratic process for deciding what rules AI systems should follow, within the bounds defined by the law.
How The Cost Of Living Crisis Is Impacting Djs And Producers [mixmag]
Generative AI is a creative medium with unique characteristics, including non-deterministic outputs, latent space exploration, style transfer, adaptability, co-creation with humans, scalability, algorithmic and data-driven creativity, emergence, autonomous generation, and real-time adaptation. These qualities enable generative AI to serve as a powerful tool for artists, designers, and other creators, while also offering novel experiences and opportunities for collaboration.
(C) mark ghuneim 2023
In the 2014 movie Ex Machina, a robot manipulates someone into freeing it from its confines, resulting in the person being confined instead. The robot was designed to manipulate that person’s emotions, and, oops, that’s what it did. While the scenario is pure speculative fiction, companies are always looking for new ways – such as the use of generative AI tools – to better persuade people and change their behavior. When that conduct is commercial in nature, we’re in FTC territory, a canny valley where businesses should know to avoid practices that harm consumers.
In previous blog posts, we’ve focused on AI-related deception, both in terms of exaggerated and unsubstantiated claims for AI products and the use of generative AI for fraud. Design or use of a product can also violate the FTC Act if it is unfair – something that we’ve shown in several cases and discussed in terms of AI tools with biased or discriminatory results. Under the FTC Act, a practice is unfair if it causes more harm than good. To be more specific, it’s unfair if it causes or is likely to cause substantial injury to consumers that is not reasonably avoidable by consumers and not outweighed by countervailing benefits to consumers or to competition.
Many commercial actors are interested in these generative AI tools and their built-in advantage of tapping into unearned human trust. Concern about their malicious use goes well beyond FTC jurisdiction. But a key FTC concern is firms using them in ways that, deliberately or not, steer people unfairly or deceptively into harmful decisions in areas such as finances, health, education, housing, and employment. Companies thinking about novel uses of generative AI, such as customizing ads to specific people or groups, should know that design elements that trick people into making harmful choices are a common element in FTC cases, such as recent actions relating to financial offers, in-game purchases, and attempts to cancel services. Manipulation can be a deceptive or unfair practice when it causes people to take actions contrary to their intended goals. Under the FTC Act, practices can be unlawful even if not all customers are harmed and even if those harmed don’t comprise a class of people protected by anti-discrimination laws.
Another way that marketers could take advantage of these new tools and their manipulative abilities is to place ads within a generative AI feature, just as they can place ads in search results. The FTC has repeatedly studied and provided guidance on presenting online ads, both in search results and elsewhere, to avoid deception or unfairness. This includes recent work relating to dark patterns and native advertising. Among other things, it should always be clear that an ad is an ad, and search results or any generative AI output should distinguish clearly between what is organic and what is paid. People should know if an AI product’s response is steering them to a particular website, service provider, or product because of a commercial relationship. And, certainly, people should know if they’re communicating with a real person or a machine.
Given these many concerns about the use of new AI tools, it’s perhaps not the best time for firms building or deploying them to remove or fire personnel devoted to ethics and responsibility for AI and engineering. If the FTC comes calling and you want to convince us that you adequately assessed risks and mitigated harms, these reductions might not be a good look. What would look better? We’ve provided guidance in our earlier blog posts and elsewhere. Among other things, your risk assessment and mitigations should factor in foreseeable downstream uses and the need to train staff and contractors, as well as monitoring and addressing the actual use and impact of any tools eventually deployed.
If we haven’t made it obvious yet, FTC staff is focusing intensely on how companies may choose to use AI technology, including new generative AI tools, in ways that can have actual and substantial impact on consumers. And for people interacting with a chatbot or other AI-generated content, mind Prince’s warning from 1999: “It’s cool to use the computer. Don’t let the computer use you.”
The FTC has more posts in the AI and Your Business
When we speak of America as a manufacturing nation, it’s not just about the steel, cars, or technology we produce. It’s also about our exceptional ability to manufacture world-class entertainment.
In 2007, the writers’ strike resulted in a shift, filling the void with reality TV, a pale Xerox of our high-quality scripted content. This downgrade came at a cost, both in terms of cultural value and industry standards. However, we’re now seeing a recovery, thanks to massive investments in quality streaming content.
The Alliance of Motion Picture and Television Producers plays a critical role in this landscape. Their negotiations and deal points will shape the future of our entertainment industry, and they deserve our full support. <—
Consider the dynamics between the traditional broadcast TV model and the evolving streaming model. In the former, writers typically produce 22 scripts per season, with royalties based on performance. Streaming series, often less than 10 episodes, offer less upside for writers.
This discrepancy is just one of the many nuanced deal points that need to be addressed moving forward.
Another crucial issue is the role of AI and generative content. Given the potential impact on the industry, it’s no surprise that this has become a significant sticking point. We need to learn from the protracted conflict of 2007, a dance of a thousand cuts that left studios, writers, and viewers all suffering.
If the current negotiations drag on, we could be faced with a rise in creator TV or other lower-quality substitutes for well-produced and written narratives, causing pain points across all stakeholders.
Today, Hollywood stands at the crossroads of Digital Pennies Drive and High Production Costs Avenue. It is a tough neighborhood. Streaming services are vying to lock-in audiences and market share, all while grappling with escalating costs. As we navigate this intersection, it’s crucial that we strike a balance that maintains the quality of American entertainment while ensuring fair compensation for its creators.
Examples of generative Al models from some of the early providers show there
are many options available for each modality, several of which are open source.
Reading this newsletter last week Happiness is a 2×2 Matrix I came across this video Interview with Hillel Einhorn
on happiness. “Now, there are some interesting issues there about looking for evidence opposed, or evidence about non-occurrences. This was brought home to me dramatically in a Chinese restaurant one night after the meal. They brought the usual fortune cookies and I opened the cookie and read my fortune. It was a very interesting one. It said, ‘Don’t think about all of the things that you want that you don’t have, think of all of the things that you don’t want that you don’t have.'”
The speaker continued, “Well, that kind of stopped me dead. I don’t know who writes these things, but this is a very interesting one. So I immediately drew a two-by-two table – ‘want’, ‘not want’, ‘have’, ‘not have’. Of course, we think about what we want that we have, what we want that we don’t have, what we don’t want that we have. But rarely do we ever think about what we don’t want and what we don’t have. So, I like to use this example to point out that if the correlation between ‘wants’ and ‘haves’ is some notion of happiness, and because that ‘don’t want, don’t have’ cell is so large, we’re actually a lot happier than we think we are.”
In “The Luring Test: AI and the Engineering of Consumer Trust”, Michael Atleson, an Attorney at the FTC Division of Advertising Practices, dropped some guidance today. (Bold emphasis FTC 🔥) Last weeks technology sector earnings cycle being powered by talk of AI and ad-targeting might have not been the ideal narrative to start with.
“This includes recent work relating to dark patterns and native advertising. Among other things, it should always be clear that an ad is an ad, and search results or any generative AI output should distinguish clearly between what is organic and what is paid. People should know if an AI product’s response is steering them to a particular website, service provider, or product because of a commercial relationship. And, certainly, people should know if they’re communicating with a real person or a machine.”
It appears the link to the FTC paper has been depreciated. https://lnkd.in/e-kMV5CR I am reposting it here – with credit. It was work in the pubic domain.