The week in AI

To say the pace of this sector is moving fast is an understatement :  Links to key advances,  moves and developments.

Microsoft has integrated AI-powered shopping tools into Bing search and Edge, including AI-curated buying guides and AI-powered review summaries.

Salesforce AI Research has released the XGen-7B, an open-source 7B LLM (Large Language Model) trained with 1.5 trillion tokens over an 8K input sequence length.

DreamDiffusion is a groundbreaking technique that generates high-quality images directly from brain EEG signals, bypassing the need to translate thoughts into text.

Microsoft has announced an AI Skills Initiative, offering free coursework in partnership with LinkedIn, a global grant challenge, and increased access to free digital learning events and resources for AI education.

Stability AI has released OpenFlamingo V2, an open-source reproduction of OpenAI’s Flamingo model with over 80% of the original performance.

Unity has introduced two AI-powered tools: Unity Muse, which generates animations and sprites using text and sketches, and Unity Sentis, which embeds an AI model into Unity Runtime for games and applications.

ElevenLabs is launching Voice Library, a platform for sharing AI-generated voices created with their voice design tool.

Merlyn Mind has released three open-source, education-specific LLMs (Large Language Models) aimed at revolutionizing the learning experience.

Amazon’s AWS has launched the Generative AI Innovation Center, a $100 million initiative leveraging AWS machine learning and AI expertise to develop and implement generative AI solutions for businesses.

Zeroscope_v2 XL is an open-source text-to-video AI model that delivers high-quality videos without watermarks.

MotionGPT is a new motion-language model designed for various motion-centric tasks.

Databricks is set to acquire the open-source startup MosaicML for $1.3 billion. MosaicML has released the MPT-30B, an open-source model licensed for commercial use.

According to Indeed’s data, generative AI-related job postings in the US increased by around 20% in May.

The DragGAN algorithm source code has been released, enabling interactive point-based manipulation on the generative image manifold.

Baidu’s ERNIE 3.5, a foundation model, outperforms ChatGPT (3.5) and even GPT-4 in several Chinese language capabilities.

Google is organizing the first-ever Machine Unlearning Challenge on Kaggle.

Adobe is offering to cover claims in lawsuits related to the use of content generated by Adobe Firefly, their generative AI image tool.

Google is launching generative AI coding features exclusively for Pro+ subscribers in the US on Google Colab.

 

Privacy preservation tactics using AI services

  • Use local models first

    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

  • Avoid sharing sensitive personal information on platforms that extensively use generative AI.
  • Use generic identifiers or pseudonyms instead of real personal details when interacting with generative AI models.
  • Report privacy concerns or suspected breaches of private data to the service provider and relevant regulatory authorities.
  • Utilize a Virtual Private Network (VPN) to anonymize user traffic, hiding location and preventing AI tracking.
  • Read and understand the privacy policies of generative AI platforms, ensuring transparency and data protection practices.  Often these are MIA. There is a reason for that.
  • Clear chat history or delete stored conversations to minimize data retention and analysis.  Request a copy first and save it.  Recommend this weekly or daily depending on your usage.
  • Use plug-in’s sparingly as there is no clear understanding of how privacy is preserved across various plug-in providers
  • Don’t use AI browser extensions that have full access to your internet activity.  Only install what you *must* install.
  • Verify if the generative AI service applies encryption and uses secure connections for data transmission.
  • Prioritize well-known and reputable generative AI platforms with a strong commitment to privacy and data protection.
  • Explore privacy-focused tools and browser extensions that block tracking scripts and enhance online privacy.
  • Maintain strong passwords, regularly update software and applications, and consider using a VPN for enhanced privacy protection.

Exploring the Modalities of AI in Song Creation

“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

Generative AI Reading List

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]

 

Unique Characteristics of Generative AI: Exploring the Creative Medium

Unique Characteristics of Generative AI: Exploring the Creative Medium

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.

10 characteristics that define Generative AI media. 

 

  1. Non-deterministic outputs: Generative AI models often produce different results each time they are run, even with the same input. This introduces an element of variability and unpredictability to the creative process.
  2. Latent space exploration: Generative AI models map high-dimensional spaces, allowing for the exploration of a vast array of possibilities and combinations within the latent space. This enables the discovery of novel and surprising outputs.
  3. Style transfer and interpolation: Generative AI allows for the blending and transfer of styles between different inputs, creating unique combinations and aesthetic experiences.
  4. Adaptability and learning: Generative AI systems can be fine-tuned and adapted to specific domains or styles, enabling them to learn and evolve in response to new data and user preferences.
  5. Co-creation with human input: Generative AI models can be used as creative collaborators, augmenting human creativity by providing novel ideas and options for artists, designers, and other creators to work with.
  6. Scalability: The generative nature of AI enables the creation of large quantities of unique content quickly, making it a valuable tool for industries that require rapid content generation, such as advertising, entertainment, and gaming.
  7. Algorithmic and data-driven creativity: Generative AI relies on mathematical algorithms and data-driven processes to generate content, resulting in a unique form of creativity that differs from traditional human-generated art and design.
  8. Emergence and complexity: Due to the complex interactions of AI algorithms and data, generative AI systems can produce intricate and emergent patterns or behaviors that may not be explicitly programmed or anticipated.
  9. Autonomous generation: Generative AI models can create content without direct human intervention, enabling the generation of art, music, or other creative outputs with minimal human guidance.
  10. Real-time generation and adaptation: Generative AI models can be used to create content in real-time, allowing for dynamic and adaptive experiences in fields like video games, virtual reality, and interactive installations.

 

(C) mark ghuneim 2023

The Luring Test: AI and the Engineering of Consumer Trust

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.”

https://lnkd.in/e-kMV5CR

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Entertainment Manufacturing: Navigating the Crossroads of Creativity and Compensation

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.

Happiness POV

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.”