Trump eliminates Biden AI policies

Trump has signed an Executive Order “eliminating harmful Biden Administration AI policies and enhancing America’s global AI dominance.” (Fact Sheet) In a Fact Sheet he calls Biden’s order(s) dangerous and onerous using the usual stifling innovation argument:

The Biden AI Executive Order established unnecessarily burdensome requirements for companies developing and deploying AI that would stifle private sector innovation and threaten American technological leadership.

There are, however, other components to the rhetoric:

  • It “established the commitment … to sustain and enhance America’s dominance to promote human flourishing, economic competitiveness, and national security.” The human flourishing seems to be
  • It directs the creation of an “AI Action Plan” within 180 days to sustain dominance. Nothing is mentioned about flourishing in regards to the plan. Presumably dominance is flourishing. This plan and review of policies will presumably where we will see the details of implementation. It sounds like the Trump administration may keep some of the infrastructure and policies. Will they, for example, keep the AI Safety Institute in NIST?
  • There is an interesting historic section reflecting back to activities of the first Trump administration noting that “President Trump also took executive action in 2020 to establish the first-ever guidance for Federal agency adoption of AI to more effectively deliver services to the American people and foster public trust in this critical technology.” Note the use of the word “trust”. I wonder if they will return to trustworthy AI language.
  • There is language about how “development of AI systems must be free from ideological bias or engineered social agendas.” My guess is that the target is AIs that don’t have “woke” guardrails.

It will be interesting to track what parts of the Biden orders are eliminated and what parts are kept.

 

Humanity’s Last Exam

Researchers with the Center for AI Safety and Scale AI are gathering submissions for Humanity’s Last Exam. The submission form is here. The idea is to develop an exam with questions from a breadth of academic specializations that current LLMs can’t answer.

While current LLMs achieve very low accuracy on Humanity’s Last Exam, recent history shows benchmarks are quickly saturated — with models dramatically progressing from near-zero to near-perfect performance in a short timeframe. Given the rapid pace of AI development, it is plausible that models could exceed 50% accuracy on HLE by the end of 2025. High accuracy on HLE would demonstrate expert-level performance on closed-ended, verifiable questions and cutting-edge scientific knowledge, but it would not alone suggest autonomous research capabilities or “artificial general intelligence.” HLE tests structured academic problems rather than open-ended research or creative problem-solving abilities, making it a focused measure of technical knowledge and reasoning. HLE may be the last academic exam we need to give to models, but it is far from the last benchmark for AI.

One wonders if it really will be the last exam. Perhaps we will get more complex exams that test for integrated skills. Andrej Karpathy criticises the exam on X. I agree that what we need are AIs able to do intern-level complex tasks rather than just answering questions.

Do we really know how to build AGI?

Sam Altman in a blog post titled Reflections looks back at what OpenAI has done and then predicts that they know how to build AGI,

We are now confident we know how to build AGI as we have traditionally understood it. We believe that, in 2025, we may see the first AI agents “join the workforce” and materially change the output of companies. We continue to believe that iteratively putting great tools in the hands of people leads to great, broadly-distributed outcomes.

It is worth noting that the definition of AGI (Artificial General Intelligence) is sufficiently vague that meeting this target could become a matter of semantics. None the less, here are some definitions of AGI from OpenAI or others about OpenAI,

  • OpenAI’s mission is to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity.” – Note the “economically valuable work”. I wonder if philosophizing or making art is valuable? Is intelligence being limited here to economics?
  • “AI systems that are generally smarter than humans” – This is somewhat circular as brings us back to defining “smartness”, another work for “intelligence”.
  • “any system that can outperform humans at most tasks” – This could be timed to the quote above and the idea of AI agents that can work for companies outperforming humans. It seems to me we are nowhere near this if you include physical tasks.
  • an AI system that can generate at least $100 billion in profits” – This is the definition used by OpenAI and Microsoft to help identify when OpenAI doesn’t have to share technology with Microsoft any more.

Can A.I. Be Blamed for a Teen’s Suicide?

The New York Times has a story about youth who committed suicide after extended interactions with a character on Character.ai. The story, Can A.I. Be Blamed for a Teen’s Suicide? describes how Sewell Setzer III has long discussions with a character called Daenerys Targaryen from the Game of Thrones series. He became isolated and got attached to Daenerys. He eventually shot himself and now his mother is suing Character.ai.

Here is an example of what he wrote in his journal,

I like staying in my room so much because I start to detach from this ‘reality,’ and I also feel more at peace, more connected with Dany and much more in love with her, and just happier.

The suit claims that Character.ai’s product was untested, dangerous and defective. It remains to be seen if these types of suits will succeed. In the meantime we need to be careful with these social AIs.

The 18th Annual Hurtig Lecture 2024: Canada’s Role in Shaping our AI Future

The video for the 2024 Hurtig Lecture is up. The speaker was Dr. Elissa Strome, Executive Director of the Pan-Canadian AI Strategy. She gave an excellent overview of the AI Strategy here in Canada and ended by discussing some of the challenges.

The Hurtig Lecture was organized by my colleague Dr. Yasmeen Abu-Laban. I got to moderate the panel discussion and Q & A after the lecture.

UNESCO – Artificial Intelligence for Information Accessibility (AI4IA) Conference

Yesterday I organized a satellite panel for the UNESCO – Artificial Intelligence for Information Accessibility (AI4IA) Conference. This full conference takes place on GatherTown, a conferencing system that feels like an 8-bit 80s game. You wander around our AI4IA conference space and talk with others who are close and watch short prerecorded video talks of which there are about 60. I’m proud that Amii and the University of Alberta provided the technical support and funding to make the conference possible. The videos will also be up on YouTube for those who don’t make the conference.

The event we organized at the University of Alberta on Friday was an online panel on What is Responsible in Responsible Artificial Intelligence with Bettina Berendt, Florence Chee, Tugba Yoldas, and Katrina Ingram.

Bettina Berendt looked at what the Canadian approach to responsible AI could be and how it might be short sighted. She talked about a project that, like a translator, lets a person “translate” their writing in whistleblowing situations into prose that won’t identify them. It helps you remove the personal identifiable signal from the text. She then pointed out how this might be responsible, but might also lead to problems.

Florence Chee talked about how responsibility and ethics should be a starting point rather than an afterthought.

Tugba Yoldas talked about how meaningful human control is important to responsible AI and what it takes for there to be control.

Katrina Ingram of Ethically Aligned AI nicely wrapped up the short talks by discussing how she advises organizations that want to weave ethics into their work. She talked about the 4 Cs: Context, Culture, Content, and Commitment.

 

System Prompts – Anthropic

From a story on Tech Crunch it seems that Anthropic has made their system prompts public. See System Prompts – Anthropic. For example, the system prompt for Claude 3.5 Sonnet starts with,

<claude_info> The assistant is Claude, created by Anthropic. The current date is {}. Claude’s knowledge base was last updated on April 2024.

These system prompts are fascinating since they describe how Anthropic hopes Claude will behave. A set of commandments, if you will. Anthropic describes the purpose of the system prompts thus:

Claude’s web interface (Claude.ai) and mobile apps use a system prompt to provide up-to-date information, such as the current date, to Claude at the start of every conversation. We also use the system prompt to encourage certain behaviors, such as always providing code snippets in Markdown. We periodically update this prompt as we continue to improve Claude’s responses. These system prompt updates do not apply to the Anthropic API.

South Korea faces deepfake porn ’emergency’

The president has addressed the growing epidemic after Telegram users were found exchanging doctored photos of underage girls.

Once again, deepfake porn is in the news as South Korea faces deepfake porn ’emergency’Teenagers have been posting deepfake porn images of people they know, including minors, on sites like Telegram.

South Korean President Yoon Suk Yeol on Tuesday instructed authorities to “thoroughly investigate and address these digital sex crimes to eradicate them”.

This has gone beyond a rare case in Spain or Winnipeg. In South Korea it has spread to hundreds of schools. Porn is proving to be a major use of AI.

When A.I.’s Output Is a Threat to A.I. Itself

As A.I.-generated data becomes harder to detect, it’s increasingly likely to be ingested by future A.I., leading to worse results.

The New York Times has a terrific article on model collapse, When A.I.’s Output Is a Threat to A.I. Itself. They illustrate what happens when an AI is repeatedly trained on its own output.

Model collapse is likely to become a problem for new generative AI systems trained on the internet which, in turn, is more and more a trash can full of AI generated misinformation. That companies like OpenAI don’t seem to respect the copyright and creativity of others makes is likely that there will be less and less free human data available. (This blog may end up the last source of fresh human text 🙂

The article also has an example of how output can converge and thus lose diversity as it trained on its own output over and over.

Perhaps the biggest takeaway of this research is that high-quality, diverse data is valuable and hard for computers to emulate.

One solution, then, is for A.I. companies to pay for this data instead of scooping it up from the internet, ensuring both human origin and high quality.

Words Used at the Democratic and Republican National Conventions

Counting frequently spoken words and phrases at both events.

The New York Times ran a neat story that used text analysis to visualize the differences between Words Used at the Democratic and Republican National Conventions. They used a number of different visualization including butterfly bar graphs like the one above. They also had a form of word bubbles that I thought was less successful.