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.
Dario Amodei of Anthropic fame has published a long essay on AI titled Machines of Loving Grace: How AI Could Transform the World for Better. In the essay he talks about how he doesn’t like the term AGI and prefers to instead talk about “powerful AI” and he provides a set of characteristics he considers important, including the ability to work on issues in sustained fashion over time.
Amodei also doesn’t worry much about the Singularity as he believes powerful AI will still have to deal with real world problems when designing more powerful AI like building physical systems. I tend to agree.
The point of the essay is, however, to focus on five categories of positive applications of AI that are possible:
The essay is long, so I won’t go into detail. What is important is that he articulates a set of positive goals that AI could help with in these categories. He calls his vision both radical and obvious. In a sense he is right – we have stopped trying to imagine a better world through technology, whether out of cynicism or attention only to details.
Throughout writing this essay I noticed an interesting tension. In one sense the vision laid out here is extremely radical: it is not what almost anyone expects to happen in the next decade, and will likely strike many as an absurd fantasy. Some may not even consider it desirable; it embodies values and political choices that not everyone will agree with. But at the same time there is something blindingly obvious—something overdetermined—about it, as if many different attempts to envision a good world inevitably lead roughly here.
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.
Alberta School Boards Association (ASBA) is pleased to announce the release of its Artificial Intelligence Policy Guidance. As Artificial Intelligence (AI) continues to shape the future of education, ASBA has […]
The ASBA Releases Artificial Intelligence Policy Guidance for K-12 Education – Alberta School Boards Association. This 14 page Policy document is clear and useful without being proscriptive. It could be a model for other educational organizations. (Note that it was authored by someone I supervised.)
It’s vital to “keep humans in the loop” to avoid humanizing machine-learning models in research
Today I was part of a panel organized by the Carnegie Council and the UNESCO Information for All Programme Working Group on AI for Information Accessibility: From the Grassroots to Policy Action. We discussed three issues starting with the issue of environmental sustainability and artificial intelligence, then moving to the issue of principles for AI, and finally policies and regulation. I am in awe of the other speakers who were excellent and introduced new ways of thinking about the issues.
Dariia Opryshko, for example, talked about the dangers of how Too Much Trust in AI Poses Unexpected Threats to the Scientific Process. We run the risk of limiting what we think is knowable to what can be researchers by AI. We also run the risk that we trust only research conducted by AI. Alternatively the misuse of AI could lead to science ceasing to be trusted. The Scientific American article linked to above is based on the research published in Nature on Artificial intelligence and illusions of understanding in scientific research.
I talked about the implications of the sorts of regulations we seen in AIDA (AI and Data Act) in C-27. AIDA takes a risk-management approach to regulating AI where they define a class of dangerous AIs called “high-risk” that will be treated differently. This allows the regulation to be “agile” in the sense that it can be adapted to emerging types of AIs. Right now we might be worried about LLMs and misinformation at scale, but five years from now it may be AIs that manage nuclear reactors. The issue with agility is that it will depend on there being government officers who stay on top of the technology or the government will end up relying on the very companies they are supposed to regulate to advise them. We thus need continuous training and experimentation in government for it to be able to regulate in an agile way.
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.
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.
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.
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.
I just got back from Replaying Japan 2024 which was at the University at Buffalo, SUNY. Taro Yoko was one of the keynotes and he was quite interesting on developing games like Nier Automata that are partly about AI in this age of AI. I was a coauthor of two papers:
A second paper on “The Slogan Game: Missions, Visions and Values in Japanese Game Companies” presented by Keiji Amano. This paper built on work documented in this Spyral notebook, Japanese Game Company Slogans, Missions, Visions, and Values. We gathered various promotional statements of Japanese game companies and analyzed them.
The conference was one of the best Replaying Japan conferences thanks to Mimi Okabe’s hard work. There were lots of participants, including virtual ones, and great papers.
