Category: Artificial Intelligence

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.

 

ASBA Releases Artificial Intelligence Policy Guidance for K-12 Education – Alberta School Boards Association

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.)

AI for Information Accessibility: From the Grassroots to Policy Action

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.

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.

Replaying Japan 2024

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 paper on “Parachuting over the Angel: Nintendo in Mexico” presented by Victor Fernandez. This paper looked at the development of a newsletter and then magazine about Nintendo in Mexico that then spread around Spanish South America.
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    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.

How to Write Poetry Using Copilot

How to Write Poetry Using Copilot is a short guide on how to use Microsoft Copilot to write different genres of poetry. Try it out, it is rather interesting. Here are some of the reasons they give for asking Copilot to write poetry:

  • Create a thoughtful surprise. Why not surprise a loved one with a meaningful poem that will make their day?
  • Add poems to cards. If you’re creating a birthday, anniversary, or Valentine’s Day card from scratch, Copilot can help you write a unique poem for the occasion.
  • Create eye-catching emails. If you’re trying to add humor to a company newsletter or a marketing email that your customers will read, you can have Copilot write a fun poem to spice up your emails.
  • See poetry examples. If you’re looking for examples of different types of poetry, like sonnets or haikus, you can use Copilot to give you an example of one of these poems.

 

Home | Constellate

The new text and data analysis service from JSTOR and Portico.

Thanks to John I have been exploring Constellate. This comes from ITHAKA that has developed JSTOR. Constellate lets you build a dataset from their collections and then visualize the data (see image above.) They also have a Jupyter lab where you can then run notebooks on your data.

They are now experimenting with AI tools.

In Ukraine War, A.I. Begins Ushering In an Age of Killer Robots

Driven by the war with Russia, many Ukrainian companies are working on a major leap forward in the weaponization of consumer technology.

The New York Times has an important story on how, In Ukraine War, A.I. Begins Ushering In an Age of Killer Robots. In short, the existential threat of the overwhelming Russian attack is creating a situation where Ukraine is developing a home-grown autonomous weapons industry that repurposes consumer technologies. Not only are all sorts of countries testing AI powered weapons in Ukraine, the Ukrainians are weaponizing cheap technologies and, in the process, removing a lot of the guardrails.

The pressure to outthink the enemy, along with huge flows of investment, donations and government contracts, has turned Ukraine into a Silicon Valley for autonomous drones and other weaponry.

There isn’t necessarily any “human in the loop” in the cheap systems they are developing. One wonders how the development of this industry will affect other conflicts. Could we see a proliferation of terrorist drone attacks put together following plans circulating on the internet?