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Margaret Mitchell

meg

http://www.m-mitchell.com

AI & ML interests

natural language processing, computer vision, ethical artificial intelligence, assistive and augmentative technology

Recent Activity

posted an update 5 days ago

New work from my socially-minded colleagues at Hugging Face, creating some foundations for AI companionship behavior evaluation. Evaluation Dataset: https://huggingface.co/datasets/AI-companionship/INTIMA Paper: https://huggingface.co/datasets/AI-companionship/INTIMA/blob/main/Companionship_Benchmark.pdf Work from @giadap , @frimelle , @yjernite .

replied to their post 10 days ago

🤖 ICYMI: Yesterday, Hugging Face and OpenAI partnered to bring open source GPT to the public. This is a Big Deal in "AI world". 0. Common ground setting: OpenAI is the ChatGPT people. An “open source” model is one whose weights are available — that means the model can be “yours”. 1. You don’t have to interact with the company directly, nor give them your interactions, to use the system. The company can't "surveil" you. 2. You can evaluate the unique contributions of their SOTA model much more rigorously than you can when there are collections of models+code behind a closed API. You can find out specifically what the model can and can't do. 3. And you can directly customize it for whatever you'd like. Fine-tuning, wherein you give the model data that's tailored to your use cases and train it some more on that data, is trivial* when you have the model weights. *Provided you have the compute. 4. You can directly benchmark whatever you'd like. Biases? Energy usage? Strengths/weaknesses? Go for it. You wants it you gots it--this transparency helps people understand SOTA *in general*, not just for this model, but points to, e.g., what's going on with closed Google models as well. 5. One of the most powerful things about "openness" that I've learned is that it cultivates ecosystems of collaborators building on top of one another's brilliance to make systems that are significantly better than they would be if created in isolation. But, caveat wrt my own philosophy... 6. I do not take it as a given that advancing LLMs is good, and have a lot more to say wrt where I think innovation should focus more. For example, a focus on *data* -- curation, measurement, consent, credit, compensation, safety -- would deeply improve technology for everyone. 7. The transparency this release provides is massive for people who want to *learn* about LLMs. For the next generation of technologists to advance over the current, they MUST be able to learn about what's happening now. (cont...)

posted an update 10 days ago

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posted an update 5 days ago

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2551

New work from my socially-minded colleagues at Hugging Face, creating some foundations for AI companionship behavior evaluation.
Evaluation Dataset: AI-companionship/INTIMA
Paper: AI-companionship/INTIMA
Work from @giadap , @frimelle , @yjernite .

2 replies

replied to their post 10 days ago

This release provides for that, will full access to:
a. What OpenAI calls "chain of thought" or "reasoning" (actually means a generated sequence of likelihood-based inferences following from the prompt and leading to a final generation).
This is super useful for debugging, explainability, foresight into future outcomes, safety, control.
b. Log probabilities of tokens. That is, the actual numeric values for how likely different outputs are--key to making informed predictions about what the model is most likely to do and not do in different scenarios. (OpenAI had made offered this in some cases before; now there's full control.)
8. Then there are the many other reasons why "openness" in technology is beneficial in general, given it's being released. Everything from enabling accountability to supporting reproducibility to lowering barriers to preserving privacy.

posted an update 10 days ago

Post

340

🤖 ICYMI: Yesterday, Hugging Face and OpenAI partnered to bring open source GPT to the public. This is a Big Deal in "AI world".

0. Common ground setting: OpenAI is the ChatGPT people. An “open source” model is one whose weights are available — that means the model can be “yours”.
1. You don’t have to interact with the company directly, nor give them your interactions, to use the system. The company can't "surveil" you.
2. You can evaluate the unique contributions of their SOTA model much more rigorously than you can when there are collections of models+code behind a closed API. You can find out specifically what the model can and can't do.
3. And you can directly customize it for whatever you'd like. Fine-tuning, wherein you give the model data that's tailored to your use cases and train it some more on that data, is trivial* when you have the model weights.
*Provided you have the compute.
4. You can directly benchmark whatever you'd like. Biases? Energy usage? Strengths/weaknesses? Go for it. You wants it you gots it--this transparency helps people understand SOTA *in general*, not just for this model, but points to, e.g., what's going on with closed Google models as well.
5. One of the most powerful things about "openness" that I've learned is that it cultivates ecosystems of collaborators building on top of one another's brilliance to make systems that are significantly better than they would be if created in isolation.
But, caveat wrt my own philosophy...
6. I do not take it as a given that advancing LLMs is good, and have a lot more to say wrt where I think innovation should focus more. For example, a focus on *data* -- curation, measurement, consent, credit, compensation, safety -- would deeply improve technology for everyone.
7. The transparency this release provides is massive for people who want to *learn* about LLMs. For the next generation of technologists to advance over the current, they MUST be able to learn about what's happening now. (cont...)

1 reply

posted an update 16 days ago

Post

438

🤖 👾 Thanks so much to BBC News and the stellar Suranjana Tewari for having me on to talk about US <—> China relationship in AI, and what it means for AI ethics.

posted an update 4 months ago

Post

2713

New launch: See the energy use of chatbot conversations, in real time. =)
jdelavande/chat-ui-energy
Great work from @JulienDelavande !

reacted to clem's post with 🔥 4 months ago

Post

4064

Energy is a massive constraint for AI but do you even know what energy your chatGPT convos are using?

We're trying to change this by releasing ChatUI-energy, the first interface where you see in real-time what energy your AI conversations consume. Great work from @jdelavande powered by spaces & TGI, available for a dozen of open-source models like Llama, Mistral, Qwen, Gemma and more.

jdelavande/chat-ui-energy

Should all chat interfaces have this? Just like ingredients have to be shown on products you buy, we need more transparency in AI for users!

3 replies

posted an update 7 months ago

Post

3395

💫...And we're live!💫 Seasonal newsletter from ethicsy folks at Hugging Face, exploring the ethics of "AI Agents"
https://huggingface.co/blog/ethics-soc-7
Our analyses found:
- There's a spectrum of "agent"-ness
- *Safety* is a key issue, leading to many other value-based concerns
Read for details & what to do next!
With @evijit , @giadap , and @sasha

Margaret Mitchell

AI & ML interests

Recent Activity

Organizations

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