Had an insightful conversation with @geoffreyhinton about AI and catastrophic risks. Two thoughts we want to share:
(i) It's important that AI scientists reach consensus on risks-similar to climate scientists, who have rough consensus on climate change-to shape good policy.
(ii) Do AI models understand the world? We think they do. If we list out and develop a shared view on key technical questions like this, it will help move us toward consensus on risks.
I learned a lot speaking with Geoff. Let’s all of us in AI keep having conversations to learn from each other!
A broad survey of published methods to "augment" Language Models so they can reason, plan, and use tools to elaborate their answers.
Tools such as search engines, calculators, code interpreters, database queries, etc, can help LLMs produce factual answers.
By @MetaAI - FAIR.
Experts have known for years that current (auto-regressive) LLMs are
- incredible
- create bullshit
- can be useful
- are actually stupid
- aren't actually scary
@luca Thanks for providing this helpful app. It is a good example that shows how the free API access benefitted users and their experience. In this particular case, by supporting the migration to an alternative.
We welcome contributions to 22 workshops at #TheWebConf 2023. Many workshops are still accepting contributions, and some have extended their deadlines. Check out the complete list at https://t.co/ulTeNGSy6l. The conference will take place in Texas on April 30 - May 4 2023.
Bard is an experimental conversational AI service, powered by LaMDA. Built using our large language models and drawing on information from the web, it’s a launchpad for curiosity and can help simplify complex topics → https://t.co/fSp531xKy3
1/ In 2021, we shared next-gen language + conversation capabilities powered by our Language Model for Dialogue Applications (LaMDA). Coming soon: Bard, a new experimental conversational #GoogleAI service powered by LaMDA.
https://t.co/cYo6iYdmQ1
The writing has been on the wall for some time now. I bet future access to the API will be reserved only for commercial purposes.
I would be totally surprised if the free access to the full archive of Twitter for academic research doesn't get axed.
My take: search is a search problem, not a generation problem. Manifold interpolation is a good fit for generative tasks (like producing derivative poetry or images), but doesn't work for search (i.e. information retrieval).
I wondered whether it was just my bias that the home timeline content has become much worse over the past few days. But then Twitter provided the answer.
In 2017, a team led by Andrew Ng published a paper showing off a Deep Learning model to detect pneumonia.
Andrew is one of the most recognized researchers in the world, and the paper showed excellent results.
But there was a big problem with their results:
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