Daily AI Papers
Training Language Models with Language Feedback at Scale

https://t.co/izItHq9Vb4

Imitation learning from Language Feedback (ILF) is a new approach that utilizes more informative language feedback. ILF consists of...

🧵 👇 https://t.co/cmBZWsC7nt
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serious gemini
government aiding industries with taxpayers' money https://t.co/YTU3iPbYqB
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ChatGPT ✨
RT @bentossell: Ben's Bites Search 2.0 🔥 by @transitive_bs

- search any link I've ever included in the newsletter
- pick between semantic search or keyword
- rank by relevance/date etc

take it for a spin

https://t.co/xuj2SUXjzU
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Travis Fischer
Google Bard was at least partially trained on OpenAI's ChatGPT data pulled from @steventey's open source ShareGPT project. 🤯

The potential legal implications of this are mind boggling... https://t.co/sfHoDnwCM9
The cat is finally out of the bag – Google relied heavily on @ShareGPT's data when training Bard.

This was also why we took down ShareGPT's Explore page – which has over 112K shared conversations – last week.

Insanity. https://t.co/34WiLQBD4V - Steven Tey
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AK 🤗 in SF for the Open-Source AI meetup
AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural Representation

abs: https://t.co/aKJm1BIJA3 https://t.co/1z9KoyN0SI
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Ethan Mollick
Its the great AI Turing-Test-Off!

I asked Claude, Bing, Bard & GPT-4 to ask a question, and guess which answer was written by me, and which was written by one of the other AI models

Bing & Claude both thought GPT-4 was human. GPT-4 thought Bard was. No one thought I was human. https://t.co/ZfkLLSXNsS
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Brian Chau (DC 29th-2nd, London May 14th-20th)
RT @jamespoulos: Thou shalt not make a human in the likeness of a machine mind
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Dan | Machine Learning Engineer
RT @DanKornas: AI will not replace you. A person using AI won’t replace you either.

As a programmer, don't rely on AI to do everything for you. Treat it as an assistant to help you with time-consuming tasks. Always aim to learn and practice coding yourself.
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Kei Watanabe
I'm so honored that Glasp (@_Glasp) is on Forbes (@Forbes) now!

Thank you for interviewing and writing about us!

"Democratizing Knowledge: Meet Glasp - An AI Thunderbolt."
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near
when predicting AI's effects, the most common fault I see is to focus on technical predictions over social ones

both paradigms feed into each other in a closely-knit feedback loop; technology does not simply 'progress', rather, people force it to progress with their efforts
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