Large Language Models (in 2023) Excellent talk on LLMs that highlights important technical details related to LLMs in <50 minutes. From scaling, pre-training to post-training techniques such as instruction tuning and reward modelling. This is a recommended watch for everyone
RESEARCH
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Large Language Models in 2023: Comprehensive Overview and Summary
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Amazing talk by @hwchung27 at Seoul National University *********************
Title of the talk “Large Language Models (in 2023)”. This was an ambitious attempt to summarize our exploding field.
************************** Video: http://
youtu.be/dbo3kNKPaUA
Slides: -
Building Trustworthy AI: A Collective Path Forward
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In the journey towards trustworthy AI and ML, we have miles to traverse. It's a collective endeavor. Let's collaborate and pave the path for a safer AI future. #TowardsTrustworthyAI
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Beyond Alarmism: Proposing Concrete AI Safety Measures
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I absolutely recognize the importance of being aware of risks. When I use the term "alarmist", I'm referring to those who solely focus on the negatives without proposing or actively working on safety measures. Simply suggesting "pausing AI" isn't a viable solution in my view.
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AI Safety and Knowledge Accessibility: Against Elitist AI Development
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I am fully aligned with Yann LeCun's sentiment. There's a vast number of us committed to building AI that's both safe and beneficial, without resorting to needless alarmism. On top of this, I'm genuinely disturbed by the push to confine AI know-how to a selected few. Progress
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Two Continua: Retrieval, Synthesis, and Program Execution
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"There is a continuum between reasoning and retrieval" is half-true. There are two continua: one between pure retrieval and novel program synthesis (with many intermediate points that consist of subroutine retrieval + partial synthesis), and one between exact program execution
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Replicating Lancet Study Prompts with DALL-E 3
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Those with access to DALL-E 3, I would appreciate your notes on attempts replication of this study in The Lancet, using (a) the literal prompts used there, and (b) minor variations thereof. https://
thelancet.com/journals/langl
o/article/PIIS2214-109X(23)00329-7/fulltext
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Beyond Turing: The Comprehension Challenge for AI Systems
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as ever, one thing that would convince me would be systematic success at the comprehension challenge I described nearly a decade ago: https://
newyorker.com/tech/annals-of
-technology/what-comes-after-the-turing-test
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Research papers and acknowledgment to Bindu Reddy
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Note: Links to papers in the image descriptions. Hat tip to @bindureddy for the original post. /17
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LLMs Self-Improvement Through Feedback and Real-Time Learning
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All of these methods explore how LLMs can self-improve based on fine-tuning, implicit human preferences and iterative prompting techniques. LLMs, like humans can take constructive feedback and become better. Now if they can only do it in real-time by just listening /16