Por un lado, se entiende la dificultad de desplegar de forma segura una tecnología tan inexplorada como esta. Por el otro, qué ridículo es querer chulear con prisas para opacar el evento de Google si luego no tienes los deberes hechos. Se deben escuchar risas desde Google.
RESEARCH
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New Preference Alignment Technique Combines Model Merging and RLHF
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We've seen a lot of new preference alignment techniques lately, but this is a really cool one combining model merging and RLHF.
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Decision Boundaries Analysis for LLM In-Context Learning Properties
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Nice analysis of the properties and smoothness of decision boundaries under different conditions for in-context learning in LLMs by @siyan_zhao, @tungnd_13 and @adityagrover_. https://t.co/wpY9yfV3oB
— Jeff Dean (@JeffDean) 25 juin 2024Nice analysis of the properties and smoothness of decision boundaries under different conditions for in-context learning in LLMs by @siyan_zhao
, @tungnd_13 and @adityagrover_
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Decision Boundaries Sensitivity to Label Names Example Order
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Interesting paper and thanks for the excellent Twitter summary thread! Regarding the decision boundaries being sensitive to label names and example order, I'd be curious what would happen if you tried inserting K different permutations of the N examples (perhaps with different
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Primer Sazze Partners A.I. Day with Professor Andrew Ng
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On July 30th, we're excited to host Primer Sazze Partners A.I. Day with Prof. Andrew Ng as the keynote speaker. I personally learned a lot from Professor Ng's classes during my deep learning journey. He always provides valuable insights in his talks. For example, he introduced
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Parallel Response Generation Model Reduces AI Hallucinations Instantly
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Imaginad un modelo que en un instante pueda generar varias respuestas "en paralelo" para minimizar alucinaciones, analizar y evaluar su propia respuesta, planificar y razonar paso a paso, etc Todo en un segundo o menos.
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Data Wall Threatens AI Progress: Innovation in Data Abundance Needed
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This is spot on from @dwarkesh_sp.
— Alexandr Wang (@alexandr_wang) 25 juin 2024
The data wall is a looming threat to continued AI progress.
Breaking through will require innovation on data abundance.
What’s become clear over the past few years is these AI models are a compression of their data, and that the only way to… https://t.co/836PSMUNo0This is spot on from @dwarkesh_sp
. The data wall is a looming threat to continued AI progress. Breaking through will require innovation on data abundance. What’s become clear over the past few years is these AI models are a compression of their data, and that the only way to -
How LLMs Acquire Factual Knowledge During Pretraining
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How do LLMs learn new facts while pre-training? Excited to have authors @hoyeon_chang and Jinho Park
answer questions on their latest paper "How Do Large Language Models Acquire Factual Knowledge During Pretraining?" Leave questions for the authors: https://
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Agent AI Interpretability: The WYSIWYG Problem Explained
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Agents have a WYSIWYG problem. For text, image, and video generation — the generated artifact is something you can look at and at a glance determine quality. For agents, the series of actions they take and the intermediate results is what they produce – often opaque.
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Raza’s Exceptional Interviewing Skills and AI Engineer Conference Preview
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Raza is an incredible interviewer and conversationalist. in <1hr he got out of me the best succinct recap of the past 1 year of the AI Engineer movement, top AI trends and lessons we are both paying attention to, and the best high level preview of the @aidotengineer conference… https://t.co/L63YFnM6OR pic.twitter.com/LbwfaNrQ9o
— swyx 🐣 (@swyx) 25 juin 2024Raza is an incredible interviewer and conversationalist. in <1hr he got out of me the best succinct recap of the past 1 year of the AI Engineer movement, top AI trends and lessons we are both paying attention to, and the best high level preview of the @aidotengineer conference