My opinion on this has changed at a recent Sequoia event where they compared to iOS. The first ~3 years of the App Store were all kinds of gimmicky apps. I think it just takes a while to process a new thing, figure out what it is and isn't and package it into products. Image:
@karpathy
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Meta’s Llama 3.1 405B: First Open Frontier LLM Available to All
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Huge congrats to @AIatMeta on the Llama 3.1 release!
Few notes: Today, with the 405B model release, is the first time that a frontier-capability LLM is available to everyone to work with and build on. The model appears to be GPT-4 / Claude 3.5 Sonnet grade and the weights are -
AGI Speed Makes AI Interaction Instantly Pleasing and Natural
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This is so cool. Feeling the AGI – you just talk to your computer and it does stuff, instantly. Speed really makes AI so much more pleasing.
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New favorite LLM test reveals inconsistent performance across SOTA models
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Wow, this has just become my favorite LLM test. I missed that this doesn't work but it really doesn't, even for SOTA LLMs. Seems to be a bit hit and miss, e.g. with GPT4o which failed 1/3 times, Claude failed 3/3 times.
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Software Issues: System Prompts and Adversarial Examples
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Of course, it's software.
Easy mode: a bad system prompt update.
Hard mode: an adversarial example in the context. -
System Robustness Against Component Failures and Adversarial Behavior
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I just feel like this is the particular problem but not the *actual* deeper problem. Any part of the system should be allowed to go *crazy*, randomly or even adversarially, and the rest of it should be robust to that. This is what you want, even if robustness is very often at
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Offline Tracker: Synthetic Data Generation for Tesla Self-Driving Networks
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This is not very different from Tesla with self-driving networks. What is the "offline tracker" (presented in AI day)? It is a synthetic data generating process, taking the previous, weaker (or e.g. singleframe, or bounding box only) models, running them over clips in an offline
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LLM Model Size Competition Moving Towards Smaller, Smarter Models
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LLM model size competition is intensifying… backwards! My bet is that we'll see models that "think" very well and reliably that are very very small. There is most likely a setting even of GPT-2 parameters for which most people will consider GPT-2 "smart". The reason current
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Free Educational Content with Revenue from Services
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Good question I do want Eureka Labs to be a proper, self-sustaining business but I also really don't want to gatekeep educational content. My default thinking is that the content itself is free and permissively licensed, the revenue comes from everything else, e.g. running the
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Eureka Labs AI launch announcement with website GitHub and social media
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Website: https://
eurekalabs.ai
GitHub: https://
github.com/EurekaLabsAI
𝕏: @EurekaLabsAI