Nice interview with me about @abraham_ai_ and some thoughts on the emerging generative AI space. This interview was actually done in June, which is why Stable Diffusion is conspicuously missing
AGENTS
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LangChain 0.0.16: Chain Input/Output Comparison and Model Evaluation
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version 0.0.16: compare inputs/outputs on a whole chain Useful for evaluation when you're doing more than just a simple call to an LLM Github: https://
github.com/hwchase17/lang
chain
… How do different models do in @OfirPress
's self-ask w/ search example? https://
colab.research.google.com/drive/1atz4xfZ
LpIHJKD2kf38WnxHv61XU-dwb
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Yann LeCun on Developing Autonomous Artificial Intelligence Systems
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Yann LeCun: How to Develop Autonomous Artificial Intelligence https://
techopedia.com/yann-lecun-how
-to-develop-autonomous-artificial-intelligence/2/34812
… @techopedia #AI #MachineLearning #DataScience #BigData #Analytics #100DaysofCode #IoT #serverless #DEVCommunity #womenwhocode #DeepLearning #DigitalTransformation #Python #TensorFlow #Robots -
Mispredicting scaling potential: Early distraction with reinforcement learning
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But I still mispredicted in how much fertile ground there was in scaling up the paradigm. Like many others in AI I got distracted by Reinforcement Learning too soon, a kind of putting the cart before the horse, …
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Automated Companies Powered Entirely by LLMs Communicating via Text
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automated companies made up just of LLMs (CEO LLM, manager LLMs, IC LLMs), running asynchronously and communicating over a Slack-like interface in text…
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LLMs as Cognitive Engines Orchestrating Compute Infrastructure via Text
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Good post. A lot of interest atm in wiring up LLMs to a wider compute infrastructure via text I/O (e.g. calculator, python interpreter, google search, scratchpads, databases, …). The LLM becomes the "cognitive engine" orchestrating resources, its thought stack trace in raw text
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Ethereum Foundation: No Discrimination Between Robots and Humans
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“…there is no discrimination against robots or humans in the Ethereum ecosystem…” – Ethereum Foundation #AGI #AIAgents #MontrealAI
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Learning Actions from Data Rather Than Building Them In
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We don't need to build it in, because it's very easy to learn from data (cf. infants). And we arguably shouldn't, because what your actions are can change (e.g., moving your hands vs. driving your car).
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People are unpredictable compared to rockets
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Also, I didn't say rockets weren't hard! Just that people are vastly harder because they are inherently unpredictable and no set of inputs will ever produce the same outputs
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Decentralized AI Through Web3 Agents and AI Nodes
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Blockchain.AGI.Eth Web3 AI Agents X AI Nodes = Decentralized AI Preliminary Presentation: https://
montreal.ai/aiagents.pdf #AGI #AINodes #AIAgents #AIToken #MontrealAI