"The ReAct model puts these pieces together (Yao et al. 2022, arxiv). ReAct takes three steps iteratively: Thought (about what is needed), Act (choice of action), and Observation (see the outcome of the action). The actions often make use of cognitive assets like search."

"The really exciting applications will be action-driven, where the model acts like an agent choosing actions. And although academics can argue all day about the true definition of AGI, an action-driven LLM is going to look a lot like AGI."

"Famously, LLMs often perform better at question-answering tasks when prompted to “think step by step.” (Kojima et al. 2022, arxiv). But they can do even better if they’re given external resources, or what I call external cognitive assets."

"The secret to OpenAI’s 002-text-davinci model seems to be attributable to a combination of instruction tuning and Reinforcement learning from Human Feedback (RLHF, blogpost), wherein humans rate the success of a given prompt."

"I suspect that the very best results will come from actual reinforcement learning where a system can actually be trained to produce better results as measured via a metric of interest."

"Some startups will become very successful creating powerful feedback loops: Solving a customer pain point (maybe bootstrapping by starting with something very simple), collecting data about how to solve that better, training their models to be more consistent, and iterating. This is roughly what a moat will look like in AI, at least for now. But as the agents get more domain-general, the spaces that can be automated and offerings that are possible will expand."

"Famously, LLMs often perform better at question-answering tasks when prompted to “think step by step.” (Kojima et al. 2022, arxiv)."

"But they can do even better if they’re given external resources, or what I call external cognitive assets."

"ReAct takes three steps iteratively: Thought (about what is needed), Act (choice of action), and Observation (see the outcome of the action). The actions often make use of cognitive assets like search."

"The LLM needs to understand the power of its own tools and it needs to know what kinds of outcomes we the user desire."

"On the left we have the External Cognitive Assets that can supercharge a model’s power. These can be any function that takes text as an input and provides text as an output, including searches, code interpreters, and chats with humans."

"Finally on the right we have the task oriented training that’s needed to make this work well. This is the hard part. Some techniques, like instruction tuning, seem fairly straightforward to implement."

"My hope is that there will be a rebalance of power of algorithms in favor of the consumer, but much remains in the air. I learned a lot building vibecheck.network."