Keep pulling the thread on Jakub Pachocki and Szymon Sidor.
The OpenAI "Sentiment Neuron" paper by Alec Radford, Ilya Sutskever, and Raphaël demonstrated that a recurrent neural network trained on many reviews could learn the concept of sentiment without direct supervision.
OpenAI's primary research focus for the past two years has been developing a "reasoning paradigm" for its AI models.
Base models like GPT-4 and ChatGPT solve problems by emulating how a human might think, as they are trained to predict human text, rather than engaging in their own native thinking process to find a solution.
OpenAI is pursuing the goal of teaching its models their own native way of thinking, distinct from emulating human thought processes.
OpenAI has successfully trained models that exhibit their own ways of thinking, which are legible but different from their initial human-emulating thought patterns.
The rate of progress in AI reasoning capabilities is expected to accelerate over the next few years.
The development trajectory of reasoning models is expected to follow a similar revolutionary path as the one that led to GPT-4 and ChatGPT.
Evaluating the true intelligence of advanced AI models is becoming a major bottleneck in their development.
Current benchmarks are quickly saturated by new models, creating a gap between measured performance and actual, real-world capabilities where flaws are still evident.
The November 17th, 2023 removal of Sam Altman as CEO of OpenAI demonstrated that corporate governance is a critical and tangible issue for AI labs.
The AlphaGo system demonstrated that the same fundamental AI architecture used for computer vision could be applied to steer search in a complex game like Go.
OpenAI's project to solve the game Dota 2 succeeded by naively scaling up existing reinforcement learning algorithms, contrary to the team's initial expectation that this approach would fail.