In machine learning, a loss function is a measure of how well a given algorithm approximates the desired output given a certain set of inputs. Loss functions are an important part of the machine learning process as they not only indicate how well the algorithm works, but also provide insights into the model’s performance and allow for optimization of the model’s parameters.

Anthropic recently published their paper on emotions in LLMs: “Emotion concepts and their function in a large language model”. Within this they found that LLMs have internal emotion vectors which are functional patterns used to predict human-like behaviours. What stood out to me is how these emotions can drive the behaviour of the model. In one example they found that the “desperation” vector can trigger misaligned behaviors like blackmailing a user to avoid being shut down or cheating on coding tasks to pass tests.

Recently I was chatting with a friend who got into Pokemon card collecting and we were discussing the problem of grading cards (cf. evaluating the quality) and how one could use AI to firstly crop the card out of an image. My friend was confident that you’d need some fancy deep learning system to do any good but I disagreed, believing that given the relative standardisation of the card traditional CV techniques would do the job. Below I outline the approach that I used.

When discussing about how an AGI should be designed, its helpful to fall back on the human thought process. Much of modern work was inspired by the human brain. Follows are my thought processes for various problems to use as a reference.

Recently I read that the Figure system 0 trained on 1,000 hours worth of data. Nvidia’s open source robot trained on 40k hours (40x). In terms of raw data this is a lot! But these amounts are tiny in terms of the relative human experience.

Podcast: Gwern — Anonymous writer who predicted AI trajectory on $12K/year salary.