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.

We had a big family gathering for Christmas which meant we decided to do a Secret Santa for gifts. I made the bold choice of a new keyboard. I wanted to share my experience so far for two reasons: I’m a big fan of custom setups, 2. it requires a decent amount of work.

My undergraduate university recently posed the question to its alumni: if you could pass one message to yourself at the time of graduation, what would it be? I thought this was a decent question to reflect on so I spent some time on it and am now sharing my thoughts below.

Embeddings are at the heart of machine learning. Embeddings allow us to represent any imaginable object as a list of numbers which can be processed by models. This idea is shockingly powerful. Literally anything, a picture of a car, a poem you wrote in fifth grade, the sound of your favourite song or something as abstract as a stream of vibrations in the Earth’s crust. By formulating all these different inputs into a consistent form we can leverage similar techniques to do useful work such as description, prediction and prescription.