2022 Letter

In 2008, I was taking COS 217 at Princeton - our final project was to write a program to play Othello competitively. We were told to use alpha-beta-pruning for grading, but that there would be a tournament at the end of class, so we could tweak our programs for that (the assignment was insanely hard, so of course no one had time to tweak).<p>To my surprise, I won the tournament, despite having done the bare implementation with no tweaks! I didn’t get a perfect grade on the assignment though - apparently I had miss implemented the queue for the alpha-beta search, and had completely reversed the queue ordering.<p>Of course, searching “in reverse” through the alpha-beta tree turned out to be the difference that won me the tournament. It was rather humiliating, but a good lesson on sheer luck’s role in academic approaches to AI.

Do any mainstream AGI researchers believe that GPT-style ML methods will plausibly get us to AGI? I have only a very shallow understanding of the state of the art, but from the outside playing with the tools it seems much more likely that we’ll get to a local maximum far below AGI and require a different approach altogether. I’d love to read some discussion on the topic if anyone has good non-hype&#x2F;biased fanboy links to share.

This is a great read, but I keep getting pulled astray by the sheer volume of interesting links that the author has embedded in the text. I&#x27;m less than a third of the way through and have just finished my 3rd rabbit hole exploration.

I think Heinlein hit an essence in The Moon is a Harsh Mistress for AGI.<p>It needs many inputs and many outputs into the real world. A true AGI sees the reaction of the world to its own actions. That is the root of sentience.

Yes, Dan Wang&#x27;s letters are outstanding.

The font on the blog is beautiful

The 286 was featured in the IBM AT, not XT.