Rick Wilson's Reviews > The Alignment Problem: Machine Learning and Human Values

It’s a good overview of a brief moment in technological advancement.

There’s a common thread in machine learning (AI, I'm going to use these terms interchangeably) research that “oh man we got to be really careful and think about how we set up these machines because they may end the world as we know it.” Thankfully this seems to be counterbalanced by the actual empirical research being done, which mostly seems like a lot of fun tricks. Similar to impressing people with your ability to open a jar by smashing it on the ground.

I love the new models coming out. As of April 2022, Open AI's DallE and GPT-3 models are super cool, (hell, I used their Davinci model to help me write a homework assignment last week) but computer “intelligence“ is intelligence the way a stick you found on the ground is like a forest. I’m sure it represents a tiny little part of it, and there’s some really cool stuff happening in the AI field right now, there’s a phenomenal convergence between computing power and new research methods, just a mind-boggling amount of funding, and a lot of brilliant people going into the field. But every time I read a book like this, I get the impression that “intelligence “is just brute force. It’s like breaking into a bank vault by unleashing a large nuclear explosive. Which is cool. But it’s not intelligence. And it’s not close to intelligence. And it always seems like the answer that these authors have is to dissect the wholeness of consciousness and human experience into constituent parts and then try to reconstruct the parts of the whole.

And that’s what this author does, compellingly. He breaks apart a lot of parts of human consciousness and thought and problem solving and then goes on to show how those have been deconstructed into machine learning algorithms. And I’m sure we can go back-and-forth with me saying that this isn’t intelligence, the author saying “ya ha,” and so on, but I find myself unconvinced that we are even on the right track. We are creating some really impressive tricks out of silicone chips, and the field is advancing it’s such a rapid state that it’s hard to keep up. But it seems like a combination of errors in that we don’t understand what’s happening anymore than we really understand ourselves. It’s like driving down a country road that says there’s a town in 10 miles. You drive on for what feels like 20 minutes, the town should be there, and then there’s another sign saying that the town is in 10 miles.


That said, this book was great. It’s a fascinating tour of the state of machine learning circa 2022. I feel like this field flips itself on its head every year, and in five years it will probably be quaint and mostly outdated. But for now I thought it was a great book. With the title “The Alignment Problem,” I thought I’d be a little more oriented towards Nick Bostrom type warnings about the dangers of AI. Instead it’s essentially a tour of an AI museum of modern machine learning models.

I thought it was well told and generally stays between the lines of speculation and hyperbole. There were some times when talking about evolutionarily psychology, I thought the author was getting a little off what my impression of modern research is. It seems like in psychology whenever we say “only humans can do this“ that thing is contradicted by some sort of niche exception almost immediately. Tool use, language, generosity. We think we are really special as humans and are so willing to come up with reasons why we are unique. I just haven’t typically seen that backed up in significant ways in replicable research. That doesn’t necessarily contradict the core of the book, but it’s becoming a pet peeve of mine. I do think the point the author is trying to make is that what separates us from say a reptile or bird is potentially what would separate us from, on the other side of the spectrum, AGI or some sort of intelligent computer. I’ll grant that, but I think there’s a better and more truthful way to portray it.

That said it this is a good book if you’re willing to get into the weeds of how modern AI is set up, the types of different structures a system can be assembled in, who did what where, and why we’ve been using those structures. It’s a fantastic overview and a strong aggregation of what I understand to be an up to date tour of the field.

Also, if you made it this far, here’s a treat (https://arxiv.org/abs/2204.06974)