I have a PhD in Computational Neuroscience from UCSD (Bachelor’s was in Biomedical Engineering with Math and Computer Science minors). Ever since junior high, I’ve been trying to figure out how to engineer artificial minds, and I’ve been coding up artificial neural networks ever since I first learned to program. Obviously, all my early designs were almost completely wrong/unworkable/poorly defined, but I think my experiences did prime my brain with inductive biases that are well suited for working on AGI.
Although I now work as a data scientist in R&D at a large medical device company, I continue to spend my free time studying the latest developments in AI/ML/DL/RL and neuroscience and trying to come up with models for how to bring it all together into systems that could actually be implemented. Unfortnately, I don’t seem to have much time to develop my ideas into publishable models, but I would love to have the opportunity to share ideas with those who do.
Of course, I’m also very interested in AI Alignment (hence the account here). My ideas on that front mostly fall into the “learn (invertible) generative models of human needs/goals and hook those up to the AI’s own reward signal” camp. I think methods of achieving alignment that depend on restricting the AI’s intelligence or behavior are about as destined to failure in the long term as Prohibition or the War on Drugs in the USA. We need a better theory of what reward signals are for in general (probably something to do with maximizing (minimizing) the attainable (dis)utility with respect to the survival needs of a system) before we can hope to model human values usefully. This could even extend to modeling the “values” of the ecological/socioeconomic/political supersystems in which humans are embedded or of the biological subsystems that are embedded within humans, both of which would be crucial for creating a better future.
As a father of two very young daughters (2 years old and 2 months old), I can really appreciate this. As someone with a background in computational neuroscience and some linguistics/NLP/ML/AI, I’ve loved watching them grow and making educated guesses about what sorts of computations could be going on inside their little brains at each developmental stage.
From the earliest days, when they can’t even focus on our faces, hard as they try (I can tell what you’re trying to do, superior coliculus and fusiform face area; you can do it!), to later on when they’re walking and talking (still working on that theory of mind, though).
Language development has been especially fun to watch. Early on, they love just staring at your mouth as you enunciate the various phonemic sequences of what will become their native language. As they become more aware, you can see them start to comprehend when you use simple sentences to narrate things within their field of attention. And they definitely learn to understand more complex language long before they can talk. Patterns built upon patterns, just like deep transformer models, yet still quite different.
When my daughter began to pronounce words, we started pausing intermittently while reading her favorite books or singing familiar songs, and we would have her complete the last word of each line. I couldn’t help but think of how large language models are often trained to perform next-token prediction in a similar way. Although, it’s clear that the human brain has some sort of extra bias that makes it easier to memorize songs and poetry than prose.
And it’s funny how trying to talk to babies reveals just how much of our adult-level world model we assume when we communicate. Once, when our oldest was trying to use a sippy cup on her own for the first time, we saw her putting it to her mouth like we did but failing to get any water. To help her out, I told her to lift the bottom of her cup to get at the water. She then proceeded to lift the entire cup above her head, which surprisingly did not help her. (Eventually she got it.)
For all their temporary limitations, it’s clear that there is a lot going on inside babies’ heads. You can learn a lot about the human brain and cognitive algorithms and biases by studying them carefully. It’s certainly the cutest way to do so.