Today on the twitters, Michael Hendricks asked, “Why do AI people bother with how animal brains work? Most good inventions work by doing things totally unlike how an animal would.”
The short answer is that animal brains can already solve the problems that AI researchers want to solve; so why not look into how they are accomplishing it?
The long answer is that in the end, the algorithms that we ultimately use may end up being dramatically different – but we need a starting point somewhere. By looking at some of the algorithms that have a neural inspiration, it is clear that by thinking about ideas of how the nervous system works machine learning/AI researchers can come up with clear solutions to their problems:
- Neural networks. In the 1940s and 50s, McCulloch, Pitts, and Hebb all contributed to modeling how a nervous system might work. In some sense, neural nets are trapped in this 1940s view of the nervous system; but why not? At an abstract level, it’s close…ish.
- Deep learning. Currently the Hot Shit in machine learning, these are like “neural networks 2.0”. Some quick history: traditionally, neural networks were done one layer at a time, with strict feedforward connectivity. One form of recurrent neural network proposed by Hopfield can be used to memorize patterns, or create ‘memories’. A variant on this, proposed by (computational neuroscientist) Terry Sejnowski and Geoff Hinton is the Boltzmann machine. If you combine multiple layers of Boltzmann machines with ideas from biological development, you get Deep Learning (and you publish it in the journal Neural Computation!).
- Independent Component Analysis. Although this story is possibly apocryphal, one of the earliest algorithms for computing ICA was developed – by Tony Bell and Terry Sejnowski (again) – by thinking about how neurons maximize their information about the physical world.
- Temporal difference learning. To quote from the Scholarpedia page: “This line of research work began with the exploration of Klopf’s 1972 idea of generalized reinforcement which emphasized the importance of sequentiality in a neuronal model of learning”
Additionally, companies like Qualcomm and the Brain Corporation are attempting to use ideas from spiking neural networks to make much more energy efficient devices.
In the other direction, neuroscientists can find that the brain appears to be implementing already-known ML algorithms (see this post on Nicole Rust). Many ideas and many biological specifics will be useless – but research is the hope of finding the tiny fraction of an idea that is useful to a new problem.
Neocognitron was the foundation for the ConvNet. Fukushima came up with the model, LeCun figured out how to train it.
Support Vector Machines This last one is quite interesting, not many people outside the neural computation community know that Support Vector machines were influenced by the neural network community. They were originally called Support Vector Networks.