Yesterday I published my brief summary of DeepMind’s paper “Neuroscience-Inspired Artificial Intelligence” – publication about how much different fields of AI are inspired by research in neuroscience.
If you are a techie person like me and you don’t know much about the human brain, there is a really great book which I would like to recommend:
The Tale of the Dueling Neurosurgeons by Sam Kean
It’s not only full of information about processes happening inside our heads or roots of our behaviors. It also shows how do we actually know what we know about our brain.
If you have at least general knowledge about AI, you will find a lot of similarities between engineering and biology. Definitely worth reading to broaden your horizons!
As a technical people, we usually see AI solutions as a bunch of really smart algorithms operating on statistical models, doing nonlinear computations. In general something extremely abstract, what its roots in programming languages.
But, as “neural network” term may suggest, many of those solutions are inspired by biology, primarily biological brain.
Some time ago, DeepMind researchers published paper: Neuroscience-Inspired Artificial Intelligence, where they highlighted some AI techniques which directly or indirectly come from neuroscience. I will try to sum it up, but if you would like to read full version, it can be found under this link:
Roots of AI
One of many definitions describes AI as hypothetical intelligence, created not by nature but artificially, in the engineering process. One of the goals of it is to create human-level, General Artificial Intelligence. Many people argue if such an intelligence is even possible, but there is one thing which proves it: it’s a human brain.
It seems natural that neuroscience is used as a guide or an inspiration for new types of architectures and algorithms. Biological computation very often works better than mathematical and logic-based methods, especially when it comes to cognitive functions.
Moreover, if current, still far-from-ideal AI techniques can be found as a core of brain functioning, it’s pretty likely that in some time in the future engineering effort pays off.
At the end, neuroscience can be also a good validation for existing AI solutions.
In current AI research, there are two key fields which took root in neuroscience — Reinforcement Learning (learning by taking actions in the environment to maximise reward) and Deep Learning (learning from examples such as a training set which correlates data with labels). Continue reading “Where does AI come from? Summary of “Neuroscience-Inspired Artificial Intelligence”“