Where does AI come from? Summary of “Neuroscience-Inspired Artificial Intelligence”

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:

https://deepmind.com/documents/113/Neuron.pdf

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 readingWhere does AI come from? Summary of “Neuroscience-Inspired Artificial Intelligence”

Understanding AlphaGo How AI beat us in Go — game of profound complexity

One of required skills as an Artificial Intelligence engineer is ability to understand and explain highly technical research papers in this field. One of my projects as a student in AI Nanodegree classes is an analysis of seminal paper in the field of Game-Playing. The target of my analysis was Nature’s paper about technical side of AlphaGo — Google Deepmind system which for the first time in history beat elite professional Go player, winning by 5 games to 0 with European Go champion — Fan Hui.

The goal of this summary (and my future publications) is to make this knowledge widely understandable, especially for those who are just starting the journey in field of AI or those who doesn’t have any experience in this area at all.

The original paper — Mastering the game of Go with deep neural networks and tree search:

http://www.nature.com/nature/journal/v529/n7587/full/nature16961.htm

Continue readingUnderstanding AlphaGo How AI beat us in Go — game of profound complexity