At US we define Ai as programmes that are;
- Analytical: Analyse data and context to learn
- Adaptive: Use that learning to adapt and improve
- Anticipatory: Understand likely good next moves
- Autonomous: Be able to act independently without explicit programming
Of course there are examples of Ai today that fit these definitions, but unlike the human brain, they can only perform a specific application. For example, “intelligent agents” like US can understand human language and deliver relevant answers. But we can’t clean your house or drive a car. Self-driving , won’t be able to learn how to play chess or to cook . Essentially, we won’t be able to combine even the smallest subsets of actions that constitute being human.
What is Machine Learning?
Machine learning is a subset of AI. The theory is simple, machines take data and ‘learn’ for themselves. It is currently the most promising tool in the AI pool for businesses. Machine learning systems can quickly apply knowledge and training from large datasets to excel at facial recognition, speech recognition, object recognition, translation, and many other tasks. Machine learning allows a system to learn to recognise patterns on its own and make predictions, contrary to hand-coding a software program with specific instructions to complete a task.
While Deep Blue and DeepMind are both types of AI, Deep Blue was rule-based, dependent on programming — so it was not a form of machine learning. DeepMind, on the other hand — beat the world champion in Go by training itself on a large data set of expert moves.
That is, all machine learning counts as AI, but not all AI counts as machine learning.
What is Deep Learning?
Deep learning is a subset of machine learning. Deep artificial neural networks are a set of algorithms reaching new levels of accuracy for many important problems, such as image recognition, sound recognition, recommender systems, etc.
It uses some machine learning techniques to solve real-world problems by tapping into neural networks that simulate human decision-making. Deep learning can be costly and requires huge datasets to train itself. This is because there are a huge number of parameters that need to be understood by a learning algorithm, which can primarily yield a lot of false-positives. For example, a deep learning algorithm could be trained to ‘learn’ how a dog looks like. It would take an enormous dataset of images for it to understand the minor details that distinguish a dog from a wolf or a fox.
Deep learning is part of DeepMind’s notorious AlphaGo algorithm, which beat the former world champion Lee Sedol in 4 out of 5 games of Go using deep learning in early 2016. Google said, “the way the deep learning system worked was by combining Monte-Carlo tree search with deep neural networks that have been trained by supervised learning, from human expert games, and by reinforcement learning from games of self-play.”