You can think of artificial intelligence (AI), machine learning and deep learning as a set of a matryoshka doll, also known as a Russian nesting doll. Deep learning is a subset of machine learning, which is a subset of AI.
Artificial intelligence is any computer program that does something smart. It can be a pile of if-then statements or a complex statistical model. AI can refer to anything from a computer program playing a game of chess, to a voice-recognition system like Amazon’s Alexa interpreting and responding to speech. The technology can broadly be categorized into three groups — Narrow AI, artificial general intelligence (AGI), and superintelligent AI.
IBM’s Deep Blue, which beat chess grandmaster Garry Kasparov at the game in 1996, or Google DeepMind’s AlphaGo, which in 2016 beat Lee Sedol at Go, are examples of narrow AI — AI that is skilled at one specific task. This is different from artificial general intelligence (AGI), which is AI that is considered human-level and can perform a range of tasks. Superintelligent AI takes things a step further. As Nick Bostrom describes it, this is “an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom, and social skills.” In other words, it is when the machines have outfoxed us.
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 kit for businesses. Machine learning systems can quickly apply knowledge and training from large data sets to excel at facial recognition, speech recognition, object recognition, translation, and many other tasks. Unlike hand-coding a software program with specific instructions to complete a task, machine learning allows a system to learn to recognize patterns on its own and make predictions.
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.
Deep learning is a subset of machine learning. Deep artificial neural networks are a set of algorithms setting new records in 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 expensive and requires massive datasets to train itself on. That’s because there are a huge number of parameters that need to be understood by a learning algorithm, which can initially produce a lot of false-positives. For instance, a deep learning algorithm could be instructed to ‘learn’ what a dog looks like. It would take a very massive 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. 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,” according to Google.
(source: Geospatial World)