A.I. is any computer program that does something smart. It can be a stack of a complex statistical model or if-then statements.
AI can refer to anything from a computer program playing chess, to a voice-recognition system like Alexa.
However,the technology can be broadly 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 beat Lee Sedol at Go in 2016,
are examples of narrow AI — AI that is skilled at one specific task. This is different from AGI — AGI is the intelligence of a machine
that could successfully perform a range of tasks intellectual task that a human being can. On the other hand, 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 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 recognize 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.
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.”