What is artificial intelligence (AI)?

ZDNet

Back in the 1950s, the fathers of the field, Minsky and McCarthy, described artificial intelligence as any task performed by a machine that would have previously been considered to require human intelligence.

That’s obviously a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not.

Modern definitions of what it means to create intelligence are more specific. Francois Chollet, an AI researcher at Google and creator of the machine-learning software library Keras, has said intelligence is tied to a system’s ability to adapt and improvise in a new environment, to generalise its knowledge and apply it to unfamiliar scenarios.

“Intelligence is the efficiency with which you acquire new skills at tasks you didn’t previously prepare for,” he said.

“Intelligence is not skill itself; it’s not what you can do; it’s how well and how efficiently you can learn new things.”

It’s a definition under which modern AI-powered systems, such as virtual assistants, would be characterised as having demonstrated ‘narrow AI’, the ability to generalise their training when carrying out a limited set of tasks, such as speech recognition or computer vision.

Typically, AI systems demonstrate at least some of the following behaviours associated with human intelligence: planning, learning, reasoning, problem-solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity.

What are the uses for AI?

AI is ubiquitous today, used to recommend what you should buy next online, to understand what you say to virtual assistants, such as Amazon’s Alexa and Apple’s Siri, to recognise who and what is in a photo, spot spam, or spot spam detect credit card fraud.

What are the different types of AI?

At a very high level, artificial intelligence can be split into two broad types: 

Narrow AI

Narrow AI is what we see all around us in computers today -- intelligent systems that have been taught or have learned how to carry out specific tasks without being explicitly programmed how to do so.

This type of machine intelligence is evident in the speech and language recognition of the Siri virtual assistant on the Apple iPhone, in the vision-recognition systems on self-driving cars, or in the recommendation engines that suggest products you might like based on what you bought in the past. Unlike humans, these systems can only learn or be taught how to do defined tasks, which is why they are called narrow AI.

General AI

General AI is very different and is the type of adaptable intellect found in humans, a flexible form of intelligence capable of learning how to carry out vastly different tasks, anything from haircutting to building spreadsheets or reasoning about a wide variety of topics based on its accumulated experience. 

This is the sort of AI more commonly seen in movies, the likes of HAL in 2001 or Skynet in The Terminator, but which doesn’t exist today – and AI experts are fiercely divided over how soon it will become a reality.

What can Narrow AI do?

There are a vast number of emerging applications for narrow AI:

Interpreting video feeds from drones carrying out visual inspections of infrastructure such as oil pipelines.

Organizing personal and business calendars.

Responding to simple customer-service queries.

Coordinating with other intelligent systems to carry out tasks like booking a hotel at a suitable time and location.

Helping radiologists to spot potential tumors in X-rays.

Flagging inappropriate content online, detecting wear and tear in elevators from data gathered by IoT devices.

Generating a 3D model of the world from satellite imagery... the list goes on and on.

What can General AI do?

A survey conducted among four groups of experts in 2012/13 by AI researchers Vincent C Müller and philosopher Nick Bostrom reported a 50 per cent chance that Artificial General Intelligence (AGI) would be developed between 2040 and 2050, rising to 90 per cent by 2075. The group went even further, predicting that so-called ‘superintelligence’ – which Bostrom defines as “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest” -- was expected some 30 years after the achievement of AGI. 

However, recent assessments by AI experts are more cautious. Pioneers in the field of modern AI research such as Geoffrey Hinton, Demis Hassabis and Yann LeCun say society is nowhere near developing AGI. Given the scepticism of leading lights in the field of modern AI and the very different nature of modern narrow AI systems to AGI, there is perhaps little basis to fears that a general artificial intelligence will disrupt society in the near future.

What are recent landmarks in the development of AI?

While modern narrow AI may be limited to performing specific tasks, within their specialisms, these systems are sometimes capable of superhuman performance, in some instances even demonstrating superior creativity, a trait often held up as intrinsically human.

There have been too many breakthroughs to put together a definitive list, but some highlights include: 

In 2009 Google showed its self-driving Toyota Prius could complete more than 10 journeys of 100 miles each, setting society on a path towards driverless vehicles.

In 2011, the computer system IBM Watson made headlines worldwide when it won the US quiz show Jeopardy!, beating two of the best players the show had ever produced. To win the show, Watson used natural language processing and analytics on vast repositories of data that is processed to answer human-posed questions, often in a fraction of a second.

The next demonstration of the efficacy of machine-learning systems that caught the public’s attention was the 2016 triumph of the Google DeepMind AlphaGo AI over a human grandmaster in Go, an ancient Chinese game whose complexity stumped computers for decades. Go has about possible 200 moves per turn compared to about 20 in Chess. Over the course of a game of Go, there are so many possible moves that are searching through each of them in advance to identify the best play is too costly from a computational point of view. Instead, AlphaGo was trained how to play the game by taking moves played by human experts in 30 million Go games and feeding them into deep-learning neural networks.

Training these deep learning networks can take a very long time, requiring vast amounts of data to be ingested and iterated over as the system gradually refines its model in order to achieve the best outcome.

What is machine learning?

Practically all of the achievements mentioned so far stemmed from machine learning, a subset of AI that accounts for the vast majority of achievements in the field in recent years. When people talk about AI today, they are generally talking about machine learning. 

Currently enjoying something of a resurgence, in simple terms, machine learning is where a computer system learns how to perform a task rather than being programmed how to do so. This description of machine learning dates all the way back to 1959 when it was coined by Arthur Samuel, a pioneer of the field who developed one of the world’s first self-learning systems, the Samuel Checkers-playing Program.

What are neural networks?

The key to machine learning success is neural networks. These mathematical models are able to tweak internal parameters to change what they output. A neural network is fed datasets that teach it what it should spit out when presented with certain data during training. In concrete terms, the network might be fed greyscale images of the numbers between zero and 9, alongside a string of binary digits -- zeroes and ones -- that indicate which number is shown in each greyscale image. The network would then be trained, adjusting its internal parameters until it classifies the number shown in each image with a high degree of accuracy. This trained neural network could then be used to classify other greyscale images of numbers between zero and 9. Such a network was used in a seminal paper showing the application of neural networks published by Yann LeCun in 1989 and has been used by the US Postal Service to recognise handwritten zip codes.