What is artificial intelligence (AI)?
It depends who you ask.
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.
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Modern definitions of what it means to create intelligence are more specific. Francois Chollet, 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.
AI is ubiquitous today, used to recommend what you should buy next online, to understanding what you say to virtual assistants, such as Amazon's Alexa and Apple's Siri, to recognise who and what is in a photo, to spot spam, or detect credit card fraud.
At a very high level, artificial intelligence can be split into two broad types: narrow AI and general AI.
As mentioned above, 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.
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.
New applications of these learning systems are emerging all the time. Graphics card designer Nvidia recently revealed an AI-based system Maxine, which allows people to make good quality video calls, almost regardless of the speed of their internet connection. The system reduces the bandwidth needed for such calls by a factor of 10 by not transmitting the full video stream over the internet and instead animating a small number of static images of the caller, in a manner designed to reproduce the callers facial expressions and movements in real time and to be indistinguishable from the video.
However, as much untapped potential as these systems have, sometimes ambitions for the technology outstrips reality. A case in point are self-driving cars, which themselves are underpinned by AI-powered systems such as computer vision. Electric car company Tesla is lagging some way behind CEO Elon Musk's original timeline for the car's Autopilot system being upgraded to "full self-driving" from the system's more limited assisted-driving capabilities, with the Full Self-Driving option only recently rolled out to a select group of expert drivers as part of a beta testing program.
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.
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A survey conducted among four groups of experts in 2012/13 by AI researchers Vincent C Mller and philosopher Nick Bostrom reported a 50% chance that Artificial General Intelligence (AGI) would be developed between 2040 and 2050, rising to 90% 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 LeCunsay society is nowhere near developing AGI. Given the skepticism 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 society will be disrupted by a general artificial intelligence in the near future.
That said, some AI experts believe such projections are wildly optimistic given our limited understanding of the human brain, and believe that AGI is still centuries away.
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 it was possible for its self-driving Toyota Prius to complete more than 10 journeys of 100 miles each, setting society on a path towards driverless vehicles.
IBM Watson competes on Jeopardy! in January 14, 2011
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 it processed to answer human-posed questions, often in a fraction of a second.
In 2012, another breakthrough heralded AI's potential to tackle a multitude of new tasks previously thought of as too complex for any machine. That year, the AlexNet system decisively triumphed in the ImageNet Large Scale Visual Recognition Challenge. AlexNet's accuracy was such that it halved the error rate compared to rival systems in the image-recognition contest.
AlexNet's performance demonstrated the power of learning systems based on neural networks, a model for machine learning that had existed for decades but that was finally realising its potential due to refinements to architecture and leaps in parallel processing power made possible by Moore's Law. The prowess of machine-learning systems at carrying out computer vision also hit the headlines that year, with Google training a system to recognise an internet favorite: pictures of cats.
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 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.
However, more recently Google refined the training process with AlphaGo Zero, a system that played "completely random" games against itself, and then learnt from the results. Google DeepMind CEO Demis Hassabis has also unveiled a new version of AlphaGo Zero that has mastered the games of chess and shogi.
And AI continues to sprint past new milestones:a system trained by OpenAI has defeated the world's top players in one-on-one matches of the online multiplayer game Dota 2.
That same year, OpenAI created AI agents that invented theirown language to cooperate and achieve their goal more effectively, shortly followed by Facebook training agents to negotiate and even lie.
2020 was the year in which an AI system seemingly gained the ability to write and talk like a human, about almost any topic you could think of.
The system in question, known as Generative Pre-trained Transformer 3 or GPT-3 for short, is a neural network trained on billions of English language articles available on the open web.
From soon after it was made available for testing by the not-for-profit organisation OpenAI, the internet was abuzz with GPT-3's ability to generate articles on almost any topic that was fed to it, articles that at first glance were often hard to distinguish from those written by a human. Similarly impressive results followed in other areas, with its ability to convincingly answer questions on a broad range of topics and even pass for a novice JavaScript coder.
But while many GPT-3 generated articles had an air of verisimilitude, further testing found the sentences generated often didn't pass muster, offering up superficially plausible but confused statements, as well as sometimes outright nonsense.
There's still considerable interest in using the model's natural language understanding as the basis of future services and it is available to select developers to build into software via OpenAI's beta API. It will also be incorporated into future services available via Microsoft's Azure cloud platform.
Perhaps the most striking example of AI's potential came late in 2020, when the Google attention-based neural network AlphaFold 2 demonstrated a result some have called worthy of a Nobel Prize for Chemistry.
The system's ability to look at a protein's building blocks, known as amino acids, and derive that protein's 3D structure could have a profound impact on the rate at which diseases are understood and medicines are developed. In the Critical Assessment of protein Structure Prediction contest, AlphaFold 2 was able to determine the 3D structure of a protein with an accuracy rivaling crystallography, the gold standard for convincingly modelling proteins.
Unlike crystallography, which takes months to return results, AlphaFold 2 can model proteins in hours. With the 3D structure of proteins playing such an important role in human biology and disease, such a speed-up has been heralded as a landmark breakthrough for medical science, not to mention potential applications in other areas where enzymes are used in biotech.
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.
To learn, these systems are fed huge amounts of data, which they then use to learn how to carry out a specific task, such as understanding speech or captioning a photograph. The quality and size of this dataset is important for building a system able to accurately carry out its designated task. For example, if you were building a machine-learning system to predict house prices, the training data should include more than just the property size, but other salient factors such as the number of bedrooms or the size of the garden.
Key to machine learning success are neural networks. These mathematical models are able to tweak internal parameters to change what they output. During training, a neural network is fed datasets that teach it what it should spit out when presented with certain data. 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.
The structure and functioning of neural networks is very loosely based on the connections between neurons in the brain. Neural networks are made up of of interconnected layers of algorithms, which feed data into each other, and which can be trained to carry out specific tasks by modifying the importance attributed to data as it passes between these layers. During training of these neural networks, the weights attached to data as it passes between layers will continue to be varied until the output from the neural network is very close to what is desired, at which point the network will have 'learned' how to carry out a particular task. The desired output could be anything from correctly labelling fruit in an image to predicting when an elevator might fail based on its sensor data.
A subset of machine learning is deep learning, where neural networks are expanded into sprawling networks with a large number of sizeable layers that are trained using massive amounts of data. It is these deep neural networks that have fuelled the current leap forward in the ability of computers to carry out tasks like speech recognition and computer vision.
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There are various types of neural networks, with different strengths and weaknesses. Recurrent Neural Networks (RNN) are a type of neural net particularly well suited to Natural Language Processing (NLP) understanding the meaning of text and speech recognition, while convolutional neural networks have their roots in image recognition, and have uses as diverse as recommender systems and NLP. The design of neural networks is also evolving, with researchers refining a more effective form of deep neural network called long short-term memory or LSTM a type of RNN architecture used for tasks such as NLP and for stock market predictions allowing it to operate fast enough to be used in on-demand systems like Google Translate.
The structure and training of deep neural networks.
Another area of AI research is evolutionary computation, which borrows from Darwin's theory of natural selection, and sees genetic algorithms undergo random mutations and combinations between generations in an attempt to evolve the optimal solution to a given problem.
This approach has even been used to help design AI models, effectively using AI to help build AI. This use of evolutionary algorithms to optimize neural networks is called neuroevolution, and could have an important role to play in helping design efficient AI as the use of intelligent systems becomes more prevalent, particularly as demand for data scientists often outstrips supply. The technique was showcased by Uber AI Labs, which released papers on using genetic algorithms to train deep neural networks for reinforcement learning problems.
Finally, there are expert systems, where computers are programmed with rules that allow them to take a series of decisions based on a large number of inputs, allowing that machine to mimic the behaviour of a human expert in a specific domain. An example of these knowledge-based systems might be, for example, an autopilot system flying a plane.
As outlined above, the biggest breakthroughs for AI research in recent years have been in the field of machine learning, in particular within the field of deep learning.
This has been driven in part by the easy availability of data, but even more so by an explosion in parallel computing power, during which time the use of clusters of graphics processing units (GPUs) to train machine-learning systems has become more prevalent.
Not only do these clusters offer vastly more powerful systems for training machine-learning models, but they are now widely available as cloud services over the internet. Over time the major tech firms, the likes of Google, Microsoft, and Tesla, have moved to using specialised chips tailored to both running, and more recently training, machine-learning models.
An example of one of these custom chips is Google's Tensor Processing Unit (TPU), the latest version of which accelerates the rate at which useful machine-learning models built using Google's TensorFlow software library can infer information from data, as well as the rate at which they can be trained.
These chips are not just used to train up models for DeepMind and Google Brain, but also the models that underpin Google Translate and the image recognition in Google Photos, as well as services that allow the public to build machine-learning models using Google's TensorFlow Research Cloud. The third generation of these chips was unveiled at Google's I/O conference in May 2018, and have since been packaged into machine-learning powerhouses called pods that can carry out more than one hundred thousand trillion floating-point operations per second (100 petaflops). These ongoing TPU upgrades have allowed Google to improve its services built on top of machine-learning models, for instance halving the time taken to train models used in Google Translate.
As mentioned, machine learning is a subset of AI and is generally split into two main categories: supervised and unsupervised learning.
Supervised learning
A common technique for teaching AI systems is by training them using a very large number of labelled examples. These machine-learning systems are fed huge amounts of data, which has been annotated to highlight the features of interest. These might be photos labelled to indicate whether they contain a dog or written sentences that have footnotes to indicate whether the word 'bass' relates to music or a fish. Once trained, the system can then apply these labels to new data, for example to a dog in a photo that's just been uploaded.
This process of teaching a machine by example is called supervised learning and the role of labelling these examples is commonly carried out by online workers, employed through platforms like Amazon Mechanical Turk.
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Training these systems typically requires vast amounts of data, with some systems needing to scour millions of examples to learn how to carry out a task effectively although this is increasingly possible in an age of big data and widespread data mining. Training datasets are huge and growing in size Google's Open Images Dataset has about nine million images, while its labelled video repositoryYouTube-8M links to seven million labelled videos.ImageNet, one of the early databases of this kind, has more than 14 million categorized images. Compiled over two years, it was put together by nearly 50,000 people most of whom were recruited through Amazon Mechanical Turk who checked, sorted, and labelled almost one billion candidate pictures.
In the long run, having access to huge labelled datasets may also prove less important than access to large amounts of compute power.
In recent years, Generative Adversarial Networks (GANs) have been used in machine-learning systems that only require a small amount of labelled data alongside a large amount of unlabelled data, which, as the name suggests, requires less manual work to prepare.
This approach could allow for the increased use of semi-supervised learning, where systems can learn how to carry out tasks using a far smaller amount of labelled data than is necessary for training systems using supervised learning today.
Unsupervised learning
In contrast, unsupervised learning uses a different approach, where algorithms try to identify patterns in data, looking for similarities that can be used to categorise that data.
An example might be clustering together fruits that weigh a similar amount or cars with a similar engine size.
The algorithm isn't set up in advance to pick out specific types of data, it simply looks for data that can be grouped by its similarities, for example Google News grouping together stories on similar topics each day.
Reinforcement learning
A crude analogy for reinforcement learning is rewarding a pet with a treat when it performs a trick. In reinforcement learning, the system attempts to maximise a reward based on its input data, basically going through a process of trial and error until it arrives at the best possible outcome.
An example of reinforcement learning is Google DeepMind's Deep Q-network, which has been used to best human performance in a variety of classic video games. The system is fed pixels from each game and determines various information, such as the distance between objects on screen.
By also looking at the score achieved in each game, the system builds a model of which action will maximise the score in different circumstances, for instance, in the case of the video game Breakout, where the paddle should be moved to in order to intercept the ball.
The approach is also used in robotics research, where reinforcement learning can help teach autonomous robots the optimal way to behave in real-world environments.
Many AI-related technologies are approaching, or have already reached, the 'peak of inflated expectations' in Gartner's Hype Cycle, with the backlash-driven 'trough of disillusionment' lying in wait.
With AI playing an increasingly major role in modern software and services, each of the major tech firms is battling to develop robust machine-learning technology for use in-house and to sell to the public via cloud services.
Each regularly makes headlines for breaking new ground in AI research, although it is probably Google with its DeepMind AI AlphaFold and AlphaGo systems that has probably made the biggest impact on the public awareness of AI.
All of the major cloud platforms Amazon Web Services, Microsoft Azure and Google Cloud Platform provide access to GPU arrays for training and running machine-learning models, with Google also gearing up to let users use its Tensor Processing Units custom chips whose design is optimized for training and running machine-learning models.
All of the necessary associated infrastructure and services are available from the big three, the cloud-based data stores, capable of holding the vast amount of data needed to train machine-learning models, services to transform data to prepare it for analysis, visualisation tools to display the results clearly, and software that simplifies the building of models.
These cloud platforms are even simplifying the creation of custom machine-learning models, with Google offering a service that automates the creation of AI models, called Cloud AutoML. This drag-and-drop service builds custom image-recognition models and requires the user to have no machine-learning expertise.
Cloud-based, machine-learning services are constantly evolving. Amazon now offers a host of AWS offerings designed to streamline the process of training up machine-learning models and recently launched Amazon SageMaker Clarify, a tool to help organizations root out biases and imbalances in training data that could lead to skewed predictions by the trained model.
For those firms that don't want to build their own machine=learning models but instead want to consume AI-powered, on-demand services, such as voice, vision, and language recognition, Microsoft Azure stands out for the breadth of services on offer, closely followed by Google Cloud Platform and then AWS. Meanwhile IBM, alongside its more general on-demand offerings, is also attempting to sell sector-specific AI services aimed at everything from healthcare to retail, grouping these offerings together under its IBM Watson umbrella, and having invested $2bn in buying The Weather Channel to unlock a trove of data to augment its AI services.
Internally, each of the tech giants and others such as Facebook use AI to help drive myriad public services: serving search results, offering recommendations, recognizing people and things in photos, on-demand translation, spotting spam the list is extensive.
But one of the most visible manifestations of this AI war has been the rise of virtual assistants, such as Apple's Siri, Amazon's Alexa, the Google Assistant, and Microsoft Cortana.
The Amazon Echo Plus is a smart speaker with access to Amazon's Alexa virtual assistant built in.
Relying heavily on voice recognition and natural-language processing, as well as needing an immense corpus to draw upon to answer queries, a huge amount of tech goes into developing these assistants.
But while Apple's Siri may have come to prominence first, it is Google and Amazon whose assistants have since overtaken Apple in the AI space Google Assistant with its ability to answer a wide range of queries and Amazon's Alexa with the massive number of 'Skills' that third-party devs have created to add to its capabilities.
Over time, these assistants are gaining abilities that make them more responsive and better able to handle the types of questions people ask in regular conversations. For example, Google Assistant now offers a feature called Continued Conversation, where a user can ask follow-up questions to their initial query, such as 'What's the weather like today?', followed by 'What about tomorrow?' and the system understands the follow-up question also relates to the weather.
These assistants and associated services can also handle far more than just speech, with the latest incarnation of the Google Lens able to translate text in images and allow you to search for clothes or furniture using photos.
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Despite being built into Windows 10, Cortana has had a particularly rough time of late, with Amazon's Alexa now available for free on Windows 10 PCs, while Microsoftrevamped Cortana's role in the operating systemto focus more on productivity tasks, such as managing the user's schedule, rather than more consumer-focused features found in other assistants, such as playing music.
It'd be a big mistake to think the US tech giants have the field of AI sewn up. Chinese firms Alibaba, Baidu, and Lenovo are investing heavily in AI in fields ranging from ecommerce to autonomous driving. As a country China is pursuing a three-step plan to turn AI into a core industry for the country,one that will be worth 150 billion yuan ($22bn) by the end of 2020, withthe aim of becoming the world's leading AI power by 2030.
Baidu has invested in developing self-driving cars, powered by its deep-learning algorithm, Baidu AutoBrain, and, following several years of tests, with its Apollo self-driving car havingracked up more than three million miles of driving in tests and carried over 100,000 passengers in 27 cities worldwide.
Baidu launched a fleet of 40 Apollo Go Robotaxis in Beijing this year and the company's founder has predicted that self-driving vehicles will be common in China's cities within five years.
Baidu's self-driving car, a modified BMW 3 series.
The combination of weak privacy laws, huge investment, concerted data-gathering, and big data analytics by major firms like Baidu, Alibaba, and Tencent, means that some analysts believe China will have an advantage over the US when it comes to future AI research, with one analyst describing the chances of China taking the lead over the US as 500 to one in China's favor.
While you could buy a moderately powerful Nvidia GPU for your PC somewhere around the Nvidia GeForce RTX 2060 or faster and start training a machine-learning model, probably the easiest way to experiment with AI-related services is via the cloud.
All of the major tech firms offer various AI services, from the infrastructure to build and train your own machine-learning models through to web services that allow you to access AI-powered tools such as speech, language, vision and sentiment recognition on-demand.
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What is AI? Everything you need to know about Artificial ...
