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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.
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.
At a very high level, artificial intelligence can be split into two broad types:
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 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.
There are a vast number of emerging applications for narrow AI:
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 of 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 is 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.
A survey conducted among four groups of experts in 2012/13 by AI researchers Vincent C Müller 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 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.
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:
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 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.
However, more recently, Google refined the training process with AlphaGo Zero, a system that played "completely random" games against itself and then learned from it. 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 their own language to cooperate and achieve their goal more effectively, followed by Facebook training agents to negotiate and 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.
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 to the basis of future services. 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 profoundly impact the rate at which diseases are understood, and medicines are developed. In the Critical Assessment of protein Structure Prediction contest, AlphaFold 2 determined 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 are important for building a system able to carry out its designated task accurately. 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.
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.
The structure and functioning of neural networks are very loosely based on the connections between neurons in the brain. Neural networks are made up of interconnected layers of algorithms that feed data into each other. They can be trained to carry out specific tasks by modifying the importance attributed to data as it passes between these layers. During the 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 that 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. These deep neural networks have fuelled the current leap forward in the ability of computers to carry out tasks like speech recognition and computer vision.
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.
Another area of AI research is evolutionary computation.
It borrows from Darwin's theory of natural selection. It 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. It 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 used to train up models for DeepMind and Google Brain and the models that underpin Google Translate and the image recognition in Google Photos and 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.
A common technique for teaching AI systems is by training them using many 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. Labelling these examples is commonly carried out by online workers employed through platforms like Amazon Mechanical Turk.
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 repository YouTube-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.
Having access to huge labelled datasets may also prove less important than access to large amounts of computing power in the long run.
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.
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 its similarities can group, for example, Google News grouping together stories on similar topics each day.
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 the 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.
With AI playing an increasingly major role in modern software and services, each major tech firm 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 have 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 tech giant 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.
Relying heavily on voice recognition and natural-language processing and 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 into images and allow you to search for clothes or furniture using photos.
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. At the same time, Microsoft revamped Cortana's role in the operating system to 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, invest heavily in AI in fields ranging from e-commerce 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 to become the world's leading AI power by 2030.
Baidu has invested in developing self-driving cars, powered by its deep-learning algorithm, Baidu AutoBrain. After several years of tests, with its Apollo self-driving car having racked up more than three million miles of driving in tests, it carried over 100 000 passengers in 27 cities worldwide.
Baidu launched a fleet of 40 Apollo Go Robotaxis in Beijing this year. The company's founder has predicted that self-driving vehicles will be common in China's cities within five years.
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 1 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.
Robots and driverless cars
The desire for robots to be able to act autonomously and understand and navigate the world around them means there is a natural overlap between robotics and AI. While AI is only one of the technologies used in robotics, AI is helping robots move into new areas such as self-driving cars, delivery robots and helping robots learn new skills. At the start of 2020, General Motors and Honda revealed the Cruise Origin, an electric-powered driverless car and Waymo, the self-driving group inside Google parent Alphabet, recently opened its robotaxi service to the general public in Phoenix, Arizona, offering a service covering a 50-square mile area in the city.
We are on the verge of having neural networks that can create photo-realistic images or replicate someone's voice in a pitch-perfect fashion. With that comes the potential for hugely disruptive social change, such as no longer being able to trust video or audio footage as genuine. Concerns are also starting to be raised about how such technologies will be used to misappropriate people's images, with tools already being created to splice famous faces into adult films convincingly.
Speech and language recognition
Machine-learning systems have helped computers recognise what people are saying with an accuracy of almost 95%. Microsoft's Artificial Intelligence and Research group also reported it had developed a system that transcribes spoken English as accurately as human transcribers.
With researchers pursuing a goal of 99% accuracy, expect speaking to computers to become increasingly common alongside more traditional forms of human-machine interaction.
Meanwhile, OpenAI's language prediction model GPT-3 recently caused a stir with its ability to create articles that could pass as being written by a human.
Facial recognition and surveillance
In recent years, the accuracy of facial recognition systems has leapt forward, to the point where Chinese tech giant Baidu says it can match faces with 99% accuracy, providing the face is clear enough on the video. While police forces in western countries have generally only trialled using facial-recognition systems at large events, in China, the authorities are mounting a nationwide program to connect CCTV across the country to facial recognition and to use AI systems to track suspects and suspicious behavior, and has also expanded the use of facial-recognition glasses by police.
Although privacy regulations vary globally, it's likely this more intrusive use of AI technology -- including AI that can recognize emotions -- will gradually become more widespread. However, a growing backlash and questions about the fairness of facial recognition systems have led to Amazon, IBM and Microsoft pausing or halting the sale of these systems to law enforcement.
AI could eventually have a dramatic impact on healthcare, helping radiologists to pick out tumors in x-rays, aiding researchers in spotting genetic sequences related to diseases and identifying molecules that could lead to more effective drugs. The recent breakthrough by Google's AlphaFold 2 machine-learning system is expected to reduce the time taken during a key step when developing new drugs from months to hours.
There have been trials of AI-related technology in hospitals across the world. These include IBM's Watson clinical decision support tool, which oncologists train at Memorial Sloan Kettering Cancer Center, and the use of Google DeepMind systems by the UK's National Health Service, where it will help spot eye abnormalities and streamline the process of screening patients for head and neck cancers.
Reinforcing discrimination and bias
A growing concern is the way that machine-learning systems can codify the human biases and societal inequities reflected in their training data. These fears have been borne out by multiple examples of how a lack of variety in the data used to train such systems has negative real-world consequences.
In 2018, an MIT and Microsoft research paper found that facial recognition systems sold by major tech companies suffered from error rates that were significantly higher when identifying people with darker skin, an issue attributed to training datasets being composed mainly of white men.
Another study a year later highlighted that Amazon's Rekognition facial recognition system had issues identifying the gender of individuals with darker skin, a charge that was challenged by Amazon executives, prompting one of the researchers to address the points raised in the Amazon rebuttal.
Since the studies were published, many of the major tech companies have, at least temporarily, ceased selling facial recognition systems to police departments.
Another example of insufficiently varied training data skewing outcomes made headlines in 2018 when Amazon scrapped a machine-learning recruitment tool that identified male applicants as preferable. Today research is ongoing into ways to offset biases in self-learning systems.
AI and global warming
As the size of machine-learning models and the datasets used to train them grows, so does the carbon footprint of the vast compute clusters that shape and run these models. The environmental impact of powering and cooling these compute farms was the subject of a paper by the World Economic Forum in 2018. One 2019 estimate was that the power required by machine-learning systems is doubling every 3.4 months.
The issue of the vast amount of energy needed to train powerful machine-learning models was brought into focus recently by the release of the language prediction model GPT-3, a sprawling neural network with some 175 billion parameters.
While the resources needed to train such models can be immense, and largely only available to major corporations, once trained the energy needed to run these models is significantly less. However, as demand for services based on these models grows, power consumption and the resulting environmental impact again becomes an issue.
One argument is that the environmental impact of training and running larger models needs to be weighed against the potential machine learning has to have a significant positive impact, for example, the more rapid advances in healthcare that look likely following the breakthrough made by Google DeepMind's AlphaFold 2.
Again, it depends on who you ask. As AI-powered systems have grown more capable, so warnings of the downsides have become more dire.
Tesla and SpaceX CEO Elon Musk has claimed that AI is a "fundamental risk to the existence of human civilization". As part of his push for stronger regulatory oversight and more responsible research into mitigating the downsides of AI, he set up OpenAI, a non-profit artificial intelligence research company that aims to promote and develop friendly AI that will benefit society as a whole. Similarly, the esteemed physicist Stephen Hawking warned that once a sufficiently advanced AI is created, it will rapidly advance to the point at which it vastly outstrips human capabilities. A phenomenon is known as a singularity and could pose an existential threat to the human race.
Yet, the notion that humanity is on the verge of an AI explosion that will dwarf our intellect seems ludicrous to some AI researchers.
Chris Bishop, Microsoft's director of research in Cambridge, England, stresses how different the narrow intelligence of AI today is from the general intelligence of humans, saying that when people worry about "Terminator and the rise of the machines and so on? Utter nonsense, yes. At best, such discussions are decades away."
The possibility of artificially intelligent systems replacing much of modern manual labour is perhaps a more credible near-future possibility.
While AI won't replace all jobs, what seems to be certain is that AI will change the nature of work, with the only question being how rapidly and how profoundly automation will alter the workplace.
There is barely a field of human endeavour that AI doesn't have the potential to impact. As AI expert Andrew Ng puts it: "many people are doing routine, repetitive jobs. Unfortunately, technology is especially good at automating routine, repetitive work", saying he sees a "significant risk of technological unemployment over the next few decades".
The evidence of which jobs will be supplanted is starting to emerge. There are now 27 Amazon Go stores and cashier-free supermarkets where customers just take items from the shelves and walk out in the US. What this means for the more than three million people in the US who work as cashiers remains to be seen. Amazon again is leading the way in using robots to improve efficiency inside its warehouses. These robots carry shelves of products to human pickers who select items to be sent out. Amazon has more than 200 000 bots in its fulfilment centers, with plans to add more. But Amazon also stresses that as the number of bots has grown, so has the number of human workers in these warehouses. However, Amazon and small robotics firms are working on automating the remaining manual jobs in the warehouse, so it's not a given that manual and robotic labor will continue to grow hand-in-hand.
Fully autonomous self-driving vehicles aren't a reality yet, but by some predictions, the self-driving trucking industry alone is poised to take over 1.7 million jobs in the next decade, even without considering the impact on couriers and taxi drivers.
Yet, some of the easiest jobs to automate won't even require robotics. At present, there are millions of people working in administration, entering and copying data between systems, chasing and booking appointments for companies as software gets better at automatically updating systems and flagging the important information, so the need for administrators will fall.
As with every technological shift, new jobs will be created to replace those lost. However, what's uncertain is whether these new roles will be created rapidly enough to offer employment to those displaced and whether the newly unemployed will have the necessary skills or temperament to fill these emerging roles.
Not everyone is a pessimist. For some, AI is a technology that will augment rather than replace workers. Not only that, but they argue there will be a commercial imperative to not replace people outright, as an AI-assisted worker -- think a human concierge with an AR headset that tells them exactly what a client wants before they ask for it -- will be more productive or effective than an AI working on its own.
There's a broad range of opinions about how quickly artificially intelligent systems will surpass human capabilities among AI experts.
Oxford University's Future of Humanity Institute asked several hundred machine-learning experts to predict AI capabilities over the coming decades.
Notable dates included AI writing essays that could pass for being written by a human by 2026, truck drivers being made redundant by 2027, AI surpassing human capabilities in retail by 2031, writing a best-seller by 2049, and doing a surgeon's work by 2053.
They estimated there was a relatively high chance that AI beats humans at all tasks within 45 years and automates all human jobs within 120 years.
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