Here's everything you need to know about artificial intelligence
An executive guide to artificial intelligence, from machine learning and general AI to neural networks.
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.
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.
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.
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% 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.
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.
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 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.
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 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.
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.
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.
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.
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.
What are other types of AI?
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.
What is fueling the resurgence in AI?
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.
What are the elements of machine learning?
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 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.
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 its similarities can group, 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 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.
Which are the leading firms in AI?
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.
Which AI services are available?
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.
Which of the major tech firms is winning the AI race?
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.
Which countries are leading the way in AI?
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.
How can I get started with AI?
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.
How will AI change the world?
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.
Fake news
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.
Healthcare
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.
Will AI kill us all?
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."
Will an AI steal your job?
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.
Machine learning is enabling computers to tackle tasks that have, until now, only been carried out by people.
From driving cars to translating speech, machine learning is driving an explosion in the capabilities of artificial intelligence – helping software make sense of the messy and unpredictable real world.
But what exactly is machine learning and what is making the current boom in machine learning possible?
What is machine learning?
At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data.
Those predictions could be answering whether a piece of fruit in a photo is a banana or an apple, spotting people crossing the road in front of a self-driving car, whether the use of the word book in a sentence relates to a paperback or a hotel reservation, whether an email is spam, or recognizing speech accurately enough to generate captions for a YouTube video.
The key difference from traditional computer software is that a human developer hasn't written code that instructs the system how to tell the difference between the banana and the apple.
Instead a machine-learning model has been taught how to reliably discriminate between the fruits by being trained on a large amount of data, in this instance likely a huge number of images labelled as containing a banana or an apple.
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Machine learning may have enjoyed enormous success of late, but it is just one method for achieving artificial intelligence.
At the birth of the field of AI in the 1950s, AI was defined as any machine capable of performing a task that would typically require human intelligence.
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AI systems will generally demonstrate at least some of the following traits: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity.
Alongside machine learning, there are various other approaches used to build AI systems, including evolutionary computation, where algorithms undergo random mutations and combinations between generations in an attempt to "evolve" optimal solutions, and expert systems, where computers are programmed with rules that allow them to mimic the behavior of a human expert in a specific domain, for example an autopilot system flying a plane.
What are the main types of machine learning?
Machine learning is generally split into two main categories: supervised and unsupervised learning.
What is supervised learning?
This approach basically teaches machines by example.
During training for supervised learning, systems are exposed to large amounts of labelled data, for example images of handwritten figures annotated to indicate which number they correspond to. Given sufficient examples, a supervised-learning system would learn to recognize the clusters of pixels and shapes associated with each number and eventually be able to recognize handwritten numbers, able to reliably distinguish between the numbers 9 and 4 or 6 and 8.
However, training these systems typically requires huge amounts of labelled data, with some systems needing to be exposed to millions of examples to master a task.
As a result, the datasets used to train these systems can be vast, with Google's Open Images Dataset having about nine million images, its labeled video repository YouTube-8M linking to seven million labeled videos and ImageNet, one of the early databases of this kind, having more than 14 million categorized images. The size of training datasets continues to grow, with Facebook announcing it had compiled 3.5 billion images publicly available on Instagram, using hashtags attached to each image as labels. Using one billion of these photos to train an image-recognition system yielded record levels of accuracy – of 85.4% – on ImageNet's benchmark.
The laborious process of labeling the datasets used in training is often carried out using crowdworking services, such as Amazon Mechanical Turk, which provides access to a large pool of low-cost labor spread across the globe. For instance, ImageNet was put together over two years by nearly 50,000 people, mainly recruited through Amazon Mechanical Turk. However, Facebook's approach of using publicly available data to train systems could provide an alternative way of training systems using billion-strong datasets without the overhead of manual labeling.
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What is unsupervised learning?
In contrast, unsupervised learning tasks algorithms with identifying patterns in data, trying to spot similarities that split that data into categories.
An example might be Airbnb clustering together houses available to rent by neighborhood, or Google News grouping together stories on similar topics each day.
Unsupervised learning algorithms aren't designed to single out specific types of data, they simply look for data that can be grouped by similarities, or for anomalies that stand out.
What is semi-supervised learning?
The importance of huge sets of labelled data for training machine-learning systems may diminish over time, due to the rise of semi-supervised learning.
As the name suggests, the approach mixes supervised and unsupervised learning. The technique relies upon using a small amount of labelled data and a large amount of unlabelled data to train systems. The labelled data is used to partially train a machine-learning model, and then that partially trained model is used to label the unlabelled data, a process called pseudo-labelling. The model is then trained on the resulting mix of the labelled and pseudo-labelled data.
The viability of semi-supervised learning has been boosted recently by Generative Adversarial Networks (GANs), machine-learning systems that can use labelled data to generate completely new data, which in turn can be used to help train a machine-learning model.
Were semi-supervised learning to become as effective as supervised learning, then access to huge amounts of computing power may end up being more important for successfully training machine-learning systems than access to large, labelled datasets.
What is reinforcement learning?
A way to understand reinforcement learning is to think about how someone might learn to play an old-school computer game for the first time, when they aren't familiar with the rules or how to control the game. While they may be a complete novice, eventually, by looking at the relationship between the buttons they press, what happens on screen and their in-game score, their performance will get better and better.
An example of reinforcement learning is Google DeepMind's Deep Q-network, which has beaten humans in a wide range of vintage video games. The system is fed pixels from each game and determines various information about the state of the game, such as the distance between objects on screen. It then considers how the state of the game and the actions it performs in game relate to the score it achieves.
Over the process of many cycles of playing the game, eventually the system builds a model of which actions will maximize the score in which circumstance, for instance, in the case of the video game Breakout, where the paddle should be moved to in order to intercept the ball.
How does supervised machine learning work?
Everything begins with training a machine-learning model, a mathematical function capable of repeatedly modifying how it operates until it can make accurate predictions when given fresh data.
Before training begins, you first have to choose which data to gather and decide which features of the data are important.
A hugely simplified example of what data features are is given in this explainer by Google, where a machine-learning model is trained to recognize the difference between beer and wine, based on two features, the drinks' color and their alcoholic volume (ABV).
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Each drink is labelled as a beer or a wine, and then the relevant data is collected, using a spectrometer to measure their color and a hydrometer to measure their alcohol content.
An important point to note is that the data has to be balanced, in this instance to have a roughly equal number of examples of beer and wine.
The gathered data is then split, into a larger proportion for training, say about 70%, and a smaller proportion for evaluation, say the remaining 30%. This evaluation data allows the trained model to be tested, to see how well it is likely to perform on real-world data.
Before training gets underway there will generally also be a data-preparation step, during which processes such as deduplication, normalization and error correction will be carried out.
The next step will be choosing an appropriate machine-learning model from the wide variety available. Each have strengths and weaknesses depending on the type of data, for example some are suited to handling images, some to text, and some to purely numerical data.
Predictions made using supervised learning are split into two main types, classification, where the model is labelling data as predefined classes, for example identifying emails as spam or not spam, and regression, where the model is predicting some continuous value, such as house prices.
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How does supervised machine-learning training work?
Basically, the training process involves the machine-learning model automatically tweaking how it functions until it can make accurate predictions from data, in the Google example, correctly labeling a drink as beer or wine when the model is given a drink's color and ABV.
A good way to explain the training process is to consider an example using a simple machine-learning model, known as linear regression with gradient descent. In the following example, the model is used to estimate how many ice creams will be sold based on the outside temperature.
Imagine taking past data showing ice cream sales and outside temperature, and plotting that data against each other on a scatter graph – basically creating a scattering of discrete points.
To predict how many ice creams will be sold in future based on the outdoor temperature, you can draw a line that passes through the middle of all these points, similar to the illustration below.
Image: Nick Heath / ZDNet
Once this is done, ice cream sales can be predicted at any temperature by finding the point at which the line passes through a particular temperature and reading off the corresponding sales at that point.
Bringing it back to training a machine-learning model, in this instance training a linear regression model would involve adjusting the vertical position and slope of the line until it lies in the middle of all of the points on the scatter graph.
At each step of the training process, the vertical distance of each of these points from the line is measured. If a change in slope or position of the line results in the distance to these points increasing, then the slope or position of the line is changed in the opposite direction, and a new measurement is taken.
In this way, via many tiny adjustments to the slope and the position of the line, the line will keep moving until it eventually settles in a position which is a good fit for the distribution of all these points. Once this training process is complete, the line can be used to make accurate predictions for how temperature will affect ice cream sales, and the machine-learning model can be said to have been trained.
While training for more complex machine-learning models such as neural networks differs in several respects, it is similar in that it can also use a gradient descent approach, where the value of "weights", variables that are combined with the input data to generate output values, are repeatedly tweaked until the output values produced by the model are as close as possible to what is desired.
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How do you evaluate machine-learning models?
Once training of the model is complete, the model is evaluated using the remaining data that wasn't used during training, helping to gauge its real-world performance.
When training a machine-learning model, typically about 60% of a dataset is used for training. A further 20% of the data is used to validate the predictions made by the model and adjust additional parameters that optimize the model's output. This fine tuning is designed to boost the accuracy of the model's prediction when presented with new data.
For example, one of those parameters whose value is adjusted during this validation process might be related to a process called regularisation. Regularisation adjusts the output of the model so the relative importance of the training data in deciding the model's output is reduced. Doing so helps reduce overfitting, a problem that can arise when training a model. Overfitting occurs when the model produces highly accurate predictions when fed its original training data but is unable to get close to that level of accuracy when presented with new data, limiting its real-world use. This problem is due to the model having been trained to make predictions that are too closely tied to patterns in the original training data, limiting the model's ability to generalise its predictions to new data. A converse problem is underfitting, where the machine-learning model fails to adequately capture patterns found within the training data, limiting its accuracy in general.
The final 20% of the dataset is then used to test the output of the trained and tuned model, to check the model's predictions remain accurate when presented with new data.
Why is domain knowledge important?
Another important decision when training a machine-learning model is which data to train the model on. For example, if you were trying to build a model to predict whether a piece of fruit was rotten you would need more information than simply how long it had been since the fruit was picked. You'd also benefit from knowing data related to changes in the color of that fruit as it rots and the temperature the fruit had been stored at. Knowing which data is important to making accurate predictions is crucial. That's why domain experts are often used when gathering training data, as these experts will understand the type of data needed to make sound predictions.
What are neural networks and how are they trained?
A very important group of algorithms for both supervised and unsupervised machine learning are neural networks. These underlie much of machine learning, and while simple models like linear regression used can be used to make predictions based on a small number of data features, as in the Google example with beer and wine, neural networks are useful when dealing with large sets of data with many features.
Neural networks, whose structure is loosely inspired by that of the brain, are interconnected layers of algorithms, called neurons, which feed data into each other, with the output of the preceding layer being the input of the subsequent layer.
Each layer can be thought of as recognizing different features of the overall data. For instance, consider the example of using machine learning to recognize handwritten numbers between 0 and 9. The first layer in the neural network might measure the intensity of the individual pixels in the image, the second layer could spot shapes, such as lines and curves, and the final layer might classify that handwritten figure as a number between 0 and 9.
The network learns how to recognize the pixels that form the shape of the numbers during the training process, by gradually tweaking the importance of data as it flows between the layers of the network. This is possible due to each link between layers having an attached weight, whose value can be increased or decreased to alter that link's significance. At the end of each training cycle the system will examine whether the neural network's final output is getting closer or further away from what is desired – for instance, is the network getting better or worse at identifying a handwritten number 6. To close the gap between between the actual output and desired output, the system will then work backwards through the neural network, altering the weights attached to all of these links between layers, as well as an associated value called bias. This process is called back-propagation.
Eventually this process will settle on values for these weights and the bias that will allow the network to reliably perform a given task, such as recognizing handwritten numbers, and the network can be said to have "learned" how to carry out a specific task.
An illustration of the structure of a neural network and how training works.
Image: Nvidia
What is deep learning and what are deep neural networks?
A subset of machine learning is deep learning, where neural networks are expanded into sprawling networks with a large number of layers containing many units 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 task like speech recognition and computer vision.
There are various types of neural networks, with different strengths and weaknesses. Recurrent neural networks are a type of neural net particularly well suited to language processing and speech recognition, while convolutional neural networks are more commonly used in image recognition. The design of neural networks is also evolving, with researchers recently devising a more efficient design for an effective type of deep neural network called long short-term memory or LSTM, allowing it to operate fast enough to be used in on-demand systems like Google Translate.
The AI technique of evolutionary algorithms is even being used to optimize neural networks, thanks to a process called neuroevolution. The approach was showcased by Uber AI Labs, which released papers on using genetic algorithms to train deep neural networks for reinforcement learning problems.
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Is machine learning carried out solely using neural networks?
Not at all. There are an array of mathematical models that can be used to train a system to make predictions.
A simple model is logistic regression, which despite the name is typically used to classify data, for example spam vs not spam. Logistic regression is straightforward to implement and train when carrying out simple binary classification, and can be extended to label more than two classes.
Another common model type are Support Vector Machines (SVMs), which are widely used to classify data and make predictions via regression. SVMs can separate data into classes, even if the plotted data is jumbled together in such a way that it appears difficult to pull apart into distinct classes. To achieve this, SVMs perform a mathematical operation called the kernel trick, which maps data points to new values, such that they can be cleanly separated into classes.
The choice of which machine-learning model to use is typically based on many factors, such as the size and the number of features in the dataset, with each model having pros and cons.
Why is machine learning so successful?
While machine learning is not a new technique, interest in the field has exploded in recent years.
This resurgence follows a series of breakthroughs, with deep learning setting new records for accuracy in areas such as speech and language recognition, and computer vision.
What's made these successes possible are primarily two factors; one is the vast quantities of images, speech, video and text available to train machine-learning systems.
But even more important has been the advent of vast amounts of parallel-processing power, courtesy of modern graphics processing units (GPUs), which can be clustered together to form machine-learning powerhouses.
Today anyone with an internet connection can use these clusters to train machine-learning models, via cloud services provided by firms like Amazon, Google and Microsoft.
As the use of machine learning has taken off, so companies are now creating specialized hardware tailored to running and training machine-learning models. An example of one of these custom chips is Google's Tensor Processing Unit (TPU), which accelerates the rate at which machine-learning models built using Google's TensorFlow software library can infer information from data, as well as the rate at which these models can be trained.
These chips are not just used to train models for Google DeepMind and Google Brain, but also the models that underpin Google Translate and the image recognition in Google Photo, 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).
In 2020, Google said its fourth-generation TPUs were 2.7 times faster than previous gen TPUs in MLPerf, a benchmark which measures how fast a system can carry out inference using a trained ML model. 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 hardware becomes increasingly specialized and machine-learning software frameworks are refined, it's becoming increasingly common for ML tasks to be carried out on consumer-grade phones and computers, rather than in cloud datacenters. In the summer of 2018, Google took a step towards offering the same quality of automated translation on phones that are offline as is available online, by rolling out local neural machine translation for 59 languages to the Google Translate app for iOS and Android.
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What is AlphaGo?
Perhaps the most famous demonstration of the efficacy of machine-learning systems is the 2016 triumph of the Google DeepMind AlphaGo AI over a human grandmaster in Go, a feat that wasn't expected until 2026. Go is an ancient Chinese game whose complexity bamboozled computers for decades. Go has about 200 possible 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 standpoint. 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 the deep-learning networks needed 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.
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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. At the Neural Information Processing Systems (NIPS) conference in 2017, Google DeepMind CEO Demis Hassabis revealed AlphaZero, a generalized version of AlphaGo Zero, had also mastered the games of chess and shogi.
DeepMind continue to break new ground in the field of machine learning. In July 2018, DeepMind reported that its AI agents had taught themselves how to play the 1999 multiplayer 3D first-person shooter Quake III Arena, well enough to beat teams of human players. These agents learned how to play the game using no more information than available to the human players, with their only input being the pixels on the screen as they tried out random actions in game, and feedback on their performance during each game.
More recently DeepMind demonstrated an AI agent capable of superhuman performance across multiple classic Atari games, an improvement over earlier approaches where each AI agent could only perform well at a single game. DeepMind researchers say these general capabilities will be important if AI research is to tackle more complex real-world domains.
The most impressive application of DeepMind's research came in late 2020, when it revealed AlphaFold 2, a system whose capabilities have been heralded as a landmark breakthrough for medical science.
AlphaFold 2 is an attention-based neural network that has the potential to significantly increase the pace of drug development and disease modelling. The system can map the 3D structure of proteins simply by analysing their building blocks, known as amino acids. In the Critical Assessment of protein Structure Prediction contest, AlphaFold 2 was able to determine the 3D structure of a protein with an accuracy rivalling crystallography, the gold standard for convincingly modelling proteins. However, while it takes months for crystallography to return results, AlphaFold 2 can accurately model protein structures in hours.
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What is machine learning used for?
Machine learning systems are used all around us and today are a cornerstone of the modern internet.
Machine-learning systems are used to recommend which product you might want to buy next on Amazon or which video you might want to watch on Netflix.
Every Google search uses multiple machine-learning systems, to understand the language in your query through to personalizing your results, so fishing enthusiasts searching for "bass" aren't inundated with results about guitars. Similarly Gmail's spam and phishing-recognition systems use machine-learning trained models to keep your inbox clear of rogue messages.
One of the most obvious demonstrations of the power of machine learning are virtual assistants, such as Apple's Siri, Amazon's Alexa, the Google Assistant, and Microsoft Cortana.
Each relies heavily on machine learning to support their voice recognition and ability to understand natural language, as well as needing an immense corpus to draw upon to answer queries.
But beyond these very visible manifestations of machine learning, systems are starting to find a use in just about every industry. These exploitations include: computer vision for driverless cars, drones and delivery robots; speech and language recognition and synthesis for chatbots and service robots; facial recognition for surveillance in countries like China; 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 in healthcare; allowing for predictive maintenance on infrastructure by analyzing IoT sensor data; underpinning the computer vision that makes the cashierless Amazon Go supermarket possible, offering reasonably accurate transcription and translation of speech for business meetings – the list goes on and on.
In 2020, OpenAI's GPT-3 (Generative Pre-trained Transformer 3) made headlines for its ability to write like a human, about almost any topic you could think of.
GPT-3 is a neural network trained on billions of English language articles available on the open web and can generate articles and answers in response to text prompts. While at first glance it was often hard to distinguish between text generated by GPT-3 and a human, on closer inspection the system's offerings didn't always stand up to scrutiny.
Deep-learning could eventually pave the way for robots that can learn directly from humans, with researchers from Nvidia creating a deep-learning system designed to teach a robot to how to carry out a task, simply by observing that job being performed by a human.
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Are machine-learning systems objective?
As you'd expect, the choice and breadth of data used to train systems will influence the tasks they are suited to. There is growing concern over how machine-learning systems codify the human biases and societal inequities reflected in their training data.
For example, in 2016 Rachael Tatman, a National Science Foundation Graduate Research Fellow in the Linguistics Department at the University of Washington, found that Google's speech-recognition system performed better for male voices than female ones when auto-captioning a sample of YouTube videos, a result she ascribed to 'unbalanced training sets' with a preponderance of male speakers.
Facial recognition systems have been shown to have greater difficultly correctly identifying women and people with darker skin. Questions about the ethics of using such intrusive and potentially biased systems for policing led to major tech companies temporarily halting sales of facial recognition systems to law enforcement.
In 2018, Amazon also scrapped a machine-learning recruitment tool that identified male applicants as preferable.
As machine-learning systems move into new areas, such as aiding medical diagnosis, the possibility of systems being skewed towards offering a better service or fairer treatment to particular groups of people is becoming more of a concern. Today research is ongoing into ways to offset bias in self-learning systems.
What about the environmental impact of machine learning?
The environmental impact of powering and cooling compute farms used to train and run machine-learning models 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.
As the size of models and the datasets used to train them grow, for example the recently released language prediction model GPT-3 is a sprawling neural network with some 175 billion parameters, so does concern over ML's carbon footprint.
There are various factors to consider, training models requires vastly more energy than running them after training, but the cost of running trained models is also growing as demands for ML-powered services builds. There is also the counter argument that the predictive capabilities of machine learning could potentially have a significant positive impact in a number of key areas, from the environment to healthcare, as demonstrated by Google DeepMind's AlphaFold 2.
Which are the best machine-learning courses?
A widely recommended course for beginners to teach themselves the fundamentals of machine learning is this free Stanford University and Coursera lecture series by AI expert and Google Brain founder Andrew Ng.
More recently Ng has released his Deep Learning Specialization course, which focuses on a broader range of machine-learning topics and uses, as well as different neural network architectures.
If you prefer to learn via a top-down approach, where you start by running trained machine-learning models and delve into their inner workings later, then fast.ai's Practical Deep Learning for Coders is recommended, preferably for developers with a year's Python experience according to fast.ai. Both courses have their strengths, with Ng's course providing an overview of the theoretical underpinnings of machine learning, while fast.ai's offering is centred around Python, a language widely used by machine-learning engineers and data scientists.
Another highly rated free online course, praised for both the breadth of its coverage and the quality of its teaching, is this EdX and Columbia University introduction to machine learning, although students do mention it requires a solid knowledge of math up to university level.
How do I get started with machine learning?
Technologies designed to allow developers to teach themselves about machine learning are increasingly common, from AWS' deep-learning enabled camera DeepLens to Google's Raspberry Pi-powered AIY kits.
Which services are available for machine learning?
All of the major cloud platforms – Amazon Web Services, Microsoft Azure and Google Cloud Platform – provide access to the hardware needed to train and run machine-learning models, with Google letting Cloud Platform users test out its Tensor Processing Units – custom chips whose design is optimized for training and running machine-learning models.
This cloud-based infrastructure includes the data stores needed to hold the vast amounts of training data, services to prepare that data for analysis, and visualization tools to display the results clearly.
Newer services even streamline 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, similar to Microsoft's Azure Machine Learning Studio. In a similar vein, Amazon has its own AWS services designed to accelerate the process of training machine-learning models.
For data scientists, Google Cloud's AI Platform is a managed machine-learning service that allows users to train, deploy and export custom machine-learning models based either on Google's open-sourced TensorFlow ML framework or the open neural network framework Keras, and which can be used with the Python library sci-kit learn and XGBoost.
Database admins without a background in data science can use Google's BigQueryML, a beta service that allows admins to call trained machine-learning models using SQL commands, allowing predictions to be made in database, which is simpler than exporting data to a separate machine learning and analytics environment.
For firms that don't want to build their own machine-learning models, the cloud platforms also offer AI-powered, on-demand services – such as voice, vision, and language recognition.
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.
Early in 2018, Google expanded its machine-learning driven services to the world of advertising, releasing a suite of tools for making more effective ads, both digital and physical.
While Apple doesn't enjoy the same reputation for cutting-edge speech recognition, natural language processing and computer vision as Google and Amazon, it is investing in improving its AI services, with Google's former chief of machine learning in charge of AI strategy across Apple, including the development of its assistant Siri and its on-demand machine learning service Core ML.
In September 2018, NVIDIA launched a combined hardware and software platform designed to be installed in datacenters that can accelerate the rate at which trained machine-learning models can carry out voice, video and image recognition, as well as other ML-related services.
The NVIDIA TensorRT Hyperscale Inference Platform uses NVIDIA Tesla T4 GPUs, which delivers up to 40x the performance of CPUs when using machine-learning models to make inferences from data, and the TensorRT software platform, which is designed to optimize the performance of trained neural networks.
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Which software libraries are available for getting started with machine learning?
There are a wide variety of software frameworks for getting started with training and running machine-learning models, typically for the programming languages Python, R, C++, Java and MATLAB, with Python and R being the most widely used in the field.
Famous examples include Google's TensorFlow, the open-source library Keras, the Python library scikit-learn, the deep-learning framework CAFFE and the machine-learning library Torch.
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