人工智能：未来决策制定的机遇与影响（英文版）.pdf

Artificial intelligence: opportunities and implications for the future of decision making Government Office for ScienceArtificial intelligence: opportunities and implications for the future of decision making Foreword We are in currently in the foothills of a new technological revolution. Artificial intelligence has the potential to be as transformative in our lifetimes as the steam-powered economy of the 19th century. Already its letting us to talk to our smartphones, recommending us music, describing photos for the visually impaired and flagging up fire risks in cities. In the near future we could see it deployed in everything from driverless cars, to intelligent energy grids, to the eradication of infectious diseases. In government too we are looking at the potential applications of this technology in the delivery of public services. Our Government Data Programme is increasing the number of projects and data scientists in government, while playing a leading role in establishing the appropriate use of these powerful new tools. As one the worlds leading digital nations, artificial intelligence presents a huge opportunity for the UK. Get this right, and we can create a more prosperous economy with better and more fulfilling jobs. We can protect our environment by using resources more efficiently. And we can make government smarter, using the power of data to improve our public services. As weve seen already in many areas, much routine cognitive work - the filing, sifting and sorting - can increasingly be automated, freeing people up to focus on the more human aspects of any job. The Prime Minister has announced an independent review of modern employment practices, so that the support we provide businesses and workers keeps pace with changes in the labour market and the economy. Artificial intelligence also poses new questions about ethics and governance, the responsible use of data and strong cyber defences. To realise the full potential of this revolution, again we have to be ready with answers. I am pleased that the Royal Society and the British Academy are conducting a review that will consider how best the UK might manage the use of artificial intelligence. This note sets out where the science is heading, describes some of the implications for society and government, and shows how we can responsibly use this technology to improve the lives and living standards of everyone in Britain. It is a timely and important piece of work from the Government Chief Scientific Adviser. Matt Hancock Minister for Digital and Culture Artificial intelligence: opportunities and implications for the future of decision making Contents Foreword . 2 Introduction . 4 What is artificial intelligence? . 4 Artificial intelligence for innovation and productivity . 8 The use of artificial intelligence by government. 10 Effects on labour markets . 12 New challenges . 14 Public dialogue . 17 Conclusion . 18 Annex A: Background . 19 Annex B: Sources . 20 Artificial intelligence: opportunities and implications for the future of decision making Introduction Artificial intelligence has arrived. In the online world it is already a part of everyday life, sitting invisibly behind a wide range of search engines and online commerce sites. It offers huge potential to enable more efficient and effective business and government but the use of artificial intelligence brings with it important questions about governance, accountability and ethics. Realising the full potential of artificial intelligence and avoiding possible adverse consequences requires societies to find satisfactory answers to these questions. This report sets out some possible approaches, and describes some of the ways government is already engaging with these issues. Artificial intelligence is not a distinct technology. It depends for its power on a number of prerequisites: computing power, bandwidth, and large-scale data sets, all of which are elements of big data, the potential of which will only be realised using artificial intelligence. If data is the fuel, artificial intelligence is the engine of the digital revolution. Much has already been written about the use of artificial intelligence and big data. This paper does not attempt to survey the whole field. Its origins lie in a seminar held at the British Academy in February 2016, chaired by Mark Walport, Government Chief Scientific Adviser and Mark Sedwill, Permanent Secretary at the Home Office, that discussed some of the legal and ethical issues around the use of artificial intelligence. The issues discussed there provide the core of this report, with additional material drawn from the views of a wide range of scientific and legal experts in the field, although we have sought to minimise detailed discussion of technical aspects in order to concentrate on the practical aspects of the debate. We hope that it serves as an introduction to the topic. The report considers the following questions: What is artificial intelligence and how is it being employed? What benefits is it likely to bring for productivity? How do we best manage any ethical and legal risks arising from its use? Sir Mark Walport Government Chief Scientific Adviser Mark Sedwill Permanent Secretary, Home Office4 Artificial intelligence: opportunities and implications for the future of decision making What is artificial intelligence? Artificial intelligence is more than the simple automation of existing processes: it involves, to greater or lesser degrees, setting an outcome and letting a computer program find its own way there. It is this creative capacity that gives artificial intelligence its power. But it also challenges some of our assumptions about the role of computers and our relationship to them. Artificial intelligence is particularly useful for sorting data, finding patterns and making predictions. Current examples in everyday life are widespread: they include translation and speech recognition services that learn from language online, search engines that rank websites on their relevance to the user, and filters for email spam that recognise junk mail based on previous examples (see box for more uses). This list of applications is growing rapidly: artificial intelligence is enabling a new wave of innovation across every sector of the UK economy. Artificial intelligence is a broad term (see box). More generally it refers to the analysis of data to model some aspect of the world. Inferences from these models are then used to predict and anticipate possible future events. Statistical models are created using series of algorithms, or step-by-step instructions that computers can follow to perform a particular task. Computer algorithms are powerful tools for automating many aspects of life today, taking the step-by-step routines that underpin the administrative and operational tasks of organisations and digitising them, making them faster and more consistent. One approach to automation is to choose a series of rules to apply to inputs, leading a particular output. Most current medical self-diagnosis systems, both in books and online, use this logic. Certain combinations of answers to questions are deterministically linked to certain individual outputs. If you provide the same answers again, the algorithm will show the same result. Some uses of artificial intelligence Product recommendations from services such as Netflix and Amazon that evolve through users web experiences are powered by machine learning. The UKs smart motorways use feedback on road conditions from embedded sensors and neural network systems to anticipate and manage traffic flow. In financial markets, high-frequency trading algorithms use pre-determined decision criteria to respond to market conditions many times faster than human traders are able to. Similar algorithms are being used by some financial advisors to automatically spot investment opportunities for clients. Cornell University worked with machine learning specialists to identify the calls of right whales more accurately, making it easier to track individual whales. Digital images from millions of satellite observations can be analysed for environmental or socio-economic trends using machine learning to identify patterns of change and development. 5 Artificial intelligence: opportunities and implications for the future of decision making In recent years, however, available data and computing power have reached the point where it has become practical to develop machine learning: algorithms that change in response to their own output, or “computer programs that automatically improve with experience” 1 . Machine learning systems have often been shown to pick up difficult-to-spot relationships in data that may otherwise have been missed. Most machine learning approaches are not restricted to producing a single prediction from given inputs. Many algorithms produce probabilistic outputs, offering a range of likely predictions with associated estimates of uncertainty. The algorithms producing these probabilistic outputs are capable of being understood by humans. However, in the case of more complex machine learning systems (such as deep learning: see box), there are many layers of statistical operations between the input and output data. These operations have been defined by an algorithm, rather than a person. Because of this, not only is the output probabilistic, as with simpler algorithms, but the process that led to it cannot be displayed in human- understandable terms. This makes machine learning fundamentally different to the kinds of algorithms used to automate standard organisational routines. There are many different kinds of algorithm used in machine learning. The key distinction between them is whether their learning is unsupervised or supervised. Unsupervised learning presents a learning algorithm with an unlabelled set of data that is, with no right or wrong answers and asks it find structure in the data, perhaps by clustering elements together for example, examining a batch of photographs of faces and learning how to say how many different people there are. Googles News service 2uses this technique to group similar news stories together, as do researchers in genomics looking for differences in the degree to which a gene might be expressed in a given population, or marketers segmenting a target audience. Supervised learning involves using a labelled data set to train a model, which can then be used to classify or sort a new, unseen set of data (for example, learning how to spot a particular person in a batch of photographs). This is useful for identifying elements in data (perhaps key phrases or physical attributes), predicting likely outcomes, or spotting anomalies and outliers. Essentially this approach presents the computer with a set of right answers and asks it to find more of the same. Terminology The range of different statistical techniques referred to here with the general term artificial intelligence have emerged over a long time from many different research fields within statistics, computer science and cognitive psychology. Consequently, authors from different disciplines tend to make different distinctions between terms like machine learning and machine intelligence, using them to refer to related but distinct ideas. As these techniques have been applied in different business areas theyve become relevant to other tasks so theyre likely to feature also in discussions about data mining and predictive analytics. While this paper looks ahead to a time when machine learning is more widespread than at present, many of the opportunities and challenges it discusses arise in other contexts too. So rather than be distracted with an academic discussion about terminology, weve chosen to use the umbrella term artificial intelligence. 6 1Mitchell, T. (1997), Machine Learning 2news.google/ Artificial intelligence: opportunities and implications for the future of decision making