人工智能分析报告-英国政府ai报告：人工智能：未来决策制定的机遇与影响artificialintelligence-opportunitiesandimplications(编辑修改稿)

人工智能分析报告-英国政府ai报告：人工智能：未来决策制定的机遇与影响artificialintelligence-opportunitiesandimplications(编辑修改稿)内容摘要：

ntelligence‘, using them to refer to related but distinct ideas. As these techniques have been applied in different business areas they‘ve bee relevant to other tasks – so they‘re 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, we‘ve chosen to use the umbrella term artificial intelligence. 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. Google‘s News service2 uses 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 outes, or spotting anomalies and outliers. Essentially this approach presents the puter with a set of ‗right answers‘ and asks it to find more of the same. 1 Mitchell, T. (1997), Machine Learning 2 Artificial intelligence: opportunities and implications for the future of decision making 7 amp。 XUUHQ W LQWHUH VW LQ PDFKLQH OHDUQL QJ LV IRFXVHG RQ 181。 GHHS OHDUQLQJ182。 ， D VXSHU YL VHG OHDUQ LQJ WHFK QLTXH FRPELQ LQ J OD\HUV RI QHXUDO QHWZRUNV WR DXWRPDWLFDOO\ LGHQWLI\ WKH IHDWXUHV RI D GDWD VHW WKDW DUH UHOHYDQW WR GHFL VLRQPDNLQ J. 39。 HHS OHDUQ LQ J LV D SRZHUIXO DGGL WLRQ WR WKH PDFK LQ H OHDU Q LQJ UHSHUWRLUH: KRZHYHU， LW UHTXLUHV YHU\ ODUJH DPRXQ WV RI GDWD WR EH HIIHFW LYH. 7KH /RQGRQEDVHG ILUP 39。 HHS0LQG (RZQHG E\ *RRJOH) LV D ZRUOG OHDGHU LQ WKLV WHFKQLTXH. 39。 HHS OHDUQ L QJ 39。 HHS OHDUQLQJ LV D VXEVHW RI PDFKLQH OHDUQLQJ WKDW GHSHQGV RQ XVLQJ OD\HUV RI QRQOLQHDU DOJRULWKPLF SURFHVVHV WR ILQG SDWWHUQV RU FODVVLI\ GDWD. 7KHUH DUH PDQ\ GLIIHUHQW WHFKQLTXHV ZLWKLQ WKLV JHQHUDO DSSURDFK 177。 EXW WKH NH\ IHDWXUH LV WKDW WKH\ HDFK XVH D OD\HUHG RU VWDJHG GHVLJQ， LQ ZKLFK RXWSXWV IURP WKH SUHYLRXV OD\HU DUH XVHG DV LQSXWV IRU WKH QH[W. amp。 HQWUDO WR WKH LQWHUHVW DURXQG PDFKLQH OHDUQLQJ LV WKH SRWHQWLDO LW RIIHUV IRU DXWRQ RP RXV GHFLVLRQP DNLQJ. 0DQ\ DOJRULWKPLF SURFHVVHV FDQ EH XVHG WR PDNH GHFLVLRQV ZLWKRXW KXPDQ LQSXW (VXFK DV ZKHQ PRUWJDJH SURYLGHUV DXWRPDW LFDOO \ DSSURYH OHQG LQJ WR LQGLYLGXDO V EDVHG RQ WKHL U FUHGLW VFRU H). %XW UHDO DXWRQRP\ FRPHV ZKHQ D V\VWHP LV DEOH WR OHDUQ FRQWLQ XRXVO \ DQG PDNH GHGXFWLRQV DERXW WKH ZRUOG ZLWKRXW KXPDQ LQSXW. )RU H[DPSOH， VHOIGULYLQJ FDUV DUH DEOH WR PDNH UHDOWLPH GHFLVLRQV DERXW VSHHG DQG GLUHFWLRQ ZLWKRXW LQSXW IURP D KXPDQ GULYHU， XVLQJ PDQ\ LQWHUOLQNHG PDFKLQH OHDUQLQJ V\VWHPV WR PDNH VHQVH RI LQIRUPDWLRQ DERXW WKHLU VXUURXQGLQJV. 7KH\ DUH QRW IROORZLQJ SUHSURJUDPPHG GHFLVLRQV EXW UHVSRQGLQJ WR FKDQJHV DURXQG WKHP. 8VLQJ DXWRQRPRXV GHFLVLRQPDNLQJ LQ RWKHU DUHDV RI VRFLHW\ ZRXOG EH D VLJQLILFDQW FKDQJH LQ WKH ZD\ ZH XVH GDWD DQG PDNH FKRLFHV， EULQJLQJ ZLWK LW LPSRUWDQW TXHVWLRQV DURXQG DFFRXQW DELOLW\ DQG WUXVW. 7KLV LV SDUWLFXODUO\ WUXH IRU LWV XVH E\ JRYHUQ PHQW ， LQ OLJKW RI WKH FRPSDFW WKDW H[LVWV EHWZH HQ LW DQG FLWL]HQ V， DQG LWV UHVSRQ VLELO LW \ IRU WKHLU ZHOOEHLQJ DQG VHFXUL W\. $W WKH SUHVHQ W WLPH WKHUH LV JHQHUDOO \ DJUHHPHQ W DPRQJVW H[SHUWV DQG SROLF\P DNHUV WKDW LPSRUWDQW GHFLVLRQV PXVW LQYROYH D 181。 KXPDQ LQ WKH ORRS182。 177。 EXW WKH H[DFW QDWXUH RI WKHLU UROH， RU WKH GHJUHH WR ZKLFK WKH\ LQIOXHQFH WKH RXWFRPH， LV VRPHWKLQJ WKDW LV OLNHO\ WR HYROYH DV WKH WHFKQRORJ\ GHYHORSV RYHU WLPH. Artificial intelligence: opportunities and implications for the future of decision making 8 Artificial intelligence for innovation and productivity Artificial intelligence holds great potential for increasing productivity, most obviously by helping firms and people use resources more efficiently, and by streamlining the way we interact with large sets of data. For example, firms like Ocado and Amazon are making use of artificial intelligence to optimise their storage and distribution works, planning the most efficient routes for delivery and making best use of their warehousing capacity. Artificial intelligence can help firms do familiar tasks in more efficient ways. Importantly, it can also enable entirely new business models and new approaches to old problems. For example, in healthcare, data from smart phones and fitness trackers that is analysed using new machine learning techniques can improve management of chronic conditions as well as predicting and preventing acute episodes of illness. Artificial intelligence can help both panies and individual employees to be more productive. Routine administrative and operational jobs can be learned by software agents (‗bots‘), which can then prioritise tasks, manage routine interactions with colleagues (or other bots), and plan schedules. Email software like Google‘s Smart Reply can draft messages to respondents based on previous responses to similar messages. Newsrooms are inc。