《出版社通信英语》课件AI-人工智能.pptx

1、 AI-人工智能In the field of computer science,artificial intelligence(AI),sometimes called machine intelligence,is intelligence demonstrated by machines,in contrast to the natural intelligence displayed by humans and other animals.artificial a.人工的，人造的intelligence n.智力，智能demonstrate v.证明，展示Kaplan and Haen

2、lein define AI as“a systems ability to correctly interpret external data,to learn from such data,and to use those learnings to achieve specific goals and tasks through flexible adaptation”.Colloquially,the term artificial intelligence is applied when a machine mimics cognitive functions that humans

3、associate with other human minds,such as learning and problem solving.interpret v.解释，翻译，说明external a.外部的，表面的adaptation n.适应，改编colloquially ad.口语的，用通俗语mimic v.模仿，模拟cognitive a.认知的，认识的The current excitement about artificial intelligence(AI),particularly machine learning(ML),is palpable and contagious.

4、The expectation that AI is poised to“revolutionize,”perhaps even take over humanity,has elicited prophetic visions and concerns from some luminaries.There is also a great deal of interest in the commercial potential of AI,which is attracting significant sums of venture capital and state-sponsored in

5、vestment globally,particularly in China.elicit v.抽出，引出prophetic a.预言的，预示的luminary n.发光体，杰出人物McKinsey,for instance,predicts the potential commercial impact of AI in several domains,envisioning markets worth trillions of dollars.All this is driven by the sudden,explosive,and surprising advances AI has

6、 made in the last 10 years or so.AlphaGo,autonomous cars,Alexa,Watson and other such systems,in game playing,robotics,computer vision,speech recognition,and natural language processing are indeed stunning advances.domain n.领域，域名envision v.想象，预想robotics n.机器人学recognition n.认出，识别stun v.使震惊，打昏 But,as w

7、ith earlier AI breakthroughs,such as expert systems in the 1980s and neural networks in the 1990s,there is also considerable hype and a tendency to overestimate the promise of these advances,as market research firm Gartner and others have noted about emerging technology.hype n.大肆宣传neural a.神经的，神经系统的

8、 The implication is that AI could eventually end up doing all“things”that humans do,and do them much betterthat is,achieve super-human performance as witnessed recently with AlphaGO and AlphaGO Zero.Historically,the term AI reflected collectively to the following branches:Game playingfor example,Che

9、ss,Go Symbolic reasoning and theorem-provingfor example,Logic Theorist,MACSYMA Roboticsfor example,self-driving cars Visionfor example,facial recognition Speech recognition,Natural language processingfor example,Siri Distributed&evolutionary AIfor example,drone swarms Hardware for AIfor example,Lisp

10、 machines Expert systems or knowledge-based systemsfor example,MYCIN,CONPHYDE Some of these are application-focused,such as game playing and vision.Others are methodological,such as expert systems and MLthe two branches that are most directly and immediately applicable to our domain.Many tasks in th

11、ese different branches of AI share certain common features.They all require pattern recognition,reasoning,and decision-making under complex conditions.And they often deal with ill-defined problems,noisy data,model uncertainties,combinatorially large search spaces,nonlinearities and the need for spee

12、dy solutions.methodological a.方法的，方法论的pattern n.模式，图案nonlinearity n.非线性，非线性特征speedy a.快的，迅速的Looking back some 30 years from now,history would recognize that there were three early milestones in AI.One is Deep Blue defeating Gary Kasparov in chess in 1997,the second Watson becoming Jeopardy champion

13、in 2011,and the third is the surprising win by AlphaGO in 2016.The AI advances that made these amazing feats possible are now poised to have an impact that goes far beyond game playing.amaze v.使吃惊AI research really started with a conference at Dartmouth College in 1956.It was a month long brainstorm

14、ing session attended by many people with interests in AI.At the conference they wrote programs that were amazing at the time,beating people at checkers or solving word problems.The Department of Defense started giving a lot of money to AI research and labs were created all over the world.Unfortunate

15、ly,researchers really underestimated just how hard some problems were.The tools they had used still did not give computers things like emotions or common sense.Funding for AI research was cut,starting an AI winter where little research was done.session n.会话，会议checker n.检验员，棋子emotion n.情感，情绪AI resear

16、ch revived in the 1980s because of the popularity of expert systems,which simulated the knowledge of a human expert.Expert systems,also called knowledge-based systems,rule-based systems,or production systems,are computer programs that mimic the problem-solving of humans with expertise in a given dom

17、ain.By 1985,1 billion dollars were spent on AI.New,faster computers convinced U.S.and British governments to start funding AI research again.However,the market for Lisp machines collapsed in 1987 and funding was pulled again,starting an even longer AI winter.popularity n.普及，流行simulate v.模仿，假装mimic v

18、.模仿，模拟AI revived again in the 90s and early 2000s with its use in data mining and medical diagnosis.This was possible because of faster computers and focusing on solving more specific problems.The excitement about expert systems waned in the 1990s due to these practical difficulties.diagnosis n.诊断，分

19、析wane v.衰落，变小 Although heuristic search and expert system proved suitable to solve well-defined,logical problems,such as playing chess,it turned out to be intractable to figure out explicit rules for solving more complex,fuzzy problems,such as image classification,speech recognition,or language tran

20、slation.Another boom of AI arose to take their place:machine learning(ML).heuristic a.启发的，探索的intractable a.棘手的，难治的explicit a.明确的，清楚的fuzzy a.模糊的，失真的boom v.使兴旺，急速发展 ML is not a new concept.In 1959,Arthur Samuel,one of the pioneers of ML,defined machine learning as a“field of study that gives computers

21、 the ability to learn without being explicitly programmed”.That is,ML programs have not been clear entered into a computer,like the if-then statements above.ML programs,in a sense,adjust themselves in response to the data theyre exposed to.explicitly 明确的，明白的 Deep learning is a kind of machine learni

22、ng,a technology that enables computer systems to improve from experience and data.Deep learning is a specific type of machine learning that has the power and flexibility to represent the vast universe as a system of nested hierarchical concepts(complex concepts defined by connections between simpler

23、 concepts,generalized to higher-level abstract representations).Artificial intelligence is a broad and active area of research,but its no longer the sole province of academics;increasingly,companies are incorporating AI into their products.Google has been a pioneer in the use of machine learning com

24、puter systems that can learn from data,as opposed to blindly following instructions.In particular,the company uses a set of machine-learning algorithms,collectively referred to as deep learning,that allow a computer to do things such as recognize patterns from massive amounts of data.province n.省，领域academics n.学术水平，学术知识algorithm n.算法，运算法则