人工智能是對人的意識、思維的信息過程的模擬。人工智能不是人的智能，但能像人那樣思考、也可能超過人的智能。
關(guān)于人工智能的英語作文優(yōu)秀范文一：
Andrew Ng is hunched over his smartphone, in a pantomime of key-pecking, squinting, typo-ridden discomfort. “This is how we do it today,” he says.
吳恩達(dá)(Andrew Ng)駝著背低著頭，略帶夸張地在他的智能手機(jī)上比劃著不停點(diǎn)擊屏幕、瞇著眼卻仍然錯(cuò)字連篇的那種不自在的樣子。“我們?nèi)缃袷沁@樣做的，”他稱。
“And this is how we should be doing it,” says the chief scientist for Baidu, China’s largest search engine. He sits back in his chair, speaking to no one in particular with his phone placed on the table. The one-finger typing agony of millions of smartphone users should one day become a thing of the past, he says. All it would take is the creation of a reasonably accurate, pocket-sized electronic version of a human brain.
“而我們應(yīng)該這樣做，”這位百度(Baidu)的首席科學(xué)家稱。他靠在座位上，沒有特定對象地說著話，手機(jī)放在桌子上。他說，數(shù)百萬智能手機(jī)用戶用一個(gè)手指敲字的痛苦有一天應(yīng)該成為過去。而這只需要?jiǎng)?chuàng)造一種達(dá)到合理精確度、與口袋大小相當(dāng)?shù)碾娮影嫒祟惔竽X。百度是中國最大的搜索引擎。
Mr Ng is an expert in deep learning, a branch of artificial intelligence that focus on teaching computers how to talk, listen, read, and think like us. The area is fast becoming a priority for the world’s biggest technology companies, including Baidu as it tackles the era of the mobile internet.
吳恩達(dá)是深度學(xué)習(xí)(deep learning)領(lǐng)域的專家，該領(lǐng)域是人工智能的一個(gè)分支，專注于讓計(jì)算機(jī)學(xué)習(xí)如何像我們一樣聽、說、讀、思。由于該領(lǐng)域與移動(dòng)互聯(lián)網(wǎng)時(shí)代緊密相連，它正迅速成為包括百度在內(nèi)的全球最大科技公司的優(yōu)先發(fā)展領(lǐng)域。
“The whole world is switching to mobile devices but no one has created a usable interface to input into the devices,” he says. With the development of artificial intelligence, “soon you’ll be able to order food and just say ‘Can I have some food delivered to my house before I get home?’ out loud.”
“整個(gè)世界都在轉(zhuǎn)向移動(dòng)設(shè)備，但是還沒人創(chuàng)造出向移動(dòng)設(shè)備輸入指令的有用接口，”他稱。隨著人工智能的發(fā)展，“很快你將可以在訂購食物時(shí)只需要大聲說一句‘能在我回家前送些食物到我家中嗎?’”
“It won’t even feel like technology, it will just be in the background.”
“感覺上甚至都不像是科技，而就在后臺里。”
In addition to better voice recognition, AI is being talked about for any number of uses from predicting advertising clicks to recognising faces.
除了更好的語音識別，從預(yù)測廣告點(diǎn)擊量到人臉識別技術(shù)的很多領(lǐng)域都在討論使用人工智能。
Since joining Baidu last year, Mr Ng has been steadily working to implement this vision. A UK native with Chinese roots, he founded in 2011 Google Brain, the US technology company’s deep learning project, and led it until he joined the Chinese company last year. Poaching him was regarded as a coup in the technology world.
自從去年加入百度以來，吳恩達(dá)一直在為實(shí)現(xiàn)這個(gè)愿景而穩(wěn)扎穩(wěn)打。作為一名出生在英國的華人，他在2011年創(chuàng)建了“谷歌大腦”(Google Brain)——谷歌的深度學(xué)習(xí)項(xiàng)目，并且在去年加入百度前一直領(lǐng)導(dǎo)著該項(xiàng)目。百度撬走吳恩達(dá)被認(rèn)為是科技界的一次政變。
He describes the advanced computers at Baidu’s Sunnyvale, California, lab as “rocket engines” whose software can be taught to mimic the functioning of the human mind. Their “fuel” is data, which he gets from Baidu’s trove of online video and audio output as he works to teach the electronic brain to listen and speak.
他把百度位于加州森尼韋爾(Sunnyvale)實(shí)驗(yàn)室中的先進(jìn)計(jì)算機(jī)比作“火箭引擎”，計(jì)算機(jī)中的軟件可以學(xué)習(xí)模擬人類思想的功能。在吳恩達(dá)教電子大腦聽和說時(shí)，它們的“燃料”就是他從百度在線視頻和音頻輸出資料庫中得到的數(shù)據(jù)。
The company has an advantage in deep-learning algorithms for speech recognition in that most video and audio in China is accompanied by text — nearly all news clips, television shows and films are close-captioned and almost all are available to Baidu and Iqiyi, its video affiliate.
百度在語音識別深度學(xué)習(xí)算法方面具有優(yōu)勢，因?yàn)橹袊蠖鄶?shù)視頻和音頻都伴有文本——幾乎所有新聞剪輯、電視節(jié)目及電影都有詳細(xì)的字幕，而百度及其視頻子公司愛奇藝(Iqiyi)可以獲得幾乎所有此類內(nèi)容。
While a typical academic project uses 2,000 hours of audio data to train voice recognition, says Mr Ng, the troves of data available to China’s version of Google mean he is able to use 100,000 hours.
吳恩達(dá)說，一個(gè)典型的學(xué)術(shù)項(xiàng)目會利用2000小時(shí)的音頻數(shù)據(jù)來訓(xùn)練語音識別，但百度——中國版谷歌——擁有的龐大數(shù)據(jù)庫意味著他可以利用10萬小時(shí)。
He declines to specify just how much the extra 98,000 hours improves the accuracy of his project, but insists it is vital.
他拒絕詳細(xì)說明額外9.8萬小時(shí)在多大程度上提升了其項(xiàng)目的精確度，但堅(jiān)稱這至關(guān)重要。
“A lot of people underestimate the difference between 95 per cent and 99 per cent accuracy. It’s not an ‘incremental’ improvement of 4 per cent; it’s the difference between using it occasionally versus using it all the time,” he says.
“許多人低估了95%精確度與99%精確度之間的區(qū)別。這不是4%的“增量”提升;這是偶爾使用與始終使用之間的區(qū)別，”他說。
Thanks to the strides made in Chinese language voice recognition — a particular challenge because of the number of homonyms and the importance of context — Baidu will soon roll out Deepspeech, a voice recognition software similar to Apple’s Siri.
由于在漢語語音識別方面取得了巨大進(jìn)步(漢語中的大量同音異義詞和語境的重要性使之極具挑戰(zhàn))，百度即將推出Deepspeech——一款類似于蘋果(Apple)的Siri的語音識別軟件。
Other Chinese companies including Alibaba and Tencent are also making advances in AI, but thanks largely to Mr Ng’s reputation Baidu is now judged by industry experts to be ahead of its domestic peers, ranking up alongside US rivals Facebook, Google, and IBM.
包括阿里巴巴(Alibaba)、騰訊(Tencent)在內(nèi)的其他中國企業(yè)在人工智能方面也取得了進(jìn)步，但主要得益于吳恩達(dá)的聲望，行業(yè)專家如今認(rèn)為百度要領(lǐng)先于國內(nèi)同行，可與美國競爭對手Facebook、谷歌和IBM比肩。
“Artificial intelligence is an oligopoly,” says Yang Jing, founder of AI Era, an association for the artificial intelligence industry in China. “It’s a game for the titans.”
“人工智能是寡頭壟斷行業(yè)，”中國人工智能行業(yè)協(xié)會新智元(AI Era)創(chuàng)始人楊靜說，“這是一個(gè)巨頭間的游戲。”
Baidu already saves Rmb17m ($2.7m) per day at its data centres by using deep-learning algorithms to predict hard drive malfunctions, and it is also using AI to optimise the use of advertisements and photos to improve clickthrough rates. It would not reveal how much it is spending on AI development overall.
百度通過在數(shù)據(jù)中心利用深度學(xué)習(xí)算法預(yù)測硬盤故障已經(jīng)可以每天節(jié)省1700萬元人民幣(合270萬美元)，而且還利用人工智能優(yōu)化廣告和相片的使用來提升點(diǎn)擊率。該公司并未透露在人工智能開發(fā)上共計(jì)投入多少資金。
But in spite of lofty long-term ambitions, translating deep learning into money-making projects is still largely on the horizon.
盡管雄心勃勃，但要將深度學(xué)習(xí)轉(zhuǎn)變成賺錢的項(xiàng)目仍有很長一段路要走。
Mr Ng is undaunted. “There’s no question that [AI] is creating huge economic value; there’s no question that this will continue to create huge advances,” he says. “There is still a huge gap between the way machines learn and the way humans learn.”
吳恩達(dá)毫無畏懼。“毫無疑問，(人工智能)正在創(chuàng)造巨大的經(jīng)濟(jì)價(jià)值;毫無疑問，這將繼續(xù)創(chuàng)造巨大的進(jìn)步，”他說，“機(jī)器的學(xué)習(xí)方式與人類的學(xué)習(xí)方式之間仍存在巨大差距。”
關(guān)于人工智能的英語作文優(yōu)秀范文二：
GWEN IFILL: Now we continue our series about artificial intelligence, A.I., where computers are able to make intelligent decisions without human input.
As computing power gets stronger and people continue to generate massive amounts of data, A.I. is making its way into the marketplace and into your doctor's examination room.
Hari Sreenivasan has the latest in series on breakthroughs in invention and innovation.
HARI SREENIVASAN: Advances in artificial intelligence continue to push the boundaries between science fiction and reality, like this brain-controlled device at the University of Minnesota. It enables users to fly a model helicopter with only their thoughts. The hope is it will soon help disabled people to operate robotic arms.
But you don't need to be in a university lab to find A.I. It's all around us.
MAN: What's the fifth planet from the sun?
HARI SREENIVASAN: Helping us search for information.
WOMAN: Jupiter is the fifth planet orbiting the sun.
HARI SREENIVASAN: Our smartphones use A.I. to navigate us, choosing the least congested traffic routes. Even the U.S. Postal Service uses it to sort mail. And on Wall Street, autonomous machines help make major financial decisions.
RAY KURZWEIL, Inventor/Futurist: At least 90 percent of the financial transactions are guided in one way or another by artificial intelligence.
HARI SREENIVASAN: Ray Kurzweil directs Google's engineering lab, but spoke to us in his capacity as an independent inventor. He's convinced that A.I. programs are already on track to solve many of the problems vexing mankind today.
RAY KURZWEIL: They're helping us find a cure for disease, helping us diagnose disease, analyzing environmental data to help us clean up the environment. Virtually every industrial process is a combination already of human and machine intelligence.
HARI SREENIVASAN: Large tech firms are betting big on the promise of A.I. Last year, Google paid $400 million to acquire DeepMind, a London startup specializing in deep learning. Facebook is raising eyebrows as it continues to pluck A.I. talent. And IBM is investing $1 billion to grow its Watson division, based out of new headquarters in New York's Silicon Alley.
Remember Watson, the supercomputer which beat a pair of “Jeopardy” game show champions in 2011?
MAN: Watson?
COMPUTER: What is Jericho?
MAN: Correct.
HARI SREENIVASAN: Well, in the four years since, IBM has sped Watson up 24-fold. What used to be a room full of computing machines can now fit into a pizza box, all accessed from the cloud.
You could say these are the brains that power Watson, but since all the data lives on the cloud, it's hard to visualize.
GURUDUTH BANAVAR, IBM: What you see is how Watson works.
HARI SREENIVASAN: Guru Banavar is vice president of cognitive computing at IBM.
GURUDUTH BANAVAR: Watson has come a very long way.
We have taken some of the underlying technologies that helped us win the “Jeopardy” game show, and applied it in many domains that matter, like health care, education, business decision-making.
HARI SREENIVASAN: Last month, IBM Introduced Watson Health, its entry into the personalized health care space. The idea is to use Watson's A.I. to make sense of vast troves of health data to deliver tailored information to physicians, insurers, researchers and hospitals.
GURUDUTH BANAVAR: The difference between any data that previously we were able to analyze and the new data that are — we have to apply artificial intelligence techniques to is that the new data is natural language. It's just written in English. Computers have never been able to understand natural language.
Typically, these are very high-end, complex information that's published by scientific researchers, and now Watson is able to read those.
HARI SREENIVASAN: At the Memorial Sloan Kettering Cancer Center, Mark Kris, a thoracic oncologist, is leading a team that is teaching Watson how to diagnose cancer.
DR. MARK G. KRIS, Memorial Sloan Kettering Cancer Center: We needed some way to help doctors deal with the deluge of information that's available now.
HARI SREENIVASAN: Watson is being trained to sort through reams of information about the patient, the most current medical research, and get it to the doctor to help make a decision, all at a pace beyond humans.
DR. MARK G. KRIS: Our kind of idea here though is that this system is going to be like what we kind of call a learned colleague.
HARI SREENIVASAN: A colleague that can assist with instant diagnoses and recommended courses of treatment. The recommendations are highly personalized based on a patient's unique genetic makeup.
DR. MARK G. KRIS: The person I'm asking about is a 55-year-old man who already has had surgery for his lung cancer. It was discovered that this cancer had spread to lymph glands that were nearby.
So, the first thing this system does is, it shows all the different treatments that are recommended. And then now I ask what kind of chemo to give, and it points to a chemo regimen, two different drugs. And if I want the more information about exactly why this decision was made, there's a little button right next to this chemo choice that takes you to the medical literature and some key publications about this regimen, the benefits it can give, and why that choice was made.
HARI SREENIVASAN: Dr. Bob Wachter is associate chair at the University of California, San Francisco, Medical School and author of a new book, “The Digital Doctor.”
DR. ROBERT WACHTER, University of California, San Francisco: In some ways, ironic that computers will probably be best at low-level tasks, pretty simple algorithmic stuff. I have a runny nose and a cough and a low-grade fever. What should I do? And high — very high-complexity stuff, like, I have an unusual form of lung cancer and I have these genetic mutations, and what should I do?
HARI SREENIVASAN: But Wachter says where computers and A.I. still struggle is in the middle.
DR. ROBERT WACHTER: A lot of medicine kind of lives in that middle ground, where it's really messy. And someone comes in to see me and they have a set of complaints and physical exam findings all that. And it could be — if you look it up in a computer, it could be some weird — it could be the Bubonic plague, but it probably is the flu.
HARI SREENIVASAN: Wachter is also concerned about fatal implications that can result from an over-reliance on computers. In his book, he writes about a teenage patient at his own hospital who barely survived after he was given 39 times the amount of antibiotics he should have received.
DR. ROBERT WACHTER: So, in two different cases, the computers threw up alerts on the computer screen that said, this is an overdose. But the alert for a 39-fold overdose and the alert for a 1 percent overdose looked exactly the same. And the doctors clicked out of it. The pharmacists clicked out of it. Why? Because they get thousands of alerts a day, and they have learned to just pay no attention to the alerts.
Where the people are relegated to being monitors of a computer system that's right most of the time, the problem is, periodically, the computer system will be wrong. And the question is, are the people still engaged or are they now asleep at the switch because the computers are so good?
HARI SREENIVASAN: That's one of many ethical questions facing scientists, and society, as artificial intelligence continues its rapid advance.
For the PBS NewsHour, I'm Hari Sreenivasan in New York.
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