①数据标签。当前大大都人工智能模型都是经过“监督学习”进行练习的。这意味着人类有必要对基础数据进行符号和分类，这可所以一个恰当大的和简略犯错的杂事。
②获得许多的练习数据集。机器学习的其时浪潮需要练习数据集，其不只被符号，还得满足大及全部。深度学习办法需要数千个数据记载才干使模型在分类使命上变得相对较好，在某些情况下，需要数百万个数据记载才干在人类层面上实施。
③ai体系的可说明性。跟着深度学习的成功和选用而不断打开，深度学习带来了更多样化和 的使用程序以及更不通明。更大更凌乱的模型使我们很难从人的视点说明为啥达到氖亟隈择(甚至在实时抵达时更难)。
④数据和算法中的误差：偏好是一种不一样的应战。当人类的偏好(有知道或无知道)在选择运用哪些数据点和无视时，可以会发生消除性的社会影响。此外，当数据搜集的进程和频率本身在整个团队和调查到的行为中不均衡时，在算法分析数据、学习和猜测数据时很简略呈现疑问。
【行文条理】提出观念（第一段）——驳斥观念（第二至四段）——提出主张（第五至六段）
ⅱbut many scientists think such promises areoverblown, naively creating false confidence in highlyfallibletechnologies. mathematics alone sets some limits on the potential usefulness of artificial intelligence. for example, physicists hykel hosni and angelo vulpiani explored the ability of computers usingmassamounts of data to improve predictions in fields such as finance, medicine, cybersecurity or even politics. the trouble, they argue, is that almost any real-world application of ai will involve a huge number ofvariables. accurately predicting the future of any such system will requireastronomicalamounts of data, far beyond what isremotelypossible to gather. the more complex the system — and that’s just where we think ai might help — the worse it gets.
ⅲthis doesn’t mean that ai won’t improve predictions, just that it won’t do so without the human factor. improved forecasts will require new conceptualinsightsas well as more data. such has been the case for weather predictions: scientists learned years ago that using more data in making forecasts often leads to less accuracy. accurate predictions today require theintentional disregardof lots of data that reflectatmosphericevents that don’t actually affect weather.
ⅳresearchers have learnedmuch the samefor biology and medicine. “big data,” as one group puts it, “needs big theory too.” in financeas well,sophisticatedusers of ai find that they get the best results bypairingmachine learning with experienced humans.
ⅴperhaps machines won’t replace humans quite asbroadlyas many fear. ai is getting better at doing what humans can do. but humans working alongside ai will be able to do what neither humans nor ai can achieve alone.
ⅵthis is crucial, as economists daron acemoglu and pascual restrepo recently noted, because we can choose how 
to develop ai for the future. most tech companies and businesses have been focusing on replacing people with ai with a narrow eye toward boosting short-term profits. we could choose to focus technology instead on creating new tasks for which humans will be asindispensableas ever. right now, they suggest, we’re developing the wrong kinds of ai. unless we change that, we won’t see new kinds of jobs, and can expect a future much like the last few decades, withstagnatingproductivity and labor demand.
2. productivity [?pr?d?k?t?v?ti] n. 出产力；出产率
3. transform [tr?ns?f?:m] v. 改进；使改观
4.fix [f?ks] v. 处置，处置（疑问等）
5. grand [gr?nd] adj. 庞大的；雄伟的
6. vision [?v??n] n. 愿望；展望
7. computing [k?m?pju:t??] n. 核算；信息处置技能
8. *overblown [???v??bl??n] adj. 夸大的；过火的
9.*fallible [?f?l?bl] adj. 会犯错的
10. mass [m?s] adj. 大批的；数量极多的
11.variable [?ve?ri?bl] n. 可变要素，变量
12.astronomical [??str??n?m?kl] adj. （数量、价格等）极端无量的
13.remotely [r??m??tli] adv. 长远地
14. insight [??nsa?t] n. 调查；深化见地
15. intentional [?n?ten??nl] adj. 成心的；有意的
16. disregard [?d?sr??gɑ:d] n. 不睬睬；无视
17. atmospheric [??tm?s?fer?k] adj. 大气的；空气的
18.much the same 几乎相同
19. as well 也；还
20. sophisticated [s??f?st?ke?t?d] adj. 水平高的；熟行的
21. pair [pe?(r)] v. 配对；使配成火伴
22. broadly [?br?:dli] adv. 多方面地；全部地
23.indispensable [??nd??spens?bl] adj. 必不可以少的
24.*stagnate [st?g?ne?t] v. 阻滞；不打开
最庞大的展望之一是……
原文例句：in the grandest of these visions, smart computing machines could automate all of scientific discovery.最庞大的展望之一是，智能核算机器能使一切科学发现主动化。
单单xx就捆绑了yy的潜在可用性。
原文例句：mathematics alone sets some limits on the potential usefulness of artificial intelligence.单单运算才能就捆绑了人工智能的潜在可用性。
这并不料味xx，只不过在这一疑问上yy必不可以少.
原文例句：this doesn’t mean that ai won’t improve predictions, just that it won’t do so without the human factor.这并不料味着人工智能不能改进猜测，只不过在这一疑问上人类的参加必不可以少.
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