人工智能托福作文

人工智能是研究、开发用于模拟、延伸和扩展人的智能的理论、方法、技术及应用系统的一门新的技术科学。 以下提供几篇有关于人工智能的托福作文，供大家参考学习。

人工智能托福作文

People do not always act how economists assume they behave, but a computer controlled agent could act how economists assume people behave, if programmed to do so, in which case all rules of economic theory would work.

This means three things. First, the behavior of artificial intelligence agents that behave under the assumptions made in economic theory can be predicted by economic theory. Second, one can test how well artificial intelligence agents do the things economists assume people do. Thirdly, artificial intelligence agents that behave under the assumptions made in economic theory can be used to do calculations of math relating to economic theory.

Of course, one must wonder why this is important. It is good to be able to predict what a group of AI agents will do. It is also good to be able to use this information in order to test how well the agents work. The final fact is particularly interesting however. We can do relatively hard math problems relatively quickly using artificial intelligence agents. By quickly, I mean the Big-O of the program is polynomial as opposed to exponential. There are many math problems out there that the best algorithms known take exponential time. This method allows us to get fairly exact answers in polynomial time.

The question arises if this is really doing math. The answer to this question is not that simple. At the very least, the end result will be a very good approximation. Economists make up a bunch of rules the same way mathematicians do, and they often provide some kind of backing of these rules the same way mathematicians prove their statements. So, if we were to create a model that followed all the assumptions of economics, then we would be doing math. Many assumptions leave room for gray area, however. Economists often make statements that assume enough time or a large enough population. Of course, it is difficult to say how much is enough. So, many people would consider these methods to result in approximations, but these approximations are not only going to be as accurate as anyone would need, but they will also be done relatively quickly.

人工智能托福作文题材

1. to unleash mass unemployment 导致大量的失业

不同的人对于机器人技术【robotics】及人工智能【artificial intelligence】是利是弊一直各执一词，争论不休。反对者反对机器人技术【robotics】及人工智能【artificial intelligence】的一个主要原因就是其会导致大量的失业【tounleash mass unemployment】。而大规模的失业【mass unemployment】使得大量劳动力【labor force】无事可做，从而会影响社会的稳定【 to take a heavy toll on social stability】。

2. to outperform 超越

然而支持者们却认为，现如今机器能够在几乎所有任务上超越人类【machines are able to outperform humans at almost any task】,所以机器人技术【robotics】及人工智能【artificialintelligence】把人们从繁重的工作中解放了出来，提高了工作效率，人们才可以更多地去享受休闲娱乐活动。只是在这个时刻确切到来之前，社会需要直面的这个问题【Society needs to confront this question before it is upon us】是如何更好地让机器人技术及人工智能为我们服务。

3. prospect of leisure-only life 只有休闲的人生前景

但是一个只有休闲的人生前景【prospect of leisure-only life】是有吸引力【appealing】的吗?有人可不这样认为，毕竟有句话说得好——生于忧患，死于安乐，工作有时候对于人类来说是必不可少的。因此人类应该警惕机器相对于人类智慧的全方位优势的实现【all-round superiority of machine to human intelligence】。

4. harness technology in a wise way 明智地利用科技

其实机器人技术及人工智能本身并没有对错之分，关键是要看人类如何明智地利用科技【to harness technology in a wise way】。比如说人工智能进展最快的领域之一是机器视觉【machine vision】,尤其是人脸识别【facial recognition】。装有人脸识别设备的车辆将会实现自动驾驶，而科学家预测相比容易出错的人类司机，自动驾驶汽车【the self-driving vehicles】将减少90%甚至更多的事故。不过与此同时，大量涉及驾驶车辆的工作岗位将会消失。

人工智能托福作文词汇

科技类话题 topic of science and technology

随着科技渗透到我们日常生活的方方面面 with technology penetrating into every aspect of our daily life

科技现代化 modernization of science and technology

人工智能 artificial intelligence

机器人技术 robotics

导致大量的失业 to unleash mass unemployment

劳动力 labor force

lao dong li labor force

影响社会稳定 to take a heavy toll on social stability

机器能够在几乎所有任务上超越人类 machines are able to outperform humans at almost any task

只是在这个时刻确切到来之前，社会需要直面这个问题 Society needs to confront this question before it is upon us

只有休闲的人生前景 prospect of leisure-only life

有吸引力的 appealing

机器相对于人类智慧的全方位优势 all-round superiority of machine to human intelligence

明智地利用科技 to harness technology in a wise way

机器视觉 machine vision

人脸识别 facial recognition