You could, for instance, ask a car fitted with a reasoning engine why it had hit the brakes, and it would be able to tell you that it thought a bicycle hidden by a van was about to enter the intersection ahead.A machine-learning program cannot do that.Besides helping improve program design, such information will, Dr Bhatt reckons, help regulators and insurance companies.It may thus speed up public acceptance of autonomous vehicles.Dr Bhatt's work is part of a long-standing debate in the field of artificial intelligence.Early AI researchers, working in the 1950s, chalked up some successes using this sort of preprogrammed reasoning.But, beginning in the 1990s, machine learning improved dramatically, thanks to better programming techniques combined with more powerful computers and the availability of more data.Today almost all AI is based on it.Dr Bhatt is not, though, alone in his scepticism.Gary Marcus, who studies psychology and neural science at New York University and is also the boss of an AI and robotics company called Robust.AI, agrees.To support his point of view, Dr Marcus cites a much-publicised result, albeit from eight years ago.This was when engineers at DeepMind (then an independent company, now part of Google) wrote a program that could learn, without being given any hints about the rules, how to play Breakout, a video game which involves hitting a moving virtual ball with a virtual paddle.
例如，如果问一辆装有推理引擎的汽车为什么踩刹车，它会告诉你它认为有一辆藏在货车后面的自行车即将进入前面的十字路口。而机器学习程序无法做到这一点。巴特博士认为，这些信息除了有助于改进项目设计之外，对监管机构和保险公司也会起到帮助作用。因此，它可能会加快公众对自动驾驶汽车的接受程度。巴特博士的工作在人工智能领域长期备受争论。20世纪50年代的早期人工智能研究人员使用这种预编程推理取得了一些成功。但是，从20世纪90年代开始，由于更好的编程技术、更强大的计算机以及更多数据的可用性，机器学习得到了显著改善。今天，几乎所有的人工智能都基于机器学习。不过，巴特博士并不是唯一持怀疑态度的人。加里・马库斯在纽约大学研究心理学和神经科学，他也是一家名为Robust.AI的人工智能和机器人公司的老板。为了支持他的观点，马库斯博士引用了一个8年前被广泛报道的结果。当时，DeepMind(当时是一家独立公司，现在隶属于谷歌)的工程师编写了一个程序，可以在不需要任何规则提示的情况下学习如何玩Breakout，这是一款电子游戏，需要用虚拟球拍击打移动中的虚拟球。
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