In the 1950s, the pioneers of artificial intelligence (AI) predicted that, by the end of this century, computers would be conversing with us at work and robots would be performing our housework. But as useful as computers are, they’re nowhere close to achieving anything remotely resembling these early aspirations for humanlike behavior. Never mind something as complex as conversation: the most powerful computers struggle to reliably recognize the shape of an object, the most elementary of tasks for a ten-month-old kid.
20世纪50年代，人工智能的先驱们预测，到本世纪末，计算机将在工作中与我们交谈，机器人将完成我们的家务。但是，尽管计算机很有用，但它们远未实现任何与早期人类行为愿望相似的目标。就算不考虑像对话这样复杂的事情：最强大的计算机也很难可靠地识别物体的形状——这是十个月大的孩子最基本的任务。
A growing group of AI researchers think they know where the field went wrong. The problem, the scientists say, is that AI has been trying to separate the highest, most abstract levels of thought, like language and mathematics, and to duplicate them with logical, step-by-step programs. A new movement in AI, on the other hand, takes a closer look at the more roundabout way in which nature came up with intelligence. Many of these researchers study evolution and natural adaptation instead of formal logic and conventional computer programs. Rather than digital computers and transistors, some want to work with brain cells and proteins. The results of these early efforts are as promising as they are peculiar, and the new nature-based AI movement is slowly but surely moving to the forefront of the field.
越来越多的人工智能研究人员认为他们知道这个领域哪里出了问题。科学家们表示，问题在于，人工智能一直试图将语言和数学等最高、最抽象的思维水平分开，并用逻辑的、循序渐进的程序来复制它们。另一方面，人工智能的一个新运动更深入地观察了自然界产生智能的更迂回的方式。这些研究人员中的许多人研究进化和自然适应，而不是形式逻辑和传统的计算机程序。有些人不想使用数字计算机和晶体管，而是想使用脑细胞和蛋白质。这些早期努力的结果既有希望，也有独特之处，新的基于自然的人工智能运动正在缓慢，但无疑走在了该领域的前沿。
Imitating the brain’s neural network is a huge step in the right direction, says computer scientist and biophysicist Michael Conrad, but it still misses an important aspect of natural intelligence. “People tend to treat the brain as if it were made up of color-coded transistors”, he explains, “but it’s not simply a clever network of switches. There are lots of important things going on inside the brain cells themselves.” Specifically, Conrad believes that many of the brain’s capabilities stem from the pattern recognition proficiency of the individual molecules that make up each brain cell. The best way to build and artificially intelligent device, he claims, would be to build it around the same sort of molecular skills.
计算机科学家和生物物理学家迈克尔·康拉德说，模仿大脑的神经网络是朝着正确方向迈出的一大步，但它仍然错过了自然智能的一个重要方面。他解释道：“人们倾向于将大脑视为由彩色编码的晶体管组成，但这不仅仅是一个聪明的开关网络。脑细胞本身有很多重要的事情在发生。”具体而言，康拉德认为，大脑的许多能力源于组成每个脑细胞的单个分子的模式识别能力。他声称，制造人工智能设备的最好方法是围绕同样的分子技能来制造。
Right now, the option that conventional computers and software are fundamentally incapable of matching the processes that take place in the brain remains controversial. But if it proves true, then the efforts of Conrad and his fellow AI rebels could turn out to be the only game in town.
目前，传统计算机和软件从根本上无法与大脑中发生的过程相匹配的选择仍然存在争议。但如果这被证明是真的，那么康拉德和他的人工智能叛军同伴的努力可能会成为城里唯一的游戏。
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