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发表于2024-11-21

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出版者:Pantheon

作者:Gary F. Marcus

出品人:

页数:288

译者:

出版时间:2019-9-10

价格:0

装帧:平装

isbn号码:9781524748258

丛书系列:

图书标签: 人工智能 AI 机器学习科普数学和计算机科学和心理学比尔盖茨推荐 TECHNOLOGY

Rebooting AI: Building Artificial Intelligence We Can Trust 2024 pdf epub mobi 电子书图书描述

Two leaders in the field offer a compelling analysis of the current state of the art and reveal the steps we must take to achieve a truly robust artificial intelligence.

Despite the hype surrounding AI, creating an intelligence that rivals or exceeds human levels is far more complicated than we have been led to believe. Professors Gary Marcus and Ernest Davis have spent their careers at the forefront of AI research and have witnessed some of the greatest milestones in the field, but they argue that a computer beating a human in Jeopardy! does not signal that we are on the doorstep of fully autonomous cars or superintelligent machines. The achievements in the field thus far have occurred in closed systems with fixed sets of rules, and these approaches are too narrow to achieve genuine intelligence.

The real world, in contrast, is wildly complex and open-ended. How can we bridge this gap? What will the consequences be when we do? Taking inspiration from the human mind, Marcus and Davis explain what we need to advance AI to the next level, and suggest that if we are wise along the way, we won't need to worry about a future of machine overlords. If we focus on endowing machines with common sense and deep understanding, rather than simply focusing on statistical analysis and gatherine ever larger collections of data, we will be able to create an AI we can trust—in our homes, our cars, and our doctors' offices. Rebooting AI provides a lucid, clear-eyed assessment of the current science and offers an inspiring vision of how a new generation of AI can make our lives better.

Rebooting AI: Building Artificial Intelligence We Can Trust 2024 pdf epub mobi 电子书

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《未来简史》大火时，我曾在某微信群开喷：“与其相信那些虚头八脑的预测论断，不如回归实际代码，因为只有后者才能告诉你关于人工智能的真实预期要低得多得多。”不出所料，立刻有人站出来：“你凭什么说简史这书不好，它哪里说的不对？”之后我闭嘴了，我知道即使自己举出各种例子也会最后陷入无意义的争论中，何况我也没那个专业素养能成功驳斥简史作者那看似完备的几百页内容，而时隔几年之后今天总算出现了一份相当靠谱的科普论证材料，在我看来这本书最大的意义是在至今为止异常的机器学习狂潮上系统性的浇上一盆冷水，即便个人并不完全赞同作者的观点，但仍无法否认此书是相关领域最值得一读的科普书，尤其相比那些在TED舞台上鼓吹奇点时代但却没有拿得出手的论文或产品的未来学家们。评价扣一星是因为书中的解决方案仍是大构想而非具体实践。

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