人工智能的神话：为什么计算机不能像我们一样思考[有声读物]

English | 10-19-21 | B09HSL7T3N | 10h 9m | M4B@125 kbps | 554 MB

Futurists insist that AI will soon eclipse the capacities of the most gifted human mind. What hope do we have against superintelligent machines? But we aren't really on the path to developing intelligent machines. In fact, we don't even know where that path might be.

Erik Larson takes us on a tour of the landscape of AI to show how far we are from superintelligence and what it would take to get there. Ever since Alan Turing, AI enthusiasts have equated artificial intelligence with human intelligence. This is a profound mistake. AI works on inductive reasoning, crunching data sets to predict outcomes. But humans don't correlate data sets: We make conjectures informed by context and experience. Human intelligence is a web of best guesses, given what we know about the world. We haven't a clue how to program this kind of intuitive reasoning, known as abduction. Yet it is the heart of common sense. That's why Alexa can't understand what you are asking and why AI can only take us so far.

Larson argues that AI hype is both bad science and bad for science. A culture of invention thrives on exploring unknowns, not overselling existing methods. Inductive AI will continue to improve at narrow tasks, but if we want to make real progress, we will need to start by more fully appreciating the only true intelligence we know - our own.

英语| 10-19-21 | B09HSL7T3N | 10小时9分钟|M4B@125kbps | 554 MB 未来学家坚持认为，人工智能很快就会超越最有天赋的人类大脑的能力。我们对超级智能机器有什么希望？但我们并没有真正走上开发智能机器的道路。事实上，我们甚至不知道这条路可能在哪里。 Erik Larson带领我们参观了人工智能的景观，展示了我们离超级智能还有多远，以及实现这一目标需要什么。自从阿兰·图灵以来，人工智能爱好者就把人工智能等同于人类智能。这是一个严重的错误。人工智能基于归纳推理，处理数据集以预测结果。但人类不会将数据集关联起来：我们根据上下文和经验做出猜测。根据我们对世界的了解，人类的智力是一个由最佳猜测组成的网络。我们不知道如何对这种被称为绑架的直觉推理进行编程。然而，这是常识的核心。这就是为什么Alexa无法理解你在问什么，为什么人工智能只能带我们走这么远。 拉尔森认为，人工智能炒作既不利于科学，也不利于科学。发明文化在探索未知事物而不是过度推销现有方法的基础上蓬勃发展。归纳式人工智能将在狭义的任务中继续改进，但如果我们想取得真正的进展，我们需要从更充分地了解我们所知道的唯一真正的智能开始——我们自己的智能。

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