AI 与机器学习rogerschank.com
IBM 并没有用 Watson 实现“认知计算”（2016）
IBM is not doing "cognitive computing" with Watson (2016)
2016 — 2018，它一次次回到首页
黑点是上首页（≥ 50 分）的那次投稿，空心点是没冲上去的投稿，点越大分数越高。
导读
这篇2016年的文章由AI研究者Roger Schank撰写，批评IBM对Watson的宣传是“欺诈性声明”，称Watson并未真正实现广告所暗示的认知计算和自然对话能力。文章引发Hacker News上关于Watson营销夸大、平台本质以及AI炒作风险的长期讨论。
为什么经久不衰：它提前揭示了AI产品营销中拟人化包装和夸大能力的问题，今天在AI热潮下回看仍有警示意义。
HN 评论精选
广告展示的开放对话能力并不存在，会误导公众。
作者特别不满的是Watson广告中有人坐下来和“Watson”对话。我在IBM Watson团队工作时也对这类广告感到不安，因为它们展示了一种IBM根本无法提供的能力。没人能提供，因为对话系统只存在于特定、严格受限的决策树中，比如语音邮件或客服提示。如果SpaceX这样打广告，他们会请名人坐在火星上的客厅里谈论火星生活多美好；那样大多数人会明白SpaceX并没有真的把人送上火星。不幸的是，很多人以为和电脑对话其实已经可行，只是自己还没体验过。
ChuckMcM查看英文原文
The author's particular gripe is that the Watson advertisements showing someone sitting down and talking to "Watson." They bother me as well (and did so when I was working at IBM in the Watson group) because they portray a capability that nothing in IBM can provide. Nobody can provide it (again to the author's point) because dialog systems (those which interact with a user through conversational speech) don't exist out side specific, tightly constrained, decision trees (like voice mail or customer support prompts). If SpaceX were to advertise like that, they would have famous people sitting in their living room, on mars, and talking about what they liked about the Martian way of life. In that case I believe that most people would understand that SpaceX wasn't already hosting people on Mars. Unfortunately many, many people think that talking to your computer in actually already possible, they just haven't experienced it yet. Not sure how we fix that.
Watson只是多个技术平台的品牌名，拟人化命名误导公众。
IBM营销部门最巧妙的伎俩是让非技术人士（从本帖看可能连技术人士也如此）以为Watson是某种单一的东西，比如一个带有不同API的大型神经网络之类。老实说，我认为大多数人就是这么理解Watson的。Watson就像Google Cloud Platform，只是一个包含一堆技术的平台名称。例如Watson Natural Language Understanding以前叫AlchemyLanguage，只是重新命名了。不过这招很聪明：用一个人名带来拟人化联想，让人以为它是某种会学习的AI。
laichzeit0查看英文原文
The most ingenious trick that the IBM marketing department pulled was to get non-technical (and probably even technical people, judging by this thread) to think that Watson is some kind of singular thing. Like that it’s a single big neural network with different APIs on it, or something. I honestly think that’s what most people think Watson refers to. Watson is like Google Cloud Platform. It’s just a name for a platform with a bunch of technologies. E.g. Watson Natural Language Understanding was previously AlchemyLanguage. It was just rebranded. It’s very clever though, I’ll give them that. Use a human name so it has all the anthropomorphic connotations and let people think it’s some kind of AI learning things.
IBM员工承认PR过头，但批评者应先实际试用服务和读论文。
如果你想弄清Watson能做什么并绕开所有营销吹嘘，可以直接试用 http://ibm.com/watsondevelopercloud 上的所有服务。我不否认公关常常太过分，这也是我们内部的大争论（我在Watson工作）。但遗憾的是，这里大多数负面意见来自那些根本懒得试用我们服务、也没读过我们团队发表的科学论文的人。
pesenti查看英文原文
If you want to figure out what Watson can do and bypass all the marketing hype, you can just try all the services available at http://ibm.com/watsondevelopercloud. I won't argue that the PR goes often too far, and that's a big debate we have internally (I work for Watson). But it's a pity that most of the negative opinions expressed here come from people who haven't even bothered to try any of the services we put out there or read any of the scientific papers that have been published by our team.
认为“欺诈”过重，Watson是传统ML算法的营销包装，过度销售可能损害AI信誉。
这篇文章印证了我近年来作为机器学习长期爱好者和推动者的一些巨大挫败感。几年前在一个机器学习会议上，我和Watson展台员工聊了几分钟，几乎立刻有同感。不过我不喜欢“欺诈”这个词，“被疯狂过度销售”更合适。我更深入看了Watson，发现它其实只是把大量传统（且不太创新）的机器学习算法包装进一个有巨额营销预算的平台。机器学习潜力巨大却常淹没在噪音和流行词中，这让我无比沮丧；平庸的企业公司和无知的媒体过度吹捧，可能在未来几年损害其可信度。
rm999查看英文原文
This article mirrors some huge frustrations I've had in recent years as a long-time lover and pusher of machine learning. I spoke to a Watson booth employee for a few minutes at a machine learning conference a couple years ago, and almost right away had similar feelings. I don't like the term 'fraud' here though, 'insanely oversold' seems more appropriate. I looked more into Watson, and realized it's really just a large number of traditional (and not very innovative) machine learning algorithms wrapped into a platform with a huge marketing budget. >AI winter is coming soon. Perhaps, if so Watson is certainly evidence of this. It frustrates me to no end that machine learning has so much potential but is often lost in a sea of noise and buzz words (as much as I love deep learning, I'm almost tempted to lump that in here too given its outsized media coverage). Machine learning is in its infancy of impact, but the overselling by mediocre enterprise companies and ignorant press may shoot its credibility for years to come.
历次讨论
同类还值得读
如何写一个拼写纠正器
How to Write a Spelling Corrector
Peter Norvig 用一个几十行的 Python 程序演示如何做拼写纠正：先用编辑距离（删除、插入、替换、换位）生成所有可能的候选词，再用从大量文本中统计出的词频概率，选出…
AI 与机器学习上首页 10 次 · 2007–2022norvig.com编辑精选面向黑客的概率编程与贝叶斯方法
Probabilistic Programming and Bayesian Methods for Hackers (2013)
《黑客的贝叶斯方法与概率编程》是一本开源的贝叶斯统计入门书，以 Python/PyMC 代码和交互式 Jupyter Notebook 为主线，强调“先实践、后数学”的学习路径，…
AI 与机器学习上首页 5 次 · 2013–2018camdavidsonpilon.github.io编辑精选神经网络黑客指南
Hacker's Guide to Neural Networks
Andrej Karpathy 写的神经网络入门教程，面向程序员，从最简单的表达式求导讲起，逐步推导出反向传播和梯度下降，最后引出神经网络。
AI 与机器学习上首页 4 次 · 2014–2019karpathy.github.io编辑精选神经网络能逼近任意函数的可视化证明
A visual proof that neural nets can approximate any function
这一章来自 Michael Nielsen 的在线书《Neural Networks and Deep Learning》，用 sigmoid 单元叠加成局部隆起/平滑阶跃的图示…
AI 与机器学习上首页 4 次 · 2014–2022neuralnetworksanddeeplearning.com编辑精选论乔姆斯基与统计学习的两种文化
On Chomsky and the Two Cultures of Statistical Learning
Norvig 回应 Chomsky 对统计语言模型批评的文章，围绕“统计学习能否解释语言”展开，比较生成与解释、少参数模型与 Shannon 式高参数统计模型。
AI 与机器学习上首页 4 次 · 2011–2025norvig.com编辑精选