I built an evolutionary algorithm for retail inventory replenishment in a product targeted at huge plant nurseries (and there are some really big, smart ones -- $200m companies).
It was probably the coolest thing I've ever worked on. Using three years of historical data, it crunched and evolved for a week straight while I was on vacation.
The end results were both positive and bizarre. Actually, I was pretty sure it was broken at first.
The algorithm was ignoring sales from the previous few weeks, giving them a weight of 0 for all indicators (which is at odds with how these guys currently work -- right now they consider the same week in the previous year and also factor in recent trends).
Eventually I realized what was going on. With the indicators the organism had to work with, over time it was more efficient to look at the same part of the previous month and ignore recent trends.
So instead of looking at the last several days, it looked at the same week in the previous month because there were some subtle but steady trends that repeat every 30 days. And they were more reliable than the more volatile day-to-day trends.
And the result was a significant and reproducable improvement in efficiency.
Unfortunately, I was so excited by this that I told the customer about it and they cancelled the project. That first run was extremely promising, but it was hard to sell as proof even though you could crunch almost any data from the last three years and see that the algorithm magically improved efficiency. EA's are not hard, but people find them convoluted at first, and the idea of doing something so arcane was just a little bit too much to swallow.
The big takeaway for me was that if I ever create something that appears a bit too magical, I should hold off on talking about it until I can put together a good presentation. :)
Slightly outside of the traditional AI realm, are HTMs (Hierachical Temporal Memory) as developed at Numenta. This technology is still in its early stages but shows promises in the targeted "WOW factor" areas.
So far the most impressive aspect of AI has been the ratio of promises to deliveries. In my opinion, the only truly viable approach to computer-based intelligence is simulated neural networks, because all of the things in the real world that we consider to be "intelligent" (humans, chimpanzees, dogs, cockroaches etc.) all possess variants of the same basic control system: a big mess of neurons hooked up to input and output devices.
Amazingly, despite this apparent truth, the Computer Science field that calls itself "neural networks" has pretty much abandoned the attempt to simulate actual biological neurons and neuronal structures. I couldn't begin to tell you why this is the case, although I suspect it's because programmers in general do not like going outside their comfort zones and learning about topics outside of Computer Science.
The only upside to this is that Terminator is still just a movie.
One of the most interesting things in AI, for me, is a very old discussion started by Rodney Brooks about his behavioral architecture called subsumption architecture.
He completely abandons all kinds of symbolic representation, and always says: take the world as your model. This saves the robot from generating a wrong world view and all complicated issues in correcting the model.
He published many interesting books and was one of the first persons in the embodied cognition approach that is used a lot in research at the moment.
Interesting reading material can be found on http://people.csail.mit.edu/brooks/index.html. Some of his later publications get very philosophical, but the earlier descriptions of the robots and how their behavior emerged from a simple set of rules and actions are worth reading.
I found the recent research of evolution and cooperation among robots very intriguing. This blog entry gives a good summary of the experiment and its results. Most interesting to me was the observed behavior of both martyr AI and "evil" AI.
You might be asking an incomplete question. You are saying "what are great answers", but just like the Hitchhikers guide to the galaxy, when the best computer gives "42" as an answer, you want to know what is the question.
There are some "best questions" that drive some great answers. Some really useful answers are in things that look mundane. The "traveling salesman problem" means a lot of cost or money for FedEx. Dijkstra's algorithm drives the paths packets on the internet actually follow.
De'Morgans laws are quite cool too - they allow minimization of gates in computer chips to do the same job. They are automated and work on the billions of gates in computer chips. It likely touches as much as a third of a trillion dollars in computer-hardware based value-creation per year. I'm not talking what people do with them, I'm just talking "them".
These may seem mundane, but they are neat to me.
I also like the evolutionary antenna. I'm pretty sure that when Musk says that AI presents an existential threat, he is referring to the power of evolutionary algorithms. There is a much more modern version of that on one of the Mars rovers - and humans couldn't invent it (alone), but they can set up computers that can.
There is an ambitious open source Java library called CIlib that provides a host of Computational Intelligence methods. It is currently being used at University level by a research group to advance their own research.
There are many impressive algorithms and software in the world of AI.
Deep learning: Deep learning is a type of machine learning that uses artificial neural networks to learn from data.
Reinforcement learning: Reinforcement learning is a type of machine learning that allows an agent to learn how to behave in an environment by trial and error.
Natural language processing: Natural language processing (NLP) is a field of computer science that deals with the interaction between computers and human (natural) languages.
Computer vision: Computer vision is a field of computer science that deals with the extraction of meaningful information from digital images or videos
Speech recognition: Speech recognition is a field of computer science that deals with the automatic recognition of human speech.
As AI technology continues to develop, we can expect to see even more innovative and powerful applications of AI in the years to come.
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我在一款针对大型植物苗圃的产品中构建了一种用于零售库存补充的进化算法（其中有一些非常大、聪明的苗圃——价值 2 亿美元的公司）。
这可能是我做过的最酷的事情。使用三年的历史数据，在我度假的时候，它连续一周进行了处理和演变。
最终的结果既积极又奇怪。事实上，一开始我很确定它坏了。
该算法忽略了前几周的销售情况，所有指标的权重均为 0（这与这些人目前的工作方式不一致——现在他们考虑的是上一年的同一周，并且还考虑了最近的趋势） ）。
最终我意识到发生了什么事。有了有机体必须使用的指标，随着时间的推移，查看上个月的同一部分并忽略最近的趋势会更有效。
因此，它不是关注过去几天，而是关注上个月的同一周，因为有一些微妙但稳定的趋势每 30 天重复一次。而且它们比波动较大的日常趋势更可靠。
结果是效率得到了显着且可重复的提高。
不幸的是，我对此感到非常兴奋，以至于我告诉了客户，他们取消了该项目。第一次运行非常有希望，但很难作为证据出售，即使你可以处理过去三年的几乎所有数据并看到该算法神奇地提高了效率。 EA 并不难，但人们一开始就觉得它们很复杂，而且做一些如此神秘的事情的想法有点难以接受。
对我来说最大的收获是，如果我创造了一些看起来有点太神奇的东西，我应该推迟谈论它，直到我能整理出一个好的演示文稿。 :)
I built an evolutionary algorithm for retail inventory replenishment in a product targeted at huge plant nurseries (and there are some really big, smart ones -- $200m companies).
It was probably the coolest thing I've ever worked on. Using three years of historical data, it crunched and evolved for a week straight while I was on vacation.
The end results were both positive and bizarre. Actually, I was pretty sure it was broken at first.
The algorithm was ignoring sales from the previous few weeks, giving them a weight of 0 for all indicators (which is at odds with how these guys currently work -- right now they consider the same week in the previous year and also factor in recent trends).
Eventually I realized what was going on. With the indicators the organism had to work with, over time it was more efficient to look at the same part of the previous month and ignore recent trends.
So instead of looking at the last several days, it looked at the same week in the previous month because there were some subtle but steady trends that repeat every 30 days. And they were more reliable than the more volatile day-to-day trends.
And the result was a significant and reproducable improvement in efficiency.
Unfortunately, I was so excited by this that I told the customer about it and they cancelled the project. That first run was extremely promising, but it was hard to sell as proof even though you could crunch almost any data from the last three years and see that the algorithm magically improved efficiency. EA's are not hard, but people find them convoluted at first, and the idea of doing something so arcane was just a little bit too much to swallow.
The big takeaway for me was that if I ever create something that appears a bit too magical, I should hold off on talking about it until I can put together a good presentation. :)
前段时间，我发现了这个系列的文章： 设计新兴人工智能。
这些文章的作者创建了一款游戏“AI 战争：舰队指挥”，该游戏以新兴 AI 为特色。也许你会发现这很有趣。
Some times ago, I've found this series of articles: Designing Emergent AI.
The author of these articles has created the game "AI War: Fleet command" that features an emergent AI. Maybe you'll find this interesting.
稍微超出传统人工智能领域的是 Numenta 开发的 HTM（分层时序记忆）。这项技术仍处于早期阶段，但在“令人惊叹的因素”目标领域显示出前景。
Slightly outside of the traditional AI realm, are HTMs (Hierachical Temporal Memory) as developed at Numenta. This technology is still in its early stages but shows promises in the targeted "WOW factor" areas.
到目前为止，人工智能最令人印象深刻的方面是承诺与交付的比率。在我看来，基于计算机的智能唯一真正可行的方法是模拟神经网络，因为现实世界中我们认为“智能”的所有事物（人类、黑猩猩、狗、蟑螂等）都拥有变体相同的基本控制系统：连接到输入和输出设备的一大堆神经元。
令人惊讶的是，尽管事实如此，自称为“神经网络”的计算机科学领域几乎放弃了模拟实际生物神经元和神经元结构的尝试。我无法开始告诉你为什么会出现这种情况，尽管我怀疑这是因为程序员一般不喜欢走出自己的舒适区并学习计算机科学之外的主题。
唯一的好处是《终结者》仍然只是一部电影。
So far the most impressive aspect of AI has been the ratio of promises to deliveries. In my opinion, the only truly viable approach to computer-based intelligence is simulated neural networks, because all of the things in the real world that we consider to be "intelligent" (humans, chimpanzees, dogs, cockroaches etc.) all possess variants of the same basic control system: a big mess of neurons hooked up to input and output devices.
Amazingly, despite this apparent truth, the Computer Science field that calls itself "neural networks" has pretty much abandoned the attempt to simulate actual biological neurons and neuronal structures. I couldn't begin to tell you why this is the case, although I suspect it's because programmers in general do not like going outside their comfort zones and learning about topics outside of Computer Science.
The only upside to this is that Terminator is still just a movie.
对我来说，人工智能中最有趣的事情之一是 Rodney Brooks 发起的关于他的行为架构的一个非常古老的讨论，称为 包容架构。
他完全抛弃了各种象征性的表征，总是说：以世界为模型。这可以避免机器人产生错误的世界观以及纠正模型时出现的所有复杂问题。
他出版了许多有趣的书籍，并且是目前在研究中大量使用的具身认知方法的第一人之一。
有趣的阅读材料可以在 http://people.csail.mit.edu/brooks 上找到/index.html。他后来的一些出版物变得非常哲学化，但早期对机器人的描述以及它们的行为如何从一组简单的规则和动作中显现出来，值得一读。
One of the most interesting things in AI, for me, is a very old discussion started by Rodney Brooks about his behavioral architecture called subsumption architecture.
He completely abandons all kinds of symbolic representation, and always says: take the world as your model. This saves the robot from generating a wrong world view and all complicated issues in correcting the model.
He published many interesting books and was one of the first persons in the embodied cognition approach that is used a lot in research at the moment.
Interesting reading material can be found on http://people.csail.mit.edu/brooks/index.html. Some of his later publications get very philosophical, but the earlier descriptions of the robots and how their behavior emerged from a simple set of rules and actions are worth reading.
查看 http://www.wolframalpha.com/ （可能更多地属于计算知识）
Check out http://www.wolframalpha.com/ (probably falls more under computational knowledge)
我发现最近关于机器人之间的进化和合作的研究非常有趣。 这篇博客文章很好地总结了实验及其结果。对我来说最有趣的是观察到的烈士人工智能和“邪恶”人工智能的行为。
I found the recent research of evolution and cooperation among robots very intriguing. This blog entry gives a good summary of the experiment and its results. Most interesting to me was the observed behavior of both martyr AI and "evil" AI.
我认为你的问题没有明确、客观的答案，所以这是我个人最喜欢的答案。
学习乐趣和学习乐趣playfun
"learnfun 和 playfun：自动化 NES 游戏的通用技术"< /a> （包含源代码和其他信息）
这是一个 YouTube 链接，如果之前的其他人会死。 Vsauce 也对此进行了专题报道。
“它不是松手，并收到‘游戏结束’，而是只是暂停了游戏。永远。[...]唯一的胜利之举就是不玩。”
I don't think there is a definite, objective answer to your question, so here is my personal favorite.
learnfun & playfun
"learnfun & playfun: A general technique for automating NES games" (with source code and other info)
Here is a youtube link if the other previous one would die. This was also featured on Vsauce.
"Rather than loose, and receive a 'game over', it just paused the game. For ever. [...] The only winning move is to not play."
您可能问了一个不完整的问题。你说的是“什么是最好的答案”，但就像银河系漫游指南一样，当最好的计算机给出“42”作为答案时，你想知道问题是什么。
有一些“最好的问题”可以带来一些很好的答案。一些真正有用的答案来自于看似平凡的事情。 “旅行推销员问题”对联邦快递来说意味着大量的成本或金钱。 Dijkstra 算法驱动互联网上数据包实际遵循的路径。
德摩根定律也很酷 - 它们允许最小化计算机芯片中的门同一份工作。它们是自动化的，并在计算机芯片中的数十亿个门上工作。每年基于计算机硬件的价值创造可能高达一万亿美元的三分之一。我不是在谈论人们用它们做什么，我只是在谈论“他们”。
这些看似平凡，但对我来说却很巧妙。
我也喜欢进化天线。我非常确定，当马斯克说人工智能构成了生存威胁时，他指的是进化算法的力量。在其中一个火星探测器上有一个更现代的版本 - 人类无法（单独）发明它，但他们可以设置可以做到的计算机。
You might be asking an incomplete question. You are saying "what are great answers", but just like the Hitchhikers guide to the galaxy, when the best computer gives "42" as an answer, you want to know what is the question.
There are some "best questions" that drive some great answers. Some really useful answers are in things that look mundane. The "traveling salesman problem" means a lot of cost or money for FedEx. Dijkstra's algorithm drives the paths packets on the internet actually follow.
De'Morgans laws are quite cool too - they allow minimization of gates in computer chips to do the same job. They are automated and work on the billions of gates in computer chips. It likely touches as much as a third of a trillion dollars in computer-hardware based value-creation per year. I'm not talking what people do with them, I'm just talking "them".
These may seem mundane, but they are neat to me.
I also like the evolutionary antenna. I'm pretty sure that when Musk says that AI presents an existential threat, he is referring to the power of evolutionary algorithms. There is a much more modern version of that on one of the Mars rovers - and humans couldn't invent it (alone), but they can set up computers that can.
有一个雄心勃勃的开源 Java 库，名为 CIlib，它提供了许多计算智能方法。目前，一个研究小组正在大学层面使用它来推进他们自己的研究。
There is an ambitious open source Java library called CIlib that provides a host of Computational Intelligence methods. It is currently being used at University level by a research group to advance their own research.
人工智能世界中有许多令人印象深刻的算法和软件。
人工神经网络从数据中学习。
学习允许代理学习如何在
环境通过反复试验。
计算机科学领域，处理之间的相互作用
计算机和人类（自然）语言。
处理从数字中提取有意义的信息
图像或视频
涉及人类语音的自动识别。
随着人工智能技术的不断发展，我们预计在未来几年将会看到更多创新和强大的人工智能应用。
There are many impressive algorithms and software in the world of AI.
artificial neural networks to learn from data.
learning that allows an agent to learn how to behave in an
environment by trial and error.
field of computer science that deals with the interaction between
computers and human (natural) languages.
deals with the extraction of meaningful information from digital
images or videos
that deals with the automatic recognition of human speech.
As AI technology continues to develop, we can expect to see even more innovative and powerful applications of AI in the years to come.