AI世界中有哪些令人印象深刻的算法或软件? [英] What are some impressive algorithms or software in the world of AI?

问题描述

我一直很喜欢AI和进化算法的想法.不幸的是，众所周知，该领域的发展速度并没有达到早期的预期.

I have always loved the idea of AI and evolutionary algorithms. Unfortunately, as we all know, the field hasn't developed nearly as fast as expected in the early days.

我要寻找的是一些具有哇"因素的示例:

What I am looking for are some examples that have the "wow" factor:

• 以意外方式适应的自学式学习系统.

• Self-directed learning systems that adapted in unexpected ways.

游戏代理商特别活跃并且产生了意想不到的策略

Game agents that were particularly dynamic and produced unexpected strategies

符号表示系统实际上产生了一些有意义而有见地的输出

Symbolic representation systems that actually produced some meaningful and insightful output

在多个代理系统中有趣的紧急行为.

Interesting emergent behavior in multiple agent systems.

让我们不了解定义AI的语义. 如果它看起来或听起来像AI，让我们听听它.

Let's not get into the semantics of what defines AI. If it looks or sounds like AI, let's hear about it.

我将首先介绍1997年的故事.

I'll go first with a story from 1997.

Dr. Adrian Thompson正在尝试使用遗传算法在FPGA中创建语音识别电路.几千年后，他成功地使设备区分了停止"和执行"语音命令.他检查了器件的结构，发现一些有源逻辑门与电路的其余部分断开了连接.当他禁用这些原本毫无用处的门时，电路就会停止工作...

我们可以尝试保持对产生令人印象深刻的技术/算法的讨论吗?如果我想阅读早期阶段成千上万的AI技术但表现出希望的AI技术，我可以在Google上搜索.

Can we try and keep the discussion to techniques/algorithms that produced something impressive? I can google if I want to read about the thousands of AI technologies that are in the early stages but showing promise.

推荐答案

我建立了一种针对大型工厂苗圃的产品中零售库存补给的进化算法(还有一些非常大，聪明的公司-2亿美元的公司)

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).

这可能是我做过的最酷的事情.利用三年的历史数据，它在我度假时连续第一个星期就崩溃和演变.

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.

最终结果既正面又奇怪.实际上，我很确定一开始它是坏的.

The end results were both positive and bizarre. Actually, I was pretty sure it was broken at first.

该算法忽略了前几​​周的销售额，所有指标的权重均为0(这与这些人当前的工作方式不符-现在他们考虑的是上一年的同一周，也考虑了这些因素在最近的趋势中.)

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).

最终我意识到发生了什么事.有了生物必须使用的指标，随着时间的推移，查看上个月的同一部分而忽略最近的趋势会更有效.

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.

因此，它没有查看过去几天，而是查看上个月的同一周，因为有些微妙但稳定的趋势每30天重复一次.而且它们比日趋动荡的趋势更加可靠.

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.

结果是效率得到了显着且可重复的提高.

And the result was a significant and reproducable improvement in efficiency.

不幸的是，我对此感到非常兴奋，以至于我告诉客户这件事，他们取消了该项目.第一次运行是非常有希望的，但是即使您可以处理最近三年中的几乎所有数据并且看到该算法神奇地提高了效率，也很难作为证明来出售. EA并不难，但是人们首先发现它们令人费解，而做如此神秘的事情的想法实在有些令人难以忍受.

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.

对我来说，最大的收获是，如果我创造出看起来有些不可思议的东西，那么我就应该坚持不懈地谈论它，直到我可以组织一个很好的演示文稿为止. :)

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. :)

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