当然，1980年以前是施乐PARC的辉煌时期。在图形用户界面、鼠标、激光打印机、互联网和个人电脑刚刚诞生的时候。(鉴于我太年轻了，不可能活在那个年代，而你几乎在努力发明所有这些东西，关于1980年的事情，我不能告诉你任何你不知道的事情，所以我们继续吧。)

The thing is, though, that the pre-1980 days were a lot more vibrant in terms of truly disruptive new technologies. That's the way it is with any new field -- hwo many game-changing technology advances have you seen in railroads in the past 100 years? How many have you seen in lightbulbs? In the printing press? Once something ignites a hype in the right circles, there is an explosive period of invention, followed by a long period of maturing. After that, you're not going to see the same kind of completely radical changes again UNLESS the basic circumstances change.

幸运的是，这可能会发生在一些领域，而且已经发生在其他一些领域:

Mobility - smart phones bring computing to a truly portable platform, which will soon include location-based services and proximity-based ad-hoc networks. It's a completely new paradigm that's potentially as game-changing as the GUI has been The WWW (HTTP, HTML and DNS) has already been mentioned and is an obvious addition to the list, since it is enabling global, inexpensive, mainstream rich communication across the globe - all thanks to a computing platform On the interface side, both touch, multitouch (Jeff Han comes to mind) and the Wiimote need mentioning. Currently, they are basically curiosities, but so were the early GUIs. OOP design patterns -- higher level solutions as best practices to hard problems. Depending on your definition of 'computing', it may or may not belong on the list, but if you count OOP as a significant advance pre-1980 (I certainly do), I think design patterns and the GoF deserve a mention too Google's PageRank and MapReduce algorithms - I am pleased to notice I wasn't the first to mention them, and seriously --- where would the world be without the principles of both of them? I vividly remember what the world looked like before them, and suffice it to say Google really IS my friend. Non-volatile memory -- it's on the hardware side, but it is going to play a significant role in the future of computing - making bootup times a thing of the past, for example, and enabling us to use computers in entirely new ways Semantic (natural language) search / analysis / classification / translation... We're not quite there yet, but companies like Powerset give the impression that we're on the brink. On that note, intelligent HTMs should be on this list as well. I am yet another believer in Jeff Hawkins' model and approach, and if it works, it will mean a complete redefinition of what computers can do, what it means to be human, and where the world can go from here. Creating a real intelligence in that way (synthetically) would be bigger than anything the human race has accomplished before. GNU + Linux 3D printing / rapid prototyping (and, in time, manufacturing) P2P (which also lead to VoIP etc.) E-ink, once the technologies mature a bit more RFID might belong on the list, but the verdict is still out on that one Quantum Computing is the most obvious element on the list, except we still haven't been able to get enough qubits to play along. However, my friends in the field tell me there's incredible progress going on even as we speak, so I'm holding my breath for that one. And finally, I want to mention a personal favourite: distributed intelligence, or its other name: artificial artificial intelligence. The idea of connecting a huge number of people in a network and allowing them access to the combined minds of everyone else through some form of question answering interface. It's been done a number of times recently, with Yahoo Answers, Askville, Amazon Mechanical Turk, and so on, but in my mind, those are all missing the mark by a LOT... much like the many implementations of distributed hypertext that came before Tim Berners-Lee's HTML, or the many web crawlers before Google. Seriously -- someone needs to build an search interface into 'the hive mind' to blow everyone else out of the water. IMHO - it is only a matter of time.

HTM系统(分层时态记忆)。

人工智能的一种新方法，由杰夫·霍金斯通过《论智能》一书发起。

现在是一家名为Numenta的公司，通过开发“真正的”人工智能来测试这些想法，并邀请社区通过sdk使用该系统来参与。

它更多的是从头开始构建机器智能，而不是试图模仿人类的推理。

我认为自20世纪80年代以来发明的最好的想法将是我们不知道的。要么是因为它们很小，无处不在，以至于不引人注意，要么是因为它们的受欢迎程度还没有真正起飞。

前者的一个例子是单击并拖动以选择文本的一部分。我相信这是1984年首次出现在麦金塔电脑上。在此之前，您有单独的按钮用于选择选择的开始和结束。相当繁重。

后者的一个例子是(可能是)可视化编程语言。我不是说像hypercard，我是说像Max/MSP, Prograph, Quartz Composer, yahoo pipes等。目前它们确实是小众的，但我认为，除了思想分享之外，没有什么能阻止它们像标准编程语言一样具有表现力和强大的功能。

可视化编程语言有效地加强了引用透明性的函数式编程范式。这对于代码来说是一个非常有用的属性。他们执行这一点的方式也不是人为的——这只是由于他们使用的比喻。

VPL让那些本来不会编程的人也能编程，比如有语言障碍的人，比如阅读困难的人，甚至只是需要简单节省时间的门外汉。专业程序员可能会对此嗤之以鼻，但就我个人而言，我认为如果编程成为一种真正无处不在的技能，就像识字一样，那就太好了。

就目前来看，VPL只是一个小众的兴趣，还没有真正成为主流。

我们应该做些什么不同的事情

all computer science majors should be required to double major- coupling the CS major with one of the humanities. Painting, literature, design, psychology, history, english, whatever. A lot of the problem is that the industry is populated with people that have a really narrow and unimaginative understanding of the world, and therefore can't begin to imagine a computer working any significantly differently than it already does. (if it helps, you can imagine that I'm talking about someone other than you, the person reading this.) Mathematics is great, but in the end it's just a tool for achieving. we need experts who understand the nature of creativity, who also understand technology.

But even if we have them, there needs to be an environment where there's a possibility that doing something new would be worth the risk. It's 100 times more likely that anything truly new gets rejected out of hand, rather viciously. (the newton is an example of this). so we need a much higher tolerance for failure. We should not be afraid to try an idea which has failed in the past. We should not fully reject our own failures- and we should learn to recognize when we have failed. We should not see failure as a bad thing, and so we shouldn't lie to ourselves or to others about it. We should just get used to it, because it is just about the only constant in this ever changing industry. Post mortems are useful in this regard.

One of the more interesting things, about smalltalk, I think, was not the language itself, but the process that was used to arrive at the design of smalltalk. The iterative design process, going through many many revisions- But also very carefully and critically identifying the flaws of the existing system, and finding solutions in the next one. The more perspectives, and the broader the perspectives we have on the situation, the better we can judge where the mistakes and problems are. So don't just study computer science. Study as many other academic subjects as you can get yourself to be interested in.

我还会推荐3D鼠标。从20世纪90年代初开始就有几种变体。对于任何使用3D的人来说，像spacenavator这样的东西让生活变得更容易。(免责声明:我与3Dconnexion没有任何关系，只是一个满意的无rsi用户。)

函数式编程研究者对单子的重新发现。单子有助于让一种纯粹的、懒惰的语言(Haskell)成为一种实用的工具;它还影响了组合子库的设计(一元解析器组合子甚至在Python中找到了自己的方式)。

Moggi的“程序模块的范畴理论解释”(1989)通常被认为是将单子引入有效计算的观点;Wadler的作品(例如，“命令式函数式编程”(1993))将单子作为实用工具。

在人机交互中使用物理学提供了另一种可理解的隐喻。这与手势和触觉相结合，很可能会取代70年代发明的、从80年代中后期开始普遍使用的当前常见GUI隐喻。

1980年的计算能力还不足以让这成为可能。我相信游戏可能引领了这一方向。iPod Touch/iPhone中的列表滚动交互便是一个很好的例子。交互机制依赖于动量和摩擦如何在现实世界中工作的直觉，以提供滚动项目列表的简单方法，而可用性依赖于导致滚动的物理手势。