现代阴影语言和现代gpu的流行。

GPU也是一个低成本的并行超级计算机，拥有CUDA和OpenCL等工具，可以快速编写高级并行代码。感谢所有在那里压低这些越来越令人印象深刻的硬件奇迹价格的玩家。在接下来的五年里，我希望每台售出的新电脑(iphone也一样)都能基本具备运行大量并行代码的能力，就像24位彩色或32位保护模式一样。

我相信单元测试、TDD和持续集成是1980年之后的重大发明。

RAID(1988)。

可以说，这只是多年前错误修正代码的应用，但可以说，计算机科学中的一切都可以简化为基本的数学，这已经存在了几千年。

计算机图形学，特殊效果和3D动画

回答“为什么新思想会消亡”和“如何应对”这两个问题?

I suspect a lot of the lack of progress is due to the massive influx of capital and entrenched wealth in the industry. Sounds counterintuitive, but I think it's become conventional wisdom that any new idea gets one shot; if it doesn't make it at the first try, it can't come back. It gets bought by someone with entrenched interests, or just FAILs, and the energy is gone. A couple examples are tablet computers, and integrated office software. The Newton and several others had real potential, but ended up (through competitive attrition and bad judgment) squandering their birthrights, killing whole categories. (I was especially fond of Ashton Tate's Framework; but I'm still stuck with Word and Excel).

怎么办呢?首先想到的是Wm。莎士比亚的建议:“让我们杀了所有的律师。”但恐怕他们现在装备太精良了。实际上，我认为最好的选择是找到某种开源计划。它们似乎比其他选择更好地保持可访问性和增量改进。但是这个行业已经变得足够大了，所以某种有机的合作机制是必要的。

I also think that there's a dynamic that says that the entrenched interests (especially platforms) require a substantial amount of change - churn - to justify continuing revenue streams; and this absorbs a lot of creative energy that could have been spent in better ways. Look how much time we spend treading water with the newest iteration from Microsoft or Sun or Linux or Firefox, making changes to systems that for the most part work fine already. It's not because they are evil, it's just built into the industry. There's no such thing as Stable Equilibrium; all the feedback mechanisms are positive, favoring change over stability. (Did you ever see a feature withdrawn, or a change retracted?)

关于SO的另一个讨论线索是臭鼬工厂综合症(参考:Geoffrey Moore):在大型组织中，真正的创新几乎总是(90%以上)出现在自发出现的未经授权的项目中，这些项目完全由个人或小团队的主动性推动(通常会受到正式的管理等级的反对)。所以:质疑权威，反抗体制。

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。