不确定1980年的情况，但人工智能社区几十年来一直是一个创意生成器，他们仍然在做。

fpga是1980年以后发明的重大突破。

受保护的内存。在保护内存之前，如果你的程序犯了错误，你可以在任何地方开始执行代码——实际上总是挂起整个机器。没错，重启时间到了!

硬件成本低。我的第一台电脑在1978年花了500美元——这在当时是一笔巨款。降低成本让每个人的办公桌上都有了电脑。

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 (Did you ever see a feature withdrawn, or a change retracted?)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。

Ctrl-C + Ctrl-V + Ctrl-X组合:)

标签，信息分类的方式。是的，就是每个问题下面的小方框。

令人惊讶的是，标签发明花了大约30年的时间。我们使用了目录和目录;我们使用了为印刷书籍优化的东西。

然而，30年比人们意识到印刷书籍可以用更小的格式的时间短得多。人们可以把书放在手里。

我认为核心CS玩家低估了标签概念。所有的研究都集中在自然语言处理(自顶向下的方法)。但是标签是第一种计算机和人类都能很好理解的语言。这是一种自底向上的方法，使计算机使用自然语言。