What I find interesting is that Hacker News manages to hold the same mentality that there's a startup bubble, <i>this time it's not different</i>, while at the same time holding the mentality that AI is going to take over, <i>this time it's different</i> (in aggregate, I'm not saying everyone holds this view, this seems to be the plurality view).<p>Many of the same arguments used to argue how AI is different this time around are the exact same arguments you can use to justify that it's not a startup bubble this time.<p>My guess is something about the fantastical sci-fi aspect of AI captures the deep-seated imagination and wishful-thinking of many engineers, whereas the rush of money, business-people, and focus on sales/growth aspect of startups draws out deep-seated disdain from many engineers.
No! Be quiet and let me raise money via overfitting my datasets and making short demo vids!<p>More seriously though, even if there's a lot of undeserved hype (I don't think so, I just think non-technical people are making up foolish expectations), all this work and investment into technical development of statistical machine learning algorithms and software is going to pay off. Much in the same way that overinvestment in internet infrastructure during the dot-com bubble paid off.
The difference this time is that 1) AI is profitable, and 2) everything is on a much larger scale.<p>As I've mentioned before, I went through Stanford CS in 1985, just as it was becoming clear that Expert Systems were not going to yield Strong AI Real Soon Now. Back then, AI research was about 20 people at Stanford, and similar sized departments at MIT and CMU, with a few other small academic groups and some people at SRI. There were a few small AI startups including Teknowledge and Denning Robotics; few if any survived. Everybody was operating on a very small scale, and nobody was shipping a useful product. Most of this was funded by the US DoD.<p>Now, machine learning, etc., is huge. There are hundreds of companies and hundreds of thousands of people in the field. Billions are being spent. Companies are shipping products and making money. This makes the field self-sustaining and keeps it moving forward. With all those people thinking, there's forward progress.<p>Also, we now have enough compute power to get something done. Kurtzweil claims a human brain needs about 20 petaFLOPS. That's about one aisle in a data center today.
One of my favorite comic artists put it more appropriately than I could - in the form of a flow chart: <a href="http://www.smbc-comics.com/index.php?id=4122" rel="nofollow">http://www.smbc-comics.com/index.php?id=4122</a><p>TL;DR - once strong AI arrives, we're as good as dead. ;-) Can't argue with a flow chart!
I remember reading the famous book by McCorduck and Feigenbaum about the Japanese fifth generation project in the early 1980s. That book had a strong influence on my career and I recall that I resolved to become an AI expert. As it happened, it turned out that the Japanese fifth generation project achieved pretty much nothing.
Our bar for achieving true Artificial Intelligence is set way to high in my opinion. This talk from CCC 2015 is very relevant <a href="https://www.youtube.com/watch?v=DATQKB7656E" rel="nofollow">https://www.youtube.com/watch?v=DATQKB7656E</a>