Cryptography, Artificial Intelligence, Computer Vision, Data Mining, Game Theory...

[phantasmagoric]

<p>

Lastly, I would really advise you to rethink entering artificial intelligence. While AI sound really interesting (and promising), you’ll find that the state of the field (overall knowledge, speed of advancement, etc.) is really disappointing. Artificial intelligence is not going anywhere fast. Everyone seems to think that AI is the next big “thing”, but after getting deep into AI, everyone changes their opinion. A prominent researcher in AI famously said: “A year in artificial intelligence is enough to convince anyone of the existence of God.”

</p>

<p>This is not true at all. I say that not just because I work in AI, but because it’s patently wrong and outdated.</p>

<p>Yes, there have been times when AI was moving very slowly and people were dissatisfied with its direction. It led to several AI winters. But that was because the approach was wrong and solutions would not be found until computers increased in speed and memory. Since they have, AI research has exploded, and it’s made a great many strides. Here’s something I posted recently:</p>

<p>

But it’s well-acknowledge in the field that we’ve learned from our mistakes; in the past, symbolic and logical methods were dominant, and those precipitated failure. But with the increase in speed of computers, esp. personal computers, that began in the early 1990s, statistical methods took off, and since then, AI has consistently produced scalable, profitable technologies. Spam filtering? Speech recognition systems? Search engines? Automatic translation? Plagiarism-detection software? Automated online assistants for trouble-shooting and customer service? Autonomous vacuums? The list goes on. And then there are all those that are still in development–self-driving cars, personal computer assistants, etc.</p>

<p>Most if not all of the major tech companies–Google, Microsoft, Yahoo, Accenture, etc.–hire people to work on AI and even have specific research groups dedicated to them. And then there are tons of smaller companies that work on AI – NetBase, Cataphora, Hakia, etc.

</p>

<p>It’s absurd to suggest that the state of the field of AI is “disappointing” when every day, you are surrounded by the fruits of AI. The only ones who are “disappointed” by AI are those who come into the field expecting everything to be figured out, wrapped into a neat little package, etc. but they don’t have that same expectation when they go into, say, physics. I think that’s probably because of movies and TV shows, which have for decades shown the public technologies which were not available (2001: A Space Odyssey, Short Circuit, Terminator, etc.). So when people discover that actually the problems in AI are much harder than that, they feel disappointed–but no other field has such high demands on it or has been expected to deliver on extremely difficult tasks so quickly. That is not a reason to leave the field; in fact, that’s why AI needs to have more people in it! We won’t get to those solutions without more people working on the problems.</p>

<p>The point is that AI is hard, but we’ve made tons of advances: that’s why there are hundreds of millions of dollars spent every year on AI research, hundreds of journals and conferences, faculty positions, labs, etc. in addition to the companies and research centers that focus on it. It’s also an extremely lucrative field (if you didn’t get that from the above quote).</p>

<p>To the OP: if you have more questions about AI, let me know–I’m starting my PhD in it soon, so I know a bit more about it than others do.</p>

<p>MIT is celebrating its 150th anniversary and recently had a symposium on AI.</p>

<p>

The central theme of “Brains, Minds and Machines” — the last of a series of symposia celebrating MIT’s 150th anniversary — is that it’s time for artificial-intelligence research, cognitive science and neuroscience to get ambitious again.

</p>

<p>

On Wednesday evening, a panel addressed the question, “Why is it time to try again?” Moderator Tomaso Poggio, the Eugene McDermott Professor in the Brain Sciences and Human Behavior and director of the Center for Biological and Computational Learning, suggested that, like the influenza virus, an infectious interest in artificial intelligence captures the scientific community about every 25 years. In the 1960s, Marvin Minsky’s work started the first wave; then around 1985, interest in machine learning and neural networks took off. Now, the time is right for another such wave, he said, in part because of the massive computing power now available.

</p>

<p><a href=“http://web.mit.edu/newsoffice/2011/mit150-brain-ai-symposium.html[/url]”>http://web.mit.edu/newsoffice/2011/mit150-brain-ai-symposium.html&lt;/a&gt;&lt;/p&gt;