For a place considered to have a high level of natural intelligence, it is funny that one would even think about Artificial Intelligence (AI) in Higher Ed. However given that Higher Ed has a lot to do with the creation of AI, we might as well think about if there is a way we can benefit from it. There has been a recent explosion in this area and there are many articles written on this that you can look up in Google. “The Most Exciting Artificial Intelligence Applications in Media” and “Top 10 Hot Artificial Intelligence (AI) Technologies” are a couple of interesting (totally randomly selected) readings.
I also encourage you to read “The Great AI Paradox” by Brian Bergstein. This article talks about the distinction between “true” AI and “Computational Statistics” and how some argue that machines are pretty far away from having “true intelligence”. Let us set aside these differences and explore if we can take advantage of what is currently being touted as AI. (more…)
I was at the NERCOMP Annual Conference last week. There were some really interesting presentations that I attended, but I should say that the first keynote by Gerard Senehi was less than optimal for a conference to open. Danah Boyd, on the other hand, was fantastic, talking about how even the younger members of our society care about privacy, contrary to the myth that they don’t.
One particular talk that I liked and want to follow up has to do with open educational resources. The powerpoint presentation is available along with the abstract, so please review it. Though some of the panelists are from institutions that are very different from us, we feel that there is something here for us to learn from and educate our community.
Artificial Intelligence has been in the news recently and frankly, trying to define it in clear terms is something I am not capable of. It has morphed over the years thanks to advances in computing. Is it possible for machines to emulate humans in the way we think? This is a loaded question as you can imagine.
Theoretically speaking, an artificial intelligence system must pass the Turing test. This test involves a party game where a man and a woman play with a third person who is trying to guess the genders accurately. The man provides all answers to convince the third person that he is a man while the woman provides tricky answers to convince the third person that she is the man. Turing proposed that if you switched one of them with a machine then the person needs to guess who is a human and who is a machine. If the person failed to guess correctly more than half the time, then the machine will be declared having passed the test (that it has enough intelligence on its own to fool the third person).
There are a lot more underlying details to this of course, because of the availability of massive amounts of data and the computing power, even the “brute force” computing can be confused with intelligence.
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