# AAAI 2011 – Day 1

I skipped the afternoon tutorial sessions to work on my presentation and go around town a bit but I did attend a very interesting morning tutorial on time series. The tutorial was on Time Series and was led by Eamonn Keogh.

He makes some bold claims but it was a very informative tutorial and he seems to have a lot of very reasonable and interesting things to say.  His basic claim is that a lot of the time people try to fit complex, fully general models to classification and prediction problems when their data is actually linear and simpler methods would be much more effective. A time series is any data set which has data points occurring in a fixed linear ordering. In response to someone’s question he did make clear that you need to make some reasonable assumptions about how much variation there is over time. So data where events can occur in arbitrary order won’t work. Also, if it’s equally likely that some event may occur every 0.1 seconds or occur every 10 hours then it probably won’t work either. But a lot of real data sets actually vary within a small time range. What time series methods can handle, apparently, is almost arbitrary variation in the magnitude of the number, in anomalies in the middle of a set of data, in outliers that don’t fit the average cases and several other types of variation.

This makes time series very powerful for sensor data and medical data of well understood processes.  Keogh’s focus is on symbolic analysis of data which has obvious applications for genetic datasets but is apparently also very effective for continuous, real valued data. The basic idea is to cluster the time series into levels and label these levels. Patterns of these labels can then be used to discover motifs in the data which often have an understandable semantic meaning.

There are several questions that arise with this approach, some of which he answered as well.  Basically, he argues that most data can be analysed using simple Euclidean distance and linear transformations on the time series with anomaly detection. The worry with this is that you may just find a pattern when there isn’t really one present if you stretch and shift the data enough.  It is important to treat a pattern detected in this way as merely a theory which is then verified by going back to the original data or to some independent data source.  He confirmed that this is what they always do but that when such anomalies arise in real datasets they very often are meaningful or can guide further exploration.

# The Chomsky/Norvig — Classical/Statistical AI Brouhaha

This post summarizes an fascinating ongoing discussion on the state of Artificial Intelligence research. It should be accessible to technical and non-technical but I’ll add more to it as the discussion heats up.

If you’ve come from my other blog Pop the Stack then you are probably of a bit more political bent,  you might be surprised to learn that this is a debate between Noam Chomsky (yes, that Noam Chomsky, everyone has their own Noam Chomsky and they’re all the same person, sort of like the Queen) and the head of Google research Peter Norvig.

No, Google hasn’t declared an extrajudicial assassination a victory or undermined democracy in favour of the Military-Industrial complex. Chomsky is a very famous in linguistics and a founding father of some of the concepts of computer science and artificial intelligence. Peter Norvig on the other hand wrote the book on modern AI and Google is the epitome of modern AI research which treats intelligence as advanced pattern recognition and  statistical analysis of huge amounts of data under uncertainty. It seems that some people disagree that this is the way to building a full artificial intelligence. And the dust is still flying.

I’m going to put up links to the parts I see and add commentary later, I don’t think this is a going to die off soon, especially with a cluster of AI conferences coming over the next few months, so stay tuned.

## The Beginning

It all began with the MIT’s Brains, Minds, and Machines symposium on May 3,-5 2011. The initial review of the event from MIT TechReview. Hopefully there will be a video at some point, doesn’t MIT videotape everything?

One theme of the discussion that has garnered attention is summarized here in this statement by Marvin Minsky:

“You might wonder why aren’t there any robots that you can send in to fix the Japanese reactors,” said Marvin Minsky, who pioneered neural networks in the 1950s and went on to make significant early advances in AI and robotics. “The answer is that there was a lot of progress in the 1960s and 1970s. Then something went wrong. [Today] you’ll find students excited over robots that play basketball or soccer or dance or make funny faces at you. [But] they’re not making them smarter.”

## Norvig’s Response

Sufficed to say Minksy’s statement is not how a lot of researchers would characterize the current state or the recent advances in the field. Peter Norvig explains the problem much more clearly than I or most people could hope to.  If you aren’t familiar with Peter Norvig then he literally wrote The Book on modern AI with Stuart Russell and he is also the director of research at Google. Be sure to read the comment section there is some real intelligent debate there as well as a little bit of flaming.

## Commentary

Here’s a great commentary by Mark Liberman on Norvig’s response (he mostly agrees) and support for his characterization of Chomsky’s views.

## Discussion

Or perhaps you’d like to hear what philosophy of linguistics people think of the discussion?

More to come…

# I’ll Take Spectacle for \$1000 Alex.

News now that the next big public spectacle in the battle between Man vs Machine will be….Jeaopardy?

Update: more detail here

You may remember that computer’s have now defeated the greatest human players of chess inspiring endless punditry and loose discussion about ‘thinking’ machines as well as inspiring awesome Arcade Fire songs. Computers are also now quite good at playing poker, have solved Checkers completely (no point playing that anymore…), provide us with frustrating ‘automated phone help’ bots and regularly vacuum the floors of geeks fairly adequately.

Sigh. Perhaps this is why the New York Times article, which is otherwise pretty clear and non-hyperbolic about the next spectacle, felt the need to throw this in:

Despite more than four decades of experimentation in artificial intelligence, scientists have made only modest progress until now toward building machines that can understand language and interact with humans.

The first half of the sentence refers to the common observation that four decades of research into AI has not produced walking,m talking androids trying to take over the world and consume us for power.  Instead it had provided tremendous research gains and advances in technology that underly  many aspects of our modern world from google to space probes, from self-driving cars to face detecting auto-focus cameras, from management of complex energy systems to medical diagnostic tools.  The second half the sentence points out that on the problem everyone on the street really cares about, walking talking androids that can ‘think’ like us and understand what we’re saying…that progress has been below society’s ridiculously high expectations.

Granted. Voice recognition has got a lot better over the years but not up to say, the 4 year old child level. But you know, we don’t even really understand how our own brains work, that makes simulating one in a less complex computing machine that the one between our ears, you know, tricky.  (A separate approach that may outflank current AI might in fact be just building an equally complex simulation of a brain and letting it go, but that’s another post.)

But I love these public spectacles, they provide a chance to explain the current level of AI and open up some of the ideas of computation in the problem that are used in more relevant applications all around us.  Having a computer up on t.v. with Alex Trebek and other contestants will be fun and we probably won’t even have the embarrassing situation Mr. Kasparov was in of the computer beating the human, not yet anyways.

It will be entertaining, some of it will be funny and hopefully some of it will be informative to viewers who live in an increasingly computational world.  Playing jeopardy well is a much harder problem than playing chess well.  The challenges it requires in terms of understanding language, meaning, searching databases, forming sentences and making strategic decisions about bids and questions are all very rich domains that have more real world application than the way chess playing programs work which is generally some kind of brute force search.

I just hope when the computer loses, the show is over and they ship the computer back to IBM labs we don’t hear another round of  ”why such modest progress”?  This ain’t rocket science people, its a lot harder than that.

# History of People in Science

In response to Nicholas’ waxing on about the history of science. I can’t resist that:

Given Google’s ambitious goals to organize all the worlds information, all of it!, I’m sure much more of your correspondence will be hanging around than you’d like for years to come. One crazy futuristic benefit of really saving (more…)

## Mark Crowley

### Researcher. Educator. Blogger. Optimist.

The best way to summarize me is to link to the places where you can read my writing.

I am a currently a postdoctoral scholar at Oregon State University on the search for a great faculty job in Computer Science. My research is generally in Artificial Intelligence and Machine Learning. Specifically, I look at the computational challenges to making good decisions about how to act in the face of uncertainty when actions need to be taken at many locations in space at each moment in time.

Currently, I am very excited and involved in a new field of research called Computational Sustainability which attempts to combine advances in AI, Machine Learning and Operations Research and other fields with the ridiculously challenging problems that exist in Ecology, Sustainable Resource Management, Forest and Wildlife Management and Climate Change.

Personally, if we want me to talk your ear off bring up politics or voting systems and democratic reform in general

Everything else I have to say tends to show up on Google+, so feel free to look me up there and say hi.