Valiant's Book Out: Probably Approximately Correct

Les Valiant has a new book out: 
Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World

I was sent a free copy last week, but was delayed in reading it due to my avocational vocation.  (I was "talking with lawyers" a bunch.)  But I wanted to make sure to finish it over the weekend.  And now I'll recommend it all to you.

It would be, I think, somewhat inappropriate for me to attempt to review the book, but I'll aim to give some description of it which may encourage you to purchase it.  The book is aptly summarized by the following two sentences from it.

"The focus here will be the unified study of the mechanisms of evolution, learning, and intelligence using the methods of computer science."

"By the end of the book I hope to have persuaded the reader that when seeking to understand the fundamental character of life, learning algorithms are good place to start."  

Needless to say, the book is ambitious in scope, what one might expect from a Turing award winner, but in particular from Les.  If you have heard his Turing award lecture (available here), you can think of it as a preview of the book.  It is hard not to read the book as a challenge, to computer science in particular, but to the sciences more generally.  It is a call to arms, a vision, a plea, an agenda.

Because of this, I would recommend it highly to all computer scientists (in any area).

I would also recommend to it all scientists, so they could see this clearly laid out research vision from one of the leaders in computer science -- and, arguably, the one who is most interested in promoting the extension of the theory of computation to other sciences.  It might, I think, spur them to consider the relationship between computing and their own area of work, even if they are not directly working on evolution, learning, or intelligence.

It is slightly harder to recommend it to a general audience.  The book tackles fundamental questions of the connections between life and computation, making it a philosophical work certainly worthy of a large and general audience.  It raises some quite deep questions about the nature of human thought from what I think for most would be a novel vantage point.  But it does not shy away from the technical, and while, as promised, "The language of mathematics will be used, but only a little, and will be explained where used.", I imagine readers without a math/computer science background could get lost at times.  Still, other technical books (e.g., anything by Lisa Randall) find a large audience, so perhaps I underestimate the population at large. 

A final personal aside:  because I work with Les, when I read it, it came out in his voice.  I think the book very much sounds like Les -- it reads, to me, like him speaking -- but perhaps that's a trick of my own mind.  

Harvard Humanities

There's been a mild hubbub toward the end of the week here, due to a report and some articles (Boston Globe, WSJ) that the number of students majoring at the humanities at Harvard is in decline.  (See also this post at Shots in the Dark.)

Happily, this appears to be much ado about nothing.  Ben Schmidt at Princeton has already run the nationwide number, and shown that the decline is really more about a bubble in the 1960's of humanities majors.  Which just goes to show, when looking at historical data, what starting point you choose is important.  (Yes, that goes in the "duh", "lies, damn lies, and statistics" category.)

At Harvard, specifically, there are a variety of potential reasons for this trend, including but not limited to the general national trend.  In computer science, we've been actively trying to attract and retain students;  the humanities just may be facing more competition.  There is some claim that Harvard's financial aid policy is having an effect;  to the extent that students are coming from less well-off backgrounds, they may be seeking an education that they feel more directly will lead to job prospects.

There has been, however, perhaps a hint (or more than a hint) in some of all of what's going around that somehow people focusing on things outside of the humanities is "anti-intellectual", with students caring more about immediate job prospects than, well, the "intellectual" humanities. 

Naturally, I resent this.  I find computer science has a very solid intellectual basis.  The nature of computation, what it means to compute efficiently, how computing is found throughout nature (more on this in my next post) -- there's a lot interesting intellectually there.  If one seeks more "moral" sorts of lessons, I think many can naturally be found throughout CS, with the right interpretation.  The challenge of tradeoffs, for instance, is an underlying concept of my own algorithms class, and certainly appeared (if less quantitatively) in the moral reasoning class I took as an undergraduate. 

On the other hand, I understand where this is coming from.  There is a sense that the humanities is under siege (particularly at state institutions);  there are politicians of the mindset that "if it's not job training, why are we providing it?"  I believe that one should study more than computer science to learn to be a more complete human being;  I am thrilled to be at an institution where history, English, religious studies, as well as Romance languages, economics, and government are studied.  When one feels under attack, one's reactions might seem a bit more extreme.   

I'm not one to say where the final balance will be, or should be.  I do believe an understanding of computation should be a fundamental part of a liberal arts education;  it is clearly one of the most powerful ideas of the last century.  And it's our goal to make it both so that every Harvard student feels welcome and able to take a computer science course, and so that many understand our excitement and choose to major in it.  For a few decades, Harvard has been a bit behind in the role computer science has played at the university, and I think now that's changed.  So to the extent that the humanities feel the competition is from us, well, I'm actually all for it.    

Thanks to Harry Lewis for various discussions on this theme.  

NSF Reviewing Trial Run

Noam Nisan points to the NSF trying out some new rules for reviewing in its upcoming SSS program. 

There's a lot here to discuss.  First, I'm glad to see the NSF is willing to try out some new reviewing approaches.  They've been using the same approach for a long time now (1 or 2 day in person meetings, a reviewer panel drawn according to who is available and willing);  I really haven't seen any discussion from the NSF as to why it's a good review system, and it's typically got some major cons (as well as, admittedly, some pros).  But as far as I know -- and perhaps some people are more knowledgeable than I am on the topic -- it's not clear at all to me why it's become the stable equilibrium point as a reviewing method.

That being said, there's some clear pros and cons to this experiment.  Some features + initial off-the-cuff commentary.

1.  No panel review.  Proposals will be split into groups of 25-40, and PIs in the group will have to review other proposals (they say 7 here) in that group.  [If there are multiple PIs on a proposal, one has to be the sacrificial lamb and take on the role of reviewer for the team.]   

I kind of like the idea that people submitting proposals have to review.  One of the big problems in the conference/journal system is that there's minimal "incentive" to review.  Good citizens pay back into the system.  Bad citizens don't.  This method handles the problem in a natural way -- you submit, you review.  There are many potential problems with this method to be sure (as we'll see in the proposed implementation below).

2.  A composite ranking will be determined, and then the "quality" of the reviews of the PIs will be judged against this composite;  then the PIs ranking may be adjusted according to the quality of their reviews.

Ugh.  Hunh?  I get the motivation here.  You've now forced people into doing reviews, who may not want to.  So you need an incentive to get them to do the reviews, and do them well.  One incentive is that if you're late in your reviews, your own proposal will be disqualified.  That seems fine to me.  But this seems --- off.  I should note, they have a whole subsection in the document labelled

Theoretical Basis:

The theoretical basis for the proposed review process lies in an area of mathematics referred to as mechanism design or, alternatively, reverse game theory.  In mathematics, a game is defined as any interaction among two or more people.  The purpose of mechanism design is to enable one to “design” the “mechanism,” namely the game, to obtain the desired result, in this case to efficiently obtain high-quality proposal review while providing the advantages noted above.  In mechanism design, this is done by formulating a set of incentives that drive behavior in the desired direction.  The mechanism presented here was devised by Michael Merrifield and Donald Saari [¹].

I suppose I now have to go read the Merrifeld and Saari paper to see if they can convince me this a good idea.  But before reading that, there are multiple things I don't like about this.

a)   Why is "reviewer quality" now going to be part of how we make decisions about what gets funded?  I'm not sure to what extent, if any, I want "reviewer quality" determining who gets money to do research.  Here's what the document says:

To promote diligence and honesty in the ranking process, PIs are given a bonus for doing a good job.  The bonus consists of moving their proposals up in the ranking in accordance with the accuracy with which their ranking agrees with the global ranking.  This movement will be sufficient to provide a strong incentive to reviewers to do a good job, but not large enough to severely distort the ranking merely as a result of the review process.  Recognizing that, if all reviewers do an excellent job of ranking the proposals they review, all PIs’ proposals will be moved up equally, which means that the ranking will not be changed, the maximum incentive bonus will be a movement of two positions, that is, a proposal could be moved up in the ranking to a position above the next two higher proposals.

With funding ratios at about 15% (I don't know what the latest is, but that seems in the ballpark), two places could be a big deal in the rankings.  

b)   Why is there the assumption that the group ranking is the "right" score -- particularly with such small samples?  I should note I've been on NSF panels where I felt I knew much better than the other people in the room what were the best proposals.  (Others can judge their confidence in whether I was likely to have been right or not.)  One of the pluses of face-to-face meetings is that a lone dissenter has a chance to convince other reviewers that they were, well, initially wrong (and this happens non-trivially often).  I'm not sure why review quality is judged by "matching the global ranking".

c)   Indeed, this seems to me to create all sorts of game theoretic problems;  my goal in reviewing does not seem to be to present my actual opinion of a paper, but to present my belief about how other reviewers will opine about the paper.  My experience suggests that this does not lead to the best reviews.  The NSF document says:

Each PI will then review the assigned subset of m proposals, providing a detailed written review and score (Poor-to-Excellent) for each, and rank order the proposals in his/her subset, placing the proposals in the order which he/she thinks the group as a whole will rank them, not in the order of his/her personal preference.

But then it says:

Each individual PI’s rankings will be compared to the global ranking, and the PI’s ranking will be adjusted in accordance with the degree to which his/her ranking matches the global ranking.  This adjustment provides an incentive to each PI to make an honest and thorough assessment of the proposals to which they are assigned as failure to do so results in the PI placing himself/herself at a disadvantage compared to others in the group.

So I'm saying I'm not clear myself how their incentive system -- based on the global ranking --- gives an incentive to make an honest and thorough assessment.  Even the document itself seems to contradict itself here.

d)  This methodology seems ripe for abuse via collusion -- which is of course against the rules:

The PIs are not permitted to communicate with each other regarding this process or a proposal’s content, and they are not informed of who is reviewing their proposals.

But offhand I see plenty of opportunities for gaming the system....

e)  This scheme is complicated.  You have to read the document to get all the details.  If it takes what seems to be a couple of pages to explain the rules of the assignment and scoring system, maybe the system is too complicated for its own good.

That came out pretty negative.  Again, I like the idea of experimenting with the review process.  I like the idea that submitters review.  I understand the concept that we somehow want to incentivize good reviews, and that's very difficult to incentivize.

This actual implementation... well, I'd love to hear other people argue why it's a good one.  And I'd certainly like to hear what people think of it after it's all done.  But it looks like the wrong way to go to me.  Maybe in the morning, with some time to think about it, and with some comments from people, it will look better to me.  Or maybe, after others' comments, it will seem even worse.  

Review are In, 2013

Teaching reviews are in!  I'm happy to say students were more forgiving than last year.  But also, I notice in the reviews the effects of 4 significant changes from last year.   (The best is saved for last.)

1.  Students knew it was my last year teaching the course.  I think they were nicer to me than normal because of it.  (Pity points!)

2.  For the last several years, students have complained that the midterm coincided with "Housing Day", the day the freshmen find out about where they'll spend their later years, and apparently it's a big party day.  This year, I was able to move the midterm.  (For didactic reasons, I assure you -- some of basic material in my course is now covered in an earlier class, saving me a lecture early in the semester.)  Students really appreciated that.  (I maintain that my midterm being the Thursday before finals precedes the advent of "Housing Day" -- someone put Housing Day on the day of my midterm, not the other way around -- but students have not sympathized with this reasoning.) 

3.  This was the first year students had the advantage of taking that new class, CS20, designed to give them more background on CS mathematics.  (We finally have the CS "Discrete Math" class we haven't had but have probably needed.)  I think this helped students, especially at the lower tail, and probably somewhat helped review scores.

4.  The final change may arguably be the most important.  In the past I've given longer assignments over usually 2 week periods;  something like 7-9 problems.  At the urging of one of my experienced TAs, who both wanted the grading split up more and thought the students would prefer it, I broke up the problem sets, so they were due weekly, and were usually 4 or 5 problems.  The feedback from many students was that they liked this approach better (obviously not from direct experience with the class previously, but from what they had heard from other students).

To me, this remains counterintuitive.  The students were getting the same problems either way, so the splitting only added an additional constraint on them.  Instead of having eight problems over two weeks, they were forced to do the first four in week one and the next four in week two.  But, clearly, for psychological reasons many students want (need?) that constraint.  As some have explained to me, they aren't going to start the assignment until they're close to the deadline, so the additional deadline prevents them from becoming overloaded and overstressed by a longer assignment.  Perhaps, beyond the psychology, part of the issue may be student collaboration -- more frequent shorter assignments introduces constraints that probably help encourage scheduling of working together.   

I worry about the time management skills of Harvard students.  Or I suppose for many it's just the way they live -- their schedules constantly packed full with deadlines serving as the basis for their priority scheduling.  I hope they experience a different lifestyle at some point.

However, lessons learned for whatever undergrad class I teach next!  Short weekly assignments.  And be sure to avoid student (non-academic) events when setting up the midterm in the class schedule.

Finally, I still suspect my reviews would be non-trivially better if they happened after grades were out.  Students often think they're doing worse than they are -- they don't see how much the curve helps them.  For example, one senior, after the final, came up to me worried that he/she did poorly enough that he/she would fail the class.  I asked how he/she had done over the semester, and said it seemed very unlikely, but I'd send mail after grading the final.  The student got a C and was in absolutely no danger of failing.  The horror stories of CS124 have been somewhat exaggerated over time -- perhaps all the more reason a "refresh" is in order.  

Anyhow, thanks again to this year's CS 124 class -- for those of you who aren't graduating, I hope to see you around, and for all the students, if you've read this far, I hope you'll send me stories when you find whatever you learned in CS 124 to be useful to you. 


      

An Unusual CS Student Blog

As I'm up working/watching a Memorial Day weekend Arrested Development marathon (OK, I'm not working that hard), I found myself wandering over to Justine Bateman's blog.  Like many teens at that time period, I surely had a crush on her during her run on Family Ties.  So I had noticed that last year she had decided to go back to school to study computer science (UCLA -- college to the stars-interested-in-math-and-science, apparently;  I'm talking about you Mayim Blalik and Danica McKellar!).  But I hadn't been reading the blog.  And it's very entertaining, if only because it sounds like a freshman college blog, albeit occasionally with some pointers you might not normally find (like interviews for LA magazines).    This post, about finishing up a big project (making a computer Battleship game), is really familiar to me in tone;  I hear stuff like this from students all the time (and, of course, lived through it myself as an undergraduate). 

The point here -- besides that I now watch and have always watched too much TV -- is that computer science is awesome.*  Awesome enough that a big star of the 1980s has gotten inspired enough to go back to school and learn how to program Battleship, and more.

*And I guess another lesson is that Justine Bateman is awesome too.  Not that she'll ever see this, but Justine -- best of luck to you sophomore year and beyond!   

Grades In

The grades are in for CS 124.  Hooray!

Interesting trend : freshmen, who make up a small fraction of the class, are highly over-represented in the A and A- grades.  This has been happening for some years now. 
Extension of the interesting trend : women freshmen*, who make up a smaller fraction of the class, are even more highly over-represented in the A and A- grades.

I'd be very excited if I were teaching the class again next year -- finding undergraduates who have the potential to be TAs for multiple years is golden -- but I'll be sure to pass the names on to the new guy.**

Other side of the trend :  2nd semester seniors performed substantially below average this year.
But on the positive side :  nobody did so badly they won't graduate.  (At least, not in my class.)  

Getting grades in really makes the class feel over.  Onto summer work!  (Research and writing...)

* Freshwomen if you prefer.
** And any freshman or sophomore in the class who got an A and is interested in TAing should let me know next November or so.

Congratulations to Justin and Jon

Justin Thaler and Jon Ullman had back-to-back thesis defenses today.  Both talks were excellent -- Justin's on his work on verification methods (for cloud computing), and Jon on his work on differential privacy.  While there is still some small paperwork matters (like the actual theses to turn in), I think we should start calling them Doctor Thaler and and Doctor Ullman, just so they start getting used to it.

Congratulations Justin and Jon!

Calling Out Stupidity

While I greatly enjoy working at Harvard, there are many painfully stupid things said and done by people here -- as I suppose is the case anywhere -- that either I don't feel merit commenting on or I don't feel it's appropriate to comment on.  (And, of course, I'm sure that sometime in the past I've done or said things that others find stupid, and I'm very happy they aren't blogged about.)

However, the recent comments by Niall Ferguson are out in the public, and so over-the-top stupid that even he quickly realized how stupid they were.  And I think it's important to point out, prominently, how stupid they are, because the idea that either childless people or gay people somehow have a discounted view of the importance of the future deeply offends me, and somehow the idea that someone prominent in my workplace would say (or believe, or say in stupidity without really believing) such a thing has made my week substantially sadder.  And so I feel the need to point it out, ideally not to sadden anyone reading this, but to emphasize how sad and stupid the comments were.   

A Boy and His Atom

Some researchers at IBM are so good at playing with atoms, they decided to make a movie (called A Boy and His Atom), by moving atoms.  Cool stuff.  Computer science connections:  implications for storage.  Personal connection:  the spouse of the scientist leading the group who made the video is a friend from high school.