Guest post by Samir Khuller on Humans, Machines, and the Future of Work (a workshop)

Guest Post from Samir Khuller on

Humans, Machines, and the Future of Work
(A Workshop)

On Dec 5th and 6th I attended, at Rice University,
a workshop on Humans, Machines, and the Future of Work.
I had a wonderful lunch with John Markoff  of NY Times,
and Moshe Vardi of Rice University (Moshe is the brains
behind this workshop).

I have rarely ever attended such meetings, that are so different
from the usual conferences I attend where the talks are all
rather technical and half the audience is lost half way through the talk.

The main theme poses the speculative question about the evolving
nature of work and how technology, and especially recent
advances in AI and Robotics (Self driving cars, Robots to look
after the elderly, self checkout machines that can simply
recognize your shopping order) can render huge categories of
jobs redundant. Why hire a human, when a robot will cook
dinner, and clean your home every day? What will people spend their time doing?

Will the work week reduce greatly? Yet, for many, their devices and

24/7 connectivity means we are working more than ever! The

speakers had varied backgrounds, from Economics to Social Science

to Roboticists.

In the end, its clear that the nature of work is constantly evolving.

Our children could have job titles, our parents could never dream of

when they were selecting a profession. However, the rapidly growing

interest in Tech and Computing is clear - these are powerful societal

forces at work. New job titles - Robot fixer, Robot Programmer,

Virtual Reality Expert, Lawyer for the protection of Robots. Demand

for graduates with those skills will continue to increase, and the

Universities that invest quickly and early will become the dominant

players. CMU,  Georgia Tech and others invested very early in colleges

of computing, and this has paid of handsomely for them. The demand for

CS degrees is going to increase even more in the next decade

with research moving more closely to the impact that technology is

having on society. We as researchers, need to pay attention to

societal impact, since its very real and immediate.

The workshop speakers were pretty amazing - talks were accessible,

and extremely well delivered. I suppose such talks meant for a really

broad audience (there actually were not many CS people in the audience)

and thus the points being made are pretty high level.

I would recommend that we all become better acquainted with some of

the social issues that interact with Computer Science, and going

to workshops like this is a great start.

Fixing the Academic Job Market

Last month I posted about the craziness of the computer science academic job market due mainly to the decentralized nature of our field. Here are some ideas of what we can do better. I've stolen some of these ideas from other fields such as math and economics.

Single Job Market System

A single website that every CS department uses to advertise positions and accept applications. Easy for applicants to apply to multiple places and, if they wish, make their materials open for any department to see. Recommenders need only upload their letter once. We could add some additional controls, methods for applicants to indicate say geographical preferences or two-body issues.

We could have an opt-in site for candidates to list where and when they have interviews. It would make coordination of job interview dates and offer timing that much simpler.

Annual Meeting

A meeting in early January that brings together all the subfields of computing so we can work as one community instead of twenty. As part of this meeting, members of recruiting committees and job candidates come and schedule short interviews with each other to make preliminary choices for on-campus interviews. CS hasn't had annual meetings since the 80's but math and econ still do.

Virtual Meetings

Every time I bring up annual meetings, people complain that we already have too many conferences in computer science. So if no physical meeting, we can set aside days to have a virtual meeting with recruiting committees and candidates talking over Skype.

Common Dates

Have some common fixed dates, just a couple of times in the spring, when departments can present offers, and when candidates must make a decision. That should reduce how long departments have to hold a position before it settles.

These last two ideas require no centralization, just willing job candidates.

Job Market Paper and Video

As the recent CRA Best Practices Memo suggests, candidates should choose a single paper to highlight their research. Each candidate should also post a short (3-5 minute) video where they describe this research at a level that any computer scientist could follow. The job talk should cover this paper only, instead of trying to wow with multiple works.

Candidate Web Page

If you are publicly looking for a job, set up a web page, linked from your home page, to your job materials: CV, research statement, teaching statement, list of references, pointers to all your papers with links to PDFs, with the aforementioned job market paper and video highlighted. Also give a pointer to your Google Scholar profile page and make sure that page is correct and up to date.

A students unusual proof might be a better proof

I asked a student to show that between any two rationals is a rational.

She did the following: if x < y are rational then take δ << y-x and rational and use x+δ

This is correct though more complicated then what I had in mind: (x+y)/2

I then asked her to prove that between two irrationals is an irrational.


She did the following: if x < y are irrational then take δ << y-x and rational and use x+δ

SAME PROOF!

I had a different proof in mind: the number of reals in (x,y) is uncountable while the number of rationals is countable, so there must be at least one (in fact uncountable many) irrationals in (x,y).
(NOTE- I originally had `the number of irrationals in (x,y) is ...' which, as comment by stu below
points out, is assuming what I want to prove. Typo on my part.)

These proofs raise questions about our criteria of goodness-of-proof.

1) Which proof for rationals is better:

The delta-proof or the (x+y)/2 proof?

The (x+y)/2 proof is simple, but the delta-proof also works for irrationals.

2) Which proof for irrationals is better:

The delta proof or the uncountable proof?

Which one is simpler?

The uncountable proof gives more information (the number of irrationals is unctble)
but is nonconstructive.

3) Are there other proofs for either theorem?

Which proof do you prefer? Why? What is your criteria?

Guest post by Samir Khuller about Visiting the Simons theory Inst for Computing

Guest post by Samir Khuller on his visit to the Simons Inst. of Computing

Visiting the Simons Institute for Theory of Computing

A few days back I had the good fortune to spend four days at the
Simons Theory inst. at UC Berkeley (for a workshop titled
Learning, Algorihtm Design, and Beyond Worst Case Analysis,)
Not only was the workshop extremely well run, the entire Institute is
amazing - from wide open spaces for collaboration to an excellent staff
and amenities for visitors (especially long term visitors who have
shared office space). We were missing a Dagstuhl like venue in the US
for a long time, and I think the Simons Theory Center partially makes
up for this deficiency. Dagstuhl is of course a bit isolated, so people
have no-where to go but interact, and work, and that is part
of the charm. Berkeley isn’t isolated and has some of the most amazing
cafes and restaurants right next to its charming campus. I ended up staying
on campus at their Faculty Club.

Simons workshops are over-subscribed and in high demand, but even on
Thursday evening (my last day there) I noticed that scores of badges
had not yet been picked up. Part of the problem lies in the fact that
registration is free. Perhaps charging $10/day for registration will

ensure that people sign up for the days that they intend to be there,

so others who want to attend are not locked out (to be fair, I don’t

know how many were locked out, but I have the impression that due to

high demand, workshops have closed registration in the past).

Kudos to both the Simons Institute  organizers as well as the ones

for the special semester and workshop! I did visit Simons once in 2014,

for just 15 minutes when we were visiting places to see for the design

of our very own Brendan Iribe Center for Computer Science and Innovation.

The construction of which launched in summer 2016, and we hope to be done

by August 2018.  Come and visit after that! This project has consumed the

last 30 months of my life.

One of the main benefits of attending these workshops is to see sub-fields

as they form - long before new courses emerge covering these topics - and

it’s inspiring to meet the leaders in our field running the

program on Algorithms and Uncertainty. Finally, if you missed it,

videos of talks are available online here.

A Few Interesting Computer Science Papers

Probabilistic Rank and Matrix Rigidity by Josh Alman and Ryan Williams

Leslie Valiant outlined an approach to proving circuit lower bounds from matrix rigidity. A matrix is rigid if you need to change many entries to significantly reduce the rank. Valiant showed that one could create a function with high algebraic circuit complexity from a rigid matrix. Of course one needs a rigid matrix and the Hadamard matrices (rows are Hadamard codes) seemed the perfect candidate. Alman and Williams say not so fast, in fact the Hadamard matrices are not that rigid and won't work for Valiant's program.

Ryoan: A Distributed Sandbox for Untrusted Computation on Secret Data by Tyler Hunt, Zhiting Zhu, Yuanzhong Xu, Simon Peter, and Emmett Witchel

You want to use Intuit's TurboTax to prepare your taxes but you don't want to actually let Intuit or their cloud provider to see your income sources. Sounds like a job for homomorphic encryption, a good solution in theory but not yet practical. Ryoan, an OSDI best paper awardee, takes a systems approach to the same problem, creating a computing sandbox where one can do secure computation without allowing the cloud or software provider access.

Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation (and related blog post) by Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes and Jeffrey Dean

Google now using a single system to translate between different pairs of languages, even pairs of languages for which it has no examples. Their analysis shows a potential internal representation of the language and the beginning of a universal translator.

Music Theory for Theorists

I fully completed and passed my first MOOC, Fundamentals of Music Theory, a Coursera course out of the University of Edinburgh. I played the tuba in college and grad school but really just practiced scales and (usually) hit the notes. I didn't really understand keys, intervals, chords and the such and I wanted to learn.

Music theory has the same problem as C++, too much overloaded notation before you really understand what's going on. So here's a quick view of music theory without the mess.

Think (theoretically) of an bi-infinite sequence of notes. Every note is equivalent to notes a factor of twelve away from them (though in different octaves). Pick a note, label it 1, then the notes labelled 1, 3, 5, 6, 8, 10, 12, 1 (an octave higher) form a major scale in the key of note 1. There are twelve major scales, depending on which note you label 1. If you start at 10, i.e., 10, 12, 1, 3, 5, 6, 8, 10 you get the relative minor scale in the key of note 10. Also twelve minor scales.

Start a new major scale starting from 8 you'll get 8, 10, 12, 1, 3, 5, 7, 8, just one number off from the previous scale. Repeating this process will run through all the major scales. This is called the circle of fifths (since 8 is the fifth note of the scale). You can go through the circle backwards by starting at 6.

A chord is typically three or four alternating notes of a scale, for example 1, 5, 8 (the tonic) and 8, 12, 3, 6 (the dominant 7th). They can be inverted using different octaves and all sorts of other good stuff.

So notation wise, there is a mapping of {A,B,C,D,E,F,G} X {Flat, Natural, Sharp} to the twelve notes with several notes getting two different names in such a way that each note of any scale can get one of the letters in cyclic order. For example the D-Major scale would be the notes D-Natural, E-Natural, F-Sharp, G-Natural, A-Natural, B-Natural, C-Sharp and back to D-Natural. You put it all together in a diagram called a musical score.

Many undergrad computing courses now use Python instead of C++ to get around the notational challenges. Alas music notation goes back a millennium or so, I don't expect any changes soon.

Should I tell HS students that if they do well I'll write them a letter OR do I want them to ...

I teach a 3-week intense course for HS students on cryptography during the summer. Some of the students are very good, interested, and working hard. I also give out some extra credit assignments that some of them do.  For such students I am glad to write letters of rec for college. I want to reward them for working hard and being interested not knowing that they will get a letter for it.

These students don't quite know about colleges and letters and that stuff. I have two choices:

1) The first day tell them that if they do well I could write a letter for them.

PRO- this will encourage some of them to work harder

CON- I want them to work hard and BE interested (not just SHOW interest, though I doubt they are that good as actors) NOT because of some reward but because they really are interested. Contrast:

Bob showed an intense interest in computer science

to

Bob showed an intense interest in computer science but it may have been just for show to get this letter. Gee, I can't tell.

2) Don't tell them. This way when they work hard and show interest its more likely genuine.

PRO- as noted, their behavior is genuine

CON- They may not work as hard.

I tend to do (1) now- tell them. One thing, don't tell them too much and make it simple. I used to say that I would write a letter even for a B student if there were extenuating circumstances or if I could tell they really were good and just happened to blow an exam, or something like that. But this just invites more questions about what they need to get a letter, and I've never  had one of those cases where a B-student is better than he looks. (This could be because my exams are of the type where there is no time pressure as evidenced by half the students leaving about half way through. They are not easy, but they depend on knowledge not cleverness, so more time does not help. Or it could be something else about my teaching and grading.)

So- better to tell them about a letter option or not?

Computer Science Academic Hiring

My faculty recruiting season in 2016 never stopped. The spring recruiting season bled through summer right into various fall recruiting activities. How I envy my friends in other fields whose faculty recruiting season runs in less than three months.

Economist Noah Smith writes about the process he went through in the econ job market in 2012.

After applications go out, employers contact grad students for interviews. These interviews take place at the AEA's Annual Meeting in early January, which is a gigantic confab where most of the economists in the country go to eat free food and hobnob. As an interviewee, you generally don't have time to go to any of the presentations or speeches; you're running back and forth from hotel to hotel, going to interview after interview. This year's AEA Meeting was in Chicago. 

You then wait a week or two, and the employers who are interested in you call you back and schedule a flyout. Flyouts, which happen in February and March, involve going to the place, meeting the people, getting taken to dinner, and presenting your research. After that, offers go out.

Ahh to be done with recruiting before April Fools Day. Why can't we do this in Computer Science? In one word: Decentralization.

Academic fields act administratively like their field. So econ creates a structured market with the goal of an efficient (in their sense of the right people get the right jobs) outcome.

Computer science abhors centralized control. We lack a strong central authority and have very little coordination between different departments. So we have a process that takes a long time to work its way through and quite often fails to get that efficient outcome. Even the CRA's modest 2010 proposal for some common earlier deadlines went nowhere.

What can we do? I welcome suggestions and will give some of my own in a future post.

Did you do research when you were an undergrad? Well.. it was a different time.

Dan is a high school student who has worked with me and is now an ugrad at College Park

DAN: Bill, did you do research when you were in high school?

BILL: No, things were different then, there wasn't really a mechanism for it. Or, as you young people would say, it wasn't a thing.

DAN: When as an undergrad did you begin to do research?

BILL: Well, I never did research as an ugrad either.

DAN: Then how did you get into Harvard? Did they accidentally put your folder in the wrong pile?

BILL: Good question. I'll ask my adviser if he recalls what went wrong back in 1980.

DAN: Really? You'll ask him?

BILL: NO!

Okay, the real question here is:

At some point (before 1980) doing research at an ugrad in college was not really a thing many people did.

And now its quite common.

What changed? Is the change for the good? I'll phrase my thoughts and questions in the form of a question (the opposite of Jeopardy).

1) Are there more magnet schools and more ways to funnel students into research early?

2) Do computers allow students to to more research than they could have back before (say) 1980?

3) Is the phrase `HS research' a bit odd-  it is more learning then doing?

4) Is there a cycle going on- since more students are doing research, more have to do research to keep up in order to get into grad school? Is this even an issue for college admissions and/or scholarships?

5) Has the quality of research at HS science competitions increased ? Ugrad research? Grad student research? Prof research?

6) Does having HS or ugrads do research with you increase or decrease your productivity? (That depends on the students of course)

7) Do colleges give faculty kudos for guiding HS or ugrad research? (That depends on the school of course)

8) Are there more grants for ugrad research then before 1980? Are REU programs fairly new?

9) Does an ugrad now NEED to have done research to get into one of the top X grad schools? If so is this a good thing or not?  Does this hurt those without the oppurtunity  to do research? Do REU programs help alleviate this problem?

10) Are students entering grad school knowing far more than they used to?

11) Some magnet schools REQUIRE HS students to do research. Is this a good thing?

12) I've been using 1980 as a reference for no good reason- when did HS research and ugrad research increase? Was it gradual or was there some big increase over some X years where X is small?

Our Role and Responsibility

It's Trump but not just Trump. Brexit. Right-wing politicians gaining power in France, Netherlands, Austria and beyond. We worry about the future of our country and the whole world at large.

But what role have we played? By we I mean those of us who work in technology-related fields and have benefited the most from the new economy. 

There's a large population who's lives haven't gotten better. Not just the poor, we're talking the lower 80% of the economy. Our changing economy hasn't greatly improved the life of the middle class. 

Our technologies have put people out of jobs. We tell them our technology will create more and better jobs, our own version of trickle-down economics. They just need to learn what we have created, drive our Ubers, at least until the Ubers can drive themselves. 

We love working at universities where we can educate the very top students. But many don't get the opportunity to go to college at all. They are honest people willing to work hard and not seeing their lives improving. And we just call them "uneducated". 

We have learned how to mine data but often forget the people behind the data and get caught by surprise when things don't go the way we expected from the data.

We've created new communication channels. But we also control those channels. We often make the choices between usability, security, privacy, fairness, free speech and preventing hatred and discrimination. But who are we to make those choices. They have little say.

Who are they? They are us, fellow Americans. We need to make sure that all of us benefit from our changing world and everyone feels that they contribute to making it better and everyone has a say. Democracy, messy as it is, means that if people don't feel they the world looks out for them, they will find someone who promises change no matter the baggage they carry. And we need to listen or we end up with Donald Trump.

And the winner is... Harambe

Tomorow is the Election for Prez of the USA! This post is non-partisan but, in the interest of full disclosure, I state my politics: I will be voting for Hillary Clinton. I urge you to read the posts by Scott Aaronson: here and by Terry Tao here or John Oliver's segment here to see why. I would have thought that 99% of  their readers, and mine, are voting for Clinton, but looking at the comments on  Scott's an Terry's blog I realize that might not be true. (ADDED LATER- A nice article that summarizes the entire election, I put it here for you and for me: here)

However, I am not blogging about who you should vote for.

I had the teachers of Discrete Math and Algorithms (two diff courses)  give their students paper ballots with the four candidates on the Maryland ballot (Clinton, Trump, Johnson, Stein, write-in) and had them vote. But with a difference:

Discrete Math did the standard voting where you vote for ONE candidate

Algorithms did approval voting- where you mark all those you approve of.

DISCRETE MATH RESULTS: 428 students.
Clinton: 305
Trump: 44
Johnson:21
Stein:11:
Abstain (or some variant like `f**k all of them')-7
Sanders-6
Harambe. The Gorilla  who was shot (see here)-3
Hugh Mungus (word play - you figure it out. Also see here)-3
Ken Bone (he asked a good question at the Town Hall Debate. Then...)-2
Vermin Supreme (Policy: All Americans must brush their teeth. See here)-2
Joe Yecco (don't know who it is. Maybe a student in the class) -2

For the people who got 1 vote see here. There were 22 of these.

ALGORITHMS RESULTS. 251 students (won't add up since its approval voting)
Clinton-155
Johnson-63
Stein-59
Trump-40
Sanders-18
Abstain-14
Clyde Kruskal (who teaches the course)-13
Tom Reinhart (a CS lecturer at UMCP)-5
Larry Herman (a CS lecturer at UMCP)-3
John Kasich-3
Evan McMullen (Third Party Candidate who is doing well in Utah)-3 See here
Vernin Supreme (see above)-3
Harambe the Gorilla (really!) -2
Michelle Obama-2
Eric Sim (don't know who this is- Maybe a student in the class)-2

For people who got 1 vote see here

For more complete information see here

What to make of all of this?

College Students are largely Liberal so the pro-Clinton vote is not surprising. Also Maryland is a strongly democratic state, though I think being a college is more of a factor than being in Maryland. I suspect that students at UT Austin are also liberal even though the state isn't.

I've had large classes vote before, but not by approval voting. Both of the classes above had more  write-ins than usual- though that is less surprising for approval voting.

Approval voting shows that Trump is very few people's second choice. That is, those that don't like him REALLY don't like him. Again, this  is among the narrow demographic of college students taking Algorithms at UMCP CS.

Any odd candidate who gets ONE vote does not surprise me. If ONE person votes for Aliko Dangote, a real (see here) Nigerian Billionaire (he has NOT emailed you) that is not surprising.

If someone who is sort-of out there get TWO or more votes  (Vernin Supreme, Hugh Mungus) I am not surprised. Those two have websites and a following, though small.

The three votes for Harambe the Gorilla surprised me. I've looked on the web, and this is NOT a thing- there is no Harmabe for Prez websites, even as a joke (I assume if they existed they would be a joke). So I am surprised he got 3 votes in DM and 2 vote in Algorithms. One of those voted for Clinton, Johnson, Stein, and Harambe. I'm assuming that's a strong anti-trump vote.

Does this poll have any predictive value? The winner of the Discrete Math Poll ended up being the real winner in 1992, 1996 (though there were more Ross Perot Votes then I would have thought),
2008, and 2012. Note that in all these cases the democratic candidate won. Not a surprise.

Some  personal opinions:

1) If Trump loses what will the republicans learn from this. NOTHING since Trump was such an odd candidate. I wish it would have been Hillary (or someone like her) vs Cruz (or someone on the hard right) so that if they lose the reps can't say, as some have, we lost because we weren't conservative enough. And one can hope that Hillary vs Cruz would have more of a contest of ideas.

2) I'm not sure what would be better for the Rep Party- a Trump loss or a Trump win.

3) Predictwise says a Hillary win is around 85% likely. Nate (the only pollster known by his first name!) says 65%. If Hillary wins it won't be clear who was right? If Trump wins then Nate would have been more right, but not sure what that means. More generally:

If two people give different predictions, in the end you can see who was right

If two people give different probabilities of an event, then its harder to see who was right.

Do We Still Need Great Minds?

An anonymous comment on a recent post on alternate histories.

As far as science is concerned I don't believe that the great minds are needed anymore. They only speed things up that would later have been developed by an industry of a great mass of brilliant but more mediocre researchers. The best examples are Godel's and Turing's work which would have been done necessarily by the next generation of logicians or theoretical computer scientists. Regarding your own contributions it is fair to say that "the future doesn't need you." Anything important would also be created sooner or later by others, only some idiosyncratic work of minor importance could be left undone forever.

Depressing. So we do either mediocre work or work that others would later do anyway.

Of course we can never know. We can't tell if some great idea today may not have existed if a single genius didn't create it. We also don't know what technology we don't have because of someone who became a playwright instead of a scientist.

I don't doubt we'd have the P v NP question without Cook, Karp and Levin, though our understanding of the problem would have a very different flavor.

Take someone like Manuel Blum. Through him and his students we got formal models of cryptography that led to zero-knowledge proofs, interactive proofs, probabilistically checkable proofs, lower bounds on approximation and major advances in coding theory, among so much more. Would we have all this work if Manuel never existed? Maybe, eventually, but we'd live in a whole different theory world without him. And that world would always look different until we find a new Manuel to unify it.

My chair wants me to post about.. Mohammad Haj wants me to post about.. I want to post about...

Maryland is looking to hire lecturers and my chai  Samir  wants me to post it on my blog. I think he overestimates the power of the blog, however, here is the link. I could use this to launch into a post about either the value of lecturers or how to handle the fact that CS enrollment is so high, but I've already posted on those in the past: here and here. (ADD- Actually my chair wanted me to post on all of our hiring which also includes professors, so see here)

Mohammad Hajiaghayi  is the Program chair of the SPAA conference and wants me to post it on my blog. I think he overestimates the power of the blog, however here is the link. I could use this to launch into a post about the prestige-conference model of academia,  or other conference issues, or parallelism, but these topics have also already appeared on the blog, mostly by Lance.


But here is the real question: Is it easier or harder to get information (e.g., UMCP CS is looking for a lecturer, SPAA call for papers is out) than it used to be.

EASIER: We have so many different mechanisms. Blogs, email, FACEBOOK, Twitter.  Conferences used to have posters as well- I don't think I"ve seen one for a while. (If someone knows where some of the
old posters are, on line, please leave a comment- some of them were real neat!).

HARDER: we all get too much email (for those still on email), and get too much input in general. Hence things can get lost. I'm on so many mailing lists for talks that I actually MISS talks I want to goto since I tend to ignore ALL of those emails.

If you WANT to find something out, is that easier. If you want to find out

when is the SPAA conference

yes- you can Google it.

For something like

what schools are advertising they want to hire a lecturer?

prob yes though I am not quite sure where to look.

There are things out there that if you knew about them you would want to know, but you are not quite sure how to look.  Do we come across them more or less than we used to. I think more since, at least in my case, people I know email me stuff they think I will care about and they are usually right (Ramsey Theory, Cake Cutting papers, and satires of Nobel Laureate Bob Dylan find there way to my inbox.)

Get Ready

My daughters, now in college, never knew a time when they couldn't communicate with anyone instantaneously. Molly, now 18, takes pride having the same birth year as Google. They have never in their memory seen a true technological change that so dramatically affects the world they live in. But they are about to.

The 50's and 60's saw a transportation revolution. The Interstate highway system made local and national car and truck travel feasible. The jet engine allowed short trips to faraway places. The shipping container made transporting a good, made anywhere in the world, cheaper than producing it.

We could have national and worldwide academic conferences. China became a superpower by shipping us low-cost goods. Dock worker jobs, the kind held by Archie Bunker, have morphed and shrunk, even as the number of imported goods has grown.

In the 90's we had a communications revolution. The cell phone kept us connected all the time. The home computer and the Internet gave us immediate access to the world's information and Google helped us sort through it.

It fundamentally changed how we interacted. No longer did we need to make plans in advance. Eventually we would have little need for encyclopedias, almanacs, maps, or physical media for music, photos and movies. Not to mention new types of companies and the transformation of how businesses could work with their employees and contractors spread across the world.

That brings us to today. We are at the brink, if it hasn't already started, of an intelligence revolution, the combination of big data, machine learning and automation. The initial obvious transformation will come from autonomous cars, which will change not only how we get from point A to point B but how we plan roads, businesses and where we live. Beyond that work itself will change as we will continue to automate an ever growing number of white collar jobs. As with every transformation, the world we live in will change in unforeseen ways, hopefully more good than bad.

I really look forward to watching these changes through my daughter's eyes, to see how this new society directly affects them as they start their working lives. And how their children will one day see an old-fashioned automobile with relics like foot pedals and a steering wheel and be shocked that their parents once drove cars themselves.

Exaggeration is one thing but this is....

This website is about the history of math and lists famous mathematicians. The ones from the 20th century are biased towards logic, but you should go there yourself and see who you think they left out.

There entry on Paul Cohen is... odd. Its here. I quote from it:

-------------------------------------
His findings were as revolutionary as Gödel’s own. Since that time, mathematicians have built up two different mathematical worlds, one in which the continuum hypothesis applies and one in which it does not, and modern mathematical proofs must insert a statement declaring whether or not the result depends on the continuum hypothesis.
-------------------------------------

When was the last time you had to put into the premise of a theorem CH or NOT-CH?
I did once here in an expository work about a problem in combinatorics that was ind of set theory since it was equivalent to CH. Before you get too excited it was a problem in infinite combinatorics having to do with coloring the reals.

I suspect that at least 99% of my readers have never had to insert a  note in a paper about if they were assuming CH or NOT-CH. If you have I'd love to hear about it in the comments. And I suspect
you are a set theorist.

Paul Cohen's work was very important--- here we have an open problem in math that will always be open (thats one interpretation). And there will sometimes be other problems that are ind of ZFC or equiv to CH or something like that. But it does not affect the typical mathematician working on a typical problem.

I have a sense that its bad to exaggerate like this. One reason would be that if the reader finds out
the truth he or she will be disillusioned. But somehow, that doesn't seem to apply here. So I leave it to the reader to comment:  Is it  bad to exaggerate Paul Cohen's (or anyone's) accomplishments? And if so,
then why?

Alternate Histories

Imagine if we had a machine that let us change some earlier moment in history and see what developed. We wouldn't actually change history--that would lead to paradoxes and other disasters, but we could see what would develop. Suppose Archduke Ferdinand was never assassinated. How would that have changed 20th century history and beyond?

The same could be said for an academic field. Science flows like a story, with building results from other results, "standing on the shoulders of giants" so to say. But not all theorems are dependent on earlier ones and suppose that things happened in a different order. How would that have changed our field?

Points to ponder:

Suppose Gauss was alive today instead of two centuries ago. Would he still be as famous? Would there be a big hole in mathematics that would have been left for the current Gauss to solve?

Suppose Fermat's last theorem was still a conjecture. Would there be more budding mathematicians inspired by the wonders of  that  famous open problem like my generation was? Would the Clay Mathematics Institute still have produced a list of those millennial problems, including P v NP?

Suppose we knew Primes in P back in 1975. Would randomized algorithms and subsequent derandomization techniques have happened without its prime example? Same for Undirected connectivity in log space. These both are small cheats as the AKS and Reingold proofs are at their core derandomization arguments and may not have happened if we didn't think about randomized algorithms.

What if someone settled P vs NP right after Cook? Would it have stopped most of the research in computational complexity theory? Would it depend on whether P = NP was answered positively or negatively?

What if you were never born? Ultimately that would be the only true measure of your influence in the world. What if your research somehow prevented other great theorems from happening? Would you even want to see the results of that experiment?

This university does not discriminate based on....

I recently came across the following (I delete the name of the school)
and also add my own comments in caps as they relate to UMCP hiring
of professors.


X-University, located in YZ, in hiring professors
does not discriminate on the basis of

race

color . COULD AN HBCU DISCRIMINATE AGAINST WHITE PROFESSORS?

religious HAS NEVER COME UP. COULD A RELIGIOUS SCHOOL DISCRIMINATE ON THIS BASIS?

creed  I LOOKED UP HOW IT DIFFERS FROM RELIGIONS- CREED COULD BE ANY SET OF BELIEFS. WHAT IF ONE OF CANDIDATES BELIEVES ARE REPREHENSIBLE BUT WOULD NOT INTERFERE WITH THEIR JOB? JUST ASKING.

age  WHAT IF YOU WANT SOMEONE THERE LONG-TERM?

gender. COULD A WOMEN'S (MEN'S) SCHOOL DISCRIMINATE AGAINST MEN (WOMEN)?

gender identity or expression  I THINK THIS IS A NEW ONE TO

COVER TRANS AND SIMILAR THINGS. ITS NOT SEXUAL ORIENTATION WHICH IS BELOW.

national origin  HAS NEVER COME UP. EVEN SO, I WOULD NEVER HIRE A WISIAN. See here for why.

marital status WE CAN"T EVEN ASK IF THEY HAVE A SPOUSE WHICH MAY BE HARD FOR TELLING THEM SOME SELLING POINTS OF THE AREA- GOOD FOR MARRIED PEOPLE? GOOD FOR SINGLE PEOPLE? COULD A RELIGIOUS SCHOOL HOLD AGAINST THEM THAT YOU WERE LIVING WITH SOMEONE BUT NOT MARRIED TO THEM?

ancestry I CAN"T IMAGINE THIS COMING UP.

present or past history of mental disorder I"M SURPRISED ABOUT THIS ONE SINCE I WOULD THINK HAVING A PRESENT MENTAL DISORDER IS RELEVANT TO THE JOB. THEN AGAIN, GODEL AND CANTOR PROB HAD SOME MENTAL DISORDERS.

learning disability  HAS NEVER COME UP.  I HAVE NEVER EVEN SEEN A GRAD STUDENT WITH A LEARNING DISABILITY SO IT MIGHT NOT COME UP FOR A WHILE. 

physical disability HAS NEVER COME UP. I CAN SEE IT COMING UP.

political belief  HAS NEVER COME UP. I WONDER IF IT COMES UP MORE IN A POLYSCI OR HISTORY DEPT.

veteran status HAS NEVER COME UP. COULD A MILITARY SCHOOL PREFER VETERANS?

sexual orientation  HAS NEVER COME UP. BUT COULD.

genetic information THIS MAY BE RELEVANT  IN THE FUTURE. PERHAPS THE NEAR FUTURE. SCARY?

non-position-related criminal record. WHICH CRIMES ARE POSITION RELATED? THIS COULD BE A LEGAL QUAGMIRE . MURDERING SOMEONE IN A BAR FIGHT IS NOT POSITION RELATED BUT MURDERING A STUDENT WHO COMPLAINED ABOUT A GRADE IS. OTHER CASES MAY NOT BE AS CLEAR CUT.

2016 Fall Jobs Post

The weather cools down, the leaves change color and you start thinking about what you plan to do after you graduate. As a public service every year about this time, we offer up links and advice for the job search for PhDs in theoretical computer science.

For computer science faculty positions best to look at the ads from the CRA and the ACM. For theoretical computer science specific postdoc and faculty positions check out TCS Jobs and Theory Announcements. If you have jobs to announce, please post to the above and/or feel free to leave a comment on this post.

It never hurts to check out the webpages of departments or to contact people to see if positions are available. Even if theory is not listed as a specific hiring priority you may want to apply anyway since some departments may hire theorists when other opportunities to hire dry up. Think global--there are growing theory groups around the world.

I expect hiring this year to be similar to recent years. Most departments looking to dramatically expand in computer science but with a preference for the more applied areas that the students and the companies that will hire them desire. You can make yourself more valuable by showing a willingness to participate and teach beyond core theory. Machine learning, data science and information security are areas of great need where theorists can play a large role.

A bad job talk can sink your job prospects. Know your audience and particularly for faculty positions create a talk that can express your results and importance to those outside of theory. Pictures help, complex formulas and heavy text work against you. Practice your talk in front of non-theory students and faculty. You will greatly increase your chances if you can sell yourself as a valuable colleague, not just a smart theorist who will prove great things in isolation.

Good luck out there and I look forward to seeing your names on the Spring 2017 jobs post.

Ideal courses for a comp sci dept/I'm glad we don't do it

I once heard it said:

In our data structures course we read Knuth and ignore the proofs

In our algorithms course we read Knuth and ignore the code.

And indeed, there are many topics where the theory-part is in one course and the programming is in another.

With that in mind, here is a different way to offer courses (some depts already do some of this).

1) A course in Algorithms AND Data Structures. Actually I have seen this title on courses but its usually just a theory course. I mean you REALLY PROOF and CODE. Might need to be a year long.

2) Crypto AND Security. You learn crypto and security together and do both. If you only did the crypto relevant to security it might be a semester long and in fact it might already be being done. But I am thinking of really combining these courses--- code and prove. Might be a year long.

3) Formal lang Theory and Practice of Compilers. You do DFA, NDFA, CFG, but also do compiler design. If you also want to do P, NP, and decidability (spell check thinks that decidability is not a word!) then might not quite connect up with compilers, then again in might with theorems like: CFG equivalence is undecidable. Might be a year long.

4) Machine learning AND Prob/stat.

PROS: Theory and Practice would be more united.

CONS: Having year-long courses is hard for the students scheduling their courses. Would having just one semester of any of the above courses make sense?

CONS: Harder to find someone to teach these courses. I'll confess that I prefer to teach formal lang theory without any programming in it.

CAVEAT: I think theorists coming out now know more of the practical counterpart of their area than I did and perhaps than people of my generation did.

CAVEAT: A much less radical thing to do is to put more security in to crypto, more about compilers into Formal Lang Theory, etc. But thats a bit odd now since we DO have a course in security, and a course in compilers. Even so, seems to be a good idea and this I know many schools are doing.

Typecasting from Avi's 60th Celebration

Lance: Live from the Institute for Advanced Study in Princeton, New Jersey, Bill and I are doing a typecast from the Avi Wigderson 60th Birthday Celebration. Hi Bill.

Bill: Hi Lance. Last time I saw you was for the Mike Sipser 60th birthday and next for Eric Allender and Michael Saks.

L: New Jersey again. The Garden State.

B: New Jersey rocks! After all Bruce Springsteen is from here.

L: So was I.

B: You both rock! You are better at math. But I don’t want to hear you sing. I got in last night. How was the first day of the conference.

L: There’s two kinds of talks at a celebration like this. Some people who survey how Avi has shaped the field and their research in particular. Scott Aaronson gave a great talk titled “Avi’s Permanent Impact on me” on how a survey talk on the permanent problem eventually led to Scott’s work on boson sampling.

Most people though are giving talks on their latest and greatest research. At least they have some good Avi jokes to start their talks.

B: So what category did Oded Goldreich’s “Canonical depth-three Boolean circuits for multi-linear functions, Multi-linear circuits with general gates, and matrix rigidity” talk fit in.

L: Oded did say the title was a joke and he did start with an Avi story, Actually it was a Silvio and Shafi story about when to leave for the airport.

B: I liked Alex Lubotzky’s ruminations on pure math versus computer science. He went into pure math thinking computer scientists had to know how to use computers. Had he known that they didn't he may have gone into computer science. He likes that his work on expanders has “applications” to computer science.

L: How did you like the rest of his talk.

B: I don’t know--it’s still going on.

L: Once I gave a talk at a logic meeting about generic oracles. People came up to me afterwards so excited that logic had such applications in computer science.

B: I liked Noga Alon’s talk “Avi, Graphs and Communication”. There is a fixed graph G on k nodes with k players each with an n bit string. How many bits do they to communicate over the edges to determine if all the strings are identical? Basically if the graph is 2-connected you need half of the trivial kn bits.

[Intermission]

L: A day has passed and we find ourselves talking again on Friday at IAS. It’s a beautiful day and we are sitting on lawn chairs on the IAS grounds. This is how Einstein must have felt.

B: Although he wouldn’t be skipping a talk on quantum physics.We are listening in through the magic of the Internet.

I liked Noam Nisan’s talk on the complexity of pricing. Perhaps surprisingly a seller can do better bundling two independent items than using individual prices. I plan to use that example in my fair division class. I may even do a short blog post.

L: I can’t wait. Madhu gave a great talk on low-degree polynomials and how error-correcting have gone from obscurity in theoretical computer science in the early 90’s to a standard tool just a few years later. And we have Madhu (inspired by Avi) to thank for that.

B: Eyal Wigderson, son of you know who, is a grad student studying neuroscience. Talked on “Brains are Better Computers than Computers” which I found flattering.

L: He was talking about rats, not your brain Bill.

B: Should I feel complemented or insulted?

L: Yes.

B: It’s kind of nice to have a birthday person’s child talk a conference like this. In TCS there are several including Paul Valiant who talked at Les Valiant’s 60th, Tal Rabin talked at Michael Rabin’s 80th, father and son Edmonds are here, and Molly Fortnow will surely talk at Lance Fortnow’s 60th. I remember when she interviewed us.

L: Let’s leave it at that.

B: Silvio Micali “knighted” Avi and used Byzantine agreement to improve bitcoin-like chains. I was  enlightened to learn bitcoin is so expensive only five companies do it regularly.

L: When people claim to solve P = NP I asked them to mine a few bitcoins and get back me. I have yet to see any bitcoins.

B: I asked them to find Ramsey of 5. I’m still waiting.

L: On that note time to say goodbye until we meet again.

B: Allender/Saks New Jersey again! Take us out Lance.

L: In a complex world, best to keep it simple, and

B/L: HAPPY BIRTHDAY AVI!

A Second Order Statement true in (R,+) but not (Q,+)

In my last post I asked

Is there a first order statement true in (R,+) but false in (Q,+)

Is there a second order statement true in (R,+) but false in (Q,+)


First order: No.

One reader said it followed from the Lowenheim-Skolem Theorem. I can believe this but don't see how.

One reader said the theory of (Q,+) is complete. True, but would like a reference.

I would prove it by a Duplicator-Spoiler argument.

Second order: Yes

Some readers thought I had < in my language. I do not so I think that examples using < do not work- unless there is someway to express < in my language.

Some readers thought that second order meant you could quantify over functions. My intent was just to be able to quantify over sets.

Some had the right answers. The one I had in mind was

There exists A,B such that A,B are subgroups with at least two elements that only intersect at 0.

Here is a writeup.

Give a second order statement true in (R,+) but false in (Q,+) or show there isn't one

Here is a logic question I will ask today and answer next week. Feel free to leave comments with
the answer- you may come up with a different proof than me and that would be great!

Our lang will have the usual logic symbols, quantification over the domain, quantification over subsets of the domain (so second order) the = sign, and the symbol +

Examples of sentences:

(∀ x)(∀ y)[ x+y=y+x]

true over Q,R,N.  False in S_n for n\ge 4 (group of perms of n elements)

(∃ x)(∀ y)[ x+y=y]

true in Q, R by taking 0. not true in {1,2,3,...}

Lets assume it is true and call the x 0

(∀ x)(∃ y)[x+y=0]

True in Q, R, Z, not true in N.

QUESTION ONE: Is there any sentence in the first order theory that is TRUE over (Q,+) but
FALSE over (R,+)?

QUESTION TWO: Is there any sentence in the second order theory that is TRUE over (Q,+)
but false over (R,+)?

Who's Afraid

The playwright  Edward Albee passed away earlier this month at the age of 88. I had never actually seen any of his plays so I took the opportunity to watch the 1966 movie Who's Afraid of Virginia Woolf? based on the play.

The play has four characters, George, a history professor in his forties and his wife Martha, the daughter of the University's president. Joining them for a late get together is a young biology professor and his wife. The movie had an incredible cast: Richard Burton, Elizabeth Taylor, George Segal and Sandy Dennis. All nominated for academy awards. The women won.

The movie covers many themes, mostly revolving around the disintegrating relationship between George and Martha. George did not live up to his potential as a drunk Martha did not hesitate to point out in front of all of them.

I actually fell for him. And the match seemed practical too. For a while Daddy thought George had the stuff to take over when he was ready to retire. Anyway, I married the S.O.B. I had it all planned out. First he'd take over the History Department, then the whole college. That's how it was supposed to be! That was how it was supposed to be. All very simple. Daddy thought it was a good idea too. For a while!

Until he started watching for a couple of years and and started thinking it wasn't such a good idea. That maybe Georgie-boy didn't have the stuff! That he didn't have it in him! George didn't have much push. He wasn't aggressive. In fact, he was sort of a flop! A great big, fat flop! 

So here I am, stuck with this flop, this bog in the History Department. Who's married to the president's daughter who's expected to be somebody. Not just a nobody! A bookworm who's so goddamn complacent he can't make anything out of himself.

The movie reflects an earlier time in academics, when all the professors were white males and success was measured by taking charge of a department.

Nevertheless Albee captures a fear many academics have. That one day we may wake up and realize we've become that bog. Stuck in our job because we can't afford to give up tenure, but just going through the motions. It's a fear that motivates us, to make us continually try to do new things and achieve new heights. But it's also a reminder that academics is a long career and one that could bog down before we even notice.

Edward Albee writes a play incredibly uncomfortable to watch yet impossible not to. So rare to see that in mainstream plays or movies today.

Boris Trakhtenbrot (1921-2016)

I woke up this morning to two pieces of news. Subhash Khot has just been named a MacArthur Fellow, the "genius" award, for his work on unique games. But I also just learned about Monday's passing of the great Russian theorist Boris Trakhtenbrot at the age of 95.

Trakhtenbrot has a number of important results in automata theory, model theory and logic to name just a few areas. In computational complexity we know him best for the Gap Theorem which he proved independently with Allan Borodin. Roughly the gap theorem states that for any computable f there is a computable time-bound t such that DTIME(t) = DTIME(f(t)), every problem solvable in time f(t) can also be solved in time t. For example there is a time bound t such that DTIME(t) = DTIME(2^t). This doesn't violate the time hierarchy since t may not be time-constructible. There is nothing special about time here, it works for space or any abstract complexity measure.

Borodin and Trakhtenbrot worked independently because they sat on different sides of the iron curtain during the cold war which very little communication in between. Boris Trakhtenbrot wrote A Survey of Russian Approaches to Perebor (Brute-Force Searches) Algorithms (PDF) that traces this early history of Russian theory. He didn't hold back discussing some of the academic politics in Russia that stifled research into computational complexity and gave us a window into the Russian scientific community in the mid-20th century.

Thankfully the iron curtain has been replaced by a global internet and we can do science together. Let's hope it stays that way.

Academic Rankings Foster Competition

This week US News and World Report released their undergraduate rankings earlier this week. A time for schools to brag. US News and World Report used to publish an actual weekly news magazine, now they mostly just focuses on rankings. Besides various categories of undergrad institutions USN&WR ranks engineering and business programs. There are many other ranking systems of varying quality but in the US we take the USN&WR rankings the most seriously.

Computer Science does not get an undergraduate ranking. Computer engineering does--not the same. CS does get rankings as a PhD program, last time in 2014. I've posted on rankings in 2005, on the failed NRC rankings of 2010, on using metrics for rankings, and Bill had his own so-called non-controversial thoughts on rankings.

In the September CACM, Moshe Vardi wrote his editor's column entitled Academic Rankings Considered Harmful

Academic rankings, in general, provide highly misleading ways to inform academic decision making by individuals. An academic program or unit is a highly complex entity with numerous attributes. An academic decision is typically a multi-objective optimization problem, in which the objective function is highly personal. A unidimensional ranking provides a seductively easy objective function to optimize. Yet such decision making ignores the complex interplay between individual preferences and programs' unique patterns of strengths and weaknesses. Decision making by ranking is decision making by lazy minds, I believe.

No potential grad student should decide based solely on rankings but neither can we expect them to solve a highly-complex multi-objective highly-personal optimization problem over all 266 PhD-granting CS departments. They will find ways to narrow down their list of schools somehow and a reasonable independent ranking of CS departments can certainly help.

More importantly rankings cause us to compete against each other. Every CS department wants to raise their rankings (or stay on top) and use that goal to work on strengthening their departments and use rankings to make the case to upper administration and alumni to get the resources needed to continue to grow. By the nature of rankings, not everyone can rise up but we all get better in the process.

Noam Nisan wins the Knuth Prize

Noam Nisan will receive the 2016 Donald E. Knuth Prize, the highest honor in theoretical computer science, for his work in computational complexity and algorithmic game theory. He'll receive the award at the upcoming FOCS conference.

I've known Noam since we were fellow grad students in Berkeley in 1985 and we have become good friends and colleagues. Noam Nisan started his career in computational complexity and Luca posts about several of his seminal works in derandomization, interactive proofs, communication and circuit complexity. I'll focus this post on Nisan's 1992 paper with Mario Szegedy, On the degree of boolean functions as real polynomials.

Let f be a Boolean function on n variables and p a n-variate polynomial over the reals and suppose for every x in {0,1}ⁿ, |f(x)-p(x)| ≤ 1/3. Nisan and Szegedy show that the decision tree complexity of f is bounded by a polynomial in the degree of f. The decision tree complexity of a function is the number of bits one has to query to determine whether f is 0 or 1.

The theorem didn't have an immediate application but soon afterwards I told Noam we found a result that followed from his paper. His ears perked up until I told him the actual result (if P = PSPACE then P = AWPP relative to a Cohen generic oracle).

Later on Nisan-Szegedy would have direct applications to quantum computing, showing that quantum, random and deterministic decision tree complexity for total functions are polynomially related. Just last STOC we saw two papers giving tighter bounds on these relationships.

In 1997 Noam Nisan walked away from complexity and soon thereafter became one of the founding players in algorithmic game theory, co-organizing the 2001 DIMACS workshop that would kickstart this field. He won the 2012 Gödel Prize for his early work on algorithmic mechanism design with Amir Ronen.

Nisan and Szegedy begat one of my most frustrating open questions on the relationship between rational functions and decision tree complexity. I have an application for it but I don't think you really want to know.

Why I Don't Believe in ET

Is there life on other planets? The naive answer is "of course, why should Earth be special." What makes a lottery winner special? We could have just won the life lottery and the losers aren't around to complain.

The more scientific approach uses variants of the Drake Equation. I'll use the variant from the recent paper A New Empirical Constraint on the Prevalence of Technological Species in the Universe by Frank and Sullivan.

We define the ‘‘A-form’’ of the Drake equation, which describes the total number of technological species that have ever evolved anywhere in the currently observable Universe:

A  = Nf_pn_pf_lf_if_t

where N is the total number of stars, f_p is the fraction of those stars that form planets, n_p is the average number of planets in the habitable zone of a star with planets, f_l is the
probability that a habitable zone planet develops life, f_i is the probability that a planet with life develops intelligence, and f_t is the probability that a planet with intelligent life develops technology (of the ‘‘energy intensive’’ kind such as that of our own civilization).

I first encountered the Drake equation in high school from Carl Sagan's book Cosmos. The conclusion that there must be life out there from the equation never satisfied me because of our inability to measure the f values.

Frank and Sullivan's computations show that we expect there to only be life on Earth, say A=0.01 then f = f_lf_if_t must be at most 2.5 x 10^-24.

Seems small but is it really? 2.5 x 10^-24 is roughly the probability of flipping 78 coin tosses and having them all come up heads. Maybe life requires 78 50/50 independent events to occur. Or 1060 independent events each with 95% probability. 1060 doesn't seem that big.

Our attempts at finding life, whether by SETI or Mars soil or UFOs have so far turned up nothing substantial. We just might be the only ones out there.

By no means am I arguing that we give up the search. I could be wrong, f could be much larger than 2.5 x 10^-24. Let's keep looking, perhaps exploring the ice caps of Mars or the moons of Jupiter, keep listening the the stars, explore new ways to probe the galaxy. It would be incredible to find extraterrestrial life, but just don't be surprised if we don't.