To scissors, rock.
Don’t jump rejoice,
Without his voice,
To guide your choice,
Of rolls or Royce.
When chance arise,
Try on for size,
This guy insider.
The one that bends.
Who letter sends.
And makes amends.
The man who plans.
Stores up his cans.
Controls his glands.
No chitter chat,
Or swagger frat.
A clever cat.
To measure right,
Looks not hindsight.
Makes best of plight
In current light.
Odds in favor?
Risk won’t waiver.
More than saver.
He never lies.
Yet more, this guy’s.
He flies. He dies.
And then he’ll rise.
He makes the grade,
He’s wealthy paid,
He so gets laid.
The lesson cinch:
Just inch by inch,
Turn your winch
On Vulcan pinch.
Take lesson stock:
No dove or hawk.
For life to rock,
Embrace your Spock.
RIP Leonard Nimoy
See also: Keep Kirk in Lurk
Moshe Babaioff served as General Chair for the conference and many other Microsoft Researchers served roles including (senior) PC members, workshop organizers, and tutorial speakers.
For research at the intersection of economics and computation, IMHO there’s no stronger “department” in the world than MSR.
Sébastien Lahaie and Jennifer Wortman Vaughan co-authored three papers each. Remarkably, Jenn accomplished that feat and gave birth!
The full list of authors are: Shipra Agrawal, Moshe Babaioff, Yoram Bachrach, Wei Chen, Sofia Ceppi, Nikhil R. Devanur, Fernando Diaz, Hu Fu, Rafael Frongillo, Daniel Goldstein, Nicole Immorlica, Ian Kash, Peter Key, Sébastien Lahaie, Tie-Yan Liu, Brendan Lucier, Yishay Mansour, Preston McAfee, Noam Nisan, David M. Pennock, Tao Qin, Justin Rao, Aleksandrs Slivkins, Siddharth Suri, Jennifer Wortman Vaughan, and Duncan Watts.
The full list of papers are:
Optimal Auctions for Correlated Bidders with Sampling
Hu Fu, Nima Haghpanah, Jason Hartline and Robert Kleinberg
Generalized Second Price Auction with Probabilistic Broad Match
Wei Chen, Di He, Tie-Yan Liu, Tao Qin, Yixin Tao and Liwei Wang
Optimising Trade‐offs Among Stakeholders in Ad Auctions
Yoram Bachrach, Sofia Ceppi, Ian Kash, Peter Key and David Kurokaw
Neutrality and Geometry of Mean Voting
Sébastien Lahaie and Nisarg Shah
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal‐Agent Problems
Chien-Ju Ho, Aleksandrs Slivkins and Jennifer Wortman Vaughan
Removing Arbitrage from Wagering Mechanisms
Yiling Chen, Nikhil R. Devanur, David M. Pennock and Jennifer Wortman Vaughan
Information Aggregation in Exponential Family Markets
Jacob Abernethy, Sindhu Kutty, Sébastien Lahaie and Rahul Sami
A General Volume‐ Parameterized Market Making Framework
Jacob Abernethy, Rafael Frongillo, Xiaolong Li and Jennifer Wortman Vaughan
Reasoning about Optimal Stable Matchings under Partial Information
Baharak Rastegari, Anne Condon, Nicole Immorlica, Robert Irving and Kevin Leyton-Brown
The Wisdom of Smaller, Smarter Crowds
Daniel Goldstein, Preston McAfee and Siddharth Suri
Incentivized Optimal Advert Assignment via Utility Decomposition
Frank Kelly, Peter Key and Neil Walton
Whole Page Optimization: How Page Elements Interact with the Position Auction
Pavel Metrikov, Fernando Diaz, Sébastien Lahaie and Justin Rao
Local Computation Mechanism Design
Shai Vardi, Avinatan Hassidim and Yishay Mansour
On the Efficiency of the Walrasian Mechanism
Moshe Babaioff, Brendan Lucier, Noam Nisan and Renato Paes Leme
Long‐run Learning in Games of Cooperation
Winter Mason, Siddharth Suri and Duncan Watts
Moshe Babaioff and Eyal Winter
Bandits with concave rewards and convex knapsacks
Shipra Agrawal and Nikhil R. Devanur
We will also consider applicants in other focus areas of the lab, including information retrieval, and behavioral & empirical economics. Additional information about these areas is included below. Please submit all application materials by January 11, 2013 for full consideration. Instructions are here.
COMPUTATIONAL SOCIAL SCIENCE
With an increasing amount of data on every aspect of our daily activities — from what we buy, to where we travel, to who we know — we are able to measure human behavior with precision largely thought impossible just a decade ago. Lying at the intersection of computer science, statistics and the social sciences, the emerging field of computational social science uses large-scale demographic, behavioral and network data to address longstanding questions in sociology, economics, politics, and beyond. We seek postdoc applicants with a diverse set of skills, including experience with large-scale data, scalable statistical and machine learning methods, and knowledge of a substantive social science field, such as sociology, economics, psychology, political science, or marketing.
ONLINE EXPERIMENTAL SOCIAL SCIENCE
Online experimental social science involves using the web, including crowdsourcing platforms such as Amazon’s Mechanical Turk, to study human behavior in “virtual lab” environments. Among other topics, virtual labs have been used to study the relationship between financial incentives and performance, the honesty of online workers, advertising impact as a function of exposure time, the implicit cost of “bad ads,” the testing of graphical user interfaces eliciting probabilistic information and also the relationship between network structure and social dynamics, related to social phenomena such as cooperation, learning, and collective problem solving. We seek postdoc applicants with a diverse mix of skills, including awareness of the theoretical and experimental social science literature, and experience with experimental design, as well as demonstrated statistical modeling and programming expertise. Specific experience running experiments on Amazon’s Mechanical Turk or related crowdsourcing websites, as well as managing virtual participant pools is also desirable, as is evidence of UI design ability.
ALGORITHMIC ECONOMICS AND MARKET DESIGN
Market design, the engineering arm of economics, benefits from an understanding of computation: complexity, algorithms, engineering practice, and data. Conversely, computer science in a networked world benefits from a solid foundation in economics: incentives and game theory. Scientists with hybrid expertise are crucial as social systems of all types move to electronic platforms, as people increasingly rely on programmatic trading aids, as market designers rely more on equilibrium simulations, and as optimization and machine learning algorithms become part of the inner loop of social and economic mechanisms. We seek applicants who embody a diverse mix of skills, including a background in computer science (e.g., artificial intelligence or theory) or related field, and knowledge of the theoretical and experimental economics literature. Experience building prototype systems, and a comfort level with modern programming paradigms (e.g., web programming and map-reduce) are also desirable.
Machine learning is the discipline of designing efficient algorithms for making accurate predictions and optimal decisions in the face of uncertainty. It combines tools and techniques from computer science, signal processing, statistics and optimization. Microsoft offers a unique opportunity to work with extremely diverse data sources, both big and small, while also offering a very stimulating environment for cutting-edge theoretical research. We seek postdoc applicants who have demonstrated ability to do independent research, have a strong publication record at top research venues and thrive in a multidisciplinary environment.]]>
We were there to promote The Signal, a partnership between Yahoo! Research and Yahoo! News to put a quantitative lens on the election and beyond. The Signal was our data-driven antidote to two media extremes: the pundits who commit to statements without evidence; and some journalists who, in the name of balance, commit to nothing. As MIT Tech Review billed it, The Signal would be the “mother of all political prediction engines”. We like to joke that that quote undersold us: our aim was to be the mother of all prediction engines, period. The Signal was a broad project with many moving parts, featuring predictions, social media analysis, infographics, interactives, polls, and games. Led by David “Force-of-Nature” Rothschild, myself, and Chris Wilson, the full cast included over 30 researchers, engineers, and news editors . We confirmed quickly that there’s a clear thirst for numeracy in news reporting: The Signal grew in 4 months to 2 million unique users per month .
On that night, though, the journalists kept coming back to the Yahoo! PR hook that brought them in the door: our insanely early election “call”. At that time in February, Romney hadn’t even been nominated.
No, we didn’t call the election, we predicted the election. That may sound like the same thing but, in scientific terms, there is a world of difference. We estimated the most likely outcome – Obama would win 303 Electoral College votes, more than enough to return him to the White House — and assigned a probability to it. Of less than one. Implying a probability of more than zero of being wrong. But that nuance is hard to explain to journalists and the public, and not nearly as exciting.
Although most of our predictions were based on markets and polls, the “303” prediction was not: it was a statistical model trained on historical data of past elections, authored by economists Patrick Hummel and David Rothschild. It doesn’t even care about the identities of the candidates.
I have to give Yahoo! enormous credit. It took a lot of guts to put faith in some number-crunching eggheads in their Research division and go to press with their conclusions. On February 16, Yahoo! went further. They put the 303 prediction front and center, literally, as an “Exclusive” banner item on Yahoo.com, a place that 300 million people call home every month.
The firestorm was immediate and monstrous. Nearly a million people read the article and almost 40,000 left comments. Writing for Yahoo! News, I had grown used to the barrage of comments and emails, some comic, irrelevant, or snarky; others hateful or alert-the-FBI scary. But nothing could prepare us for that day. Responses ranged from skeptical to utterly outraged, mostly from people who read the headline or reactions but not the article itself. How dare Yahoo! call the election this far out?! (We didn’t.) Yahoo! is a mouthpiece for Obama! (The model is transparent and published: take it for what it’s worth.) Even Yahoo! News editor Chris Suellentrop grew uncomfortable, especially with the spin from Homepage (“Has Obama won?”) and PR (see “call” versus “predict”), keeping a tighter rein on us from then on. Plenty of other outlets “got it” and reported on it for what it was – a prediction with a solid scientific basis, and a margin for error.
This morning, with Florida still undecided, Obama had secured exactly 303 Electoral College votes.
Just today Obama wrapped up Florida too, giving him 29 more EVs than we predicted. Still, Florida was the closest vote in the nation, and for all 50 other entities — 49 states plus Washington D.C. — we predicted the correct outcome back in February. The model was not 100% confident about every state of course, formally expecting to get 6.8 wrong, and rating Florida the most likely state to flip from red to blue. The Hummel-Rothschild model, based only on a handful of variables like approval rating and second-quarter economic trends, completely ignored everything else of note, including money, debates, bail outs, binders, third-quarter numbers, and more than 47% of all surreptitious recordings. Yet it came within 74,000 votes of sweeping the board. Think about that the next time you hear an “obvious” explanation for why Obama won (his data was biggi-er!) or why Romney failed (too much fundraising!).
Kudos to Nate Silver, Simon Jackman, Drew Linzer, and Sam Wang for predicting all 51 states correctly on election eve.
As Felix Salmon said, “The dominant narrative, the day after the presidential election, is the triumph of the quants.” Mashable’s Chris Taylor remarked, “here is the absolute, undoubted winner of this election: Nate Silver and his running mate, big data.” ReadWrite declared, “This is about the triumph of machines and software over gut instinct. The age of voodoo is over.” The new news quants “bring their own data” and represent a refreshing trend in media toward accountability at least, if not total objectivity, away from rhetoric and anecdote. We need more people like them. Whether you agree or not, their kind — our kind — will proliferate.
Congrats to David, Patrick, Chris, Yahoo! News, and the entire Signal team for going out on a limb, taking significant heat for it, and correctly predicting 50 out of 51 states and an Obama victory nearly nine months prior to the election.
 Here was the day-before guest list for the February 15 Yahoo! press dinner, though one or two didn’t make it:
– New York Times, John Markoff
– New York Times, David Corcoran
– Fast Company, EB Boyd
– Forbes, Tomio Geron
– MIT Tech Review, Tom Simonite
– New Scientist, Jim Giles
– Scobleizer, Robert Scoble
– WIRED, Cade Metz
– Bloomberg/BusinessWeek, Doug MacMillan
– Reuters, Alexei Oreskovic
– San Francisco Chronicle, James Temple
 The extended Signal cast included Kim Farrell, Kim Capps-Tanaka, Sebastien Lahaie, Miro Dudik, Patrick Hummel, Alex Jaimes, Ingemar Weber, Ana-Maria Popescu, Peter Mika, Rob Barrett, Thomas Kelly, Chris Suellentrop, Hillary Frey, EJ Lao, Steve Enders, Grant Wong, Paula McMahon, Shirish Anand, Laura Davis, Mridul Muralidharan, Navneet Nair, Arun Kumar, Shrikant Naidu, and Sudar Muthu.
 Although I continue to be amazed at how greener the grass is at Microsoft compared to Yahoo!, my one significant regret is not being able to see The Signal project through to its natural conclusion. Although The Signal blog was by no means the sole product of the project, it was certainly the hub. In the end, I wrote 22 articles and David Rothschild at least three times that many.]]>
In that vein, I am thrilled to announce the beta launch of PredictWiseQ, a fully operational example of our latest combinatorial prediction market design: “A tractable combinatorial market maker using constraint generation”, published in the 2012 ACM Conference on Electronic Commerce.
You read the paper.1 Now play the game.2 Help us close the loop.
PredictWiseQ is our greedy attempt to scarf up as much information as is humanly possible and use it, wisely, to forecast nearly every possible detail about the upcoming US presidential election. For example, we can project how likely it is that Romney will win Colorado but lose the election (6.2%), or that the same party will win both Ohio and Pennsylvania (77.6%), or that Obama will paint a path of blue from Canada to Mexico (99.5%). But don’t just window shop, go ahead and customize and buy a prediction or ten for yourself. Your actions help inform the odds of your own predictions and, crucially, thousands of other related predictions at the same time.
For example, a bet on Obama to win both Ohio and Florida can automatically raise his odds of winning Ohio alone. That’s because our market maker knows and enforces the fact that Obama winning OH and FL can never be more likely than him winning OH. After every trade, we find and fix thousands of these logical inconsistencies. In other words, our market maker identifies and cleans up arbitrage wherever it finds it. But there’s a limit to how fastidious our market maker can be. It’s effectively impossible to rid the system of all arbitrage: doing so is NP-hard, or computationally intractable. So we clean up a good bit of arbitrage, but there should be plenty left.
So here’s a reader’s challenge: try to identify arbitrage on PredictWiseQ that we did not. Go ahead and profit from it and, when you’re ready, please let me and others know about it in the comments. I’ll award kudos to the reader who finds the simplest arbitrage.
Why not leave all of the arbitrage for our traders to profit from themselves? That’s what nearly every other market does, from Ireland-based Intrade, to Las Vegas bookmakers, to the Chicago Board Options Exchange. The reason is, we’re operating a prediction market. Our goal is to elicit information. Even a completely uninformed trader can profit from arbitrage via a mechanical plug-and-chug process. We should reserve the spoils for people who provide good information, not those armed (solely) with fast or clever algorithms. Moreover, we want every little crumb of information that we get, in whatever form we get it, to immediately impact as many of the thousands or millions of predictions that it relates to as possible. We don’t want to wait around for traders to perform this propagation on their own and, besides, it’s a waste of their brain cells: it’s a job much better suited for a computer anyway.
Intrade offers an impressive array of predictions about the election, including who will win in all fifty states. In a sense, PredictWiseQ is Intrade to the 57th power. In a combinatorial market, a prediction can be any (Boolean) function of the state outcomes, an ungodly degree of flexibility. Let’s do some counting. In the election, there are actually 57 “states”: 48 winner-takes-all states, Washington DC, and two proportional states — Nebraska and Maine — that can split their electoral votes in 5 and 3 unique ways, respectively. Ignoring independent candidates, all 57 base “states” can end up colored Democratic blue or Republican Red. So that’s 2 to the power 57, or 144 quadrillion possible maps that newscasters might show us after the votes are tallied on November 6th. A prediction, like “Romney wins Ohio”, is the set of all outcomes where the prediction is true, in this case all 72 quadrillion maps where Ohio is red. The number of possible predictions is the number of sets of outcomes, or 2 to the power 144 quadrillion. That’s more than a googol, though less than a googolplex (maybe next year). To get a sense of how big that is, if today’s fastest supercomputer starting counting at the instant of the big bang, it still wouldn’t be anywhere close reaching a googol yet.
Create your own league to compare your political WiseQ among friends. If you tell us how much each player is in for, we’ll tell you how to divvy things up at the end. Or join the “Friends Of Dave” (FOD) league. If you finish ahead of me in my league, I’ll buy you a beer (or beverage of your choice) the next time I see you, or I’ll paypal you $5 if we don’t cross paths.
PredictWiseQ is part of PredictWise, a fascinating startup of its own. Founded by my colleague David Rothschild, PredictWise is the place to go for thousands of accurate, real-time predictions on politics, sports, finance, and entertainment, aggregated and curated from around the web. The PredictWiseQ Game is a joint effort among David, Miro, Sebastien, Clinton, and myself.
The academic paper that PredictWiseQ is based on is one of my favorites — owed in large part to my coauthors Miro and Sebastien, two incredible sciengineers. As is often the case, the theory looks bulletproof on paper. But I’ve learned the hard way many times that you don’t really know if a design is good until you try it. Or more accurately, until you build it and let a crowd of other people try it.
So, dear crowd, please try it! Bang on it. Break it. (Though please tell me how you did, so we might fix it.) Tell me what you like and what is horribly wrong. Mostly, have fun playing a market that I believe represents the future of markets in the post-CDA era, a.k.a the digital age.
1 Or not.
2 Or not.
However, when Google’s or Bing’s crawlers arrive to index my corner of the web, they see a different <title> altogether — Buy Cheap Cialis Online — and immediately roll their eyes. (Actually even if you run
'curl http://blog.oddhead.com', you’ll see the spam keywords.) The effect of the attack is a kind of reverse cloaking. Cloaking is the black-hat SEO practice of serving legitimate content to crawlers and spam content to people. Here, the spam content is shown to the crawlers and the legitimate content to the people.
Once the crawlers report this appalling information back to their respective mother ships, the search engines have no choice but to delist and demote my blog in their pagerankings. Right now, if you search for or within Oddhead Blog on Google, you’ll see how poorly the bots in Mountain View think of me:
You can hardly find any deep links into my blog by searching Google. For example, try searching for Bem+Wom, my invented term for “BEtter Mousetrap, Word of Mouth”. Even try “Bem+Wom oddhead blog”. You”ll find aggregators republishing my content, but no links to the original source, my blog, anywhere in sight. (Note to self: the Bing results for Bem+Wom are awful.)
Once again I am at a loss to understand my attacker’s motivation. Clearly it’s not to sell Cialis to my users, as they remain blissfully ignorant of any changes. The only benefit to anyone is to remove one relatively obscure blog from the search engine rankings and thus to move the attacker one slot up. Having a blog tangentially about gambling probably puts me into a shady neighborhood of the web, yet reverse-cloaking your competition (even if it can be somewhat automated and strike more than one competitor) seems like an awfully indirect way to improve one’s standing in Google. It’s also possible this is an act of pure vandalism.
So what should I do? Although I partly blame WordPress for writing insecure software, I may end up paying WordPress protection money to make this problem go away. I am seriously considering giving up on self hosting and moving my whole operation to worpress.com’s hosted service, where presumably security is tighter, or at least it’s not my responsibility any more. My web hosting service, DreamHost, may also be partly to blame, yet I like the company and have been quite happy with them in many respects. Any advice, dear reader? WordPress.com? Blogger? Try again and hope the fourth time is the charm? Should I be looking to ditch DreamHost as well?]]>
But that’s not all. Together with fourteen other founding members (seven of whom I can name: Duncan Watts, John Langford, David Rothschild, Sharad Goel, Dan Goldstein, Jake Hofman, and Sid Suri), we are cutting the ribbon on a new outpost for Microsoft Research in New York City. We will report to Jennifer Chayes, the founder and director of Microsoft Research New England in Cambridge, MA. It’s been amazing to watch her up close pursue a goal relentlessly with boundless positive energy. I get the feeling it’s how she approaches everything she does, a realization that played no small part in my decision. The New England Lab, like us, is an interdisciplinary research group that blends computer science, social science, and machine learning, yet from different enough perspectives to make this an almost perfect marriage. It’s no exaggeration to say that helping to found and lead a new research group amid the bursting tech scene in New York City, with the resources of Microsoft behind us, is — as Duncan says — a once-in-a-career opportunity.
The press coverage Thursday was gratifying, including nice pieces in PCMag (source of the sweet logo above), NYTimes.com, AllThingsD, and dozens more. Here is the official press release. For science perspectives, see John Langford’s, Lance Fortnow’s, Dan Goldstein’s, and Jennifer Chayes’s blog posts. One of the coolest moments came when New York Mayor Michael Bloomberg tweeted about us.
Note that, despite the attrition, Yahoo! Labs lives on, probably more applied but not solely so. Ron Brachman, the new head of Yahoo! Labs, is terrific and may be able to do something special there. The Barcelona group remains largely intact and just got 7 (!) papers into SIGIR. Other groups remain intact as well.
The reception within Microsoft research and product orgs has been swift and very warm. The breadth and scope of the place can be daunting at first but invigorating. The ability to impact products that touch hundreds of millions of people’s lives is, as always, a rewarding draw of corporate research. Yet one of the deciding factors for many of us in joining Microsoft is the freedom to interact with universities in research, service, teaching, hosting visitors, hiring interns and postdocs, etc. In addition, we’d like to play our part in the New York City tech scene, including the startup, venture-capitalist, and hack/make communities, plus the new Cornell-Technion campus, contributing to Mayor Bloomberg’s vision of New York City as a tech hub.
An interesting side note that bodes well for my two daughters ages 7 and 4 is that my primary decision boiled down to working for one of two brilliant and accomplished women: Jennifer Chayes at Microsoft, or Corinna Cortes at Google, who is absolutely terrific. Google is a incredible place, a model of efficiency, innovation, and ambition, with an impressive roster of people, and the company is in a very strong position. But this opportunity at Microsoft simply proved to be too good to pass up. I can’t believe how perfectly everything fell into place. I’m beyond thrilled at the outcome and excited to begin this next chapter of my career.]]>
——– Original Message ——–
Subject: last Yodle (and last corny Yodle joke)
Date: Wed, 25 Apr 2012 16:44:31 -0400
From: David Pennock
After 8 wonderful years (almost 10 if you include Overture), it is with
very mixed emotions that I leave Yahoo!. My last day is tomorrow,
2526. You can reach me in plenty of ways and I hope you do:
[my email address]
Y!IM pennockd | facebook pennockd | twitter pennockd | linkedin
http://dpennock.com | http://blog.oddhead.com
I’ve grown to love this company (purple blood, yada yada) and one of the
deep ironies is that I have a feeling Scott Thompson may actually know
what he is doing and that maybe just maybe Yahoo!’s return to revenue
growth and good public perception will finally come (note I didn’t say
return to profitability — a steady $1 billion in cashmoney profit in
our pocket every year is very far from shabby). I plan to hold on to
some of my stock.
In the early 2000s Google was an amazing Bem+Wom story yet almost no one
(me included) had a clue how they would make money. In 2002, Gary Flake
introduced me to Overture, a company already making hundreds of millions
on search, and suddenly it was clear. I joined Gary in what became
Overture Research and later, under Usama Fayyad’s protective wing, the
inception of “Yahoo! Research Labs”. When Gary left, we hired Prabhakar
and Ron. The rest is history. Andrei, Andrew, Raghu, Ravi, Ricardo,
Preston, Duncan. An absolutely amazing place that was my pleasure to
watch grow and mature. I still remember the excitement of our first
offsite at Half Moon Bay to map out the future of the place.* I remember
a fateful week when Preston, Duncan, and David Reiley simultaneously
gave up their tenure to stay at Yahoo!.
From the beginning Prabhakar saw the importance of including social
science research in the mix for online media. In my little corner, where
we mixed computer science and economics (“algorithmic economics” we
called it), I believe we had enormous effect both internally and
externally. In 2007, Jeff MacKie-Mason, one of our Big Thinker lecturers
and now Dean of the School of Information at the University of Michigan,
wrote (ok, informally to me in email) that our group was “the most
exciting and successful group I’ve seen crossing the CS/Econ boundary”.
If imitation is the sincerest form of flattery, I believe we had a
significant positive impact on the growth in hiring in the social
sciences and in algorithmic economics at both Google and Microsoft. In
our group alone, we published more than 70 papers including at least two
award winners (Arpita just this year). We literally wrote the book
(chapters) on sponsored search and prediction markets. We co-founded the
Ad Auctions Workshop and NYCE Day. People who left often did
fantastically well, including Yiling Chen to Harvard, Mohammad Mahdian
to Google, and Dan Reeves to found his own successful startup Beeminder.
We filed dozens of patents (take that fb!). Former intern Nicolas
Lambert who is now a Stanford professor once told me he hoped to one day
say “it all started at Yahoo!”. I just left a Ph.D. student’s defense
whose three (!) weeks at Yahoo! were good for two chapters in his
thesis. We’ve had academic visitors leave after a week here and follow
up that they wanted to apply for a job — the environment was that great.
Inside Yahoo!, we worked on sponsored search (“squashing” and so much
more by the incomparable Sebastien Lahaie, who we recently discovered is
the central hub of research in New York), display ads, and UGC among
many topics. My passion has been in prediction (markets), and some of my
best memories have been trying to play product manager for a day (or a
couple months) for Predictalot and The Signal. Often it felt more like
operating a startup but with incredible advantages in resources, people,
and of course access to that monster traffic firehose. This was Yahoo!
at it’s best — marshaling talent from all over the globe in many
divisions and specialties to produce a product that no one had ever seen
before, and that no one including us even knew would work. One of the
saddest parts of departing now is leaving The Signal behind, an
incredible effort and in many ways our biggest and best, led by David
“force-of-nature” Rothschild and so many people behind it. Sadly, some
were let go and others are leaving on the own accord, and we’ll never
know what could have been in a counterfactual universe. Yet I believe
The Signal will live on in the good hands of those who remain, including
Chris Wilson, Alex, Ingemar, and the absolutely phenomenal Bangalore team.
By far the best part of working at Yahoo! was the people. It’s been my
pleasure to work with so many fantastic colleagues in Labs and
throughout the company. In the recent turmoil many in Labs have been, as
Preston said, “evaluated by the market”, and came out looking pretty
darn good, with calls, interviews, and offers from the best companies
(Facebook, Google, Microsoft) and universities. Early on we set a goal
to always hire above the mean, and I truly believe we did that. (Having
been here from the beginning, you can see where that leaves me in this
incredible crowd.) It’s a cliche but a true one: I am only as good as
the people working with me, and I’ve truly been blessed with amazing
colleagues, bosses, employees, postdocs, and interns. To Sebastien,
Arpita, Giro, and David Rothschild, plus Mridul, Navneet, Sudar, Arun,
Shrikant, Kim, Chris, Janet, Ron, Michael and dozens more and everyone
who has come before, from Preston & Prabhakar on down, I can’t thank you
enough and I owe you almost everything.
Goodbye for now,
* For history buffs, these were the people at the initial Yahoo!
Research offsite: Prabhakar Raghavan, Dennis DeCoste, David Pennock,
Omid Madani, Shyam Kapur, Andrew Tomkins, Winton Davies, Ravi Kumar,
Bernard Mangold, Ron Brachman, Marc Davis, Michael Mahoney, Kevin Lang,
Seung-Taek Park, and Dan Fain.
** I also remember the first few days of Yahoo! Research New York in
2005, with just Ron, John, and I. It’s amazing to see what we have
*** An even more arcane note of history: the Overture control room made
a cameo as NASA Mission Control in James Cameron’s 2003 movie Ghosts of
the Abyss. I am on somewhere on the cutting room floor trying to muster
that awestruck look one gets upon seeing alien life for the first time.
——– Original Message ——–
Subject: one more thing
Date: Thu, 26 Apr 2012 11:20:01 -0400
From: David Pennock
I’ll abuse my final act of spam to add one more thing. For those of you
remaining, you’re in good hands with Ron. I believe he can do something
special with Labs. In case you’re not familiar with his background, Ron
is frighteningly smart (Princeton undergrad, Harvard Ph.D.), was a
pioneer in artificial intelligence, wrote a seminal book on Knowledge
Representation, served as President of AAAI, the main AI society, ran
research groups at Bell Labs & AT&T, and is a highly organized, fair,
diligent manager who listens actively, gets things done, and, in
addition is a genuinely nice person. Best of luck to everyone.
Next post: A dream job come true.]]>
In short, Kiva Systems designs, builds, and operates intelligent autonomous robots to pick and stow products in giant distribution centers for companies like Toys R Us, Walgreens, and Zappos. (The latter is an Amazon subsidiary.) The best way to understand Kiva Systems is to watch their robots in action: an amazing sight to see. Here is a clip from IEEE Spectrum:
In 2003, I remember sitting in the back seat of a car with Pete, him excitedly demo-ing the concept to me via an animated simulation on his laptop, little dots representing robots weaving in and out of each on the screen. (Pete’s laptop was a mac. In grad school, Pete was every bit the Apple fan I was and more. He and I programmed HyperCard and Newton together. Pete advocated for simplicity in design before it was cool. When I briefly switched to Windows, he never wavered.)
By 2006, the robots were real. Pete took me and our shared academic parent, Mike Wellman (who I believe also played an early role in the company), on a tour. Dots on a laptop had become squat orange robots receiving orders, fetching products, avoiding each other, seeking power, and otherwise navigating around a complex environment with computational minds of their own. The designs were inspired: for example, to lift a box, the robot spun underneath it to extend a corkscrew so that the product wouldn’t get jarred. They even added noise in the robots’ paths, so their wheels wouldn’t wear grooves in the floor (call it a floorsaver algorithm).
By coincidence, a few weeks ago, I was speaking to someone from Amazon who works on optimizing the way people (ha!) retrieve, store, and pack items in their distribution centers and I mentioned Pete’s company. He said “until that happens” he would focus on optimizing their current systems. Little did we (or at least I) know how quickly “until” would come.
Kiva Systems isn’t just an incredibly cool company run by amazing people. It’s a harbinger of things to come as the world moves inexorably toward an Automated Economy.
By the way, if you’re worried that robots will take jobs away from people, don’t. The world is a better place with mechanical devices doing mechanical tasks, leaving people to do more interesting and creative things, for example turning crazy ideas into companies. Remember that the purpose of jobs is to produce valuable things and improve the world. Despite political rhetoric, jobs are not an end to themselves. Otherwise, we should all be happy digging ditches and filling them back up, or pumping gas for people who would rather do it themselves. Think about where society should go in fifty or a hundred years when automation can handle more and more tasks. It would be a real shame if at that time people were still “working for a living” in jobs they don’t enjoy simply for the sake of keeping them occupied.]]>