# Upcoming AISTATS paper

In about a week, our paper Unifying Local Consistency and MAX SAT Relaxations for Scalable Inference with Rounding Guarantees by Steve, me, and Lise will appear at AISTATS in San Diego. (Steve will be giving a talk on it Monday morning.)

The paper title is a mouthful, and it includes a pretty technical result, so here’s a weak attempt at explaining the intuition and take-away messages.

The paper is about MAP inference, or finding the most likely state in a probability distribution. Specifically, it’s about MAP inference in what we are calling logical Markov random fields (MRFs), which are MRFs whose potentials are defined by weighted logical clauses of a particular form. We show equivalences between two different approaches (and a third bonus one) for approximating MAP inference in these logical MRFs. These equivalences are theoretically interesting, but at least as importantly, they allow us to get the benefits of each approach, leading to fast MAP approximations that have constant-factor approximation guarantees.

What’s special about logical MRFs is that MAP inference is equivalent to finding the maximum weighted satisfying assignment to the logical variables, aka the MAX SAT problem. On the surface, this equivalence doesn’t seem exciting, because it equates one NP-hard problem to another. What makes it exciting is that there are some nice approximation algorithms for MAX SAT with quality guarantees, and these algorithms can therefore be applied to get quality guarantees for MAP. Unfortunately, the approximation method by the famous computer science duo, Michael Goemans and David Williamson, requires solving a linear program (LP) that scales not-so-well in practice, when using off-the-shelf linear programming algorithms.

Another approach for approximating MAP inference borrows from recent developments on doing fast inference in MRFs using local-consistency relaxations. At a very high level, these approaches relax the space of possible marginal probabilities (i.e., the marginal polytope) to a simpler space that only requires local consistency between marginal probabilities of variables and factors (i.e., the local marginal polytope). By solving the corresponding optimization over this simpler, relaxed set of constraints, many very fast message-passing algorithms have been discovered in the past few years. So it’s natural to try to use one of these local-consistency relaxation message-passing algorithms to do MAP inference in a logical MRF.

The main result we show in the paper is that these two seemingly different approaches are equivalent. This equivalence means that when we use these fast-in-practice local-consistency relaxation algorithms that pass messages to quickly find an approximate solution, we’re also able to get the solution to the linear-program subproblem of the MAX SAT approximation algorithm. Using the LP solution, we can perform a special rounding scheme that guarantees a constant-factor approximation.

The last bonus equivalence is that both of these approaches are also equivalent to the linear form of a hinge-loss Markov random field, which we’ve been studying over the past few years as a powerful class of probabilistic models with efficiently optimizable energy functions. The conversion from logic to hinge-loss MRFs (i.e., the principles behind probabilistic soft logic) had previously been motivated by connections to fuzzy logic, and now we have these other relationships to MAX SAT relaxation and local-consistency relaxation.

Behind the scenes, this last bonus piece is how we happened to find these equivalences. We initially were working with these hinge-loss MRFs and we had thought that these other approaches to doing inference in logically-defined MRFs seemed so different, that it’d be interesting to compare them. So we ran a few tests and discovered they were behaving similarly… very similarly; they were returning solutions with differences that were small enough to be numerical errors. This surprising behavior led to confusion, derivations, and finally understanding.

Finally, the super weird part: any (nondeterministic factored) discrete MRF can be converted to an equivalent logical MRF. This, in some sense, means the constant-factor quality guarantees that come from the equivalence between logical MRF inference and MAX SAT, also apply to any discrete MRF. But along the way of converting a discrete MRF to the restricted logical MRF form, the strength of this constant-factor guarantee must be weakened, especially since we know that MAP can’t be approximated to a constant factor unless P = NP. But it’s still an open question how this all fits together. Check out the paper, and maybe we can brainstorm about this in San Diego.

# My Spring Conference Reviewing Data

Last night, I finally finished a marathon month+ of reviewing for machine learning and machine-learning-adjacent conferences. Because of my own poor calendar organization, I foolishly agreed to join the program committees for IJCAI 2105 (Machine Learning Track), KDD 2015, ICML 2015, and UAI 2015. These conferences all had reviewing periods during the month of March and this first bit of April.

My paper assignments for these conferences were six for IJCAI, five for KDD, six for ICML, and five for UAI. While I was reviewing these 22 papers, I was recording my initial overall recommendation (prior to discussion and author response) for each of these papers, just to measure how I tend to score papers. I figured I’d post some of these recordings here, with the major caveat that these are still tiny sample sizes and they are heavily biased by what papers and topics I like to bid on. I’m also going to convert all scores to a scale of [strong reject, weak reject, weak accept, strong accept] to both simplify and muddy up my data a bit to prevent any chance of some smartypants somehow de-anonymizing based on my silly blog post.

• For IJCAI, my recommendations for my six papers were one reject, one weak reject, three weak accepts, and one strong accept.
• For KDD, my recommendations for my five papers were three rejects, one weak reject, and one strong accept.
• For ICML, my recommendations for my six papers were two weak rejects, three weak accepts, and one strong accept.
• For UAI, my recommendations for my five papers were two rejects, two weak rejects, and one weak accept.

Overall, I recommended four rejects, six weak rejects, eight weak accepts, and three strong accepts. I gave zero strong reject recommendations. If my initial vote was the only one that counted, my accept rate for each conference is 66% for IJCAI, 20% for KDD, 66% for ICML, and 20% for UAI. Overall, my acceptance rate was a rather high 45%.

So what is the takeaway message? I’m not sure. I guess this still isn’t enough data to really tell anything. Let me attempt to make some claims.

• The numbers suggest that I like ICML and IJCAI papers better than UAI and KDD papers. I would be pretty surprised if this is true and not just a result of randomness. It’s hard to tell with the IJCAI ML track being a brand new idea. I usually imagine myself as liking UAI papers the most of all the medium-sized ML conferences.
• The numbers suggest that I like ICML papers about graphical models, structured prediction, and relational learning. Since these are the topic areas I usually bid on and that Toronto Paper Matching usually assigns to me. This is plausible, but not consistent with my low accept rate for UAI.
• By a similar argument, the numbers suggest that I don’t like KDD papers on graph mining and relational models. This is also plausible, but surprising. I think in this case, I really like the problem area of data mining from complex network data, but maybe I’m often unsatisfied by the methods people propose. It’s possible I’m too critical of this kind of work.

Sorry these are all pretty weak analyses. The sample size is just too small. If I want to understand my own biases better, I need to volunteer to review even more (note to self: do not do this), or keep better records from previous years of reviewing.

Only one thing is absolutely clear from this month of reading all these submissions: seriously everyone needs to stop using the word “employ.”

This is just an announcement regarding a FAQ primarily for the Virginia Tech community. I’m scheduled to teach CS5824/ECE5424 Advanced Machine Learning. This course is named this way because it’ll be taught in conjunction with CS4824/ECE4424 for undergrads, which is called “Machine Learning.” So the “Advanced” modifier indicates that you’ll be graded at an advanced, graduate level, but the course in both cases will be intended for students who want an introduction to machine learning. In other words, it’ll be my version of this course, currently being taught by Dhruv.

Sorry for the confusion! The university needed these courses to have different names.

Dhruv will be teaching a seminar course on deep learning in the fall (not yet in the course catalog), if you’re looking for a course to continue learning after the intro, and I’m working out details on teaching a graphical models seminar-level course in Spring 2016. So there will be plenty of chances for more advanced study in the next academic year, but “Advanced Machine Learning” is “Introduction to Machine Learning for Graduate Students.”

# The Plurality of Data

Plural Data (I know that’s not Data on the right… or is it?)

Let’s set this straight. The English word “data” should almost always be used as a mass noun, which means it shouldn’t be treated as a plural noun. Lots of people make the mistake of using it as a plural noun because they think it sounds fancier its original Latin form was a plural of “datum.” But the way we use it in modern English, especially in computer science, makes the concept of a “datum” make no sense.

Data pluralists think of data sets as many “datums,” but what is a datum? An example? A measurement? A dimension of a measurement? A bit in the binary representation of a dimension of a measurement in a IEEE floating point representation?

It is none of these. Data, especially high-dimensional and complex data, does not consist of countable singulars in any way that actually corresponds to what we mean when we talk about it, so it should not be a plural. Instead, it should be treated synonymously to “information.” We analyze “this information” or “this data,” not “these information” or “these data.”

I’m pretty darn certain of this reasoning. But is there any example where another word is (justifiably) plural when its singulars are uncountable or undefined? Or is there any scenario in modern usage where datums can be reasonably defined? If there are, then those that data might change my mind.

# Contraction-based convergence proofs

Every now and then, teaching gives us some really valuable experiences. I’m teaching graduate level AI, and I had a really great experience learning something I had never paid much attention to before. Specifically, I read a section of Russell and Norvig’s book that deals with the convergence of Bellman’s utility updates for Markov decision processes.

When it comes to convergence proofs, the kind I’ve seen usually reason about the high-dimensional curvature of some objective function, or they do nasty things like analyzing the unrolled recursion tree. Yuck! For this reason, when I saw a section on a convergence proof, my immediate instinct was to run away and skip it for my and my students’ sakes.

But the convergence of Bellman’s updates can be proven using the concept of contraction, which says that an operator $f$ on two inputs $x$ and $x'$ makes them “closer,” according to some measure of distance $d$.

$d(x, x') < d(f(x), f(x'))$

This property means that repeating the operator, e.g., $f(f(f(f(x))))$ and $f(f(f(f(x'))))$, makes any two different initializations $x$ and $x'$ get closer and closer, and if the contraction is strong enough, makes them converge to the same value, giving a proof of convergence and uniqueness for the update \$\latex f\$. Beautiful, no?

The work comes in proving the contraction bound. There’s freedom in choosing the distance metric. The stronger the contraction, the faster the convergence. The Bellman updates contract by a constant factor, which I imagine is about as fast a convergence as you can get. And I imagine there can be some traps when things contract, but the amount of contraction becomes infinitesimal; in that case, this argument doesn’t actually show convergence.

I’m now curious what other convergent algorithms can be proven to converge using a contraction argument. Or more importantly, whether I can reach into my bag of algorithms-that-I-can’t-yet-prove-convergence-of and find one that I can finally solve using this approach.

I’m also curious if some type of contraction is a necessary condition for a convergent operator with a unique fixed point. I’m sure I could find that by doing some reading. But first, I have to put out a million tiny fires (this is how I describe my life now as a professor).

# A quick Keynote tip

A few months ago, I discovered that Keynote has connection lines, which dynamically connect objects that you draw. I used to draw graphs and diagrams in Omnigraffle for this reason, but with native Keynote connection lines, you can animate them directly, etc. The ability to animate graphs is super important, and I’m a huge proponent in using animation in talks (in the right places) to help audiences visualize some of the absurdly complex things we present.

But it still wasn’t perfect, because Keynote doesn’t have built-in keyboard shortcuts to insert connection lines, so you have to select the objects you want, go to the pulldown menu, and select the command. This process is not good for avoiding repetitive-stress injury.

Well, I recently discovered that Mac OS lets you define keyboard shortcuts for any application. Maybe this is common knowledge, but in System Preferences, the Keyboard module lets you add App Shortcuts.

I set mine to cmd-L for a “Straight Connection Line” and cmd-option-L for a “Curved Connection Line.” And now I draw graphs natively in Keynote.

Unfortunately, Magic Move doesn’t work great with connection lines when they have to change orientation. They disappear as the nodes move and reappear once the nodes settle into their location on the next slide. That would be a really cool and useful animation to help visualize graphs. Oh well. Can’t win ’em all.

# On the NIPS Experiment and Review Process

A lot of attention has been focused on the NIPS experiment on its own reviewing system, Eric Price’s blog post on it, and unfortunately my flippant tweet pointing out some inaccuracies in the blog post. So I’ll try to clarify what I meant here.

Edit: folks looking for more info on the NIPS experiment should check out Neil Lawrence’s initial blog post on it and stay tuned, as he and Corinna Cortes will be eventually releasing a more thorough writeup on the results.

Eric Price’s Post

In the spirit of Eric’s post, here’s a TL;DR: the minor inaccuracies in Eric’s post should not detract from the main message that the results from the NIPS experiment are concerning, but we should be careful to get the details right before trying to explain the scary results with faulty information.

The three details that popped out at me as inaccurate were the idea that the program committee was split into two independent committees, that the committee only discussed papers with an average score between 6.0 and 6.5, and that area chairs could not see if a paper was a duplicate. In Eric’s defense, a lot of these details and more were clarified in the comments and discussion below his post, so what I’m writing here is somewhat redundant (e.g., he points out in the comments that NIPS does not have a fixed acceptance rate).

On the first point. I’m not completely sure how the program chairs implemented the duplication. But I don’t think the concept of splitting the program committee in half is correct or makes sense the way NIPS reviewing is organized. Most of the area chairing, fostering discussion, quality control of reviews, etc., is done independently by each area chair, so there no real concept of split independent committees. I’m not even sure what that would mean. The committee is in some sense already split 92 ways, for each area chair, but reviewers may be assigned papers with different chairs, so it’s not really independent. The way Eric’s post describes it invokes an image of two huge conference rooms of area chairs talking about the papers, which is a model that doesn’t scale to the absurd size of the NIPS conference nowadays.

As for the issue of which papers were discussed, I believe this was simply a recantation of a half-joking description of what the review process was, but the tongue-in-cheek tone is lost in writing. First, the reviewers discussed any paper that wasn’t immediately an obvious, high-confidence consensus. Then the area chairs examined all these reviews, papers, discussions, joining in the discussion in most cases. Then the area chairs met in pairs to go over all their papers together (except conflicted papers), making pair judgement calls on each and identifying controversial or tricky decisions that needed to be discussed at the larger meetings. After that, the chairs met in groups of four with one of the program chairs to go over these tricky decisions. Nowhere in this process does the average score even come up other than as a very rough way for area chairs to sort the papers, all of which they have to individually consider. But we also had much better heuristics to sort by, like controversiality, spread of scores, and low confidence scores. I’ll write more about my own personal experience with this process later.

Lastly, area chairs, who had access to author identities (because we are partially responsible for preventing conflicts of interest), could in fact see if a paper was a duplicate. To make the experiment work on CMT, the program chairs had to create a fake additional author, whose name was something like NIPS Dup1. So it was pretty obvious. It’s not clear how this affected the experiment. Different area chairs might have reacted differently to it. I did my best to ignore this bit of information, to preserve the experiment, but there’s no way that my awareness of this didn’t affect my behavior. Did I give these papers more attention because I knew my area chairing would be compared to someone else’s? Or did I give them less attention because I wanted to focus on my actual assigned papers? I wanted to do neither, but who knows how successful I was. This fact surely contaminates the experimental results and any conclusions we might be able to definitely make about it.

I think the reason I was irked by these nitpicky details was partially because the discussion in the comments seemed to suggest that they were the problem with NIPS and the reason for the inconsistency. But I was hasty to criticize on Twitter, because Eric’s post really brings up a few important points and interesting discussion and hypotheses. It is truly great that people are talking about it, and lots of non-machine-learning communities have been made aware of the NIPS experiment through Eric’s post. I’d like some more caution about overgeneralizing from the experimental results, like Eric’s TL;DR does, but I suppose that’s inevitable, so we might as well get right to it with the first public writeup on the results. Hopefully people read on past that, since the rest of his analysis is pretty level-headed and thoughtful.

My own experience as area chair

Since this was my first year on the senior program committee, I got a new perspective on the NIPS review process that might be helpful to share to others. It wasn’t a huge difference from what I’d seen as a reviewer in the past, but since I was responsible for chairing 21 papers, I got a somewhat larger sample size. But it was still only 21 papers, which is much less than the number of papers that the duplication experiment used, and my sample is super biased toward my subject areas. So take this with a HUGE grain of salt. It’s just an anecdote, not a scientific study.

My initial experience with the process was disappointing. I watched the reviewers enter their reviews, and more often than not, these reported opinions that were unsubstantiated, unsupported in the review writeups, late, and unproofread. As a rough guess of the ratio here, each paper had three reviewers initially, and I would say about 3 out of 4 papers had one thoughtful review. After that, the reviewers with crappy reviews became very hard to reach. They didn’t participate in the discussion without a ton of prodding, they didn’t respond to the author rebuttals, and when they did respond, they surprisingly tended to stick to their original, seemingly unsubstantiated opinions.

So that was terrible. The good news is all that happened afterwards. Like I mentioned above, we area chairs met in pairs. (Even though I have only good things to say about the area chairs I met with, I’ll leave names off to preserve the anonymity of the review process.) My AC partner met with me on Skype for (I think) about two hours, maybe three, and we talked through each of our papers. The easy decisions, where all reviewers agreed, reported high confidence, and demonstrated high confidence through their words, were very quick discussions, but most of our time was spent debating about the more difficult decisions. Throughout this discussion, it was clear to me how thoughtfully my partner had considered the reviews and the papers, understanding when reviewers were making valid arguments and when they were being unfair. In many ways, I was reassured that another area chair was trying as hard as I was to find the signal in the noise.

The next stage was a meeting with four area chairs and a program chair. Again, we met on Skype, and this time went through a filtered list of the papers our respective AC pairs had decided needed further discussion. This list mostly included papers on the borderline of acceptance or ones where the reviewers were unwilling to agree on a decision. Reading the reviews and looking at the papers, we did our best to make a decision during the meeting, and again, anytime we couldn’t reach a decision, it was marked for further discussion and consideration as a borderline paper by the next level of the hierarchy: the program chairs.

After that point, I’m in the dark as to what the PCs’ decision process was. I know at some point they had to cut off acceptances for reasons of physical venue space, but it’s my understanding that the acceptance rate before that point is already pretty close to the usual 25%-ish rate.

So what now?

So my experience with the whole process could be summarized by saying that I saw some really disappointing reviewers, but was rescued from losing faith in NIPS by the thoughtfulness of the area chairs I worked with and the program chairs. But even within the sea of bad reviews, there were some standout individuals who anchored (mixed maritime metaphor…) these discussions and decisions in reason, fairness, and perspective. So I think as NIPS continues expanding to accommodate the growing interest in its topics, we’ll have to figure out how to address the growing proportion of bad reviewers that we’ll need to recruit to handle its scale. Maybe the answer is better reviewer training, or maybe the answer is more work for the more competent reviewers, or maybe there is no answer.

One important aspect to consider is, when we talk about peer review being broken, unscalable, or any other complaint, that the primary purpose of these huge processes is not to assign credit for peoples’ work, it’s to decide what content to present in a conference, and despite all the noise, and all the flaws in the system, the quality of the conferences I’ve been attending has always been consistently high. More specifically, to not generalize beyond what I’m discussing in this post, NIPS 2014 was a great conference. Of course, in the real world, assigning credit is a super-important part of this whole deal. (Luckily, the process for journals and for funding decisions tends to happen at a smaller scale, so my guess is it’s less noisy and less crappy, but that certainly isn’t perfect either.) So something does have to be fixed, but it’s not as broken as it may feel at times.

(There are people experimenting with new models of publication to try to address these issues, e.g., see the talks from the ICML 13 Workshop on Peer Review, and the International Conference on Learning Representations (ICLR))

Lastly, I’ll conclude with two questions I’ve been asking myself and my colleagues lately. Of all the peer review experiences you’ve had as a reviewer, a chair, or an author, how often do all the reviewers understand the paper and make a valid decision based on this understanding? For me, the answer is nearly zero percent of the time. Reviewers almost never understand the papers I submit, whether they accept or reject, and when I’m a reviewer or chair, reviewers almost always have different interpretations of what’s going on in the paper, which means they can’t all be correct. So peer review is broken, right? Maybe, but as a second question, how often is the final decision the right decision? For me, the answer is pretty close to always. E.g., the papers I’ve had rejected for dumb reasons have always needed a lot of improvement in retrospect, or maybe the papers reviewers don’t get aren’t written or thought through well enough. Maybe despite reviewers not knowing what they’re reading, their B.S. detectors still work fine. But we shouldn’t just settle for that if it’s true. I dunno. Lots to think about…

# Our NIPS 2014 Workshop papers

Two of our papers appeared at the NIPS 2014 workshops this past week.

• On the Collective Stability of Variational Inference,” by Ben London, me, and Lise Getoor, shows some of our theoretical analyses on bounding the curvature of variational entropy surrogates. Since a lot of inference methods get around the computational difficulty of computing and optimizing the entropy of graphical models, people often replace the entropy term with a convex surrogate that’s efficient to work with. In the paper, we specifically analyze two of these: tree reweighting, and region-based counting-number adjustment. We provide new, tighter bounds on how strongly convex tree entropies can be, and provide new conditions for the counting numbers of regions that guarantee strongly convex entropies (whereas the previous conditions only guaranteed strict convexity). In both cases, we identify conditions under which the modulus of strong convexity does not grow as the number of variables grows, which our previous theory suggests is necessary for generalization.
• Rounding Guarantees for Message-Passing MAP Inference with Logical Dependencies,” by Stephen H. Bach, me, and Lise Getoor, recounts our recent discovery that the local LP relaxation that is extremely popular for fast inference in discrete MRFs is actually equivalent to an old LP-based approximation algorithm for weighted MAX SAT, for a certain class of graphical models. This equivalence implies that the local LP relaxations, which our community has developed some blazing-fast algorithms to solve, automatically inherits the 3/4 approximation guarantees associated with the old MAX SAT method. And even better, the equivalence class also happens to include a huge subset of hinge-loss MRFs, which we’ve been looking at over the past few years.

Thanks to everyone who came to talk to us during the workshops! I was bouncing back and forth between the two workshops during the poster times, but the conversations I had were super interesting and helpful.

# Data Science Research for Social Good

In a few weeks, there will be a workshop at KDD on data science for social good, run by Rayid Ghani and folks. Rayid has also been heading the Data Science for Social Good fellowship program the past couple years.

I really love the whole mission behind the workshop and fellowship (and Stephen H. Bach, Lise Getoor, and I have submitted a short paper to the workshop on how probabilistic soft logic is particularly useful for problems that have social benefit). But I wanted to point out a distinction that I think will help bring in more of the best minds in machine learning research to applications with potential to do social good.

My view of the mission behind the existing effort to encourage social good applications of data science is that it is driven by the question, “What can state-of-the-art data science do to benefit society?”

This is an unarguably important question, and answers to this question should improve the lives of many immediately. The projects that are being tackled by the Data Science for Social Good Fellows should have measurable impact on the well-being of people now, in 2014. This type of immediate impact is crucially missing from most research, making it rather attractive to spend research effort on these problems.

On the other hand, what can easily be lost in the excitement over immediate tangible impact is the advancement of scientific knowledge, in algorithms or theory. When working on applied machine learning research, one typically explores what known, existing methods can be applied to novel problems.

Because of this tendency, I suggest emphasizing another important question, which exists in all novel applications to some degree, but is often paid less attention. The question is, “What can’t state-of-the-art data science do to benefit society?” I.e., what are we missing, that we need new algorithms, new models, or new theory to support?

We have developed a huge toolbox of machine learning over the past few decades, so the first question is a completely valid one to focus solely on, but it happens so often that the form of data relevant to new problems is intrinsically different from the assumed form of known tools. This discrepancy means that we often need new machine learning research to do data analysis, whether that is truly new, undiscovered research, or simply new research that has only recently been discovered.

For example, human data is inherently social and thus contains statistical dependencies that make it an ill-fit for traditional statistical tools that rely on independent samples. While machine learning research has developed methods to handle heavily dependent and structured data over the past decade, these methods are often overlooked in practice. The technology transfer has not been fully or successfully executed between researchers and practitioners.

Part of the reason for all the excitement over data science is that everyone is noticing the gap created as knowledge of machine learning and analytic tools is outpacing their application in the real world. Most of the energy now is in filling that gap by finding new applications, and this direction is completely justified. We have not filled this gap yet as a whole, but as we start using machine learning on new applications, we will start inverting parts of this gap by identifying situations where known machine learning tools are insufficient for the problems we want to solve. These situations are where a lot of important scientific research can happen, and I can think of few better settings where I want this to happen than the context of social good.

# Research in “the cloud” and the honeymoon phase

A few days ago, I watched a TED talk by physicist Uri Alon on the emotional experience of scientific research. It is a fun talk, and I thought the message is important.

See the video here.

Uri argues that science is presented in a manner that poorly sets up expectations for scientists: the presented story is that we scientists brilliantly devise a hypothesis, run some experiments, and verify this hypothesis. Then we hit print and science is done. Instead, Uri sheds light on the actual process, which involves a taxing trek through what he calls “the cloud.” The real story is that a scientist has a hypothesis, tests it, finds that it is wrong, and then enters this cloud, which is a place of uncertainty, despair, and confusion. The scientist has to challenge his or her basic assumptions, and only by doing so, will be able to produce a new hypothesis that truly advances human knowledge.

Though Uri is a physicist, I think this message is just as relevant for computer science. I can’t speak for all fields within computer science, but at least in machine learning research, this is almost always the way progress occurs.

One problem of applying Uri’s message to computer science is the nomenclature. The cloud (or is it the Cloud?) is a buzzword that is already somewhat defined (but not very well). So using it in this context could lead to confusion (which is a bit too meta to be useful). We need another term for the computer science version of the cloud. Or we could just use it and stop using “the cloud” to describe the Internet, because we already have a word for that.

I would add to the process Uri describes another important concept to prepare researchers for the emotional roller coaster of science: the honeymoon phase.

The honeymoon phase is what I call the period when I’ve come up with an idea, perhaps it’s an algorithm, a theorem, or an application, that I think will work. As I start incrementally testing the idea–coding up the algorithm, for example–it starts to seem more and more correct. A subroutine of the algorithm works exactly as it should on paper! The learning algorithm correctly learns from a synthetic data set! If we assume a lemma, the proof actually works out! These small victories invoke a sense of euphoria and often come with daydreams of how easily this new research will lead to publication and recognition.

In reality, the honeymoon phase is almost always followed by a discovery that something is wrong, which leads to a sharp turn directly into the cloud. This contrast from the highs of the honeymoon phase to the lows of the cloud is jarring.

Like the message from the TED talk, I believe acknowledging that this sharp transition is part of a shared experience could help limit the emotional distress cause by the process of research. I’m not sure if there is any particular strategy for intelligently handling the highs of the honeymoon phase better, and I’m hesitant to suggest to anyone not to enjoy it while it’s happening.

Next time on Terrible Emotions in Science: Rejection…