With paper submissions rocketing and the pool of experienced researchers stagnant, machine learning conferences, backs to the wall, have made the inevitable choice to inflate the ranks of peer reviewers, in the hopes that a fortified pool might handle the onslaught.
With nearly every professor and senior grad student already reviewing at capacity, conference organizers have gotten creative, finding reviewers in unlikely places. Reached for comment, ICLR’s program chairs declined to reveal their strategy for scouting out untapped reviewing talent, indicating that these trade secrets might be exploited by rivals NeurIPS and ICML. Fortunately, on condition of anonymity, several (less senior) ICLR officials agreed to discuss a few unusual sources they’ve tapped:
All of /r/machinelearning
Twitter users who follow @ylecun
Holders of registered .ai & .ml domains
Commenters from ML articles posted to Hacker News
YouTube commenters on Siraj Raval deep learning rap videos
Employees of entities registered as owners of .ai & .ml domains
Everyone camped within 4° of Andrej Karpathy at Burning Man
GitHub handles forking TensorFlow, Pytorch, or MXNet in last 6 mos.
A joint venture with Udacity to make reviewing for ICLR a course project for their Intro to Deep Learning class
Collectively, machine learning (ML) researchers are engaged in the creation and dissemination of knowledge about data-driven algorithms. In a given paper, researchers might aspire to any subset of the following goals, among others: to theoretically characterize what is learnable, to obtain understanding through empirically rigorous experiments, or to build a working system that has high predictive accuracy. While determining which knowledge warrants inquiry may be subjective, once the topic is fixed, papers are most valuable to the community when they act in service of the reader, creating foundational knowledge and communicating as clearly as possible.
What sort of papers best serve their readers? We can enumerate desirable characteristics: these papers should (i) provide intuition to aid the reader’s understanding, but clearly distinguish it from stronger conclusions supported by evidence; (ii) describe empirical investigations that consider and rule out alternative hypotheses ; (iii) make clear the relationship between theoretical analysis and intuitive or empirical claims ; and (iv) use language to empower the reader, choosing terminology to avoid misleading or unproven connotations, collisions with other definitions, or conflation with other related but distinct concepts .
Recent progress in machine learning comes despite frequent departures from these ideals. In this paper, we focus on the following four patterns that appear to us to be trending in ML scholarship:
Failure to distinguish between explanation and speculation.
Failure to identify the sources of empirical gains, e.g. emphasizing unnecessary modifications to neural architectures when gains actually stem from hyper-parameter tuning.
Mathiness: the use of mathematics that obfuscates or impresses rather than clarifies, e.g. by confusing technical and non-technical concepts.
Misuse of language, e.g. by choosing terms of art with colloquial connotations or by overloading established technical terms.
Last week, I flew from London to Tel Aviv. The man sitting to my right was a road warrior, just this side of a late-night bender in London. He was rocking an ostentatious pair of headphones and a pair of pants ripped wide apart at both knees. Perhaps a D.J.? At some point, circumstances emerged for us to commiserate over the experience of flying on Easyjet (not the easiest). Soon after, we stumbled through the obligatory airplane smalltalk: Where are you going? What do you do?
Turns out I was flying next to the CEO of an AI+Blockchain startup.
It’s always a bit surreal when I learn of entrepreneurs combining AI with blockchain technology. For the past few years, whenever I found my myself bored among Silicon Valley socialites, this was my go-to satirical startup. What do you do? Startup CEO. What does your startup do? Deep learning on the blockchain… in The Cloud. Whoa.Continue reading “The Blockchain Bubble will Pop, What Next?”
Before committing all future posts to the coming revolution, or abandoning the blog altogether to beseech good favor from our AI overlords atthe AI church, perhaps we should ask, why are today’s headlines, startups and even academic institutions suddenly all embracing the term artificial intelligence (AI)?
In this blog post, I hope to prod all stakeholders (researchers, entrepreneurs, venture capitalists, journalists, think-fluencers, and casual observers alike) to ask the following questions:
What substantive transformation does this switch in the nomenclature from machine learning (ML) to artificial intelligence (AI) signal?
If the research hasn’t categorically changed, then why are we rebranding it?
What are the dangers, to both scholarship and society, of mindlessly shifting the way we talk about research to maximize buzz?
Within hours, I received multiple emails. Parents, friends, old classmates, my girlfriend all sent emails. Did you see the article? Maybe they wanted me to know what riches a life in private industry had in store for me? Perhaps they were curious if I was already bathing in Cristal, shopping for yachts, or planning to purchase an atoll among the Maldives? Perhaps the communist sympathizers in my social circles had renewed admiration for my abstention from such extreme opulence.
In 2014, Szegedy et al. published an ICLR paper with a surprising discovery: modern deep neural networks trained for image classification exhibit the following vulnerability: by making only slight alterations to an input image, it’s possible to drastically fool a model that would otherwise classify the image correctly (say, as a dog), into outputting a completely wrong label (say, as a banana). Moreover, this attack is possible even with perturbations that are so tiny that a human couldn’t distinguish the altered image from the original.
These doctored images are called adversarial examples and the study of how to make neural networks robust to these attacks is an increasingly active area of machine learning research.
It’s January 28th and I should be working on my paper submissions. So should you! But why write when we can meta-write? ICML deadlines loom only twelve days away. And KDD follows shortly after. The schedule hardly lets up there, with ACL, COLT, ECML, UAI, and NIPS all approaching before the summer break. Thousands of papers will be submitted to each.
The tremendous surge of interest in machine learning along with ML’s democratization due to open source software, YouTube coursework, and the availability of preprint articles are all exciting happenings. But every rose has a thorn. Of the thousands of papers that hit the arXiv in the coming month, many will be unreadable. Poor writing will damn some to rejection while others will fail to reach their potential impact. Even among accepted and influential papers, careless writing will sow confusion and damn some papers to later criticism for sloppy scholarship (you better hope Ali Rahimi and Ben Recht don’t win another test of time award!).
But wait, there’s hope! Your technical writing doesn’t have to stink. Over the course of my academic career, I’ve formed strong opinions about how to write a paper (as with all opinions, you may disagree). While one-liners can be trite, I learned early in my PhD from Charles Elkan that many important heuristics for scientific paper writing can be summed up in snappy maxims. These days, as I work with younger students, teaching them how to write clear scientific prose, I find myself repeating these one-liners, and occasionally inventing new ones.
The following list consists of easy-to-memorize dictates, each with a short explanation. Some address language, some address positioning, and others address aesthetics. Most are just heuristics so take each with a grain of salt, especially when they come into conflict. But if you’re going to violate one of them, have a good reason. This can be a living document, if you have some gems, please leave a comment.
[This article is also cross-posted to the Deep Safety blog.]
Something I oftenhear in the machine learning community and media articles is “Worries about superintelligence are a distraction from the *real* problem X that we are facing today with AI” (where X = algorithmic bias, technological unemployment, interpretability, data privacy, etc). This competitive attitude gives the impression that immediate and longer-term safety concerns are in conflict. But is there actually a tradeoff between them?
We can make this question more specific: what resources might these two types of issues be competing for?
[This article originally appeared on the Deep Safety blog.]
This year’s NIPS gave me a general sense that near-term AI safety is now mainstream and long-term safety is slowly going mainstream. On the near-term side, I particularly enjoyed Kate Crawford’s keynote on neglected problems in AI fairness, the ML security workshops, and the Interpretable ML symposium debate that addressed the “do we even need interpretability?” question in a somewhat sloppy but entertaining way. There was a lot of great content on the long-term side, including several oral / spotlight presentations and the Aligned AI workshop.
On the BBC’s anthology series Black Mirror, each episode explores a near-future dystopia. In each episode, a small extrapolation from current technological trends leads us into a terrifying future. The series should conjure modern-day Cassandras like Cathy O’Neil, who has made a second career out of exhorting caution against algorithmic decision-making run amok. In particular, she warns that algorithmic decision-making systems, if implemented carelessly, might increase inequality, twist incentives, and perpetrate undesirable feedback loops. For example, a predictive policing system might direct aggressive policing in poor neighborhoods, drive up arrests, depress employment, orphan children, and lead, ultimately, to more crime.