Technical and Social Perspectives on Machine Learning
Author: Zachary C. Lipton
Zachary Chase Lipton is an assistant professor at Carnegie Mellon University. He is interested in both core machine learning methodology and applications to healthcare and dialogue systems. He is also a visiting scientist at Amazon AI, and has worked with Amazon Core Machine Learning, Microsoft Research Redmond, & Microsoft Research Bangalore.
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.
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.
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.
In a shocking tweet, organizers of the 35th International Conference on Machine Learning (ICML 2018) announced today, through an official Twitter account, that this year’s conference has sold out. The announcement came as a surprise owing to the timing. Slated to occur in July, 2018, the conference has historically been attended by professors and graduate student authors, who attend primarily to present their research to audience of peers. With the submission deadline set for February 9th and registrations already closed, it remains unclear if and how authors of accepted papers might attend.
In July of this year, NYU Professor of Psychology Gary Marcus argued in the New York Times that AI is stuck, failing to progress towards a more general, human-like intelligence. To liberate AI from it’s current stuckness, he proposed a big science initiative. Covetously referencing the thousands of bodies (employed at) and billions of dollars (lavished on) CERN, he wondered whether we ought to launch a concerted international AI mission.
Perhaps owing to my New York upbringing, I admire Gary’s contrarian instincts. With the press pouring forth a fine slurry of real and imagined progress in machine learning, celebrating any story about AI as a major breakthrough, it’s hard to state the value of a relentless critical voice reminding the community of our remaining shortcomings.
But despite the seductive flash of big science and Gary’s irresistible chutzpah, I don’t buy this particular recommendation. Billion-dollar price tags and frightening head counts are bugs, not features. Big science requires getting those thousands of heads to agree about what questions are worth asking. A useful heuristic that applies here:
The larger an organization, the simpler its elevator pitch needs to be.
Machine learning research doesn’t yet have an agreed-upon elevator pitch. And trying to coerce one prematurely seems like a waste of resources. Dissent and diversity of viewpoints are valuable. Big science mandates overbearing bureaucracy and some amount of groupthink, and sometimes that’s necessary. If, as in physics, an entire field already agrees about what experiments come next and these happen to be thousand-man jobs costing billions of dollars, then so be it
Starting Friday, August 18th and lasting two days, Northeastern University in Boston hosted the eighth annual Machine Learning for Healthcare (MLHC) conference. This year marked MLHC’s second year as a publishing conference with an archival proceedings in the Journal of Machine Learning Research (JMLR). Incidentally, the transition to formal publishing venue in 2016 coincided with the name change to MLHC from Meaningful Use of Complex Medical Data, denoted by the memorable acronym MUCMD (pronounced MUCK-MED).
From its beginnings at Children’s Hospital Los Angeles as a non-archival symposium, the meeting set out to address the following problem:
Machine learning, even then, was seen as a powerful tool that can confer insights and improve processes in domains with well-defined problems and large quantities of interesting data.
In the course of treating patients, hospitals produce massive streams of data, including vital signs, lab tests, medication orders, radiologic imaging, and clinical notes, and record many health outcomes of interest, e.g., diagnoses. Moreover, numerous tasks in clinical care present as well-posed machine learning problems.
However, despite the clear opportunities, there was surprisingly little collaboration between machine learning experts and clinicians. Few papers at elite machine learning conferences addressed problems in clinical health and few machine learning papers were submitted to the elite medical journals.
It’s about time someone developed an anime series about deep learning. In the last several years, I’ve paid close attention to deep learning. And while I’m far from an expert on anime, I’ve watched a nonzero number of anime cartoons. And yet through neither route did I encounter even one single anime about deep learning.
There were some close calls. Ghost in the Shell gives a vague pretense of addressing AI. But the character might as well be a body-jumping alien. Nothing in this story speaks to the reality of machine learning research.
In Knights of Sidonia, if you can muster the superhuman endurance required to follow the series past its only interesting season, you’ll eventually find out that the flying space-ship made out of remnants of Earth on which Tanikaze and friends photosynthesize, while taking breaks from fighting space monsters, while wearing space-faring versions of mecha suits … [breath] contains an artificially intelligent brain-emulating parasitic nematode. But no serious consideration of ML appears.
If you were looking to anime for a critical discourse on artificial intelligence, until recently you’d be disappointed.