Zachary C. Lipton – Approximately Correct

A Pedant’s Guide to MLHC 2017

By David Kale and Zachary Lipton

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.

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Death Note: Finally, an Anime about Deep Learning

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.

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Do I really have to cite an arXiv paper?

With peak submission season for machine learning conferences just behind us, many in our community have peer-review on the mind. One especially hot topic is the arXiv preprint service. Computer scientists often post papers to arXiv in advance of formal publication to share their ideas and hasten their impact.

Despite the arXiv’s popularity, many authors are peeved, pricked, piqued, and provoked by requests from reviewers that they cite papers which are only published on the arXiv preprint.

“Do I really have to cite arXiv papers?”, they whine.

“Come on, they’re not even published!,” they exclaim.

The conversation is especially testy owing to the increased use (read misuse) of the arXiv by naifs. The preprint, like the conferences proper is awash in low-quality papers submitted by band-wagoners. Now that the tooling for deep learning has become so strong, it’s especially easy to clone a repo, run it on a new dataset, molest a few hyper-parameters, and start writing up a draft.

Of particular worry is the practice of flag-planting. That’s when researchers anticipate that an area will get hot. To avoid getting scooped / to be the first scoopers, authors might hastily throw an unfinished work on the arXiv to stake their territory: we were the first to work on X. All that follow must cite us. In a sublimely cantankerous rant on Medium, NLP/ML researcher Yoav Goldberg blasted the rising use of the (mal)practice. Continue reading “Do I really have to cite an arXiv paper?”

The Futurist’s Dilemma

The following passage is a musing on the futility of futurism. While I present a perspective, I am not married to it.

When I sat down to write this post, I briefly forgot how to spell “dilemma”. Fortunately, Apple’s spell-check magnanimously corrected me. But it seems likely, if I were cast away on an island without any automatic spell-checkers or other people to subject my brain to the cold slap of reality, that my spelling would slowly deteriorate.

And just yesterday, I had a strong intuition about trajectories through weight-space taken by neural networks along an optimization path. For at least ten minutes, I was reasonably confident that a simple trick might substantially lower the number of updates (and thus the time) it takes to train a neural network.

But for the ability to test my idea against an unforgiving reality, I might have become convinced of its truth. I might have written a paper, entitled “NO Need to worry about long training times in neural networks” (see real-life inspiration for farcical clickbait title). Perhaps I might have founded SGD-Trick University, and schooled the next generation of big thinkers on how to optimize neural networks.

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Press Failure: The Guardian’s “Meet Erica”

Meet Erica, the world’s most human-like autonomous android. From its title alone, this documentary promises a sensational encounter. As the screen fades in from black, a marimba tinkles lightly in the background and a Japanese alleyway appears. Various narrators ask us:

“What does it mean to think?”

“What is human creativity?”

“What does it mean to have a personality?”

“What is an interaction?”

“What is a minimal definition of humans?”

The title, these questions, and nearly everything that follows mislead. This article is an installment in a series of posts addressing the various sources of misinformation feeding the present AI hype cycle.

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DeepMind Solves AGI, Summons Demon

In recent years, the rapid advance of artificial intelligence has evoked cries of alarm from billionaire entrepreneur Elon Musk and legendary physicist Stephen Hawking. Others, including the eccentric futurist Ray Kurzweil, have embraced the coming of true machine intelligence, suggesting that we might merge with the computers, gaining superintelligence and immortality in the process. As it turns out, we may not have to wait much longer.

This morning, a group of research scientists at Google DeepMind announced that they had inadvertently solved the riddle of artificial general intelligence (AGI). Their approach relies upon a beguilingly simple technique called symmetrically toroidal asynchronous bisecting convolutions. By the year’s end, Alphabet executives expect that these neural networks will exhibit fully autonomous self-improvement. What comes next may affect us all.

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Fake News Challenge – Revised and Revisited

The organizers of the The Fake News Challenge have subjected it to a significant overhaul. In this light, many of my criticisms of the challenge no longer apply.

Some context:

Last month, I posted a critical piece addressing the fake news challenge. Organized by Dean Pomerleau and Delip Rao, the challenge aspires to leverage advances in machine learning to combat the epidemic viral spread of misinformation that plagues social media. The original version of the the challenge asked teams to take a claim, such as “Hillary Clinton eats babies”, and output a prediction of its veracity together with supporting documentation (links culled from the internet). Presumably, their hope was that an on-the-fly artificially-intelligent fact checker could be integrated into social media services to stop people from unwittingly sharing fake news.

My response criticized the challenge as both ill-specified (fake-ness not defined), circular (how do we know the supporting documents are legit?) and infeasible (are teams supposed to comb the entire web?)

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