[This article is also cross-posted to the Deep Safety blog.]
Something I often hear 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?
Continue reading “What are the tradeoffs between immediate and longer term AI safety efforts?”
[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.
Continue reading “Cathy O’Neil Sleepwalks into Punditry”
Consider a little science experiment we’ve all done, to find out if a switch controls a light. How many data points does it usually take to convince you? Not many! Even if you didn’t do a randomized trial yourself, and observed somebody else manipulating the switch you’d figure it out pretty quickly. This type of science is easy!
One thing that makes this easy is that you already know the right level of abstraction for the problem: what a switch is, and what a bulb is. You also have some prior knowledge, e.g. that switches typically have two states, and that it often controls things like lights. What if the data you had was actually a million variables, representing the state of every atom in the switch, or in the room?
Continue reading “Macro-causality and social science”
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.
Continue reading “ICML 2018 Registrations Sell Out Before Submission Deadline”
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
Continue reading “Embracing the Diffusion of AI Research in Yerevan, Armenia”
EMNLP – the conference on Empirical Methods for Natural Language Processing – was held this year in Copenhagen, the capital of the small state of Denmark. Nevertheless, this year’s conference had the largest attendance in EMNLP’s history.
The surge in attendance should not be too surprising, as it follows similarly frothy demand for other academic machine learning conferences, such as NIPS (which recently sold out before workshop authors could even submit their papers).
The EMNLP conference focuses on data-driven approaches to NLP, which really describes all work in NLP, so I suppose we can call it a venue for “very data-driven NLP”. It’s a popular conference, and the premier conference of ACL’s SIGDAT (ACL’s special interest group for linguistic data and corpus-based approaches to NLP).
This event went off without a hitch, with plenty of eating and socializing space in the vicinity. For 1200 people. Must’ve been a lot of hard work. Continue reading “A Random Walk Through EMNLP 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.
Continue reading “A Pedant’s Guide to MLHC 2017”
[This article originally appeared on the Deep Safety blog.]
Long-term AI safety is an inherently speculative research area, aiming to ensure safety of advanced future systems despite uncertainty about their design or algorithms or objectives. It thus seems particularly important to have different research teams tackle the problems from different perspectives and under different assumptions. While some fraction of the research might not end up being useful, a portfolio approach makes it more likely that at least some of us will be right.
In this post, I look at some dimensions along which assumptions differ, and identify some underexplored reasonable assumptions that might be relevant for prioritizing safety research. In the interest of making this breakdown as comprehensive and useful as possible, please let me know if I got something wrong or missed anything important.
Continue reading “Portfolio Approach to AI Safety Research”
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.
Continue reading “Death Note: Finally, an Anime about Deep Learning”