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”
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”
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 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.
Continue reading “The Futurist’s Dilemma”
Last week, on April 27th and 28th, I attended Algorithms and Explanations, an interdisciplinary conference hosted by NYU Law School’s Information Law Institute. The thrust of the conference could be summarized as follows:
- Humans make decisions that affect the lives of other humans
- In a number of regulatory contexts, humans must explain decisions, e.g.
- Bail, parole, and sentencing decisions
- Approving a line of credit
- Increasingly, algorithms “make” decisions traditionally made by man, e.g.
- Risk models already used to make decisions regarding incarceration
- Algorithmically-determined default risks already used to make loans
- This poses serious questions for regulators in various domains:
- Can these algorithms offer explanations?
- What sorts of explanations can they offer?
- Do these explanations satisfy the requirements of the law?
- Can humans actually explain their decisions in the first place?
The conference was organized into 9 panels. Each featured between 3 and 5 20-minute talks followed by a moderated discussion and Q&A. The first panel, moderated by Helen Nissenbaum (NYU & Cornell Tech), featured legal scholars (including conference organizer Katherine Strandburg) and addressed the legal arguments for explanations in the first place. A second panel featured sociologists Duncan Watts (MSR) and Jenna Burrell (Berkeley) as well as Solon Borocas (MSR), an organizer of the Fairness, Accountability and Transparency in Machine Learning workshop.
Katherine Jo Strandburg, NYU Law professor and conference organizer
Continue reading “NYU Law’s Algorithms and Explanations”
(This article originally appeared here. Thanks to Janos Kramar for his feedback on this post.)
The overall theme of the ICLR conference setting this year could be summarized as “finger food and ships”. More importantly, there were a lot of interesting papers, especially on machine learning security, which will be the focus on this post. (Here is a great overview of the topic.)
On the attack side, adversarial perturbations now work in physical form (if you print out the image and then take a picture) and they can also interfere with image segmentation. This has some disturbing implications for fooling vision systems in self-driving cars, such as impeding them from recognizing pedestrians. Adversarial examples are also effective at sabotaging neural network policies in reinforcement learning at test time.
Continue reading “Machine Learning Security at ICLR 2017”
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.
Continue reading “Press Failure: The Guardian’s “Meet Erica””
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.
Continue reading “DeepMind Solves AGI, Summons Demon”
On Monday, I posted an article titled The AI Misinformation Epidemic. The article introduces a series of posts that will critically examine the various sources of misinformation underlying this AI hype cycle.
The post came about for the following reason: While I had contemplated the idea for weeks, I couldn’t choose which among the many factors to focus on and which to exclude. My solution was to break down the issue into several narrower posts. The AI Machine Learning Epidemic introduced the problem, sketched an outline for the series, and articulated some preliminary philosophical arguments.
To my surprise, it stirred up a frothy reaction. In a span of three days, the site received over 36,000 readers. To date, the article received 68 comments on the original post, 274 comments on hacker news, and 140 comments on machine learning subreddit.
To ensure that my post contributes as little novel misinformation as possible, I’d like to briefly address the response to the article and some common misconceptions shared by many comments. Continue reading “Notes on Response to “The AI Misinformation Epidemic””