Machine Learning (Theory)

4/22/2017

Fact over Fiction

Tags: politics,Research jl@ 1:23 pm

Politics is a distracting affair which I generally believe it’s best to stay out of if you want to be able to concentrate on research. Nevertheless, the US presidential election looks like something that directly politicizes the idea and process of research by damaging the association of scientists & students, funding for basic research, and creating political censorship.

A core question here is: What to do? Today’s March for Science is a good step, but I’m not sure it will change many minds. Unlike most scientists, I grew up in a a county (Linn) which voted overwhelmingly for Trump. As a consequence, I feel like I must translate the mindset a bit. For the median household left behind over my lifetime a march by relatively affluent people protesting the government cutting expenses will not elicit much sympathy. Discussion about the overwhelming value of science may also fall on deaf ears simply because they have not seen the economic value personally. On the contrary, they have seen their economic situation flat or worsening for 4 decades with little prospect for things getting better. Similarly, I don’t expect history lessons on anti-intellectualism to make much of a dent. Fundamentally, scientists and science fans are a small fraction of the population.

What’s needed is a campaign that achieves broad agreement across the population and which will help. One of the roots of the March for Science is a belief in facts over fiction which may have the requisite scope. In particular, there seems to be a good case that the right to engage in mass disinformation has been enormously costly to the United States and is now a significant threat to civil stability. Internally, disinformation is a preferred tool for starting wars or for wealthy companies to push a deadly business model. Externally, disinformation is now being actively used to sway elections and is self-funding.

The election outcome is actually less important than the endemic disagreement that disinformation creates. When people simply believe in different facts about the world how can you expect them to agree? There probably are some good uses of mass disinformation somewhere, but I’m extremely skeptical the value exceeds the cost.

Is opposition to mass disinformation broad enough that it makes a good organizing principle? If mass disinformation was eliminated or greatly reduced it would be an enormous value to society, particularly to the disinformed. It would not address the fundamental economic stagnation of the median household in the United States, but it would remove a significant threat to civil society which may be necessary for such progress. Given a choice between the right to mass disinform and democracy, I choose democracy.

A real question is “how”? We are discussing an abridgment of freedom of speech so from a legal perspective the basis must rest on the balance between freedom of speech and other constitutional rights. Many abridgements exist like censuring a yell of “fire” in a crowded theater unnecessarily.

Voluntary efforts (as Facebook and Twitter have undertaken) are a start, but it seems unlikely to go far enough as many other “news” organizations have made no such commitments. A system where companies commit to informing over disinforming and in return become both more trusted and simultaneously liable for disinformation damages (due to the disinformed) as assessed by civil law may make sense. Right now organizations are mostly free to engage in disinformation as long as it is not directed at an individual where libel laws apply. Penalizing an organization for individual mistakes seems absurd, but a pattern of errors backed by scientific surveys verifying an anomalously misinformed status of viewers/readers/listeners is cause for action. Getting this right is obviously a tricky thing—we want a solution that a real news organization with an existing mimetic immune system prefers to the status quo because it curbs competitors that disinform. At the same time, there must be enough teeth to make disinformation uneconomical or the problem only grows.

Should disinformation have criminal penalties? One existing approach here uses RICO laws to counter disinformation from Tobacco companies. Reading the history, this took an amazing amount of time—enough that it was ineffective for a generation. It seems plausible that an act directly addressing disinformation may be helpful.

What about technical solutions? Technical solutions seem necessary for success, perhaps with changes to law incentivizing this. It’s important to understand that something going before the courts is inherently slow, particularly because courts tend to be deeply overloaded. A significant increase in the number of cases going before courts makes an approach nonviable in practice.

Would we regret this? There is a long history of governments abusing laws to censor inconvenient news sources so caution is warranted. Structuring new laws in a manner such that they cannot be abused is an important consideration. It is obviously important to leave satire fully intact which seems entirely possibly by making the fact that it is satire unmistakable. This entire discussion is also not relevant to individuals speaking to other individuals—that is not what creates a problem.

Is this possible? It might seem obvious that mass disinformation should be curbed but there should be no doubt that powerful forces will work to preserve mass disinformation by subtle and unethical means.

Overall, I fundamentally believe that people in a position to inform or disinform have a responsibility to inform. If they don’t want that responsibility, then they should abdicate the position to someone who does, similar in effect to the proposed fiduciary rule for investments. I’m open to any approach towards achieving this.

Edit: also at CACM.

4/12/2017

The Decision Service is Hiring

The Decision Service is a first-in-the-world project making tractable reinforcement learning easily used by developers everywhere. We are hiring for devel opers, data scientist, and a product manager. Please consider joining us to do something interesting this life :-)

1/4/2017

EWRL and NIPS 2016

I went to the European Workshop on Reinforcement Learning and NIPS last month and saw several interesting things.

At EWRL, I particularly liked the talks from:

  1. Remi Munos on off-policy evaluation
  2. Mohammad Ghavamzadeh on learning safe policies
  3. Emma Brunskill on optimizing biased-but safe estimators (sense a theme?)
  4. Sergey Levine on low sample complexity applications of RL in robotics.

My talk is here. Overall, this was a well organized workshop with diverse and interesting subjects, with the only caveat being that they had to limit registration :-)

At NIPS itself, I found the poster sessions fairly interesting.

  1. Allen-Zhu and Hazan had a new notion of a reduction (video).
  2. Zhao, Poupart, and Gordon had a new way to learn Sum-Product Networks
  3. Ho, Littman, MacGlashan, Cushman, and Austerwell, had a paper on how “Showing” is different from “Doing”.
  4. Toulis and Parkes had a paper on estimation of long term causal effects.
  5. Rae, Hunt, Danihelka, Harley, Senior, Wayne, Graves, and Lillicrap had a paper on large memories with neural networks.
  6. Hardt, Price, and Srebro, had a paper on Equal Opportunity in ML.

Format-wise, I thought the 2 sessions was better than 1, but I really would have preferred more. The recorded spotlights are also pretty cool.

The NIPS workshops were great, although I was somewhat reminded of kindergarten soccer in terms of lopsided attendance. This may be inevitable given how hot the field is, but I think it’s important for individual researchers to remember that:

  1. There are many important directions of research.
  2. You personally have a much higher chance of doing something interesting if everyone else is not doing it also.

During the workshops, I learned about ADAM (a momentum form of Adagrad), testing ML systems, and that even TenserFlow is finally looking into synchronous updates for parallel learning (allreduce is the way).

(edit: added one)

12/8/2016

Vowpal Wabbit version 8.3 and tutorial

I just released Vowpal Wabbit 8.3 and we are planning a tutorial at NIPS Saturday over the lunch break in the ML systems workshop. Please join us if interested.

8.3 should be backwards compatible with all 8.x series. There have been big changes since the last version related to

  1. Contextual bandits, particularly w.r.t. the decision service.
  2. Learning to search for which we have a paper at NIPS.
  3. Logarithmic time multiclass classification.

8/26/2016

ICML 2016 videos and statistics

Tags: Conferences,Machine Learning jl@ 2:04 pm

The ICML 2016 videos are out.

I also wanted to share some statistics from registration that might be of general interest.

The total number of people attending: 3103.

Industry: 47% University: 46%

Male: 83% Female: 14%

Local (NY, NJ, or CT): 27%

North America: 70% Europe: 18% Asia: 9% Middle East: 2% Remainder: <1% including 2 from Antarctica :-)

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