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Striking Against Climate Change

Climate change will kill us.

Gambling a planetary climate capable of sustaining human life for short-term profits exhibits a recklessness, greed, and nihilism of a scale unparalleled in the whole of written human history. Even the environmental and economic disasters of previous eras were minuscule in scope compared with the planetary-scale degradation we now are burdened with reversing or perishing by.

Quite frankly, I don’t give a shit about the whales, the polar bears, the penguins. But when we have a climate system that’s incapable of supporting those creatures, it becomes one incapable of supporting us. Human agriculture exists and functions because we have a climate system which is capable of supporting it. Disrupting that system is—to put it in dry, neoliberal terms—“very unprofitable” in the long run.

In some ways, we are all deniers. We go on about our lives, making long-term plans, as if a Venusian climate isn’t a possibility in a few decades. In many ways, this is a form of climate denial. Perhaps we’re not as guilty as those who present absurd, distracting, or fraudulent arguments that everything is fine, or we’re not as guilty as the executives at fossil fuel companies who knew better but chose to obstruct. But so long we do nothing, we acquiesce the decimation or destruction of life on Earth.

On September 23rd, I will be participating in a general strike, as part of an international call and effort for strikes in cities around the world. The goal of our strike in DC is to pressure policy makers to stop climate change from killing all of us.

For me, the justification is clear. I’ve imagined a future both where I participated in the strike and a future where I skipped it, and the future where I skipped it and went on with business as usual is unbearable. As we choke to death in our own industrial excrement, knowing that I didn’t even take a modicum of action is too cowardly for me to bear.

The hope is, these strikes will be a step in the right direction. Strike, as an action, is both powerful in forcing change and transformative for its participants. This strike will not solve climate change, but it seeds the coalescence of a movement that will.

When I look back on my life, I want to look back and know I fought for the right things.
I want to be able both to live with myself and to die with myself.

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“there’s no business in Russia:” Speaking in Implicatures

During his testimony to congress, Michael Cohen reported, with respect to Donald Trump’s Russian enterprises:

In conversations we had during the campaign, at the same time I was actively negotiating in Russia for [Donald Trump], he would look me in the eye and tell me there’s no business in Russia and then go out and lie to the American people by saying the same thing. In his way, he was telling me to lie.

Michael Cohen

What does he mean “he was telling me to lie,” exactly? The linguistic subfield of pragmatics — how meaning is communicated beyond the mere logic of an utterance — illuminates this quite clearly. In short, it’s through a conversational implicature that Trump told Cohen to lie.

Continue reading ““there’s no business in Russia:” Speaking in Implicatures”
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Revert to Normal

Studying astronomy, one of the facts that becomes apparent is the universality of death. More than anything else, the universe is empty space. Of the parts that have matter, it’s mostly stuff that can’t make planets or anything solid—hydrogen and helium. Of the parts that are made of solid stuff, most are inhospitable hellscapes or deserts dry in ways beyond our normal experience—on Mars, for example, any liquid water dumped on the surface would boil away in an instant, despite being cold enough to snow here on Earth.

This contrasts our normal experience, where rampant life is the norm. Everything decays, and in its own way, “returns to nature.” Anything left alone too long is consumed by decomposers. Abandoned buildings crumble with plants penetrating their hollowed walls. Ivy over time, almost as if by magic, climbs buildings. Dead trees sprout fungi and rot. Dirt left alone springs grass of its own. Passing places undergoing these processes over time, we watch life render much to chaos throughout our lives. It gives life an apparent inevitability, the appearance that nothing will stop its relentless crawl throughout the planet.

This apparent inevitability of life on earth is a visceral part of our experience. Life as we know it thrives so vibrantly that it must be pushed back consistently: lawns mowed, fields plowed, bushes trimmed. It’s difficult to comprehend, if a good portion of your life has been spent pushing back life, that life really isn’t so relentless after all.

But most of the universe is not that way. When you realize death is the norm, it becomes apparent how singular Earth is. This is the only place in the universe we know of where life can thrive. Likely, there is life elsewhere, but of completely different forms and adaptations. This is likely the only place in the universe where this life can thrive: the plants, animals, bacteria, and fungi that human existence depends on.

“Mars,” some cry out, “we’ll move to Mars.” Not only does liquid water boil there at below freezing temperatures, the soil is a toxic rust powder and the atmosphere is little but a thin veil of carbon dioxide without gravitational and magnetic fields strong enough to retain it. The idea we can just show up, make an ocean, and have a barbecue in no time is applying this old idea that life is relentless to Mars—an idea that only works on Earth.

To the astronomer, the changes we’ve made to the climate could revert Earth toward normalcy—toward being completely inhospitable, like everything else in the universe. It’s not so much that we change the climate with every ounce of effort—rather, it’s that we disturb a delicate balance that makes Earth no longer exceptional, an exceptionality that we depend on for survival.

When this terror sets in, it becomes clear that climate change is the only issue that matters in the long run. No matter what you’re fighting for, it won’t matter what rights you have when there’s no one left to have them.

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AI Snake Oil (Part 3): Evaluation

In the last post, I discussed training data: mostly that you ought to have it or a way of getting it. If someone pitches you an idea without even a reasonable vision of what the training data would be, they’ve got a lot less credibility. In other words, if you can’t even envision training data for a given task, then the task itself may be impractical.

No offense to the creators of this actual robot; I just needed an image with a CC license.
A ridiculous image to get the idea of “AI evaluation” into your head. (No offense to the creators of this actual robot; the giant red letter “F” is not an actual evaluation of your robot. I just needed an image with a CC license. I hope it ping-ponged well.)

Next, let’s talk about evaluation with respect to application development, namely, if someone pitches an AI application idea to you:

Question: Do they have an evaluation procedure built into their application development process?

Arguably, evaluation is more important than training data. I chose to discuss training data in the first post because thinking in terms of training data gives you intuitions about what’s possible. It eliminates the infinite, but still leaves you with dreams. Evaluation is where your dreams are torn to shreds, whether or not you have training data.

Fundamentally, I want to cover three things here: why we evaluate, how do we evaluate, and how do we score the results. Understanding these three things is essential to understanding what makes a suitable evaluation; a crappy evaluation sows false confidence, something worse than no evaluation at all.

Continue reading “AI Snake Oil (Part 3): Evaluation”

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AI Snake Oil (Part 2): Training Data

First in this series, I want to address the simplest and most important question to ask about a machine learning start-up or application:

Question: Is there existing training data? If not, how do they plan on getting it?

To sufficiently understand the answers to this question, you have to understand what training data is and, from there, what tasks or ideas would be extremely difficult to capture within training data. I’ll be addressing those in this post.

Most useful AI applications require training data: examples of the phenomenon they’re trying to replicate with the computer. If some start-up or group proposes a solution to a problem and they don’t have training data, you should be much more skeptical of their proposed solution; it’s now meandering into magic and/or expensive.

I like to think of training data as artificial intelligence’s dirty secret. It never gets mentioned in the press, but it is the topic of Day 1 of any Machine Learning class and forms the theoretical basis for what you learn the rest of the semester. Techniques like these that use training data are called often statistical methods, since they gather statistics about the data they’re provided to make predictions; this is in contrast to the rule-drive methods that were used prior to this.

Continue reading “AI Snake Oil (Part 2): Training Data”

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AI Snake Oil (Part 1): Golden Lunar Toilet

A lot of over-hyped AI claims are being thrown around right now. In a lot of cases, leveraging this hype, some individuals make promises they can’t keep, no matter how dedicated or incredibly talented they are as developers. Steve Jobs may have had a so called “reality distortion field,” but that didn’t ever spawn a conscious AI, and neither will these people.

What I do want to describe is how to tell if someone is trying to sell you AI snake oil—bullshit claims on what they can actually achieve in a realistic time and budget. Sure, with infinite resources, I could build you a gold toilet on the moon, but no one has that kind of cash lying around. Shit needs to get done, and the time and material for doing so is finite.

Anything is possible. The only limit is yourself.
Anything is possible. I will make this happen for $412 billion dollars. Please provide in gold bullion so I can melt it down into the toilet of my own secret Swiss bank account.

If you’re approached by someone trying to sell you artificial intelligence-related software, or you read a piece in the popular press about what profession AI will uncannily crush in the next year, these are the questions you should ask. Depending on the answers, you can determine whether they’re bluffing or that they’ve done their homework and are worth taking seriously.

I was originally going to make this one post, but it’s grown too large to fit into one. In this series, each post is centered around a question you should ask when someone wants to do something in the real world with natural language processing, machine learning, or other AI components. These questions are:

Each post will detail what you should expect for an answer. As I write, I might add to or revise some of these questions, so don’t consider this list definitive quite yet.

All said and done, there are some really great things happening in AI right now; it’s part of why I chose to invest 6 years of my life getting involved in computational linguistics as a field. However, on any big wave of technology, there’s also a big wave of exploitation.  When people exploit the gap in knowledge between researchers and the public with hyperbole, it comes back to hurt those of us who work so hard to actually make shit that works. I hope that these posts can help non-researchers think more critically about AI and provide researchers a way to inform the public without dragging them through the equivalent graduate level coursework.

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It’s Good* That Word Embeddings Are Sexist

A lot of news has been fluttering around about word embeddings being racist and sexist, e.g:

This is a good thing, but not in the sense that sexism and racism are good. It’s good because people who work on quantitative problems don’t believe things are real without quantitative evidence. This is quantitative evidence of that sexism and racism are real things.

Per my initial reaction, I was surprised how much alarm there is about this. When you live in a world that is glaringly *ist, take data from that world, and learn in an unsupervised manner, you’re going to acquire *ist knowledge. But then again, I’ve done my graduate education in a linguistics department with a strong group of sociolinguists. I was exposed to these ideas years ago and have been taught to have an awareness and sensitivity to these issues and to be critically aware of how language can construct and reinforce racist and sexist norms, especially though prescriptivism.

I suspect a lot of the shock is coming from the stronger CS end of things–a side of the university that is more strictly quantitative. My undergrad was in physics, which I suspect has a similar distribution of social science coursework–namely, just what the university requires. A student might have to take sociology or anthropology, and that’s only if the university requires it. My undergrad did not; I took macroeconomics en lieu of either of those.

When you’re in a quantitative program, there exists a lot of hidden assumptions. One is that quantitative analysis is the only way to do anything–any other way of approaching any problem of any kind is bullshit. This is because any other approach can involve biases that a researcher is unaware of. Abstraction and measurement help to remove the preferences of the researcher from the process, mitigating the effect of their biases. The procedure and the numbers are what count.

Hard-core context control.
Hard-core context control.

This works great for particles in a vacuum, for problems where the context can be completely controlled, but the assumption that these standards can be universally maintained bleeds into other problems for which doing so realistically is impossible. However, the air of non-bias around quantitative methods remains despite that the conditions that purged that bias in the first place are lost.

This assumption of non-bias holds into AI research–that a machine built on quantitative principles will be capable of arriving, logically and deductively, at perfect, non-biased truth–the objective truth that’s obscured by those pesky, confounding social factors.

If only Tay had taken advice from Dr. Dre: "I don't smoke weed or sess / Cause it's known to give a brother brain damage / And brain damage on the mic don't manage nothing / But making a sucker and you equal..."
“I don’t smoke weed or sess / Cause it’s known to give a brother brain damage / And brain damage on the mic don’t manage…”

This hope is at ends with AI’s Dark Secret–the one that never seems to make it into the press with its claims about AI’s up-and-coming “singularity”–solutions to the most interesting problems in AI rely entirely on training data. Some of this is supervised, some it is unsupervised, but it all still relies on the data it’s fed. With that, it comes to replicate whatever it’s been provided: garbage in, garbage out.

And so, this is where the shock comes from. For the first time, white, male quantitative researchers are smacked upside the head with the reality that the world exhibits sexist and racist tendencies. The data they’ve provided is digested and learned into biases. It turns out, building that perfectly logically deductive system, free from bias–a consciousness liberated from the social confines of human existence–isn’t just hard, but possibly impossible.

This isn’t a bad thing–perhaps disappointing to a slowly dying vision of AI. The upside though is that the majority of the evidence up to the present for *ist tendencies in society has been qualitative. You have to trust individuals synopses of their aggregate subjective experiences that privilege and bias exist. Right here, we’re seeing quantitative evidence that supports their testimonies.

There’s a two fold effect there: hopefully, it opens up quantitative researchers to acknowledging better the validity of qualitative research. Simultaneously, it confirms the discoveries of a lot of that qualitative research through discovering the same things from a totally different angle. That sort of independent confirmation is ideal in scientific work, and this convergence is just exactly that. We’re seeing decades of social science research supported by evidence from entirely different methods. In a discipline filled with men, this is unequivocal evidence that there are issues that need to be addressed, derived from the methods within that discipline. Sexism and racism suck, but with AI finally bumping into them and providing firm support for them as real issues, perhaps we can have better luck garnering public support in the larger social sphere.

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“Bing bing, bong bong bong, bing bing.”

In the class I’ve been teaching this summer, for the last few days, we’ve been using a parsed version of the Donald Trump speech corpus that Ryan McDermott posted to Github a few days ago. One of my students mentioned that Donald Trump had made a speech where he said, quote, “Bing bing, bong bong bong, bing bing.”

I was wondering if this particular speech were actually in the corpus. As a teaching activity, we started searching for instances of /[Bb][io]ng/. I also wanted to see what the parser would do with a string like “bing bong bing bing bong”. There’s a possibility that the parser would assume this is a normal sentence and produce something like:

[NP bing bong] [VP bing [NP bing bong]]

Another student asked why were we doing this–searching for such an obscure, non-sense lexical item, when we could be searching for something that is actually meaningful?

The answer I had, in part, was that it’s not that obscure. As it turns out, these items are quite characteristic of Trump’s speech. In this corpus alone—which lacks the famous original “bing bing, bong bong” speech cited above—it appears 24 times (16 if you remove duplicates), often in clusters of three:

“And that’s what we ended up getting–the king of teleprompters.  But, so when I look at these things here I say you know what, it’s so much easier, it would be so nice, just bah, pa, bah, pa, bah, bing, bing, bing.  No problems, get off stage, everybody falls asleep and that’s the end of that.  But we have to do something about these teleprompters.”

“I hear where they don’t want me to use the hairspray. They want me to use the pump because the other one, which I really like better than going bing, bing, bing, and then it comes out in big globs, right? And then you’re stuck in your hair and you say, ‘Oh my God, I have to take a shower again. My hair’s all screwed up.’ ”

“You know, in the old days everything was better right? The car seats. You’d sit in your car and you want to move forward and back, you press a button. Bing, bing. Now, you have to open up things, press a computer, takes you 15 minutes.”

“You know, when you have so many people running – we had 17 and then they started to drop. Ding. Bing. I love it. I love it.”

“On the budget – I’m really good at these things – economy, budgets. I sort of expected this. On the budget, Trump – this is with 15 people remaining – Trump 51%. Everyone else bing.”

“In Paris, I call him the guy with the dirty filthy hat. Okay? Not a smart guy. A dummy. Puts people in there – mastermind – bing, bing, bing, it’s like shooting everybody. You’ve got to be a mastermind.”

“I was like the establishment. They’d all come to me, and I’d give them all money I write checks sometimes to Senators whatever the max – bing, bing, bing.”

The communicative goals of these tokens could constitute an entire discourse paper, but let’s just stick with the basics now. He seems to use it to indicate some kind of quick, repetitive action. It doesn’t seem to have a particular sentiment associated with it: bribing senators, competitors dropping out of the race, committing mass murder, moving the chair conveniently in a car, being annoyed with pump style hair gels, politicians reading off teleprompters.

It’s undoubtedly characteristic of his speech, though. To say that it’s a mere aberration–something to ignore–is prescriptive.  If we look at counts of lemmas throughout the corpus (using SpaCy—a little easier to break out than digging through CoreNLP’s XML), the lemma “bing” appears 11 times, the other 13 times being lemmatized as “be.” In those cases, the lemmatizer assumed “bing” was a VBG, essentially a misspelling of “being.”

Of the whole corpus, compared with all 24 counts of “bing,” Trump said “bing” more often than he said:

  • situation: 23
  • donor: 21
  • dangerous: 21
  • migration: 20
  • weak: 20
  • economic: 19
  • freedom: 18
  • mexican: 18
  • illegally: 14
  • muslim: 13
  • god: 11
  • kasich: 11
  • bernardino: 10
  • criminal: 9
  • hispanic: 9
  • chinese: 8

 

Among many, many other word types. You can get the full list of lemma counts here (when I get around to posting it), though note that “bing” appears at 11 in that list because a lot of the results were merged with “be” erroneously.

To go back to the critical student’s original question, though, it’s a difference in expectations, I suspect. While NLP tools are helpful, they don’t totally address the problem of meaning in text. Meaning is still in large part up to the programmer using the tool, not the tool itself. There’s still a lot of work to be done in that regard, in any application. Sometimes “bing bing bong bong” is really the best we can do.

 

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Buzz in the Flesh: A Microcosm for Science in America

I had the opportunity a few months ago, largely thanks to @sociolinguista, to see Buzz Aldrin speak. It was pretty cool; even at 85, he’s still charming and sharp. Now-a-days, he’s mostly advocates for Martian exploration and colonization, and this comprised the bulk of the discussion. The session was part interview–done by Aldrin’s own son–part Q&A.

Before Buzz came out, a video explained his grand plan to reach Mars. Then, Buzz talked about optimal plans, etc. putting stations at L1, and choices for transfer orbits and hyperbolic intersects.

As much as I respect Buzz’s plans, I wonder what good they do. After all, the problem isn’t having a science plan. Planning is fun; there are entire video games where you plan and complete space missions. The problem is money and public interest. We have a public that doesn’t know or care, and as a result, there’s no money for the program.

As someone who studied physics (and has played enough KSP that it’s unhealthy), I understood what he was going on about. I don’t know that the majority of people in the audience did. There’s ways he could have helped, but didn’t bother, either by replacing jargon with a few extra words, or just taking a moment to explain some key concept briefly. A few extra words can go a long way.

Scientists have a duty, both to do honest science, but also to explain that science to others. That’s been done rather poorly over the last 50 years, and now we’ve got a significant segment of the public actively ignoring us, because no one really explained to them what’s going on in a way they could understand. It’s not that they can’t understand, it’s that we have to do a better job in helping them to do so.

 

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Master Key

When it comes to infosec, the magnitude of ignorance amongst people astounds me. People like this actually get taken seriously, requesting backdoors in encryption algorithms so government officials can take a peek once they get a warrant. That sounds like a good idea when he frames it that way, but encryption, data, and computers in general are really abstract. Let me give you an analogy that’s a little more concrete, and then I wanna poke at why they even want this shit in the first place.

Let’s say FBI Guy were proposing a mandate for a national master key. Any door in the country, and with a warrant, an officer of the law could get a copy of the national master key and open the door to the house.

Totally creepy, of course, knowing that at any time some guy could just show up with a magic key that opens the door to your house. Even ignoring the potential for abuse–“pretty please, we promise not to abuse our national master key privileges”–there’s the inevitability that someone could figure out what the national master key is. If there’s one of these things built into every house in the country–even if there’s a special master key for each house–there’s some pattern to figure out. Someone’s gonna want to find out that pattern, because all the national mandate has done is create a puzzle to crack.

And these kinds of puzzles always get cracked. Especially when the prize is so big–access to literally every house in the country–it will get cracked. The solution will get plastered all over the Internet as a big “fuck you” right back at the people that failed to grasp the consequences of their poorly planned policies. It’s happened before, and it will happen again.

If the consequences are so bad–the neutering of every lock in the country–why does the NSA, FBI, and seemingly every other triple letter agency want something like this?

Roughly speaking though, the FBI already has that national master key–a state monopoly on coercive force. With a warrant, they can kick down your door, shoot your dog, throw you in jail, and throw all your personal belongings into duffel bags to get torn apart in a forensics lab.

They can’t do that with encrypted data, not without millions of computer hours for decryption. That’s not as easy as kicking your door down and stealing seizing all of your shit. That’s what they really want; from their point of view, encrypted data is a domain beyond the reach of brute force, and they want to reel it back in.

Maybe, in the end, they shouldn’t be focused on breaking encryption, but strengthening it for everyone, including themselves. While the FBI was busy petitioning for laws that break encryption, another massive government data breach was revealed, probably including personal information about Mr. Steinbach–the very official begging for weaker standards. We’re stuck with 20th century barons imposing 20th century standards on 21st century problems.