Joe McKendrick at Forbes has posted a concise and thoughtful column about
Everyday Chaos, including four rules to guide your expectations about machine learning.

It’s great to see a pre-publication post so on track about what the book says and how it applies to business.

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Well, actually less. And more. Allow me to explain:

The first half of the first chapter of Everyday Chaos is now available at Medium. (An Editor’s Choice, no less!)

You can also read the first half of the chapter on how our model of models is changing at the Everyday Chaos site (Direct link: pdf).

At that site you’ll also find a fifteen minute video (Direct link: video) in which I attempt to explain why I wrote the book and what it’s about.

Or, you can just skip right to the pre-order button (Direct link: Amazon or IndieBound) :)

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In 1948 when Claude Shannon was inventing information science [pdf] (and, I’d say, information itself), he took as an explanatory example a simple algorithm for predicting the element of a sentence. For example, treating each letter as equiprobable, he came up with sentences such as:

XFOML RXKHRJFFJUJ ZLPWCFWKCYJ FFJEYVKCQSGHYD QPAAMKBZAACIBZLHJQD.

If you instead use the average frequency of each letter, you instead come up with sentences that seem more language-like:

OCRO HLI RGWR NMIELWIS EU LL NBNESEBYA TH EEI ALHENHTTPA OOBTTVA NAH BRL.

At least that one has a reasonable number of vowels.

If you then consider the frequency of letters following other letters—U follows a Q far more frequently than X does—you are practically writing nonsense Latin:

ON IE ANTSOUTINYS ARE T INCTORE ST BE S DEAMY ACHIN D ILONASIVE TUCOOWE AT TEASONARE FUSO TIZIN ANDY TOBE SEACE CTISBE.

Looking not at pairs of letters but triplets Shannon got:

IN NO IST LAT WHEY CRATICT FROURE BIRS GROCID PONDENOME OF DEMONSTURES OF THE REPTAGIN IS REGOACTIONA OF CRE.

Then Shannon changes his units from triplets of letters to triplets of words, and gets:

THE HEAD AND IN FRONTAL ATTACK ON AN ENGLISH WRITER THAT THE CHARACTER OF THIS POINT IS THEREFORE ANOTHER METHOD FOR THE LETTERS THAT THE TIME OF WHO EVER TOLD THE PROBLEM FOR AN UNEXPECTED.

Pretty good! But still gibberish.

Now jump ahead seventy years and try to figure out which pieces of the following story were written by humans and which were generated by a computer:

In a shocking finding, scientist discovered a herd of unicorns living in a remote, previously unexplored valley, in the Andes Mountains. Even more surprising to the researchers was the fact that the unicorns spoke perfect English.

The scientist named the population, after their distinctive horn, Ovid’s Unicorn. These four-horned, silver-white unicorns were previously unknown to science.

Now, after almost two centuries, the mystery of what sparked this odd phenomenon is finally solved.

Dr. Jorge Pérez, an evolutionary biologist from the University of La Paz, and several companions, were exploring the Andes Mountains when they found a small valley, with no other animals or humans. Pérez noticed that the valley had what appeared to be a natural fountain, surrounded by two peaks of rock and silver snow.

Pérez and the others then ventured further into the valley. “By the time we reached the top of one peak, the water looked blue, with some crystals on top,” said Pérez.

“Pérez and his friends were astonished to see the unicorn herd”Pérez and his friends were astonished to see the unicorn herd. These creatures could be seen from the air without having to move too much to see them – they were so close they could touch their horns.

While examining these bizarre creatures the scientists discovered that the creatures also spoke some fairly regular English. Pérez stated, “We can see, for example, that they have a common ‘language,’ something like a dialect or dialectic.”

Dr. Pérez believes that the unicorns may have originated in Argentina, where the animals were believed to be descendants of a lost race of people who lived there before the arrival of humans in those parts of South America.

While their origins are still unclear, some believe that perhaps the creatures were created when a human and a unicorn met each other in a time before human civilization. According to Pérez, “In South America, such incidents seem to be quite common.”

However, Pérez also pointed out that it is likely that the only way of knowing for sure if unicorns are indeed the descendants of a lost alien race is through DNA. “But they seem to be able to communicate in English quite well, which I believe is a sign of evolution, or at least a change in social organization,” said the scientist.

The answer: The first paragraph was written by a human being. The rest was generated by a machine learning system trained on a huge body of text. You can read about it in a fascinating article (pdf of the research paper) by its creators at OpenAI. (Those creators are: Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever.)

There are two key differences between this approach and Shannon’s.

First, the new approach analyzed a very large body of documents from the Web. It ingested 45 million pages linked in Reddit comments that got more than three upvotes. After removing duplicates and some other cleanup, the data set was reduced to 8 million Web pages. That is a lot of pages. Of course the use of Reddit, or any one site, can bias the dataset. But one of the aims was to compare this new, huge, dataset to the results from existing sets of text-based data. For that reason, the developers also removed Wikipedia pages from the mix since so many existing datasets rely on those pages, which would smudge the comparisons.

(By the way, a quick google search for any page from before December 2018 mentioning both “Jorge Pérez” and “University of La Paz” turned up nothing. “The AI is constructing, not copy-pasting.”The AI is constructing, not copy-pasting.)

The second distinction from Shannon’s method: the developers used machine learning (ML) to create a neural network, rather than relying on a table of frequencies of words in triplet sequences. ML creates a far, far more complex model that can assess the probability of the next word based on the entire context of its prior uses.

The results can be astounding. While the developers freely acknowledge that the examples they feature are somewhat cherry-picked, they say:

When prompted with topics that are highly represented in the data (Brexit, Miley Cyrus, Lord of the Rings, and so on), it seems to be capable of generating reasonable samples about 50% of the time. The opposite is also true: on highly technical or esoteric types of content, the model can perform poorly.

There are obviously things to worry about as this technology advances. For example, fake news could become the Earth’s most abundant resource. For fear of its abuse, its developers are not releasing the full dataset or model weights. Good!

Nevertheless, the possibilities for research are amazing. And, perhaps most important in the longterm, one by one the human capabilities that we take as unique and distinctive are being shown to be replicable without an engine powered by a miracle.

That may be a false conclusion. Human speech does not consist simply of the utterances we make but the complex intentional and social systems in which those utterances are more than just flavored wind. But ML intends nothing and appreciates nothing. “Nothing matters to ML.”Nothing matters to ML. Nevertheless, knowing that sufficient silicon can duplicate the human miracle should shake our confidence in our species’ special place in the order of things.

(FWIW, my personal theology says that when human specialness is taken as conferring special privilege, any blow to it is a good thing. When that specialness is taken as placing special obligations on us, then at its very worst it’s a helpful illusion.)

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For the past six months I’ve been a writer in residence embedded in a machine learning research group — PAIR (People + AI Research) — at the Google site in Cambridge, MA. I was recently renewed for another 6 months.

No, it’s not clear what a “writer in residence” does. So, I’ve been writing occasional posts that try to explain and contextualize some basic concepts in machine learning from the point of view of a humanities major who is deeply lacking the skills and knowledge of a computer scientist. Fortunately the developers at PAIR are very, very patient.

Here are three of the posts:

Machine Learning’s Triangle of Error: “…machine learning systems ‘think’ about fairness in terms of three interrelated factors: two ways the machine learning (ML) can go wrong, and the most basic way of adjusting the balance between these potential errors.”

Confidence Everywhere!: “… these systems are actually quite humble. It may seem counterintuitive, but we could learn from their humility.”

Hashtags and Confidence: “…in my fever dream of the future, we routinely say things like, “That celebrity relationship is going to last, 0.7 for sure!” …Expressions of confidence probably (0.8) won’t take exactly that form. But, then, a decade ago, many were dubious about the longevity of tagging…”

I also wrote about five types of fairness, which I posted about earlier: “…You appoint five respected ethicists, fairness activists, and customer advocates to figure out what gender mix of approved and denied applications would be fair. By the end of the first meeting, the five members have discovered that each of them has a different idea of what’s fair…”

I’ve also started writing an account of my attempt to write my very own machine learning program using TensorFlow.js: which lets you train a machine learning system in your browser; TensorFlow.js is a PAIR project. This project is bringing me face to face with the details of implementing even a “Hello, world”-ish ML program. (My project aims at suggesting tags for photos, based on a set of tagged images (Creative Commons-ed) from Flickr. It’s a toy, of course.)

I have bunch of other posts in the pipeline, as well as a couple of larger pieces on larger topics. Meanwhile, I’m trying to learn as much as I possibly can without becoming the most annoying person in Cambridge. But it might be too late to avoid that title…

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Primavera De Filippi is an expert in blockchain-based tech. She is giving a ThursdAI talk on Plantoid, an event held by Harvard’s Berkman Klein Center for Internet & Society and the MIT Media Lab. Her talk is officially on operational autonomy vs. decisional autonomy, but it’s really about how weird things become when you build a computerized flower that merges AI and the blockchain. For me, a central question of her talk was: Can we have autonomous robots that have legal rights and can own and spend assets, without having to resort to conferring personhood on them the way we have with corporations?

Ben Green is giving a ThursdAI talk on “The Limits, Perils, and Challenges of ‘Fair’ Algorithms for Criminal Justice Reform.”

In 2016, the COMPAS algorithm
became a household name (in some households) when ProPublica showed that it predicted that black men were twice as likely as white men to jump bail. People justifiably got worried that algorithms can be highly biased. At the same time, we think that algorithms may be smarter than humans, Ben says. These have been the poles of the discussion. Optimists think that we can limit the bias to take advantage of the added smartness.

There have been movements to go toward risk assessments for bail, rather than using money bail. E.g., Rand Paul and Kamala Harris have introduced the Pretrial Integrity and Safety Act of 2017. There have also been movements to use scores only to reduce risk assessments, not to increase them.

But are we asking the right questions? Yes, the criminal justice system would be better if judges could make more accurate and unbiased predictions, but it’s not clear that machine learning can do this. So, two questions: 1. Is ML an appropriate tool for this. 2. Is implementing MK algorithms an effective strategy for criminal justice reform?

#1 Is ML and appropriate tool to help judges make more accurate and unbiased predictions?

ML relies on data about the world. This can produce tunnel vision by causing us to focus on particular variables that we have quantified, and ignore others. E.g., when it comes to sentencing, a judge balances deterrence, rehabilitation, retribution, and incapacitating a criminal. COMPAS predicts recidivism, but none of the other factors. This emphasizes incapacitation as the goal of sentencing. This might be good or bad, but the ML has shifted the balance of factors, framing the decision without policy review or public discussion.

Q: Is this for sentencing or bail? Because incapacitation is a more important goal in sentencing than in bail.

A: This is about sentencing. I’ll be referring to both.

Data is always about the past, Ben continues. ML finds statistical correlations among inputs and outputs. It applies those correlations to the new inputs. This assumes that those correlations will hold in the future; it assumes that the future will look like the past. But if we’re trying reform the judicial system, we don’t want the future to look like the past. ML can thus entrench historical discrimination.

Arguments about the fairness of COMPAS are often based on competing mathematical definitions of fairness. But we could also think about the scope of what we couint as fair. ML tries to make a very specific decision: among a population, who recidivates? If you take a step back and consider the broader context of the data and the people, you would recognize that blacks recidivate at a higher rate than whites because of policing practices, economic factors, racism, etc. Without these considerations, you’re throwing away the context and accepting the current correlations as the ground truth. Even if we were to change the base data, the algorithm wouldn’t make the change, unless you retrain it.

Q: Who retrains the data?

A: It depends on the contract the court system has.

Algorithms are not themselves a natural outcome of the world. Subjective decisions go into making them: which data to input, choosing what to predict, etc. The algorithms are brought into court as if they were facts. Their subjectivity is out of the frame. A human expert would be subject to cross examination. We should be thinking of algorithms that way. Cross examination might include asking how accurate the system is for the particular group the defendant is in, etc.

Q: These tools are used in setting bail or a sentence, i.e., before or after a trial. There may not be a venue for cross examination.

In the Loomis case, an expert witness testified that the algorithm was misused. That’s not exactly what I’m suggesting; they couldn’t get to all of it because of the trade secrecy of the algorithms.

Back to the framing question. If you can make the individual decision points fair we sometimes think we’ve made the system fair. But technocratic solutions tend to sanitize rather than alter. You’re conceding the overall framework of the system, overlooking more meaningful changes. E.g., in NY, 71% of voters support ending pre-trial jail for misdemeanors and non-violent felonies. Maybe we should consider that. Or, consider that cutting food stamps has been shown to increases recidivism. Or perhaps we should be reconsidering the wisdom of preventative detention, which was only introduced in the 1980s. Focusing on the tech de-focuses on these sorts of reforms.

Also, technocratic reforms are subject to political capture. E.g., NJ replaced money bail with a risk assessment tool. After some of the people released committed crimes, they changed the tool so that certain crimes were removed from bail. What is an acceptable risk level? How to set the number? Once it’s set, how is it changed?

Q: [me] So, is your idea that these ML tools drive out meaningful change, so we ought not to use them?

A: Roughly, yes.

[Much interesting discussion which I have not captured. E.g., Algorithms can take away the political impetus to restore bail as simply a method to prevent flight. But sentencing software is different, and better algorithms might help, especially if the algorithms are recommending sentences but not imposing them. And much more.]

2. Do algorithms actually help?

How do judges use algorithms to make a decision? Even if the algorithm were perfect, would it improve the decisions judges make? We don’t have much of an empirical answer.

Ben was talking to Jeremy Heffner at Hunch Lab. They make predictive policing software and are well aware of the problem of bias. (“If theres any bias in the system it’s because of the crime data. That’s what we’re trying to address.” — Heffner) But all of the suggestions they give to police officers are called “missions,” which is in the military/jeopardy frame.

People are bad at incorporating quantitative data into decisions. And they filter info through their biases. E.g., the “ban the box” campaign to remove the tick box about criminal backgrounds on job applications actually increased racial discrimination because employers assumed the white applicants were less likely to have arrest records. (Agan and Starr 2016) Also, people have been shown to interpret police camera footage according to their own prior opinions about the police. (sommers 2016)

Evidence from Kentucky (Stevenson 2018): after mandatory risk assessments for bail only made a small increase in pretrial release, and these changes eroded over time as judges returned to their previous habits.

So, we need to be asking the empirical question of how judges actual use these decisions. And should judges incorporate these predictions into their decisions?

Ben’s been looking at the first question:L how do judges use algorithmic predictions? He’s running experiments on Mechanical Turk showing people profiles of defendants — a couple of sentences about the crime, race, previous record arrest record. The Turkers have to give a prediction of recidivism. Ben knows which ones actually recidivated. Some are also given a recommendation based on an algorithmic assessment. That risk score might be the actual one, random, or biased; the Turkers don’t know that about the score.

Q: It might be different if you gave this test to judges.

A: Yes, that’s a limitation.

Q: You ought to give some a percentage of something unrelated, e.g., it will rain, just to see if the number is anchoring people.

A: Good idea

Q: [me] Suppose you find that the Turkers’ assessment of risk is more racially biased than the algorithm…

A: Could be.

[More discussion until we ran out of time. Very interesting.]

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“If a lion could talk, we could not understand him.”
— Ludwig Wittgenstein, Philosophical Investigations, 1953.

“If an algorithm could talk, we could not understand it.”
— Deep learning, Now.

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Patrick Sharkey [twitter: patrick_sharkey] uses a Twitter thread to evaluate the evidence about a possible relationship between exposure to lead and crime. The thread is a bit hard to get unspooled correctly, but it’s worth it as an example of:

1. Thinking carefully about complex evidence and data.

2. How Twitter affects the reasoning and its expression.

3. The complexity of data, which will only get worse (= better) as machine learning can scale up their size and complexity.

Note: I lack the skills and knowledge to evaluate Patrick’s reasoning. And, hat tip to David Lazer for the retweet of the thread.

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Robert Epstein argues in Aeon against the dominant assumption that the brain is a computer, that it processes information, stores and retrieves memories, etc. That we assume so comes from what I think of as the informationalizing of everything.

The strongest part of his argument is that computers operate on symbolic information, but brains do not. There is no evidence (that I know of, but I’m no expert. On anything) that the brain decomposes visual images into pixels and those pixels into on-offs in a code that represents colors.

In the second half, Epstein tries to prove that the brain isn’t a computer through some simple experiments, such as drawing a dollar bill from memory and while looking at it. Someone committed to the idea that the brain is a computer would probably just conclude that the brain just isn’t a very good computer. But judge for yourself. There’s more to it than I’m presenting here.

Back to Epstein’s first point…

It is of the essence of information that it is independent of its medium: you can encode it into voltage levels of transistors, magnetized dust on tape, or holes in punch cards, and it’s the same information. Therefore, a representation of a brain’s states in another medium should also be conscious. Epstein doesn’t make the following argument, but I will (and I believe I am cribbing it from someone else but I don’t remember who).

Because information is independent of its medium, we could encode it in dust particles swirling clockwise or counter-clockwise; clockwise is an on, and counter is an off. In fact, imagine there’s a dust cloud somewhere in the universe that has 86 billion motes, the number of neurons in the human brain. Imagine the direction of those motes exactly matches the on-offs of your neurons when you first spied the love of your life across the room. Imagine those spins shift but happen to match how your neural states shifted over the next ten seconds of your life. That dust cloud is thus perfectly representing the informational state of your brain as you fell in love. It is therefore experiencing your feelings and thinking your thoughts.

That by itself is absurd. But perhaps you say it is just hard to imagine. Ok, then let’s change it. Same dust cloud. Same spins. But this time we say that clockwise is an off, and the other is an on. Now that dust cloud no longer represents your brain states. It therefore is both experiencing your thoughts and feeling and is not experiencing them at the same time. Aristotle would tell us that that is logically impossible: a thing cannot simultaneously be something and its opposite.

Anyway…

Toward the end of the article, Epstein gets to a crucial point that I was very glad to see him bring up: Thinking is not a brain activity, but the activity of a body engaged in the world. (He cites Anthony Chemero’s Radical Embodied Cognitive Science (2009) which I have not read. I’d trace it back further to Andy Clark, David Chalmers, Eleanor Rosch, Heidegger…). Reducing it to a brain function, and further stripping the brain of its materiality to focus on its “processing” of “information” is reductive without being clarifying.

I came into this debate many years ago already made skeptical of the most recent claims about the causes of consciousness by having some awareness of the series of failed metaphors we have used over the past couple of thousands of years. Epstein puts this well, citing another book I have not read (and another book I’ve consequently just ordered):

In his book In Our Own Image (2015), the artificial intelligence expert George Zarkadakis describes six different metaphors people have employed over the past 2,000 years to try to explain human intelligence.

In the earliest one, eventually preserved in the Bible, humans were formed from clay or dirt, which an intelligent god then infused with its spirit. That spirit ‘explained’ our intelligence – grammatically, at least.

The invention of hydraulic engineering in the 3rd century BCE led to the popularity of a hydraulic model of human intelligence, the idea that the flow of different fluids in the body – the ‘humours’ – accounted for both our physical and mental functioning. The hydraulic metaphor persisted for more than 1,600 years, handicapping medical practice all the while.

By the 1500s, automata powered by springs and gears had been devised, eventually inspiring leading thinkers such as René Descartes to assert that humans are complex machines. In the 1600s, the British philosopher Thomas Hobbes suggested that thinking arose from small mechanical motions in the brain. By the 1700s, discoveries about electricity and chemistry led to new theories of human intelligence – again, largely metaphorical in nature. In the mid-1800s, inspired by recent advances in communications, the German physicist Hermann von Helmholtz compared the brain to a telegraph.

Maybe this time our tech-based metaphor has happened to get it right. But history says we should assume not. We should be very alert to the disanologies, which Epstein helps us with.

Getting this right, or at least not getting it wrong, matters. The most pressing problem with the informationalizing of thought is not that it applies a metaphor, or even that the metaphor is inapt. Rather it’s that this metaphor leads us to a seriously diminished understanding of what it means to be a living, caring creature.

I think.

Hat tip to @JenniferSertl for pointing out the Aeon article.

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I’m at the STEAM ed Finland conference in Jyväskylä where Mariia Gavriushenko is talking about personalized learning environments.

Web-based learning systems are being more and more widely used in large part because they can be used any time, anywhere. She points to two types: Learning management systems and game-based systems. But they lack personalization that makes them suitable for particular learners in terms of learning speed, knowledge background, preferences in learning and career, goals for future life, and their differing habits. Personalized systems can provide assistance in learning and adapt their learning path. Web-based learning shouldn’t just be more convenient. It should also be better adapted to personal needs.

But this is hard. But if you can do it, it can monitor the learner’s knowledge level and automatically present the right materials. In can help teachers create suitable material and find the most relevant content and convert it into comprehensive info. It can also help students identify the best courses and programs.

She talks about two types of personalized learning systems: 1. systems that allow the user to change the system or 2. the sysytem changes itself to meet the users needs. The systems can be based on rules and context or can be algorithm driven.

Five main features of adaptive learning systems:

• Pre-test

• Pacing and control

• Feedback and assessment

• Progress tracking and reports

• Motivation and reward

The ontological presentation of every learner keeps something like a profile for each user, enabling semantic reasoning.

She gives an example of this model: automated academic advising. It’s based on learning analytics. It’s an intelligent learning support system based on semantically-enhanced decision support, that identifies gaps, and recommends materials and courses. It can create a personal study plan. The ontology helps the system understand which topics are connected to others so that it can identify knowledge gaps.

An adaptive vocabulary learning environment provides cildren with an adaptive way to train their vocabulary, taking into account the individuality of the learner. It assumes the more similar the words, the harder they are to recognize.

Mariia believes we will make increasing use of adaptive educational tech.

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