Google has just launched a new podcast that Yannick Assogba (twitter: @tafsiri) and I put together. Yannick is a software engineer at Google PAIR where I was a writer-in-residence for two years, until mid-June. I am notably not a software engineer. Throughout the course of the nine episodes, Yannick helps me train machine learning models to play Tic Tac Toe and then a much more complex version of it. Then our models fight! (Guess who wins? Never mind.)

This is definitely not a tutorial. We’re focused on getting beneath the metaphors we usually use when talking about machine learning. In so doing, we keep coming back to the many human decisions that have to be made along the way.

So the podcast is for anyone who wants to get a more vivid sense of how ML works and the ways in which human intentions and assumptions shape each and every ML application. The podcast doesn’t require any math or programming skills.

It’s chatty and fun, and full of me getting simple things wrong. And Yannick is a fantastic teacher. I miss seeing him every day :(

All nine episodes are up now. They’re about 25 mins each. You can find them wherever you get your podcasts, so long as it carries ours.

Podcast: https://pair.withgoogle.com/thehardway/

Two-minute teaser:  https://share.transistor.fm/s/6768a641

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I was going through a shelf of books I haven’t visited in a couple of decades and found a book I used in 1986 when I taught Introduction to Computer Science in my last year as a philosophy professor. (It’s a long story.) Ethical Issues in the Use of Computers was a handy anthology, edited by Deborah G. Johnson and John W. Snapper (Wadsworth, 1985).

So what were the ethical issues posed by digital tech back then?

The first obvious point is that back then ethics were ethics: codes of conduct promulgated by professional societies. So, Part I consists of eight essays on “Codes of Conduct for the Computer Professions.” All but two of the articles present the codes for various computing associations. The two stray sheep are “The Quest for a Code of Professional Ethics: An Intellectual and Moral Confusion” (John Ladd) and “What Should Professional Societies do About Ethics?” (Fay H. Sawyier).

Part 2 covers “Issues of Responsibility”, with most of the articles concerning themselves with liability issues. The last article, by James Moor, ventures wider, asking “Are There Decisions Computers Should Not Make?” About midway through, he writes:

“Therefore, the issue is not whether there are some limitations to computer decision-making but how well computer decision making compares with human decision making.” (p. 123)

While saluting artificial intelligence researchers for their enthusiasm, Moor says “…at this time the results of their labors do not establish that computers will one day match or exceed human levels of ability for most kinds of intellectual activities.” Was Moor right? It depends. First define basically everything.

Moor concedes that Hubert Dreyfus’ argument (What Computers Still Can’t Do) that understanding requires a contextual whole has some power, but points to effective expert systems. Overall, he leaves open the question whether computers will ever match or exceed human cognitive abilities.

After talking about how to judge computer decisions, and forcefully raising Joseph Weizenbaum’s objection that computers are alien to human life and thus should not be allowed to make decisions about that life, Moor lays out some guidelines, concluding that we need to be pragmatic about when and how we will let computers make decisions:

“First, what is the nature of the computer’s competency and how has it been demonstrated? Secondly given our basic goals and values why is it better to use a computer decision maker in a particular situation than a human decision maker?”

We are still asking these questions.

Part 3 is on “Privacy and Security.” Four of the seven articles can be considered to be general introductions fo the concept of privacy. Apparently privacy was not as commonly discusssed back then.

Part 4, “Computers and Power,” suddenly becomes more socially aware. It includes an excerpt from Weizenbaum’s Computer Power and Human Reason, as well as articles on “Computers and Social Power” and “Peering into the Poverty Gap.”

Part 5 is about the burning issue of the day: “Software as Property.” One entry is the Third Circuit Court of Appeals finding in Apple vs. Franklin Computer. Franklin’s Ace computer contained operating system code that had been copied from Apple. The Court knew this because in addition to the programs being line-by-line copies, Franklin failed to remove the name of one of the Apple engineers that the engineer had embedded in the program. Franklin acknowledged the copying but argued that operating system code could not be copyrighted.

That seems so long ago, doesn’t it?

Because this post mentions Joseph Weizenbaum, here’s the beginning of a blog post from 2010:

I just came across a 1985 printout of notes I took when I interviewed Prof. Joseph Weizenbaum in his MIT office for an article that I think never got published. (At least Google and I have no memory of it.) I’ve scanned it in; it’s a horrible dot-matrix printout of an unproofed semi-transcript, with some chicken scratches of my own added. I probably tape recorded the thing and then typed it up, for my own use, on my KayPro.

In it, he talks about AI and ethics in terms much more like those we hear today. He was concerned about its use by the military especially for autonomous weapons, and raised issues about the possible misuse of visual recognition systems. Weizenbaum was both of his time and way ahead of it.

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Note that this is not late-breaking news. It’s breaking news brought to you late: Android contains a really great Google transcription tool.

Live Transcribe transcribes spoken text in real time. So far, it seems pretty awesome at it. And its machine learning model is loaded on your device, so it works even when you’re offline — convenient and potentially less intrusive privacy-wise. (Only potentially, because Google could upload your text when you connect if it wanted to.)

You can download Live Transcribe from the Play Store, but if you’re like me, it will only give you an option to uninstall it. Oddly, it doesn’t show up in my App drawer. You have to go to your phone’s Settings > Accessibility screen and scroll all the way down to find the Live Transcribe option.

Once you turn it on, you’ll get an icon all the way at the bottom of your screen, to the right of the Home button. Weird that it’s given that much status, but there it is.

I expect I will be using this tool with surprising frequency … although if I expect it, it won’t be surprising.

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Of the 4.67 gazillion pages on the Web, exactly 1.87 nano-bazillion are understandable by children. Suppose there were a convention and a service for making child-friendly versions of any site that wanted to increase its presence and value?

That was the basic idea behind our project at the MindCET Hackathon in the Desert a couple of weeks ago.

MindCET is an ed tech incubator created by the Center for Educational Technology (CET) in Israel. “Automatically generates grade-specific versions? Hahaha.”Its founder and leader is Avi Warshavsky, a brilliant technologist and a person of great warmth and character, devoted to improving education for all the world’s children. Over the ten years that I’ve been on the CET tech advisory board, Avi has become a treasured personal friend.

In Yeruham on the edge of the Negev, 14 teams of 6-8 people did the hackathon thing. Our team — to my shame, I don’t have a list of them — pretty quickly settled on thinking about what it would take to create a world-wide expectation that sites that explain things would have versions suitable for children at various grade levels.

So, here’s our plan for Onderstand.com.

Let’s say you have a site that provides information about some topic; our example was a page about how planes fly. It’s written at a normal adult level, or perhaps it assumes even more expertise about the topic. You would like the page to be accessible to kids in grade school.

No problem! Just go to Onderstand.com and enter the page’s URL. Up pops a form that lets you press a button to automatically generate versions for your choice of grade levels. Or you can create your own versions manually. The form also lets you enter useful metadata, including what school kid questions you think your site addresses, such as “How do planes fly?”, “What keeps planes up?”, and “Why don’t planes crash?” (And because everything is miscellaneous, you also enter tags, of course.)

Before I go any further, let me address your question: “It automatically generates grade-specific versions? Hahaha.” Yes, it’s true that in the 36 hours of the hackathon, we did not fully train the requisite machine learning model, in the sense that we didn’t even try. But let’s come back to that…

Ok, so imagine that you now have three grade-specific versions of your page about how planes fly. You put them on your site and give Onderstand their Web addresses as well as the metadata you’ve filled in. (Perhaps Onderstand.com would also host or archive the pages. We did not work out all these details.)

Onderstand generates a button you can place on your site that lets the visitor know that there are kid-ready versions.

The fact that there are those versions available is also recorded by Onderstand.com so that kids know that if they have a question, they can search Onderstand for appropriate versions.

Our business model is the classic “We’re doing something of value so someone will pay for it somehow.” Of course, we guarantee that we will never sell, rent, publish, share or monetize user information. But one positive thing about this approach: The service does not become valuable only once there’s lots of content. “Because sites get the kid-ready button, they get value from it”Because sites get the kid-ready button, they get value from it even if the Onderstand.com site attracts no visitors.

If the idea were to take off, then a convention that it establishes would be useful even if Onderstand were to fold up like a cheap table. The convention would be something like Wikipedia’s prepending “simple” before an article address. For example, the Wikipedia article “Airplane” is a great example of the problem: It is full of details but light on generalizations, uses hyperlinks as an excuse for lazily relying on jargon rather than readable text, and never actually explains how a plane flies. But if you prepend “simple” to that page’s URL — https://simple.wikipedia.org/wiki/Fixed-wing_aircraft — you get taken to a much shorter page with far fewer details (but also still no explanation of how planes fly).

Now, our hackathon group did not actually come up with what those prepensions should be. Maybe “grade3”, “grade9”, etc. But we wouldn’t want kids to have to guess which grade levels the site has available. So maybe just “school” or some such which would then pop up a list of the available versions. What I’m trying to say is that that’s the only detail left before we transform the Web.

The machine learning miracle

Machine learning might be able to provide a fairly straightforward, and often unsatisfactory, way of generating grade-specific versions.

“The ML could be trained on a corpus of text that has human-generated versions for kids.”The ML could be trained on a corpus of text that has human-generated versions for kids. The “simple” Wikipedia pages and their adult equivalents could be one source. Textbooks on the same subjects designed for different class levels might be another, even though — unlike the Wikipedia “simple” pages — they are not more or less translations of the same text. There are several experimental simplification applications discussed on the Web already.

Even if this worked, it’s likely to be sub-par because it would just be simplifying language, not generating explanations that creatively think in kids’ terms. For example, to explain flight to a high schooler, you would probably want to explain the Bernoulli effect and the four forces that act on a wing, but for a middle schooler you might start with the experiment in which they blow across a strip of paper, and for a grade schooler you might want to ask if they’ve ever blown on the bottom of a bubble.

So, even if the ML works, the site owner might want to do something more creative and effective. But still, simply having reduced-vocabulary versions could be helpful, and might set an expectation that a site isn’t truly accessible if it isn’t understandable.

Ok, so who’s in on the angel funding round?

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Back when I was a lad, we experienced the absurdity of life by watching as ordinary things in the world shed their meanings the way the Nazi who opens the chest in Raiders of the Lost Ark loses his skin: it just melts away.

In this experience of meaninglessness, though, what’s revealed is not some other layer beneath the surface, but the fact that all meaning is just something we make up and project over things that are indifferent to whatever we care to drape over them.

If you don’t happen to have a holy ark handy, you can experience this meaninglessness writ small by saying the word “ketchup” over and over until it becomes not a word but a sound. The magazine “Forbes” also works well for this exercise. Or, if you are a Nobel Prize winning writer and surprisingly consistently wrong philosopher like Jean Paul Sartre, perhaps a chestnut tree will reveal itself to you as utterly alien and resistant to the meaning we keep trying to throw on to it.

That was meaninglessness in the 1950s and on. Today we still manage to find our everyday world meaningless, but now we don’t see ourselves projecting meanings outwards but instead imagine ourselves to be in a computer simulation. Why? Because we pretty consistently understand ourselves in terms of our dominant tech, and these days the video cards owned by gamers are close to photo realistic, virtual reality is creating vivid spatial illusions for us, and AI is demonstrating the capacity of computers to simulate the hidden logic of real domains.

So now the source of the illusory meaning that we had taken for granted reveals itself not to be us projecting the world out from our skull holes but to be super-programmers who have created our experience of the world without bothering to create an actual world.

That’s a big difference. Projecting meaning only makes sense when there’s a world to project onto. The experience of meaninglessness as simulation takes that world away.

The meaninglessness we experience assigns the absurdity not to the arbitrariness that has led us to see the world one way instead of another, but to an Other whom we cannot see, imagine, or guess at. We envision, perhaps, children outside of our time and space playing a video game (“Sims Cosmos”), or alien computer scientists running a test to see what happens using the rules they’ve specified this time. For a moment we perhaps marvel at how life-like are the images we see as we walk down a street or along a forest path, how completely the programmers have captured the feeling of a spring rain on our head and shoulders but cleverly wasted no cycles simulating any special feeling on the soles of our feet. The whole enterprise – life, the universe, and everything – is wiped out the way a computer screen goes blank when the power is turned off.

In the spirit of the age, the sense of meaninglessness that comes from the sense we’re in a simulation is not despair, for it makes no difference. Everything is different but nothing has changed. The tree still rustles. The spring rain still smells of new earth. It is the essence of the simulation that it is full of meaning. That’s what’s being simulated. It’s all mind without any matter, unlike the old revelation that the world is all matter without meaning. The new meaninglessness is absurd absurdity, not tragic absurdity. We speculate about The Simulation without it costing a thing. The new absurdity is a toy of thought, not a problem for life.

I am not pining for my years suffering from attacks of Old School Anxiety. It was depressing and paralyzing. Our new way of finding the world meaningless is playful and does not turn every joy to ashes. It has its own dangers: it can release one from any sense of responsibility – “Dude, sorry to have killed your cat, but it was just a simulation” – and it can sap some of the sense of genuineness out of one’s emotions. But not for long because, hey, it’s a heck of a realistic simulation.

But to be clear, I reject both attempts to undermine the meaningfulness of our experience. I was drawn to philosophical phenomenology precisely because it was a way to pay attention to the world and our experience, rather than finding ways to diminish them both.

Both types of meaninglessness, however, think they are opening our eyes to the hollowness of life, when in fact they are privileging a moment of deprivation as a revelation of truth, as if the uncertainty and situatedness of meaning is a sign that it is illusory rather than it being the ground of every truth and illusion itself.

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The first in a series of six posts about my experiences learning how to train a machine learning system has just been posted here. There’s no code and no math in it. Instead it focuses on the tasks and choices involved in building one of these applications. How do you figure out what sort of data to provide? How do you get that data into the system? How can you tell when the system has been trained? What types of controls do the developers have over the outcomes? What sort of ways can I go wrong? (Given that the title of the series is “The Adventures of a TensorFlow.js n00b” the answer to that last question is: Every way.)

I was guided through this project by Yannick Assogba, a developer in the machine learning research group — People + AI Research –I’m embedded in at Google as a writer in residence. Yannick is natural born teacher, and is preternaturally patient.

The series is quite frank. I make every stupid mistake possible. And for your Schadenfreude, five more posts in this series are on their way…

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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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