Book Club: How to Create a Mind, pt 2/2

Ray Kurzweil, writer, inventor, thinker

Welcome back to EvX’s Book Club. Today  are finishing Ray Kurzweil’s How to Create a Mind: The Secret of Human thought Revealed.

Spiders are interesting, but Kurzweil’s focus is computers, like Watson, which trounced the competition on Jeopardy!

I’ll let Wikipedia summarize Watson:

Watson was created as a question answering (QA) computing system that IBM built to apply advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to the field of open domain question answering.[2]

The sources of information for Watson include encyclopedias, dictionaries, thesauri, newswire articles, and literary works. Watson also used databases, taxonomies, and ontologies. …

Watson parses questions into different keywords and sentence fragments in order to find statistically related phrases.[22] Watson’s main innovation was not in the creation of a new algorithm for this operation but rather its ability to quickly execute hundreds of proven language analysis algorithms simultaneously.[22][24] The more algorithms that find the same answer independently the more likely Watson is to be correct.[22] Once Watson has a small number of potential solutions, it is able to check against its database to ascertain whether the solution makes sense or not.[22]

Kurzweil opines:

That is at least one reason why Watson represents such a significant milestone: Jeopardy! is precisely such a challenging language task. … What is perhaps not evident to many observers is that Watson not only had to master the language in the unexpected and convoluted queries, but for the most part its knowledge was not hand-coded. It obtained that knowledge by actually reading 200 million pages of natural-language documents, including all of Wikipedia… If Watson can understand and respond to questions based on 200 million pages–in three seconds!–here is nothing to stop similar systems from reading the other billions of documents on the Web. Indeed, that effort is now under way.

A point about the history of computing that may be petty of me to emphasize:

Babbage’s conception is quite miraculous when you consider the era in which he lived and worked. However, by the mid-twentieth century, his ideas had been lost in the mists of time (although they were subsequently rediscovered.) It was von Neumann who conceptualized and articulated the key principles of the computer as we know it today, and the world recognizes this by continuing to refer to the von Neumann machine as the principal model of computation. Keep in mind, though, that the von Neumann machine continually communicates data between its various units and within those units, so it could not be built without Shannon’s theorems and the methods he devised for transmitting and storing reliable digital information. …

You know what? No, it’s not petty.

Amazon lists 57 books about Ada Lovelace aimed at children, 14 about Alan Turing, and ZERO about John von Neumann.

(Some of these results are always irrelevant, but they are roughly correct.)

“EvX,” you may be saying, “Why are you counting children’s books?”

Because children are our future, and the books that get published for children show what society deems important for children to learn–and will have an effect on what adults eventually know.

I don’t want to demean Ada Lovelace’s role in the development of software, but surely von Neumann’s contributions to the field are worth a single book!

*Slides soapbox back under the table*

Anyway, back to Kurzweil, now discussing quantum mechanics:

There are two ways to view the questions we have been considering–converse Western an Easter perspective on the nature of consciousness and of reality. In the Western perspective, we start with a physical world that evolves patterns of information. After a few billion years of evolution, the entities in that world have evolved sufficiently to become conscious beings In the Eastern view, consciousness is the fundamental reality, the physical word only come into existence through the thoughts of conscious beings. …

The East-West divide on the issue of consciousness has also found expression in opposing schools of thought in the field of subatomic physics. In quantum mechanics, particles exist in what are called probability fields. Any measurement carried out on them by a measuring device causes what is called a collapse of the wave function, meaning that the particle suddenly assumes a particular location. A popular view is that such a measurement constitutes observation by a conscious observer… Thus the particle assume a particular location … only when it is observed. Basically particles figure that if no one is bothering to look at them, they don’t need to decide where they are. I call this the Buddhist school of quantum mechanics …

Niels Bohr

Or as Niels Bohr put it, “A physicist is just an atom’s way of looking at itself.” He also claimed that we could describe electrons exercised free will in choosing their positions, a statement I do not think he meant literally; “We must be clear that when it comes to atoms, language can be used only as in poetry,” as he put it.

Kurzweil explains the Western interpretation of quantum mechanics:

There is another interpretation of quantum mechanics… In this analysis, the field representing a particle is not a probability field, but rather just a function that has different values in different locations. The field, therefore, is fundamentally what the particle is. … The so-called collapse of the wave function, this view holds, is not a collapse at all. … It is just that a measurement device is also made up of particles with fields, and the interaction of the particle field being measured and the particle fields of the measuring device result in a reading of the particle being in a particular location. The field, however, is still present. This is the Western interpretation of quantum mechanics, although it is interesting to note that the more popular view among physicists worldwide is what I have called the Eastern interpretation.

Soviet atomic bomb, 1951

For example, Bohr has the yin-yang symbol on his coat of arms, along with the motto contraria sunt complementa, or contraries are complementary. Oppenheimer was such a fan of the Bhagavad Gita that he read it in Sanskrit and quoted it upon successful completion of the Trinity Test, “If the radiance of a thousand suns were to burst at once into the sky, that would be like the splendor of the mighty one,” and “Now I am become death, the destroyer of worlds.” He credited the Gita as one of the most important books in his life.

Why the appeal of Eastern philosophy? Is it something about physicists and mathematicians? Leibnitz, after all, was fond of the I Ching. As Wikipedia says:

Leibniz was perhaps the first major European intellectual to take a close interest in Chinese civilization, which he knew by corresponding with, and reading other works by, European Christian missionaries posted in China. Having read Confucius Sinarum Philosophus on the first year of its publication,[153] he concluded that Europeans could learn much from the Confucian ethical tradition. He mulled over the possibility that the Chinese characters were an unwitting form of his universal characteristic. He noted with fascination how the I Ching hexagrams correspond to the binary numbers from 000000 to 111111, and concluded that this mapping was evidence of major Chinese accomplishments in the sort of philosophical mathematics he admired.[154] Leibniz communicated his ideas of the binary system representing Christianity to the Emperor of China hoping it would convert him.[84] Leibniz may be the only major Western philosopher who attempted to accommodate Confucian ideas to prevailing European beliefs.[155]

Leibniz’s attraction to Chinese philosophy originates from his perception that Chinese philosophy was similar to his own.[153] The historian E.R. Hughes suggests that Leibniz’s ideas of “simple substance” and “pre-established harmony” were directly influenced by Confucianism, pointing to the fact that they were conceived during the period that he was reading Confucius Sinarum Philosophus.[153]

Perhaps it is just that physicists and mathematicians are naturally curious people, and Eastern philosophy is novel to a Westerner, or perhaps by adopting Eastern ideas, they were able to purge their minds of earlier theories of how the universe works, creating a blank space in which to evaluate new data without being biased by old conceptions–or perhaps it is just something about the way their minds work.

As for quantum, I favor the de Broglie-Bohm interpretation of quantum mechanics, but obviously I am not a physicist and my opinion doesn’t count for much. What do you think?

But back to the book. If you are fond of philosophical ruminations on the nature of consciousness, like “What if someone who could only see in black and white read extensively about color “red,” could they ever achieve the qualia of actually seeing the color red?” or “What if a man were locked in a room with a perfect Chinese rulebook that told him which Chinese characters to write in response to any set of characters written on notes passed under the door? The responses are be in perfect Chinese, but the man himself understands not a word of Chinese,” then you’ll enjoy the discussion. If you already covered all of this back in Philosophy 101, you might find it a bit redundant.

Kurzweil notes that conditions have improved massively over the past century for almost everyone on earth, but people are increasingly anxious:

A primary reason people believe life is getting worse is because our information about the problems of the world has steadily improved. If there is a battle today somewhere on the planet, we experience it almost as if we were there. During World War II, tens of thousand of people might perish in a battle, and if the public could see it at all it was in a grainy newsreel in a movie theater weeks later. During World War I a small elite could read about the progress of the conflict in the newspaper (without pictures.) During the nineteenth century there was almost no access to news in a timely fashion for anyone.

As for the future of man, machines, and code, Kurzweil is even more optimistic than Auerswald:

The last invention that biological evolution needed to make–the neocortex–is inevitably leading to the last invention that humanity needs to make–truly intelligent machines–and the design of one is inspiring the other. … by the end of this century we will be able to create computation at the limits of what is possible, based on the laws of physics… We call matter and energy organized in this way “computronium” which is vastly more powerful pound per pound than the human brain. It will not jut be raw computation but will be infused with intelligent algorithms constituting all of human-machine knowledge. Over time we will convert much of the mass and energy in our tiny corner of the galaxy that is suitable for this purpose to computronium. … we will need to speed out to the rest of the galaxy and universe. …

How long will it take for us to spread our intelligence in its nonbiological form throughout the universe? … waking up the universe, and then intelligently deciding its fate by infusing it with our human intelligence in its nonbiological form, is our destiny.

Whew! That is quite the ending–and with that, so will we. I hope you enjoyed the book. What did you think of it? Will Humanity 2.0 be good? Bad? Totally different? Or does the Fermi Paradox imply that Kurzweil is wrong? Did you like this shorter Book Club format? And do you have any ideas for our next Book Club pick?

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Book Club: How to Create a Mind by Ray Kurzweil pt 1/2

Welcome to our discussion of Ray Kurzweil’s How to Create a Mind: The Secret of Human thought Revealed. This book was requested by one my fine readers; I hope you have enjoyed it.

If you aren’t familiar with Ray Kurzweil (you must be new to the internet), he is a computer scientist, inventor, and futurist whose work focuses primarily on artificial intelligence and phrases like “technological singularity.”

Wikipedia really likes him.

The book is part neuroscience, part explanations of how various AI programs work. Kurzweil uses models of how the brain works to enhance his pattern-recognition programs, and evidence from what works in AI programs to build support for theories on how the brain works.

The book delves into questions like “What is consciousness?” and “Could we recognize a sentient machine if we met one?” along with a brief history of computing and AI research.

My core thesis, which I call the Law of Accelerating Returns, (LOAR), is that fundamental measures of of information technology follow predictable and exponential trajectories…

I found this an interesting sequel to Auerswald’s The Code Economy and counterpart to Gazzaniga’s Who’s In Charge? Free Will and the Science of the Brain, which I listened to in audiobook form and therefore cannot quote very easily. Nevertheless, it’s a good book and I recommend it if you want more on brains.

The quintessential example of the law of accelerating returns is the perfectly smooth, doubly exponential growth of the price/performance of computation, which has held steady for 110 years through two world was, the Great Depression, the Cold War, the collapse of the Soviet Union, the reemergence of China, the recent financial crisis, … Some people refer to this phenomenon as “Moore’s law,” but… [this] is just one paradigm among many.

From Ray Kurzweil,

Auerswald claims that the advance of “code” (that is, technologies like writing that allow us to encode information) has, for the past 40,000 years or so, supplemented and enhanced human abilities, making our lives better. Auerswald is not afraid of increasing mechanization and robotification of the economy putting people out of jobs because he believes that computers and humans are good at fundamentally different things. Computers, in fact, were invented to do things we are bad at, like decode encryption, not stuff we’re good at, like eating.

The advent of computers, in his view, lets us concentrate on the things we’re good at, while off-loading the stuff we’re bad at to the machines.

Kurzweil’s view is different. While he agrees that computers were originally invented to do things we’re bad at, he also thinks that the computers of the future will be very different from those of the past, because they will be designed to think like humans.

A computer that can think like a human can compete with a human–and since it isn’t limited in its processing power by pelvic widths, it may well out-compete us.

But Kurzweil does not seem worried:

Ultimately we will create an artificial neocortex that has the full range and flexibility of its human counterpart. …

When we augment our own neocortex with a synthetic version, we won’t have to worry about how much additional neocortex can physically fit into our bodies and brains, as most of it will be in the cloud, like most of the computing we use today. I estimated earlier that we have on the order of 300 million pattern recognizers in our biological neocortex. That’s as much as could b squeezed into our skulls even with the evolutionary innovation of a large forehead and with the neocortex taking about 80 percent of the available space. As soon as we start thinking in the cloud, there will be no natural limits–we will be able to use billions or trillions of pattern recognizers, basically whatever we need. and whatever the law of accelerating returns can provide at each point in time. …

Last but not least, we will be able to back up the digital portion of our intelligence. …

That is kind of what I already do with this blog. The downside is that sometimes you people see my incomplete or incorrect thoughts.

On the squishy side, Kurzweil writes of the biological brain:

The story of human intelligence starts with a universe that is capable of encoding information. This was the enabling factor that allowed evolution to take place. …

The story of evolution unfolds with increasing levels of abstraction. Atoms–especially carbon atoms, which can create rich information structures by linking in four different directions–formed increasingly complex molecules. …

A billion yeas later, a complex molecule called DNA evolved, which could precisely encode lengthy strings of information and generate organisms described by these “programs”. …

The mammalian brain has a distinct aptitude not found in any other class of animal. We are capable of hierarchical thinking, of understanding a structure composed of diverse elements arranged in a pattern, representing that arrangement with a symbol, and then using that symbol as an element in a yet more elaborate configuration. …

I really want to know if squids or octopuses can engage in symbolic thought.

Through an unending recursive process we are capable of building ideas that are ever more complex. … Only Homo sapiens have a knowledge base that itself evolves, grow exponentially, and is passe down from one generation to another.

Kurzweil proposes an experiment to demonstrate something of how our brains encode memories: say the alphabet backwards.

If you’re among the few people who’ve memorized it backwards, try singing “Twinkle Twinkle Little Star” backwards.

It’s much more difficult than doing it forwards.

This suggests that our memories are sequential and in order. They can be accessed in the order they are remembered. We are unable to reverse the sequence of a memory.

Funny how that works.

On the neocortex itself:

A critically important observation about the neocortex is the extraordinary uniformity of its fundamental structure. … In 1957 Mountcastle discovered the columnar organization of the neocortex. … [In 1978] he described the remarkably unvarying organization of the neocortex, hypothesizing that it was composed of a single mechanism that was repeated over and over again, and proposing the cortical column as the basic unit. The difference in the height of certain layers in different region noted above are simply differences in the amount of interconnectivity that the regions are responsible for dealing with. …

extensive experimentation has revealed that there are in fact repeating units within each column. It is my contention that the basic unit is a pattern organizer and that this constitute the fundamental component of the neocortex.

As I read, Kurzweil’s hierarchical models reminded me of Chomsky’s theories of language–both Ray and Noam are both associated with MIT and have probably conversed many times. Kurzweil does get around to discussing Chomsky’s theories and their relationship to his work:

Language is itself highly hierarchical and evolved to take advantage of the hierarchical nature of the neocortex, which in turn reflects the hierarchical nature of reality. The innate ability of human to lean the hierarchical structures in language that Noam Chomsky wrote about reflects the structure of of the neocortex. In a 2002 paper he co-authored, Chomsky cites the attribute of “recursion” as accounting for the unique language faculty of the human species. Recursion, according to Chomsky, is the ability to put together small parts into a larger chunk, and then use that chunk as a part in yet another structure, and to continue this process iteratively In this way we are able to build the elaborate structure of sentences and paragraphs from a limited set of words. Although Chomsky was not explicitly referring here to brain structure, the capability he is describing is exactly what the neocortex does. …

This sounds good to me, but I am under the impression that Chomsky’s linguistic theories are now considered outdated. Perhaps that is only his theory of universal grammar, though. Any linguistics experts care to weigh in?

According to Wikipedia:

Within the field of linguistics, McGilvray credits Chomsky with inaugurating the “cognitive revolution“.[175] McGilvray also credits him with establishing the field as a formal, natural science,[176] moving it away from the procedural form of structural linguistics that was dominant during the mid-20th century.[177] As such, some have called him “the father of modern linguistics”.[178][179][180][181]

The basis to Chomsky’s linguistic theory is rooted in biolinguistics, holding that the principles underlying the structure of language are biologically determined in the human mind and hence genetically transmitted.[182] He therefore argues that all humans share the same underlying linguistic structure, irrespective of sociocultural differences.[183] In adopting this position, Chomsky rejects the radical behaviorist psychology of B. F. Skinner which views the mind as a tabula rasa (“blank slate”) and thus treats language as learned behavior.[184] Accordingly, he argues that language is a unique evolutionary development of the human species and is unlike modes of communication used by any other animal species.[185][186] Chomsky’s nativist, internalist view of language is consistent with the philosophical school of “rationalism“, and is contrasted with the anti-nativist, externalist view of language, which is consistent with the philosophical school of “empiricism“.[187][174]

Anyway, back to Kuzweil, who has an interesting bit about love:

Science has recently gotten into the act as well, and we are now able to identify the biochemical changes that occur when someone falls in love. Dopamine is released, producing feelings of happiness and delight. Norepinephrin levels soar, which lead to a racing heart and overall feelings of exhilaration. These chemicals, along with phenylethylamine, produce elevation, high energy levels, focused attention, loss of appetite, and a general craving for the object of one’s desire. … serotonin level go down, similar to what happens in obsessive-compulsive disorder….

If these biochemical phenomena sound similar to those of the flight-or-fight syndrome, they are, except that we are running toward something or someone; indeed, a cynic might say toward rather than away form danger. The changes are also fully consistent with those of the early phase of addictive behavior. …  Studies of ecstatic religious experiences also show the same physical phenomena, it can be said that the person having such an experiences is falling in love with God or whatever spiritual connection on which they are focused. …

Religious readers care to weigh in?

Consider two related species of voles: the prairie vole and the montane vole. They are pretty much identical, except that the prairie vole has receptors for oxytocin and vasopressin, whereas the montane vole does not. The prairie vole is noted for lifetime monogamous relationships, while the montane vole resorts almost exclusively to one-night stands.

Learning by species:

A mother rat will build a nest for her young even if she has never seen another rat in her lifetime. Similarly, a spider will spin a web, a caterpillar will create her own cocoon, and a beaver will build a damn, even if no contemporary ever showed them how to accomplish these complex tasks. That is not to say that these are not learned behavior. It is just that he animals did not learn them in a single lifetime… The evolution of animal behavior does constitute a learning process, but it is learning by the species, not by the individual and the fruits of this leaning process are encoded in DNA.

I think that’s enough for today; what did you think? Did you enjoy the book? Is Kurzweil on the right track with his pattern recognizers? Are non-biological neocortexes on the horizon? Will we soon convert the solar system to computronium?

Let’s continue this discussion next Monday–so if you haven’t read the book yet, you still have a whole week to finish.

 

A Little Review of Big Data Books

I recently finished three books on “big data”– Big Data: A Revolution That Will Transform How We Live, Work, and Think, by Viktor Mayer-Schönberger and Kenneth Cukier; Everybody Lies: Big Data, New Data, and What the Internet can tell us about who we Really Are, by Seth Stephens-Davidowitz; and Big Data At Work: Dispelling the Myths, Uncovering the opportunities, by Thomas H. Davenport.

None of these books was a whiz-bang thriller, but I enjoyed them.

Big Data was a very sensible introduction. What exactly is “big data”? It’s not just bigger data sets (though it is also that.) It’s the opportunity to get all the data.

Until now, the authors point out, we have lived in a data poor world. We have had to carefully design our surveys to avoid sampling bias because we just can’t sample that many people. There’s a whole bunch of math done over in statistics to calculate how certain we can be about a particular result, or whether it could just be the result of random chance biasing our samples. I could poll 10,000 people about their jobs, and that might be a pretty good sample, but if everyone I polled happens to live within walking distance of my house, is this a very representative sample of everyone in the country? Now think about all of those studies on the mechanics of sleep done on whatever college students or homeless guys a scientist could convince to sleep in a lab for a week. How representative are they?

Today, though, we suddenly live in a data rich world. An exponentially data rich world. A world in which we no longer need to correct for bias in our sample, because we don’t have to sample. We can just get… all the data. You can go to Google and find out how many people searched for “rabbit” on Tuesday, or how many people misspelled “rabbit” in various ways.

Data is being used in new and interesting (and sometimes creepy) ways. Many things that previously weren’t even considered data are now being quantitized–like one researcher quantitizing people’s backsides to determine whether a car is being driven by its owner, or a stranger.

One application I find promising is using people’s searches for various disease symptoms to identify people who may have various diseases before they seek out a doctor. Catching cancer patients earlier could save millions of lives.

I don’t have the book in front of me anymore, so I am just going by memory, but it made a good companion to Auerswald’s The Code Economy, since the modern economy runs so much on data.

Everybody Lies was a much more lighthearted, annecdotal approach to the subject, discussing lots of different studies. Davidowitz was inspired by Freakonomics, and he wants to use Big Data to uncover hidden truths of human behavior.

The book discusses, for example, people’s pornographic searches, (as per the title, people routinely lie about how much porn they look at on the internet,) and whether people’s pornographic preferences can be used to determine what percent of people in each state are gay. It turns out that we can get a break down of porn queries by state and variety, allowing a rough estimate of the gay and straight population of each state–and it appears that what people are willing to tell pollsters about their sexuality doesn’t match what they search for online. In more conservative states, people are less likely to admit to pollsters that they are gay, but plenty of supposedly “straight” people are searching for gay porn–about the same number of people as actually admit to being gay in more liberal states.

Stephens-Davidowitz uses similar data to determine that people have been lying to pollsters (or perhaps themselves) about whom they plan to vote for. For example, Donald Trump got anomalously high votes in some areas, and Obama got anomalously low votes, compared to what people in those areas told pollsters. However, both of these areas correlated highly with areas of the country where people made a lot of racist Google searches.

Most of the studies discussed are amusing, like the discovery of the racehorse American Pharaoh. Others are quite important, like a study that found that child abuse was probably actually going up at a time when official reports said it wasn’t–the reports probably weren’t showing abuse due to a decrease in funding for investigating abuse.

At times the author steps beyond the studies and offers interpretations of why the results are the way they are that I think go beyond what the data tells, like his conclusion that parents are biased against their daughters because they are more concerned with girls being fat than with boys, or because they are more likely to Google “is my son a genius?” than “is my daughter a genius?”

I can think of a variety of alternative explanations. eg, society itself is crueler to overweight women than to overweight men, so it is reasonable, in turn, for parents to worry more about a daughter who will face cruelty than a boy who will not. Girls are more likely to be in gifted programs than boys, but perhaps this means that giftedness in girls is simply less exceptional than giftedness in boys, who are more unusual. Or perhaps male giftedness is different from female giftedness in some way that makes parents need more information on the topic.

Now, here’s an interesting study. Google can track how many people make Islamophobic searches at any particular time. Compared against Obama’s speech that tried to calm outrage after the San Bernardino attack, this data reveals that the speech was massively unsuccessful. Islamophobic searches doubled during and after the speech. Negative searches about Syrian refugees rose 60%, while searches asking how to help dropped 35%.

In fact, just about every negative search we cold think to test regarding Muslims shot up during and after Obama’s speech, and just about every positive search we could think to test declined. …

Instead of calming the angry mob, as everybody thought he was doing, the internet data tells us that Obama actually inflamed it.

However, Obama later gave another speech, on the same topic. This one was much more successful. As the author put it, this time, Obama spent little time insisting on the value of tolerance, which seems to have just made people less tolerant. Instead, “he focused overwhelmingly on provoking people’s curiosity and changing their perceptions of Muslim Americans.”

People tend to react positively toward people or things they regard as interesting, and invoking curiosity is a good way to get people interested.

The author points out that “big data” is most likely to be useful in fields where the current data is poor. In the case of American Pharaoh, for examples, people just plain weren’t getting a lot of data on racehorses before buying and selling them. It was a field based on people who “knew” horses and their pedigrees, not on people who x-rayed horses to see how big their hearts and lungs were. By contrast, hedge funds investing in the stock market are already up to their necks in data, trying to maximize every last penny. Horse racing was ripe for someone to become successful by unearthing previously unused data and making good predictions; the stock market is not.

And for those keeping track of how many people make it to the end of the book, I did. I even read the endnotes, because I do that.

Big Data At Work was very different. Rather than entertain us with the success of Google Flu or academic studies of human nature, BDAW discusses how to implement “big data” (the author admits it is a silly term) strategies at work. This is a good book if you own, run, or manage a business that could utilize data in some way. UPS, for example, uses driving data to minimize package delivery routes; even a small saving per package by optimizing routes leads to a large saving for the company as a whole, since they deliver so many packages.

The author points out that “big data” often isn’t big so much as unstructured. Photographs, call logs, Facebook posts, and Google searches may all be “data,” but you will need some way to quantitize these before you can make much use of them. For example, companies may want to gather customer feedback reports, feed them into a program that recognizes positive or negative language, and then quantitizes how many people called to report that they liked Product X vs how many called to report that they disliked it.

I think an area ripe for this kind of quantitization is medical data, which currently languishes in doctors’ files, much of it on paper, protected by patient privacy laws. But people post a good deal of information about their medical conditions online, seeking help from other people who’ve dealt with the same diseases. Currently, there are a lot of diseases (take depression) where treatment is very hit-or-miss, and doctors basically have to try a bunch of drugs in a row until they find one that works. A program that could trawl through forum posts and assemble data on patients and medical treatments that worked or failed could help doctors refine treatment for various difficult conditions–“Oh, you look like the kind of patient who would respond well to melatonin,” or “Oh, you have the characteristics that make you a good candidate for Prozac.”

The author points out that most companies will not be able to keep the massive quantities of data they are amassing. A hospital, for example, collects a great deal of data about patient’s heart rates and blood oxygen levels every day. While it might be interesting to look back at 10 years worth of patient heart rate data, hospitals can’t really afford to invest in databanks to store all of this information. Rather, what companies need is real-time or continuous data processing that analyzes current data and makes predictions/recommendations for what the company (or doctor) should do now.

For example, one of the books (I believe it was “Big Data”) discussed a study of premature babies which found, counter-intuitively, that they were most likely to have emergencies soon after a lull in which they had seemed to be doing rather well–stable heart rate, good breathing, etc. Knowing this, a hospital could have a computer monitoring all of its premature babies and automatically updating their status (“stable” “improving” “critical” “likely to have a big problem in six hours”) and notifying doctors of potential problems.

The book goes into a fair amount of detail about how to implement “big data solutions” at your office (you may have to hire someone who knows how to code and may even have to tolerate their idiosyncrasies,) which platforms are useful for data, the fact that “big data” is not all that different from standard analytics that most companies already run, etc. Once you’ve got the data pumping, actual humans may not need to be involved with it very often–for example you may have a system that automatically updates drives’ routes with traffic reports, or sprinklers that automatically turn on when the ground gets too dry.

It is easy to see how “big data” will become yet another facet of the algorithmization of work.

Overall, Big Data at Work is a good book, especially if you run a company, but not as amusing if you are just a lay reader. If you want something fun, read the first two.

Do Sufficiently Large Organizations Start Acting Like Malevolent AIs? (pt 2)

(Part 1 is here)

As we were discussing on Monday, as our networks have become more effective, our ability to incorporate new information may have actually gone down. Ironically, as we add more people to a group–beyond a certain limit–it becomes more difficult for individuals with particular expertise to convince everyone else in the group that the group’s majority consensus is wrong.

The difficulties large groups experience trying to coordinate and share information force them to become dominated by procedures–set rules of behavior and operation are necessary for large groups to operate. A group of three people can use ad-hoc consensus and rock-paper-scissors to make decisions; a nation of 320 million requires a complex body of laws and regulations. (I once tried to figure out just how many laws and regulations America has. The answer I found was that no one knows.)

An organization is initially founded to accomplish some purpose that benefits its founders–generally to make them well-off, but often also to produce some useful good or service. A small organization is lean, efficient, and generally exemplifies the ideals put forth in Adam Smith’s invisible hand:

It is not from the benevolence of the butcher, the brewer, or the baker, that we expect our dinner, but from their regard to their own interest. We address ourselves, not to their humanity but to their self-love, and never talk to them of our necessities but of their advantages. —The Wealth Of Nations, Book I

As an organization ages and grows, its founders retire or move on, it becomes more dependent on policies and regulations and each individual employee finds his own incentives further displaced from the company’s original intentions. Soon a company is no longer devoted to either the well-being of its founders or its customers, but to the company itself. (And that’s kind of a best-case scenario in which the company doesn’t just disintegrate into individual self-interest.)

I am reminded of a story about a computer that had been programmed to play Tetris–actually, it had been programmed not to lose at Tetris. So the computer paused the game. A paused game cannot lose.

What percentage of employees (especially management) have been incentivized to win? And what percentage are being incentivized to not lose?

And no, I don’t mean that in some 80s buzzword-esque way. Most employees have more to lose (ie, their jobs) if something goes wrong as a result of their actions than to gain if something goes right. The stockholders might hope that employees are doing everything they can to maximize profits, but really, most people are trying not to mess up and get fired.

Fear of messing up goes beyond the individual scale. Whole companies are goaded by concerns about risk–“Could we get sued?” Large corporation have entire legal teams devoted to telling them how they could get sued for whatever their doing and to filing lawsuits against their competitors for whatever they’re doing.

This fear of risk carries over, in turn, to government regulations. As John Sanphillipo writes in City Regulatory Hurdles Favor Big Developers, not the Little Guy:

A family in a town I visited bought an old fire station a few years ago with the intention of turning it into a Portuguese bakery and brewpub. They thought they’d have to retrofit the interior of the building to meet health and safety standards for such an establishment.

Turns out the cost of bringing the landscape around the outside of the building up to code was their primary impediment. Mandatory parking requirements, sidewalks, curb cuts, fire lanes, on-site stormwater management, handicapped accessibility, drought-tolerant native plantings…it’s a very long list that totaled $340,000 worth of work. … Guess what? They decided not to open the bakery or brewery. …

Individually it’s impossible to argue against each of the particulars. Do you really want to deprive people in wheelchairs of the basic civil right of public accommodation? Do you really want the place to catch fire and burn? Do you want a barren landscape that’s bereft of vegetation? …

I was in Hamtramck, Michigan a couple of years ago to participate in a seminar about reactivating neighborhoods through incremental small-scale development. …

While the event was underway the fire marshal happened to drive by and noticed there were people—a few dozen actual humans—occupying a commercial building in broad daylight. In a town that has seen decades of depopulation and disinvestment, this was an odd sight. And he was worried. Do people have permission for this kind of activity? Had there been an inspection? Was a permit issued? Is everything insured? He called one of his superiors to see if he should shut things down in the name of public safety.

It’s a good article. You should read the whole thing.

Back in Phillipe Bourgeois’s In Search of Respect: Selling Crack in el Barrio, Phillipe describes one drug dealer’s attempt to use the money he’d made to go into honest business by opening a convenience store. Unfortunately, he couldn’t get the store complaint with NYC disability-access regulations, and so the store never opened and the owner went back to dealing drugs. (What IQ, I wonder, is necessary to comply with all of these laws and regulations in the first place?)

Now, I’m definitely in favor of disabled people being able to buy groceries and use bathrooms. But what benefits a disabled person more: a convenience store that’s not fully wheel-chair accessible, or a crack house?

In My IRB Nightmare, Scott Alexander writes about trying to do a simple study to determine whether the screening test already being used to diagnose people with bipolar disorder is effective at diagnosing them:

When we got patients, I would give them the bipolar screening exam and record the results. Then Dr. W. would conduct a full clinical interview and formally assess them. We’d compare notes and see how often the screening test results matched Dr. W’s expert diagnosis.

Remember, they were already using the screening test on patients and then having them talk to the doctor for a formal assessment. The only thing the study added was that Scott would compare how well the screening results matched the formal assessment. No patients would be injected, subject to new procedures, or even asked different questions. They just wanted to compare two data sets.

After absurd quantities of paperwork and an approval process much too long to summarize here, the project got audited:

I kept the audit report as a souvenier. I have it in front of me now. Here’s an example infraction:

The data and safety monitoring plan consists of ‘the Principal Investigator will randomly check data integrity’. This is a prospective study with a vulnerable group (mental illness, likely to have diminished capacity, likely to be low income) and, as such, would warrant a more rigorous monitoring plan than what is stated above. In addition to the above, a more adequate plan for this study would also include review of the protocol at regular intervals, on-going checking of any participant complaints or difficulties with the study, monitoring that the approved data variables are the only ones being collected, regular study team meetings to discuss progress and any deviations or unexpected problems. Team meetings help to assure participant protections, adherence to the protocol. Having an adequate monitoring plan is a federal requirement for the approval of a study. See Regulation 45 CFR 46.111 Criteria For IRB Approval Of Research. IRB Policy: PI Qualifications And Responsibility In Conducting Research. Please revise the protocol via a protocol revision request form. Recommend that periodic meetings with the research team occur and be documented.

… Faced with submitting twenty-seven new pieces of paperwork to correct our twenty-seven infractions, Dr. W and I gave up. We shredded the patient data and the Secret Code Log. We told all the newbies they could give up and go home. … We told the IRB that they had won, fair and square; we surrendered unconditionally.

The point of all that paperwork and supervision is to make sure that no one replicates the Tuskegee Syphilis Experiment nor the Nazi anything. Noble sentiments–but as a result, a study comparing two data sets had to be canceled.

I’ve noticed recently that much of the interesting medical research is happening in the third world/China–places where the regulations aren’t as strong and experiments (of questionable ethics or not) can actually get done.

Like the computer taught not to lose at Tetris, all of these systems are more focused on minimizing risk–even non-existent risk–than on actually succeeding.

In his review of Yudkowsky’s Inadequate Equilibria, Scott writes:

…[Yudkowsky] continues to the case of infant parenteral nutrition. Some babies have malformed digestive systems and need to have nutrient fluid pumped directly into their veins. The nutrient fluid formula used in the US has the wrong kinds of lipids in it, and about a third of babies who get it die of brain or liver damage. We’ve known for decades that the nutrient fluid formula has the wrong kind of lipids. We know the right kind of lipids and they’re incredibly cheap and there is no reason at all that we couldn’t put them in the nutrient fluid formula. We’ve done a bunch of studies showing that when babies get the right nutrient fluid formula, the 33% death rate disappears. But the only FDA-approved nutrient fluid formula is the one with the wrong lipids, so we just keep giving it to babies, and they just keep dying. Grant that the FDA is terrible and ruins everything, but over several decades of knowing about this problem and watching the dead babies pile up, shouldn’t somebody have done something to make this system work better?

The doctors have to use the FDA-approved formula or they could get sued for malpractice. The insurance companies, of course, only cover the FDA-approved formula. The formula makers are already making money selling the current formula and would probably have to go through an expensive, multi-year review system (with experiments far more regulated than Scott’s) to get the new formula approved, and even then they might not actually get approval. In short, on one side are people in official positions of power whose lives could be made worse (or less convenient) if they tried to fix the problem, and on the other side are dead babies who can’t stand up for themselves.

The Chankiri Tree (Killing Tree) where infants were fatally smashed, Choeung Ek, Cambodia.

Communism strikes me as the ultimate expression of this beast: a society fully transformed into a malevolent AI. It’s impossible to determine exactly how many people were murdered by communism, but the Black Book of Communism estimates a death toll between 85 and 100 million people.

Capitalism, for all its faults, is at least somewhat decentralized. If you make a bad business decision, you suffer the consequences and can hopefully learn from your mistakes and make better decisions in the future. But in communist systems, one central planner’s bad decisions can cause suffering for millions of other people, resulting in mass death. Meanwhile, the central planner may suffer for correcting the bad decision. Centralized economies simply lack the feedback loops necessary to fix problems before they start killing people.

While FDA oversight of medicines is probably important, would it be such a bad thing if a slightly freer market in parenteral nutrition allowed parents to chose between competing brands of formula, each promising not to kill your baby?

Of course, capitalism isn’t perfect, either. SpottedToad recently had an interesting post, 2010s Identity Politics as Hostile AI:

There’s an interesting post mortem on the rise and fall of the clickbait liberalism site Mic.com, that attracted an alleged 65 million unique visitors on the strength of Woketastic personal stories like “5 Powerful Reasons I’m a (Male) Feminist,” …

Every time Mic had a hit, it would distill that success into a formula and then replicate it until it was dead. Successful “frameworks,” or headlines, that went through this process included “Science Proves TK,” “In One Perfect Tweet TK,” “TK Reveals the One Brutal Truth About TK,” and “TK Celebrity Just Said TK Thing About TK Issue. Here’s why that’s important.” At one point, according to an early staffer who has since left, news writers had to follow a formula with bolded sections, which ensured their stories didn’t leave readers with any questions: The intro. The problem. The context. The takeaway.

…But the success of Mic.com was due to algorithms built on top of algorithms. Facebook targets which links are visible to users based on complex and opaque rules, so it wasn’t just the character of the 2010s American population that was receptive to Mic.com’s specific brand of SJW outrage clickbait, but Facebook’s rules for which articles to share with which users and when. These rules, in turn, are calibrated to keep users engaged in Facebook as much as possible and provide the largest and most receptive audience for its advertisers, as befits a modern tech giant in a two-sided market.

Professor Bruce Charlton has a post about Head Girl Syndrome–the Opposite of Creative Genius that is good and short enough that I wish I could quote the whole thing. A piece must suffice:

The ideal Head Girl is an all-rounder: performs extremely well in all school subjects and has a very high Grade Point Average. She is excellent at sports, Captaining all the major teams. She is also pretty, popular, sociable and well-behaved.

The Head Girl will probably be a big success in life, in whatever terms being a big success happens to be framed …

But the Head Girl is not, cannot be, a creative genius. …

The more selective the social system, the more it will tend to privilege the Head Girl and eliminate the creative genius.

Committees, peer review processes, voting – anything which requires interpersonal agreement and consensus – will favour the Head Girl and exclude the creative genius.  …

*

We live in a Head Girl’s world – which is also a world where creative genius is marginalized and disempowered to the point of near-complete invisibility.

The quest for social status is, I suspect, one of the things driving the system. Status-oriented people refuse to accept information that comes from people lower status than themselves, which renders system feedback even more difficult. The internet as a medium of information sharing is beautiful; the internet as a medium of status signalling is horrible.

So what do you think? Do sufficiently large organization start acting like malevolent (or hostile) AIs?

(Back to Part 1)

Do Sufficiently Large Organizations Start Acting Like Malevolent AIs? (pt 1)

(and Society is an Extremely Large Organization)

What do I mean by malevolent AI?

AI typically refers to any kind of intelligence or ability to learn possessed by machines. Malevolent AI occurs when a machine pursues its programmed objectives in a way that humans find horrifying or immoral. For example, a machine programmed to make paperclips might decide that the easiest way to maximize paperclip production is to enslave humans to make paperclips for it. Superintelligent AI is AI that has figured out how to make itself smarter and thus keeps getting smarter and smarter. (Should we develop malevolent superintelligent AI, then we’ll really have something to worry about.)

Note: people who actually study AI probably have better definitions than I do.

While we like to think of ourselves (humans) as unique, thinking individuals, it’s clear that many of our ideas come from other people. Chances are good you didn’t think up washing your hands or brushing your teeth by yourself, but learned about them from your parents. Society puts quite a bit of effort, collectively speaking, into teaching children all of the things people have learned over the centuries–from heliocentrism to the fact that bleeding patients generally makes diseases worse, not better.

Just as we cannot understand the behavior of ants or bees simply by examining the anatomy of a single ant or single bee, but must look at the collective life of the entire colony/hive, so we cannot understand human behavior by merely examining a single human, but must look at the collective nature of human societies. “Man is a political animal,” whereby Aristotle did not mean that we are inherently inclined to fight over transgender bathrooms, but instinctively social:

Hence it is evident that the state is a creation of nature, and that man is by nature a political animal. And he who by nature and not by mere accident is without a state, is either above humanity, or below it; he is the ‘Tribeless, lawless, hearthless one,’ whom Homer denounces—the outcast who is a lover of war; he may be compared to a bird which flies alone.

Now the reason why man is more of a political animal than bees or any other gregarious animals is evident. Nature, as we often say, makes nothing in vain, and man is the only animal whom she has endowed with the gift of speech. And whereas mere sound is but an indication of pleasure or pain, and is therefore found in other animals (for their nature attains to the perception of pleasure and pain and the intimation of them to one another, and no further), the power of speech is intended to set forth the expedient and inexpedient, and likewise the just and the unjust. And it is a characteristic of man that he alone has any sense of good and evil, of just and unjust, and the association of living beings who have this sense makes a family and a state. –Aristotle, Politics

With very rare exceptions, humans–all humans, in all parts of the world–live in groups. Tribes. Families. Cities. Nations. Our nearest primate relatives, chimps and bonobos, also live in groups. Primates are social, and their behavior can only be understood in the context of their groups.

Groups of humans are able to operate in ways that individual humans cannot, drawing on the collective memories, skills, and knowledge of their members to create effects much greater than what could be achieved by each person acting alone. For example, one lone hunter might be able to kill a deer–or if he is extremely skilled, hardworking, and lucky, a dozen deer–but ten hunters working together can drive an entire herd of deer over a cliff, killing hundreds or even thousands. (You may balk at the idea, but many traditional hunting societies were dependent on only a few major hunts of migrating animals to provide the majority of their food for the entire year–meaning that those few hunts had to involve very high numbers of kills or else the entire tribe would starve while waiting for the animals to return.)

Chimps have never, to my knowledge, driven megafauna to extinction–but humans have a habit of doing so wherever they go. Humans are great at what we do, even if we aren’t always great at extrapolating long-term trends.

But the beneficial effects of human cooperation don’t necessarily continue to increase as groups grow larger–China’s 1.3 billion people don’t appear to have better lives than Iceland’s 332,000 people. Indeed, there probably is some optimal size–depending on activity and available communications technology–beyond which the group struggles to coordinate effectively and begins to degenerate.

CBS advises us to make groups of 7:

As it turns out, seven is a great number for not only forming an effective fictional fighting force, but also for task groups that use spreadsheets instead of swords to do their work.

That’s according to the new book Decide & Deliver: 5 Steps to Breakthrough Performance in Your Organization (Harvard Business Press).

Once you’ve got 7 people in a group, each additional member reduces decision effectiveness by 10%, say the authors, Marcia W. Blenko, Michael C. Mankins, and Paul Rogers.

Unsurprisingly, a group of 17 or more rarely makes a decision other than when to take a lunch break.

Princeton blog reports:

The trope that the likelihood of an accurate group decision increases with the abundance of brains involved might not hold up when a collective faces a variety of factors — as often happens in life and nature. Instead, Princeton University researchers report that smaller groups actually tend to make more accurate decisions, while larger assemblies may become excessively focused on only certain pieces of information. …

collective decision-making has rarely been tested under complex, “realistic” circumstances where information comes from multiple sources, the Princeton researchers report in the journal Proceedings of the Royal Society B. In these scenarios, crowd wisdom peaks early then becomes less accurate as more individuals become involved, explained senior author Iain Couzin, a professor of ecology and evolutionary biology. …

The researchers found that the communal ability to pool both pieces of information into a correct, or accurate, decision was highest in a band of five to 20. After that, the accurate decision increasingly eluded the expanding group.

Couzin found that in small groups, people with specialized knowledge could effectively communicate that to the rest of the group, whereas in larger groups, they simply couldn’t convey their knowledge to enough people and group decision-making became dominated by the things everyone knew.

If you could travel back in time and propose the idea of democracy to the inhabitants of 13th century England, they’d respond with incredulity: how could peasants in far-flung corners of the kingdom find out who was running for office? Who would count the votes? How many months would it take to tally up the results, determine who won, and get the news back to the outlying provinces? If you have a printing press, news–and speeches–can quickly and accurately spread across large distances and to large numbers of people, but prior to the press, large-scale democracy simply wasn’t practical.

Likewise, the communism of 1917 probably couldn’t have been enacted in 1776, simply because society at that time didn’t have the technology yet to gather all of the necessary data on crop production, factory output, etc. (As it was, neither did Russia of 1917, but they were closer.)

Today, the amount of information we can gather and share on a daily basis is astounding. I have at my fingertips the world’s greatest collection of human knowledge, an overwhelming torrent of data.

All of our these information networks have linked society together into an increasingly efficient meta-brain–unfortunately, it’s not a very smart meta-brain. Like the participants in Couzin’s experiments, we are limited to what “everyone knows,” stymied in our efforts to impart more specialized knowledge. (I don’t know about you, but I find being shouted down by a legion of angry people who know less about a subject than I do one of the particularly annoying features of the internet.)

For example, there’s been a lot of debate lately about immigration, but how much do any of us really know about immigrants or immigrant communities? How much of this debate is informed by actual knowledge of the people involved, and how much is just people trying to extend vague moral principles to cover novel situations? I recently had a conversation with a progressive acquaintance who justified mass-immigration on the grounds that she has friendly conversations with the cabbies in her city. Heavens protect us–I hope to get along with people as friends and neighbors, not just when I am paying them!

One gets the impression in conversation with Progressives that they regard Christian Conservatives as a real threat, because that group that can throw its weight around in elections or generally enforce cultural norms that liberals don’t like, but are completely oblivious to the immigrants’ beliefs. Most of our immigrants hail from countries that are rather more conservative than the US and definitely more conservative than our liberals.

Any sufficiently intelligent democracy ought to be able to think critically about the political opinions of the new voters it is awarding citizenship to, but we struggle with this. My Progressive acquaintance seems think that we can import an immense, conservative, third-world underclass and it will stay servile indefinitely, not vote its own interests or have any effects on social norms. (Or its interests will be, coincidentally, hers.)

This is largely an information problem–most Americans are familiar with our particular brand of Christian conservatives, but are unfamiliar with Mexican or Islamic ones.

How many Americans have intimate, detailed knowledge of any Islamic society? Very few of us who are not Muslim ourselves speak Arabic, and few Muslim countries are major tourist destinations. Aside from the immigrants themselves, soldiers, oil company employees, and a handful of others have spent time in Islamic countries, but that’s about it–and no one is making any particular effort to listen to their opinions. (It’s a bit sobering to realize that I know more about Islamic culture than 90% of Americans and I still don’t really know anything.)

So instead of making immigration policy based on actual knowledge of the groups involved, people try to extend the moral rules–heuristics–they already have. So people who believe that “religious tolerance is good,” because this rule has generally been useful in preventing conflict between American religious groups, think this rule should include Muslim immigrants. People who believe, “I like being around Christians,” also want to apply their rule. (And some people believe, “Groups are more oppressive when they’re the majority, so I want to re-structure society so we don’t have a majority,” and use that rule to welcome new immigrants.)

And we are really bad at testing whether or not our rules are continuing to be useful in these new situations.

 

Ironically, as our networks have become more effective, our ability to incorporate new information may have actually gone down.

The difficulties large groups experience trying to coordinate and share information force them to become dominated by procedures–set rules of behavior and operation are necessary for large groups to operate. A group of three people can use ad-hoc consensus and rock-paper-scissors to make decisions; a nation of 320 million requires a complex body of laws and regulations.

But it’s getting late, so let’s continue this discussion in the next post.