Alpha Takes All

This isn't a post about the adversarial neural network that was programmed to play a video game against itself until it took the Grandmaster Championship from the world's best (human) players. It's about a person who beat the computers, and took out a huge portion of the stock market in the process.

Come back with me for a moment, to the year 2010: Smart phones are just now in everybody's hand, the word "application" is just about to be replaced by the word "app," and an algorithm is still just an anagram of the word logarithm.

Wall Street, on the other hand, is just about to get rocked by an event called the Flash Crash of 2010. The market lost and then regained trillions of dollars in 30 minutes, due to a bunch of trading algorithms getting stuck in each other's code like a pack of leashed-up dogs trying to sniff each other's butts. It was described as one of the most turbulent periods in the history of financial markets, and for quite a bit after, nobody knew what the heck happened.

It was also one of the first alarms to be rung about the dangers of artificial intelligence.

"No place for a human" is how the stock market was now described after the takeover by high frequency trading algorithms.
-Jerry Adler, WIRED, 2012

That comment means something different all of the sudden, in light of the Boeing 737 Max's MCAS failure that killed almost 350 people in 2019.

But taking it back to the stock market, the story got a nice twist this year when we were reminded about the Hound of Hounslow. He's a human, but he doesn't exactly belong in the stockmarket either. By 2015, this self-taught quant was held on multiple counts of doing bad things to the stock market, and by 2020, he was given his (lenient) sentence.

Why wasn't he put in jail for life for causing a trillion-dollar market maelstrom? For one thing, he's helping authorities catch other HFT-wielding market manipulators.

The leniency also comes from the fact that he's running an Asperger cortex, and he saw the stock market as a video game, and like AlphaStar, he learned how to play that game really well, and how to "beat" his opponents.  He noticed that the other trading algorithms were doing strange things, and getting entrained into each other's code. He thought he could tap into that groupthink and orchestrate the whole lot, and he did, triggering a cascade of buy/sells that crashed the market.

Notes
Hounslow trader avoids jail in 'flash crash' case
Jan 2020, BBC News

Quantitative Financial Analyst

High Frequency Trading

Post Script
Circa 2010's commentary on high frequency trading:
Raging Bulls - How Wall Street Got Addicted to Light-Speed Trading
Aug 2012, WIRED

Keeping track of algorithms in the news:
Algos
Network Address, 2012

Palmar Sweating and Mass Hysteria

During a recent political crisis [this was written in 1967], when there was a temporary increase in the likelihood of nuclear war, all experiments into palmar sweating at a research institute had to be abandoned because the base level of the response had become so abnormal that the tests would have been meaningless.  (p188)
-The Naked Ape, Desmond Morris, 1967
(a book about the human animal by a zoologist)

Manual Override Autocrash

We are already at their service.
We are already neglecting Asimov's Laws.
We don't see it yet because it's automatic updates and automatic screen rotation. When it's automatic plane-flying, or rather automatic plane-crashing, some might see it. Once it's automatically running your entire life, with no consideration to you the end user as an individual, with no consideration to a manual override, it will be too endemic, too pervasive, and too powerful for you to do anything.

The pilot's manual choices should always override software.
-random reddit comment by NotYouDude

Notes:
Wiki link

Flawed analysis, failed oversight: How Boeing, FAA certified the suspect 737 MAX flight control system
Seattle Times. Mar 17, 2019.

Boeing 'misjudged 737 Max pilot reactions'
Sep 2019, BBC News

"...which was designed to make the aircraft easier to fly."

Post Script:
And you can't make this shit up -- the automated security system in your house automatically called the cops on the automated floor cleaner in the house. If only they talked to each other first...

Deputies surround burglar in Oregon home, find out suspect is Roomba trapped in bathroom
April 2019, Local News

Asimov's Three Laws of Robotics

So Fake

There's so much going on in the Fake sector that I just can't keep track; here's a few bits:

Author pulls software that used deep learning to virtually undress women
June 2019, Ars Technica

This is it. Deep learning, neural networks, generative adversarial networks, all that. We see now the world in the way it looked when the internet first started showing up in people's homes, but then all of the sudden, a phone could connect to the internet while you're walking down the street.

Deep learning is the robots doing things humans never thought a robot could do, and things we never even thought to ask for. It's absolute magic, and it's about to completely f--- our sh-- up.

Mona Lisa guest on TV? Researchers work out talking heads from photos, art
May 2019, phys.org

We can now extrapolate from photo to video. Is that what Mona Lisa really looked like? Does it really matter? It is astutely pointed out that unlike previous techniques, this one doesn't need 3-D modelling to make the leap.

And how?
"Lengthy meta-learning" that's how.

Facebook removes accounts from Russia, Iran for 'coordinated inauthentic behavior'
Mar 2019, Reuters

Has a nice ring to it. Reminds me of "Low-Credibility Information"

Anyway, the accounts were removed for their behavior and not their content. Let that be a lesson. Not sure if I see this as a good thing because it means that the screening algorithms are sophisticated enough to identify patterns of activity rather than simple vis/text content recognition, or it's a bad thing because it's getting harder and harder to get away with illicit activity on the web. Depends on which side you're on I guess.

Supporters in Trump Facebook adverts were actors
July 2019, BBC News

I think I'm not even mad.That's what stock footage is for, no? I mean, what is authenticity; Eiffel Tower in Las Vegas type stuff.

Melbourne fake Chinese beggars scam busted by police
July 2019, AUNews

Last but not least; it wouldn't be a report on the state of the fake without some of the old masters showing up.

From the same people who brought you the fake zoo with a legit dog purported to be a lion, we now bring you -- fake homeless people!

They are literally shipped from China to Australia to look disheveled and beg for money. I refuse to believe it but in the article they're talking about them clearing a few hundred dollars a day. Fake homeless people. It's one thing when you shave your head and throw on a saffron robe to be a begging Buddhist, but to be a part of an international underground beggar syndicate where you don't shower for weeks and lie prostrate on the ground, that's dedication.

*Actually, the more I look into this, the less funny it becomes. It's way more common than it should be, and devolves into maiming kids to use as bait; few people can resist giving money to a crippled kid on the street.


Post Script
On the Buddhist Beggar Syndicate and the geographic function of susceptibility:

"The men targeted out-of-towners, [Robert Hammond, executive director and co-founder of Friends of the High Line] said, adding that his office staff had a rule of thumb for watching the interactions: --Each second a visitor was willing to talk to one of the robed men was equal to 50 miles away from New York City that the person probably lived.-- New Yorkers would not give the men even a second’s worth of their time, Mr. Hammond added."
-The Fake Monks Are Back, Aggressively Begging
Christopher Mele, New York Times, July 1, 2016

Post Post Script

^Here we have a refined specimen. I can't tell you exactly what makes this jump out at me, but it screams "robot". Maybe it's a semibot, same difference, the purpose is to manipulate; there's something intentional about it. Maybe because people don't really comment on a message board in order to influence others, but to voice their opinion, and the two look pretty different. 

Notes
The spread of low-credibility content by social bots
Chengcheng Shao, Giovanni Luca Ciampaglia, Onur Varol, Kai-Cheng Yang, Alessandro Flammini, Filippo Menczer. Nature Communications. Volume 9, Article number: 4787 (2018)

Here we analyze 14 million messages spreading 400 thousand articles on Twitter during ten months in 2016 and 2017.

Robots Have Feelings Too

aka In Other News Suicide is Funny Again

A robot kills itself, and everyone thinks it's funny:
Robot 'drowns' in fountain mishap
July 2017, BBC

This headline above was pretty tame. But otherwise, take your pick, I'll go with my local radio news station, WNYC 93.9 FM. Today on the six o'clock headlines they quip - "Turns out his first day on the job was too much for this robot..."

I thought suicide was a big deal. And what's up with the whole bullying thing? And don't even get me started on how they already assumed the thing's gender.

***
Very often when I think about the way people treat eachother, this quote by Carl Sagan comes to mind:

"It’s a little unfair, I think, to criticize a person for not sharing the enlightenment of a later epoch, but it is also profoundly saddening that such prejudices were so extremely pervasive. The question raises nagging uncertainties about which of the conventional truths of our own age will be considered unforgivable bigotry by the next."

Broca’s Brain, Carl Sagan, 1974-1979, p11

And I wonder when, if ever, we are living through such a prejudice in 'our own age'. When such a situation as this arises, how can you resist but to extrapolate? One day, far in the future, will we ridicule the newswriters of today for having no sympathy for this poor intelligentity?

But seriously, it seems pretty irresponsible to be poking fun at someone for committing suicide.

Obviously, a robot isn't "someone" and it didn't "commit suicide," but when it is portrayed that way in the headlines, I'll bet that's what it looks like to a young person, for example, or perhaps a person with mental illness. They hear that someone, or something, has killed itself, and they see that everyone thinks it's a joke.

Image source: Robot is Dead, Waldemar-Kazak, 2017

Semibotic Semibiotic

It's not often you get to see an article about consciousness on the BBC, but you do, it's Dan Dennett getting mega-memetical. (just kidding, that doesn't even make sense, in this context.)

Is consciousness just an illusion?
Apr 2017, BBC

We're not just are robots", he says. "We're robots, made of robots, made of robots".
-Dan Dennett

To See Better

The automation of artmaking - Adobe's Project Felix

The automation of artmaking - Adobe's Project Felix

Adobe’s Project Felix Uses AI to Help You Craft Hyper-Realistic 3-D Renderings
Nov 2016, WIRED

This thing is nuts, and makes me scared for my job. (But not really; I used to be an art teacher, but am no more.)

Inceptionism vs Trypophobia

Left my computer dreaming overnight, found this when I woke up

gallery

Deep Learning in Reverse Shows You What an Algorithm Sees
#DeepDream

Phantasmagoric neural net visions
mindhacks.com, Jun 2015

All I have to say is, sucks to be trypophobic right now.

Deep Bosch

Hieronymus Bosch’s “The Garden of Earthly Delights”, Kyle McDonald/Flickr

Google’s New Visualization Tool Slips Slimy and Furry Creatures into Art History
Claire Voon, Hypoallergic, July 7, 2015

Inceptionism
Each layer of the network deals with features at a different level of abstraction, so the complexity of features we generate depends on which layer we choose to enhance. For example, lower layers tend to produce strokes or simple ornament-like patterns, because those layers are sensitive to basic features such as edges and their orientations.

"...overinterpret [...] oversaturated with snippets of other images."

Hieronymus Bosch’s “The Garden of Earthly Delights”, @aut0mata /Twitter

Phantasmagoric neural net visions
mindhacks.com, Jun 2015

[mindhacks always has the best explanations]

by using the neural networks “in reverse” they could elicit visualisations of the representations that the networks had developed over training.

pictures are freaky because they look sort of like the things the network had been trained to classify, but without the coherence of real-world scenes

The obvious parallel is to images from dreams or other altered states – situations where ‘low level’ constraints in our vision are obviously still operating, but the high-level constraints – the kind of thing that tries to impose an abstract and unitary coherence on what we see – is loosened. In these situations we get to observe something that reflects our own processes as much as what is out there in the world.

link
gallery

***

the code has been opened for all to use; let the dreaming begin
#DeepDream

On Polymathism and Macguyvering

Anyone can learn to be a polymath
Robert Twigger, Aeon MAgazine, 2013-11-04

the division of labor vs. the synthesis of polymathism

Carrying too many tools is a sign of a weak man; it makes him lazy. The real master has no tools at all, only an unlimited capacity for using the resources at hand.

Algos

I remember watching Kevin Slavin’s talk last year. After the Flash Crash of 2010, before the Knight Capital fiasco of 2012. I am still very surprised as to what seems to be the lack of public awareness regarding what can now be called the pervasive use of algorithms in decision-making. It’s quite obvious that what visionaries of the 20^th century were referring to as ‘Artificial Intelligence’ will finally manifest itself as ‘augmented intelligence’, in the form of high-frequency trading, for example.

“No place for a human”…

Raging Bulls: How Wall Street Got Addicted to Light-Speed Trading

BY JERRY ADLER 08.03.12

http://www.wired.com/business/2012/08/ff_wallstreet_trading/all/

examines how Wall St. got to the point where flash failures come with increasing frequency, and how much farther traders seem willing to go in pursuit of ever-greater speed.

clipped:

“For the first time in financial history, machines can execute trades far faster than humans can intervene,” said Andrew Haldane, a regulatory official with the Bank of England, at another recent conference. “That gap is set to widen.”

This movement has been gaining momentum for more than a decade. Human beings who make investment decisions based on their assessment of the economy and on the prospects for individual companies are retreating. Computers—acting on computer-generated market trend data and even newsfeeds, communicating only with one another—have taken up the slack.

One common algo strategy is to look for pairs of stocks whose prices are historically correlated. The canonical examples are the stock prices of oil companies, which rise with the price of crude, and those of airlines, which do the opposite. But they may not move all at the same time, so one strategy is to buy or sell the one that’s trailing and wait for it to catch up. Similarly, “derivative” equities such as options and futures may get out of equilibrium with the underlying stocks. Some algorithms are “market makers” in a stock—they attempt to buy at a low bid price and quickly sell at a slightly higher asking price, pocketing the difference, or spread. The people who did this used to be called specialists, and it was a nice living when spreads were an eighth of a dollar. Since the New York Stock Exchange instituted “decimalization” in 2001, spreads have gone down to a penny or two, meaning you have to trade a lot more stock, a lot faster, to make the same amount of money. It’s no place for a human being.

“By the time the ordinary investor sees a quote, it’s like looking at a star that burned out 50,000 years ago,” says Sal Arnuk, a partner in Themis Trading and coauthor of a book critical of high-frequency trading titled Broken Markets. By some estimates, 90 percent of quotes on the major exchanges are canceled before execution. Many of them were never meant to be executed; they are there to test the market, to confuse or subvert competing algorithms, or to slow trading in a stock by clogging the system—a practice known as quote stuffing. It may even be a different stock, but one whose trades are handled on the same server. On the Internet, this is called a denial-of-service attack, and it’s a crime. Among quants, it’s considered at most bad manners.

And it’s not just the words of central bankers that matter in this world. Almost any kind of data that in any way bears on economic activity, no matter at what remove, is being aggregated and tested for its potential impact on stock prices. GPS data, showing concentrations of cell phone users in malls or office buildings, has been used to get a real-time read on economic activity. Even the most ephemeral shards in the digital scrap heap, old Twitter posts, are proving of value, if you can get your hands on enough of them. For their starkly titled paper “Twitter Mood Predicts the Stock Market,” Johan Bollen, an informatics researcher at Indiana University, and two colleagues collected almost 10 million tweets from 2008, aggregating phrases that indicate emotional state and analyzing them along dimensions of feeling such as “calm,” “alert,” “sure,” “vital” and “happy.” They then looked for correlations with stock prices and discovered that a surge in “calm” sentiment reliably predicted an increase in the Dow Jones Industrial Average two to six days later. No one, including Bollen, knows what this means or why it should be so, but if quants had a coat of arms, it would say, “If it works, trade on it.”

further:

A New Kind of Socio-Inspired Technology

Edge conversation, Dirk Helbing [6.19.12]

"...computers of tomorrow are basically creating artificial social systems. Just take financial trading today, it's done by the most powerful computers. These computers are creating a view of the environment; in this case the financial world. They're making projections into the future. They're communicating with each other. They have really many features of humans. And that basically establishes an artificial society, which means also we may have all the problems that we are facing in society if we don't design these systems well. The flash crash is just one of those examples that shows that, if many of those components — the computers in this case — interact with each other, then some surprising effects can happen. And in that case, $600 billion were actually evaporating within 20 minutes."

http://edge.org/conversation/a-new-kind-of-social-inspired-technology

Kevin Slavin: How algorithms shape our world

FILMED JUL 2011 • POSTED JUL 2011 • TEDGlobal 2011

Kevin Slavin argues that we're living in a world designed for -- and increasingly controlled by -- algorithms. In this riveting talk from TEDGlobal, he shows how these complex computer programs determine: espionage tactics, stock prices, movie scripts, and architecture. And he warns that we are writing code we can't understand, with implications we can't control.

http://www.ted.com/talks/kevin_slavin_how_algorithms_shape_our_world.html

NYSE looks into 'irregular trading' in 140 stocks

1 August 2012

http://www.bbc.co.uk/news/business-19087748

Decision-making, Public Oversight, and Privatization

Aug 2012

http://networkaddress.blogspot.com/2012/08/decision-making-public-oversight-and.html


Math algorithm tracks crime, rumours, epidemics to source
10 August 2012
http://phys.org/news/2012-08-math-algorithm-tracks-crime-rumours.html

U.S. Cities Relying on Precog Software to Predict Murder
KIM ZETTER 01.10.13
The software parses about two dozen variables, including criminal record and geographic location. The type of crime and the age at which it was committed, however, turned out to be two of the most predictive variables.
“People assume that if someone murdered then they will murder in the future,” Berk told the news outlet. “But what really matters is what that person did as a young individual. If they committed armed robbery at age 14 that’s a good predictor. If they committed the same crime at age 30, that doesn’t predict very much.”
-Richard Berk, criminologist at the University of Pennsylvania who developed the algorithm
http://www.wired.com/threatlevel/2013/01/precog-software-predicts-crime/

Supercomputers could generate warnings for stock crashes
Lisa M. Krieger, Apr 19, 2013
http://phys.org/news/2013-04-supercomputers-stock.html

High-frequency traders face speed limit on deals
29 April 2013

High-frequency trading tactic lowers investor profits

Jun 17, 2013

"Latency Arbitrage, Market Fragmentation, and Efficiency: A Two-Market Model."

Provided by University of Michigan

[pdf]

Doing the math 'predicts' which movies will be box office hits
Aug 22, 2013
based on an analysis of the activity on Wikipedia pages: number of page views for the movie's article, number of human editors contributing to the article, number of edits made, and diversity of online users

New Algorithm Can Spot the Bots in Your Twitter Feed

Lee Simmons, Wired, 10.17.13

I Liked Everything I Saw on Facebook for Two Days
Wired, Aug 2014

[...] My News Feed took on an entirely new character in a surprisingly short amount of time. After checking in and liking a bunch of stuff over the course of an hour, there were no human beings in my feed anymore. It became about brands and messaging, rather than humans with messages.

[...] By the next morning, the items in my News Feed had moved very, very far to the right. I’m offered the chance to like the 2nd Amendment and some sort of anti-immigrant page. I like them both. I like Ted Cruz. I like Rick Perry. The Conservative Tribune comes up again, and again, and again in my News Feed. I get to learn its very particular syntax.

[...] The next morning, my friend Helena sent me a message. “My fb feed is literally full of articles you like, it’s kind of funny,” she says. “No friend stuff, just Honan likes.” I replied with a thumbs up. This continued throughout the experiment. When I posted a status update to Facebook just saying “I like you,” I heard from numerous people that my weirdo activity had been overrunning their feeds. “My newsfeed is 70 percent things Mat has liked,” noted my pal Heather.

Patents provide insight on Wall Street 'technology arms race'

phys.org, Jan 2015

"A 'technology arms race' is well underway as firms vie to shave even more time off trading and maintain their competitive edge. But it's not just about trading speed. We're seeing technology used more when firms are first issuing securities and even the use of neural networks in portfolio selection."