Week 16
About a dozen trucks from major manufacturers like Volvo and Daimler just completed a week of largely autonomous driving across Europe, the first such major exercise on the continent. The trucks set off from their bases in three European countries and completed their journeys in Rotterdam in the Netherlands today (Apr. 6). One set of trucks, made by the Volkswagen subsidiary Scania, traveled more than 2,000 km and crossed four borders to get there [..]
While self-driving cars from Google or Ford get most of the credit for capturing the public imagination, commercial uses for autonomous or nearly autonomous vehicles, like tractors from John Deere, have been quietly putting the concept to work in a business setting.
When trucks autonomously follow one another, it’s called “platooning.” They’re connected by wifi and can leave a much smaller gap between vehicles than when humans are at the wheel. Platooning can reduce fuel use by up to 15%, prevent human error from causing accidents, and reduce congestion, according to a study by research firm TNO.
When money is cheap, borrowing becomes too easy, then money can go to places we may not like it to go to - useless M & A is one example. The big fat bond the beermaker InBev dropped recently will mostly be used for a merger IMO (low rates triggered an intense "search for yield" elsewhere, after the Inbev bond was issued the market ate that shit up so fast, it was comical). Heroic acts are becoming necessary to "stop" tax-avoiding mergers, but the fact that heroics are required signals the the "evolutionary landscape" has problems; if there is a swamp, there are flies. Virgin America was bought by Alaska Air, the former's founder Branson said he could not stop it due to a US legalistic quirk that did not give him proper voting rights; another useless merger where the friggin founder is not happy and, the word is, the consumers are not happy either.
Bunch of Useless M&A..
"[Commenting on the dangers of FED keeping interest rates too low for too long] Back in late 2003, I remember we had nine percent nominal growth, seven percent real growth. The economy was very, very strong, but we had one percent interest rates, and we also had a tag on them that they were going to remain there for a considerable period, and I just felt at the time that fed policy was unnecessarily easy [..]
[I]f you look at the situation [today], stock prices, household net worth per capita, are at record highs. By the way, they went to record highs in 2013, and they’ve been going up for two straight years, so I’m not sure exactly what the fed is trying to achieve in terms of the reward here; particularly, since if you look at what is going on, we’ve had a tremendous amount of debt growth; particularly, in the corporate sector, and, unfortunately, the productivity of that debt, if it was measurable, I would opine to say is at an all-time low.
Why did I say that? Because there’s good debt growth, and there’s bad debt growth. Good debt growth is when you borrow money, and it goes into the real economy. You do capital spending. You build businesses. But by most calculations, almost 98 percent of the current debt growth has gone into M &A [short for mergers and acquisitions] cooperate buy-backs, by the way, at record prices, leveraged buy-outs, so where it’s going is into financial engineering, and I can’t prove it, but I would pretty much feel very confident that a trillion dollars in buy-backs, and dividends in the last year and four trillion is the forecast this year for M & A, is a job reducer, an economic reducer, so I don’t exactly what they think they’re getting out of the zero percent rates."
Fascinating.
"DNA as Hard Drive.. DNA can fit almost 1 billion terabytes of data into just one gram. That makes it far more efficient than any other known form of computer storage.
And it also manages to last for a long time, as can be seen in the fact that the DNA of woolly mammoths has stayed accessible tens of thousands of years after they died. Experts suggest that storing data in DNA would allow it to last for 2,000 years or more, making it far more long-lasting than traditional data storage.
But DNA remains expensive. The US start-up that Microsoft bought the DNA from charges about 10 cents for a custom DNA sequence, though it hopes to make it much cheaper in the future.
Accessing it is similarly expensive, because it relies on genetic sequencing. Costs have dropped massively - the human genome project cost about $3 billion in the 13 years it took from 1990, but would cost $1,000 now.
Microsoft says that initial trials of the technology have seen all of the data stored on it retrieved.
“We’re still years away from a commercially-viable product, but our early tests with Twist demonstrate that in the future we’ll be able to substantially increase the density and durability of data storage,” said Doug Carmean, the Microsoft partner who worked on the technology"
Blinkety blink motherblinking abso-blinking-lutely blinking awesome.
Dude Where Is My Solar Impulse 2?!!
"A solar-powered airplane reached the San Francisco Bay area and performed a fly-by over the Golden Gate Bridge on Saturday afternoon, some 56 hours after leaving Oahu as part of its journey around the world.
Solar Impulse 2 took off from Kalaeloa Airport early Thursday morning.
“I crossed the bridge. I am officially in America,” pilot Bertrand Piccard declared as he flew over the iconic span as spectators watched the narrow aircraft with extra wide wings from below"
But the success of [investment firm AHL's] machine learning experiments in recent years led the company to plough more money into the field, and it is now the single biggest investment area at AHL [..]. AHL has been researching machine learning — a field of artificial intelligence where dynamic algorithms pore over vast data sets for patterns — for five years, and has been using the technique in trading for the past three years. The results have been encouraging, according to executives at the hedge fund.
A machine learning strategy helped one of AHL’s funds swing from a narrow loss to a narrow gain in August last year, when markets were convulsed by concerns over China, by autonomously buying and selling stock at vital junctures in the turmoil. Many traders initially stood on the sidelines, unable to quantify rapidly-changing data.
“It learnt to buy the dip,” said Nick Granger, co-head of research at AHL and deputy chief investment officer. “No one taught it to do this, it learnt how to do this when we showed it a lot of data.
Genuine advances in this field are welcome, but watch out
Ernie Chan: "There was an article in the New York Times a short while ago about a new hedge fund launched by Mr. Ray Kurzweil, a poineer in the field of artificial intelligence. (Thanks to my fellow blogger Yaser Anwar who pointed it out to me.) The stock picking decisions in this fund are supposed to be made by machines that "... can observe billions of market transactions to see patterns we could never see". While I am certainly a believer in algorithmic trading, I have become a skeptic when it comes to trading based on "aritificial intelligence".
At the risk of over-simplification, we can characterize artificial intelligence as trying to fit past data points into a function with many, many parameters. This is the case for some of the favorite tools of AI: neural networks, decision trees, and genetic algorithms. With many parameters, we can for sure capture small patterns that no human can see. But do these patterns persist? Or are they random noises that will never replay again? Experts in AI assure us that they have many safeguards against fitting the function to transient noise. And indeed, such tools have been very effective in consumer marketing and credit card fraud detection. Apparently, the patterns of consumers and thefts are quite consistent over time, allowing such AI algorithms to work even with a large number of parameters. However, from my experience, these safeguards work far less well in financial markets prediction, and over-fitting to the noise in historical data remains a rampant problem. As a matter of fact, I have built financial predictive models based on many of these AI algorithms in the past [Chan has a PhD in machine learning]. Every time a carefully constructed model that seems to work marvels in backtest came up, they inevitably performed miserably going forward. The main reason for this seems to be that the amount of statistically independent financial data is far more limited compared to the billions of independent consumer and credit transactions available. (You may think that there is a lot of tick-by-tick financial data to mine, but such data is serially-correlated and far from independent.)
This is not to say that quantitative models do not work in prediction. The ones that work for me are usually characterized by these properties:
• They are based on a sound econometric or rational basis, and not on random discovery of patterns;
• They have few or even no parameters that need to be fitted to past data;
• They involve linear regression only, and not fitting to some esoteric nonlinear functions;
• They are conceptually simple.
Only when a trading model is philosophically constrained in such a manner do I dare to allow testing on my small, precious amount of historical data. Apparently, Occam’s razor works not only in science, but in finance as well"
Yes
Chan is the author of two books on algorithmic / quantitative trading - so he knows what he is talking about. In another post he mentions of feeling unease whenever he hears of some neural net based trading model that'll have gazillion free parameters to fit, an obvious non-linear approach and prone to overfitting on serially dependent data.
There is a lot of beautiful mathematics to use on finance and trading, but they might not always fall under the data mining / AI approach. If data mining approaches are used, they need to be handled with care - with a keen eye for the statistical aspects on how the algorithms behave on the data at hand.
Overall though, more quantitative approaches are a welcome innovation - they bring more rationality to the market, and also more liquidity. Speculation is a good thing - and it is the kind that we want, mathematical, on open exchanges, rather than the ones through over-the-counter, and too-connected-and-big-to-fail sausage makers / banks.
Question
But ppl in finance are not curing cancer.
Not everyone will work on that kind of research
.. no matter the incentive - not everyone should either. While on this topic, I must say I am a little frustrated by this constant degrading of finance, as if it the whole industry is born of an evil seed. Providing liquidity to grain, metal producers, buyers, sellers is a good thing. During the dot-com boom there were sites offering pet-services, or things like "online-laundry". Are these truly essential services compared to finance? All this stuff is luxury at the end of the day, isn't it? So is getting a haircut, watching sports, sitting at Starbucks for that matter. If we scratch the surface on all economic activity a little, almost nothing will remain standing, except basic goods and services.
Question
But while these [online laundry, pet upkeep, etc] services are being developed, they spur innovation in related tech (coding of the backend, handling of data, prediction for CRM so forth), math, and management techniques.
Right
But you can say that about anything. Side-innovation is especially potent in finance, since it is mostly if not all about information.
Question
There is inequality. Health care is broken. People don't spend.
Give people free money
Knowledge driven 3W economy brings with it more uncertainties, non-permanent jobs / gigs, life is too dynamic. This is the result - on the one hand people are forced to fight a cage match to "earn", on the other hand they are asked to consume what are essentially luxury services.
No wonder company earnings are in a do-doo.
More innovation will also require a better safety net BTW. So however we look at it, it all comes to the same thing.
Washington Post
There’s little doubt that what has happened to America’s middle class has helped to create the climate that has fueled Trump’s [i.e. fascism's] sudden rise [..] For most families, the two recessions have wiped out previous gains and widened the wealth and income gap between the wealthiest and all others. “The losses were so large that only upper-income families realized notable gains in wealth over the span of 30 years from 1983 to 2013,” according to the Pew study.
And there is that
Question
Why not give people services?
Too old school
.. because the needs are too varied (see the answer above), and now we know about them (due to freer flow of information).
If my memory serves correctly, the first exampe of a "social" service took place a few centruries ago, the state delivered a bottle of milk at the doorstep of each home. A seemingly nice thing to do - but it also demonstrated the second-wave industrial age type of thinking. Nearly half the people in the world have an allergy for milk - they can't digest the shit. But 2W at the time was producing many such one-size-fits-all products, it saw the society that way, it saw life that way. The state simply took that milk that was being produced en masse, and gave it to people, en masse. Now we know this cannot work. It's better to give people money so some buy milk, but others a haircut, some healthcare, some bread, .. whatever.
Question
What will government do then?
Inspect food, watch borders, smart regulation, fund research
There have been extinction level events triggered by asteroid hits on the Earth, but it is also known Jupiter with its massive size protected Earth before, pulling asteroids to itself. Are these views contradictory?
No
With the Planet 9 explanation, it all makes sense. Jupiter is massive (in fact some jokingly refer to our solar system as "Jupiter and some debris"), it saved us before, but P9, being a planet itself, can enter deep into the solar system, knocking stuff in the direction of Earth. Jupiter would not be able to run interference against those.
Some more info on the subject: about the periodicity and measurement of extinction events, paper. My notebook that looks at frequencies of extinction events [geek] using the Lomb-Scargle method that computes statistical significance of peaks, rather than direct Fourier Analysis [/geek]. I reverse-engineered the graph here with image processing to get its raw data (who da man!) and compute the periods of extinction events. The result is this, and periods in this. There is one period of 25 million years, and one for around 70 - dinosaurs were wiped out 66 million years ago during one of these I assume.
Question
Favorite phone prank?
Bodankadonk by Tracy Morgan. Still unsurpassed.
Strong evidence for the existence of a ninth planet (9th because the previous 9th, Pluto, was demoted). Fascinating stuff.. It takes P9 almost 20,000 years to go around the sun. Some also claim (backed up by reputable research) that this planet passes through a region of space full of icy objects every 27 million years, knocking them towards the Earth, creating mass extinction events. It seems space travel is not a luxury, it's a necessity.
#colbert #conan
https://youtu.be/rfxCy0wj4uw
https://youtu.be/TaxAsHhex10
The History of Medical Billing
Bernie Sanders: "The model of the Democratic Party is failing"
https://youtu.be/LZv-0f46Abw