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jerryking : arcane_knowledge   3

Art market ripe for disruption by algorithms
MAY 26, 2017 | Financial Times | by John Dizard.

Art consultants and dealers are convinced that theirs is a high-touch, rather than a high-tech business, and they have arcane skills that are difficult, if not impossible, to replicate..... better-informed collectors [are musing about] how to compress those transaction costs and get that price discovery done more efficiently.....The art world already has transaction databases and competing price indices. The databases tend to be incomplete, since a high proportion of fine art objects are sold privately rather than at public auctions. The price indices also have their issues, given the (arguably) unique nature of the objects being traded. Sotheby’s Mei Moses index attempts to get around that by compiling repeat-sales data, which, given the slow turnover of particular works of art, is challenging.....Other indices, or value estimations, are based on hedonic regression, which is less amusing than it sounds. It is a form of linear regression used, in this case, to determine the weight of different components in the pricing of a work of art, such as the artist’s name, the work’s size, the year of creation and so on. Those weights in turn are used to create time-series data to describe “the art market”. It is better than nothing, but not quite enough to replace the auctioneers and dealers.....the algos are already on the hunt....people are watching the auctions and art fairs and doing empirics....gathering data at a very micro level, looking for patterns, just to gather information on the process.....the art world and its auction markets are increasingly intriguing to applied mathematicians and computer scientists. Recognising, let alone analysing, a work of art is a conceptually and computationally challenging problem. But computing power is very cheap now, which makes it easier to try new methods.....Computer scientists have been scanning, or “crawling”, published art catalogues and art reviews to create semantic data for art works based on natural-language descriptions. As one 2015 Polish paper says, “well-structured data may pave the way towards usage of methods from graph theory, topic labelling, or even employment of machine learning”.

Machine-learning techniques, such as software programs for deep recurrent neural networks, have already been used to analyse and predict other auction processes.
algorithms  disruption  art  art_finance  auctions  collectors  linear_regression  data_scientists  machine_learning  Sotheby’s  high-touch  pricing  quantitative  analytics  arcane_knowledge  art_market 
june 2017 by jerryking
Wilbur Ross brings art of restructuring to Team Trump
JANUARY 21, 2017 | FT| by: Philip Delves Broughton.

“When you start out with your adversary understanding that he or she is going to have to make concessions, that’s a pretty good background to begin.”

So all this stuff about tariffs and walls and protectionism turns out to be pure gamesmanship.......In his career as an investment banker at NM Rothschild and then running his own business, WL Ross & Co, he has shown repeatedly how he can dive into an industrial dung heap and emerge with a fistful of dollars and not a speck on his silk tie......... Working on his own account, Mr Ross’s most famous deal was his purchase of an ailing group of US steelmakers in 2002, shortly before President George W Bush imposed tariffs on imports of steel. Mr Ross used the protection to fix the operations, cut debt and draft new contracts with workers. He was able to take the company public in 2003 and sell it two years later to the Indian steel mogul Lakshmi Mittal.

He has pulled off similar tricks, mostly successfully in coal mining, textiles and banking, immersing himself again and again in new industries and the minutiae of the laws, trade rules and contracts that govern them.

As a student at Harvard Business School, Mr Ross was mentored by Georges Doriot, a pioneering advocate for venture capital, who said: “People who do well in life understand things that other people don’t understand.”
For bothering to understand things that most people don’t, Mr Ross deserves more credit than he gets. He is often easily dismissed as a vulture or someone who buys low and sells high. But what he has done is hard. The devil in restructuring is in the grinding detail of voluminous contracts and difficult, often highly emotional negotiations.
Wilbur_Ross  negotiations  steel  Georges_Doriot  HBS  vulture_investing  new_industries  sophisticated  bankruptcy  messiness  thinking_tragically  dispassion  preparation  leverage  emotions  Lakshmi_Mittal  moguls  restructurings  tariffs  imports  gamesmanship  unsentimental  hard_work  minutiae  protectionism  arcane_knowledge  inequality_of_information  Philip_Delves_Broughton  vulttion 
january 2017 by jerryking
The Sensor-Rich, Data-Scooping Future -

Sensor-rich lights, to be found eventually in offices and homes, are for a company that will sell knowledge of behavior as much as physical objects....The Internet will be almost fused with the physical world. The way Google now looks at online clicks to figure out what ad to next put in front of you will become the way companies gain once-hidden insights into the patterns of nature and society.

G.E., Google and others expect that knowing and manipulating these patterns is the heart of a new era of global efficiency, centered on machines that learn and predict what is likely to happen next.

“The core thing Google is doing is machine learning,” Eric Schmidt....The great data science companies of our sensor-packed world will have experts in arcane reaches of statistics, computer science, networking, visualization and database systems, among other fields. Graduates in those areas are already in high demand.

Nor is data analysis just a question of computing skills; data access is also critically important. As a general rule, the larger and richer a data set a company has, the better its predictions become. emerging area of computer analysis known as “deep learning” will blow away older fields.

While both Facebook and Google have snapped up deep-learning specialists, Mr. Howard said, “they have far too much invested in traditional computing paradigms. They are the equivalent of Kodak in photography.” Echoing Mr. Chui’s point about specialization, he said he thought the new methods demanded understanding of specific fields to work well.

It is of course possible that both things are true: Big companies like Google and Amazon will have lots of commodity data analysis, and specialists will find niches. That means for most of us, the answer to the future will be in knowing how to ask the right kinds of questions.
sensors  GE  GE_Capital  Quentin_Hardy  data  data_driven  data_scientists  massive_data_sets  machine_learning  automated_reasoning  predictions  predictive_analytics  predictive_modeling  layer_mastery  core_competencies  Enlitic  deep_learning  niches  patterns  analog  insights  latent  hidden  questions  Google  Amazon  aftermath  physical_world  specialization  consumer_behavior  cyberphysical  arcane_knowledge  artificial_intelligence  test_beds 
april 2015 by jerryking

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