From The Daily Express, I believe this was last week:
By way of Coconuts, (Bali edition):
Click for live view, the mini-earthquakes are coming every two - to - three minutes now.
Click for live view, the mini-earthquakes are coming every two - to - three minutes now.
Market watchers are increasingly eyeing the popular bet against volatility as ground zero for the next financial crisis.
More than $2 trillion has flowed into the “global short volatility trade,” says Artemis Capital Management founder Christopher Cole. Those wagers both influence and are influenced by stock market turbulence. They also could backfire, threatening the eight-year-old bull market.
The tactics include options selling strategies by the largest pension funds and asset managers alongside exchange-traded products tracking the CBOE Volatility Index, or VIX, and VIX futures. Exchange-traded products betting on a pickup in volatility are some of the riskiest, and some investors could see extreme losses if the VIX jumps, Mr. Cole wrote in an Oct. 18 note.
Among the other strategies are ones that utilize volatility to make investing decisions, such as so-called risk parity strategies, that tend to bank on assets like stocks and bonds moving in different directions. If stocks and bonds rise and fall at the same time, it could hurt the billions of dollars in investments that rely on this relationship, he said.Here's Mr. Cole's Artemis Capital Management:
“The danger is that the multi-trillion dollar short volatility trade, in all its forms, will contribute to a violent feedback loop of higher volatility resulting in a hyper-crash,” wrote Mr. Cole.
If that happens, there is no limit to how high volatility could go, Mr. Cole wrote, a moment markets experienced 30 years ago to the day. The Dow Jones Industrial Average plummeted 22.6% on Black Monday, fueled by many of the drivers that markets are experiencing right now, he said.
Artemis is not alone in flagging these risks. The International Monetary Fund recently warned in its Global Financial Stability Report that low volatility can heighten how sensitive a financial system is to risks because when things are calm, investors ramp up exposure to financial assets. Investors also use more leverage, which can amplify any swings when they do happen.
Ironically, so-called volatility-targeting investment strategies, meant to keep portfolio fluctuations at a certain level, can lead to market disruptions and heavy selling when market jitters pick up...MORE
Volatility and the Alchemy of RiskPreviously from Mr. Cole:
The Ouroboros , a Greek word meaning ‘tail devourer’, is the ancient symbol of a snake consuming its own body in perfect symmetry. The imagery of the Ouroboros evokes the infinite nature of creation from destruction . The sign appears across cultures and is an important icon in the esoteric tradition of Alchemy. Egyptian mystics first derived the symbol from a real phenomenon in nature. In extreme heat a snake, unable to self - regulate its body temperature, will experience an out - of - control spike in its metabolism. In a state of mania, the snake is unable to differentiate its own tail from its prey, and will attack itself, self - cannibalizing until it perishes. In nature and markets, when randomness self - organizes into too perfect symmetry, order becomes the source of chaos (1) .
The Ouroboros is a metaphor for the financial alchemy driving the modern Bear Market in Fear. Volatility across asset classes is at multi - generational lows. A dangerous feedback loop now exists between ultra - low interest rates, debt expansion, asset volatility, and financial engineering that allocates risk based on that volatility. In this self - reflexive loop volatility can reinforce itself both lower and higher. In a market where stocks and bonds are both overvalued, financial alchemy is the only way to feed our global hunger for yield, until it kills the very system it is nourishing .
The Global Short Volatility trade now represents an estimated $2 + trillion in financial engineering strategies that simultaneously exert influence over, and are influenced by, stock market volatility (2). We broadly define the short volatility trade as any financial strategy that relies on the assumption of market stability to generate returns, while using volatility itself as an input for risk taking. Many popular institutional investment strategies, even if they are not explicitly shorting derivatives, generate excess returns from the same implicit risk factors as a portfolio of short optionality, and contain hidden fragility.
Volatility is now an input for risk taking and the source of excess returns in the absence of value. Lower volatility is feeding into even lower volatility, in a self - perpetuating cycle, pushing variance to the zero bound. To the uninitiated this appears to be a magical formula to transmute ether into gold... volatility into riches... however financial alchemy is deceptive. Like a snake blind to the fact it is devouring its own body, the same factors that appear stabilizing can reverse into chaos. The danger is that the multi - trillion - dollar short volatility trade, in all its forms, will contribute to a violent feedback loop of higher volatility resulting in a hyper - crash. At that point the snake will die and there is no theoretical limit to how high volatility could go.
Thirty years ago to the day we experienced that moment. On October 19th, 1987 markets around the world crashed at record speed, including a - 20% loss in the S&P 500 Index, and a spike to over 150% in volatility. Many forget that Black Monday occurred during a booming stock market, economic expansion, and rising interest rates. In retrospect, we blame portfolio insurance for creating a feedback loop that amplified losses. In this paper we will argue that rising inflation was the spark that ignited 1987 fire, while computer trading served as explosive nitroglycerin that amplified a normal fire into a cataclysmic conflagration. The multi - trillion - dollar short volatility trade, broadly defined in all its forms, can play a similar role today if inflation forces central banks to raise rates into any financial stress. Black Monday was the first modern crash driven by machine feedback loops, and it will not be the last....MUCH MORE (19 page PDF)
Baby Got Black (Swan)Genius or madman?
(With apologies to Sir Mix-a-Lot)
I like… fat… tails and I cannot lie
You vol sellers can’t deny
When a hot trend breaks with a well-timed stop
and a great big black swan pop you get
Paid… P&L year gets made
‘Cause you noticed that trade was packed
Buncha mean reversion suckers got jacked
Oh baby I wanna get lumpy
Long gamma for when it gets bumpy
Central banks tried to haze me,
But those carry trades just don’t faze me!...MORE
When DeepMind’s AlphaGo artificial intelligence defeated Lee Sedol, the Korean Go champion, for the first time last year, it stunned the world. Many, including Sedol himself, didn’t expect an AI to have mastered the complicated board game, but it won four out of five matches—proving it could compete with the best human players. More than a year has passed, and today’s AlphaGo makes last year’s version seem positively quaint.
Google’s latest AI efforts push beyond the limitations of their human developers. Its artificial intelligence algorithms are teaching themselves how to code and how to play the intricate, yet easy-to-learn ancient board game Go.
This has been quite the week for the company. On Monday, researchers announced that Google’s project AutoML had successfully taught itself to program machine learning software on its own. While it’s limited to basic programming tasks, the code AutoML created was, in some cases, better than the code written by its human counterparts. In a program designed to identify objects in a picture, the AI-created algorithm achieved a 43 percent success rate at the task. The human-developed code, by comparison, only scored 39 percent on the task.
On Wednesday, in a paper published in the journal Nature, DeepMind researchers revealed another remarkable achievement. The newest version of its Go-playing algorithm, dubbed AlphaGo Zero, was not only better than the original AlphaGo, which defeated the world’s best human player in May. This version had taught itself how to play the game. All on its own, given only the basic rules of the game. (The original, by comparison, learned from a database of 100,000 Go games.) According to Google’s researchers, AlphaGo Zero has achieved superhuman-level performance: It won 100–0 against its champion predecessor, AlphaGo.
But DeepMind’s developments go beyond just playing a board game exceedingly well. There are important implications that could positively impact AI in the near future.“By not using human data—by not using human expertise in any fashion—we’ve actually removed the constraints of human knowledge,” AlphaGo Zero’s lead programmer, David Silver, said at a press conference.
Until now, modern AIs have largely relied on learning from vast data sets. The bigger the data set, the better. What AlphaGo Zero and AutoML prove is that a successful AI doesn’t necessarily need those human-supplied data sets—it can teach itself.
This could be important in the face of our current consumer-facing AI mess. Written by human programmers and taught on human-supplied data, algorithms (such as the ones Google and Facebook use to suggest articles you should read) are subject to the same defects as their human overlords. Without that human interference and influence, future AI’s could be far superior to what we’re seeing employed in the wild today. A dataset can be flawed or skewed—for example, a facial recognition algorithm that has trouble with black faces because their white programmers didn’t feed it a diverse enough set of images. AI, teaching itself, wouldn’t inherently be sexist or racist, or suffer from those kinds of unconscious biases.
In the case of AlphaGo Zero, its reinforcement-based learning is also good news for the computational power of advanced AI networks. Early AlphaGo versions operated on 48 Google-built TPUs. AlphaGo Zero works on only four. It’s far more efficient and practical than its predecessors. Paired with AutoML’s ability to develop its own machine learning algorithms, this could seriously speed up the pace of DeepMind’s AI-related discoveries....MOREPreviously:
There's yet another complication in Alphabet's relationship with start-up giant Uber.
CapitalG, an investment arm within Google-parent Alphabet, will lead a $1 billion funding round for Lyft, the companies said on Thursday. CapitalG Partner David Lawee will join Lyft's board.
Alphabet's self-driving car unit, Waymo, confirmed earlier this year it would partner with Lyft on a self-driving car project. Lyft noted in a statement that its service is now available to 95 percent of the U.S. population — up from 54 percent at the beginning of the year.
The funding round comes as a chummy relationship between Uber and Google has soured, in part due to a lawsuit between Waymo and Uber. Waymo alleges that Uber is using one of Waymo's trade secrets for its autonomous vehicle sensors....MORE
No matter what it took, David Hall was going to kill that clown. He maneuvered Drillzilla for another ramming run. The robot was squat and heavy, with serrated blades coming off one end and a sharp drill whirling on the other. Across the arena, Conquering Clown awaited. It had the face of a goofy jokester, but its hands were a pair of smashing hammers and its body was equipped with a pair of circular saws.Drillzilla managed to flank the clown, then ram it, sneaking blades under its body and lifting it up off its wheels. Its opponent was helpless, and Drillzilla pushed it onto a waiting geyser of flames. As the audience cheered, the clown’s grinning face melted away. “We were in there to make great TV,” recalls Hall with a chuckle, “and damn it, we were successful.”The year was 2001, and this was the third round of the Robot Wars Annihilator Challenge. The show pitted homemade gladiators against one another. Hall, an eccentric inventor, was best known as the creator of a high-end subwoofer. His company, Velodyne, had around 60 employees and a few million dollars in annual sales. But Hall had grown bored with the audio industry, and was trying his hand at building robots. Perhaps, he thought, Velodyne could find a new product to manufacture. At the very least he could have some fun.Hall admits he “bent the rules a little” to win the competition — he camouflaged the robot’s wheels as “legs” — and, after other teams complained, the judges banned Hall’s approach for future seasons. He took that as a sign to move on. But he was eager to continue his work on vehicles in the public eye.He got his chance in 2004, when the US Army’s research division, DARPA, held its first Grand Challenge. Teams were asked to design an automobile that could autonomously navigate its way through a 150-mile course. Hall took a bunch of the motor controls and code from his robots and got to work on a self-driving truck.There were 15 vehicles in that first race, all competing for a $1 million prize. Not a single one finished the course. Hall used stereo cameras to see the road and avoid obstacles. “I could see some of the road all the time, and all the road some of the time,” he says now. But the vehicles were constantly confused. “There was all kinds of weird artifacts. It would see a fence five feet in front of it and throw on the brakes.”A few of the teams were using a technology called LIDAR, which uses laser beams to sense objects and measure their range. “I didn’t know what a LIDAR was,” admits Hall, “but Jim McBride from Ford kept bending my ear about how LIDAR was the solution for all his problems. I kinda made a mental note — maybe I’ll look into it when I’m bored. A few months later, I started looking into it, and the more I did, the more intrigued I was.”Hall returned the next year with a custom LIDAR unit he had designed and built. Instead of putting a laser scanner on the front of his car, as most teams had done, he put it on the roof. And instead of looking forward, it scanned in all directions at once. It was a radically different approach to the technology.He didn’t win the race, but his design impressed competitors, and the DARPA challenge convinced a number of the participants that driverless cars were no longer an unattainable fantasy. The teams from Stanford and CMU would go on to help found the self-driving projects at Google and Uber. And as those massive corporations built out their self-driving ambitions, they turned to Velodyne for LIDAR.At age 66, Hall finds himself in an enviable position. LIDAR has eclipsed Velodyne’s audio business; the company now has over 400 full-time employees people and generates hundreds of millions in annual revenue. Velodyne’s most recent round of funding made Hall a billionaire. And earlier this year, the company announced it was on the verge of rolling out a new kind of LIDAR, one that would make the technology radically cheaper, allowing it to move from expensive test vehicles to a standard piece of an affordable consumer sedan.
After four decades of inventing, five product categories, and a lot of wrong turns, Hall has stumbled onto a high-tech widget that is poised to become an essential piece of a trillion-dollar self-driving auto industry. Setting aside Steve Jobs’ return to Apple, it might be Silicon Valley’s greatest second act.
There are many ways of seeing the world. Humans rely on our eyes, which interpret incoming rays of light. On a sunny day, they allow us to see a richly nuanced view of the world. In total darkness, they aren’t much use. Some animals, like bats, use echolocation. While hunting small insects at night, they emit high-pitched noises, then interpret the sound waves that bounce back to get a picture of the world.Humans have created technologies that mimic echolocation. Sonar, used by submarines, emits a pulse of sound, then reads the waves that bounce back. Radar does the same thing, using radio waves instead. It’s found countless applications, from spotting incoming missiles to catching drivers who break the speed limit. LIDAR, short for “light detection and ranging,” adopts Radar’s approach, but uses lasers in place of radio waves. Scientists at Hughes Research Lab demonstrated the first functioning lasers in 1960, and LIDAR attempts quickly followed. Early on, it was used by government research agencies to map and measure the natural world, from cloud formations to sea floors to the surface of the Moon.By the late 1980s, LIDAR had found its way into autonomous cars. Researchers at Carnegie Mellon University’s Navlab used a laser scanner to help detect obstacles and determine their range back in 1989, but it was not their primary sensor. “We would see, with our scanning lasers, these very grainy updates every half second,” says Dean Pomerleau, a Navlab researcher. “A kid on a bicycle would just look like a blob.” The lab used LIDAR primarily because it was good at detecting reflective materials, like lane markings and road signs. In the late ‘90s, Mitsubishi actually tried LIDAR in its driver assistance system, but its high cost made it prohibitive. As the 21st century arrived, the auto industry moved to radar and cameras, which were much cheaper....MUCH MORE
Richard H. Thaler, the US economist who elevated the word “nudge” from transitive verb to political catchphrase, can now add “Nobel laureate” to his impressive biography....MUCH MORE
On Monday, the Royal Swedish Academy of Sciences in Stockholm announced that Thaler, who teaches at the Booth School of Business at the University of Chicago, had won the 2017 prize in economics “for his contributions to behavioral economics.”
In a statement, the Royal Swedish Academy said that Thaler “has incorporated psychologically realistic assumptions into analyses of economic decision-making. By exploring the consequences of limited rationality, social preferences, and lack of self-control, he has shown how these human traits systematically affect individual decisions as well as market outcomes.”
Thaler is perhaps best-known for his popular book about choices: Nudge: Improving Decisions about Health, Wealth, and Happiness — how we make them and what we can do to improve how we make them. His “nudge” theory is also credited with inspiring former UK prime minister David Cameron’s Behavioural Insights Team (BIT) — or “Nudge Unit.” Thaler reportedly “visited Britain in 2008 to promote his theory, met Cameron, and made such an impression that for a time he acted as unpaid adviser to the Tory leader.”
Former US president Barack Obama also officially adopted the “nudge” approach when he created the Social and Behavioral Science Team (SBST), which sought to integrate behavioral science research into policy making.
Shortly after the announcement from Sweden, fellow economist Tyler Cowen wrote:
“This is a prize that is easy to understand. It is a prize for behavioral economics, for the ongoing importance of psychology in economic decision-making, and for ‘Nudge,’ his famous and also bestselling book co-authored with Cass Sunstein.”
Cowen also noted that “perhaps unknown to many, Thaler’s most heavily cited piece is on whether the stock market overreacts for psychological reasons.”
Perhaps also unknown to many is that Thaler spoke at the 70th CFA Institute Annual Conference in Philadelphia this past May.
My colleague, Shreenivas Kunte, CFA, wrote a recap of the session, entitled “Richard Thaler: To Intervene or Not to Intervene.” Another colleague, Ron Rimkus, CFA, conducted a 13-minute interview with Thaler.
Thaler has also contributed to the CFA Institute Financial Analysts Journal®, among other CFA Institute publications over the years.
In his prescient conclusion to the 1999 piece, “The End of Behavioral Finance,” he wrote:...
Recently, we have seen a rise in class actions filed against employers for improperly classifying their employees as independent contractors. While misclassification issues are nothing new, the proliferation of nontraditional jobs grows every year—especially with the advancement of technology and the ability of service providers to work remotely from anywhere in the world. In this brave new world, employers may struggle with how to define their workforce. Current labor laws recognize workers providing services can be categorized as either an independent contractor or an employee, and employees are generally protected by more employment rights. On one hand, classifying service providers as independent contractors can be more efficient and cost-effective for a company. On the other hand, misclassifying service providers can have dire consequences, leaving a company exposed to expensive class actions for wage, hour, and other Labor Code violations— not to mention staggering governmental fines and penalties.
In this article, we outline the current legal landscape governing classification of service providers and give guidance for employers on how to properly classify their work force.
Classification StandardsBoth the federal government and various individual state governments have their own individual independent tests to determine whether a service provider is an employee or an independent contractor. To make things even more complicated, various departments within the federal and state governments may also have their own differing tests. However, at their common core, all these tests are primarily focused on the degree of control a company exerts over the service provider and the independence of the provider. By way of example, we highlight below the standards used by two federal departments most often interested in provider classification—i.e., the United States Department of Labor and the United States Internal Revenue Service—as well as by a state agency.
United States Department of LaborThe Department of Labor (“DOL”) is tasked with overseeing compliance with the Fair Labor Standards Act (“FLSA”)1. The FLSA2 includes minimum wage and overtime pay requirements for nonexempt employees.3 The DOL generally relies on the six elements identified by the U.S. Supreme Court4 and subsequent case law to determine whether to apply the FLSA.5 While the factors considered can vary and no one set of factors is exclusive, these are the following six elements generally considered when determining whether an employment relationship exists under the FLSA:
- The extent to which the work performed is an integral part of the employer’s business. If the work performed by a worker is integral to the employer’s business, it is more likely that the worker is economically dependent on the employer and less likely that the worker is in business for himself or herself.
- Whether the worker’s managerial skills affect his or her opportunity for profit and loss. Analysis of this factor focuses on whether the worker exercises managerial skills and, if so, whether those skills affect that worker’s opportunity for both profit and loss.
- The relative investments in facilities and equipment by the worker and the The worker must make some investment compared to the employer’s investment, and bear some risk for a loss, in order for there to be an indication that he/ she is an independent contractor in business for himself or herself.
- The worker’s skill and initiative. To indicate possible independent contractor status, the worker’s skills should demonstrate that he or she exercises independent business judgment. Further, the fact that a worker is in open market competition with others would suggest independent contractor status.
- The permanency of the worker’s relationship with the Permanency or indefiniteness in the worker’s relationship with the employer suggests that the worker is an employee, as opposed to an independent contractor.
- The nature and degree of control by the Analysis of this factor includes who sets pay amounts and work hours and who determines how the work is performed, as well as whether the worker is free to work for others and hire helper.
United States Internal Revenue ServiceThe Internal Revenue Service (“IRS”) administers federal payroll taxes, including social security, Medicare, federal unemployment insurance, and federal income tax withholding, and ensures that employers pay taxes, make the appropriate withholdings, and obtain certain insurance coverage on behalf of their employees. To determine whether a service provider is an employee or an independent contractor, the IRS utilizes a test different from the DOL’s six-element test. Historically, the IRS utilized a 20-Factor Test, but the IRS has recently grouped the 20 factors into three primary categories of evidence to support the level of control and independence.6
The first category—“Behavioral”— refers to facts showing whether a company has a right to direct or control how the worker does the work. A worker is an employee when the business has the right to direct and control the worker. Within this category, the IRS examines four subcategories:...
When Thomas Piketty’s Capital in the Twenty-First Century first appeared many economists demurred on the theory but heaped praise on the empirical work. “Even if none of Piketty’s theories stands up,” Larry Summers argued, his “deeply grounded” and “painstaking empirical research” was “a Nobel Prize-worthy contribution”.
Theory is easier to evaluate than empirical work, however, and Phillip Magness and Robert Murphy were among the few authors to actually take a close look at Piketty’s data and they came to a different conclusion:...MORE
We find evidence of pervasive errors of historical fact, opaque methodological choices, and the cherry-picking of sources to construct favorable patterns from ambiguous data.Magness and Murphy, however, could be dismissed as economic history outsiders with an ax to grind. Moreover, their paper was published in an obscure libertarian-oriented journal. (Chris Giles and Ferdinando Giugliano writing in the FT also pointed to errors but they could be dismissed as journalists.) The Magness and Murphy conclusions, however, have now been verified (and then some) by a respected figure in economic history, Richard Sutch.
I have never read an abstract quite like the one to Sutch’s paper...
Farmers Insurance, State Farm, Liberty, Allstate, Travelers, Nationwide Mutual, Chubb, AIG, Tokio Marine, National General, Allianz and QBE, are all among the top property insurers in the state of California, so likely to share the bulk of these losses....Here's the latest from Artemis, October 18:
The Northern California wildfires that started on Sunday, October 8th, could drive insurance industry losses of $4.6 billion or higher, adding further pressure to the profitability of the property & casualty (P&C) industry following an active third-quarter, says Moody’s.
The fires broke out on October 8th, and spread rapidly with the help of high winds, low humidity, high temperatures and dry conditions, claiming 40 lives as it tore through Northern parts of the state, as of Saturday.
The number of structures reportedly damaged and historical fire data suggests the insured loss total for the California wildfires will be in the billions of dollars, says Moody’s, providing an initial loss estimate of around $4.6 billion, which it feels could rise.
The figure is based on reports from the California Department of Forestry and Fire Protection (CAL FIRE) that 5,700 homes and commercial structures had been destroyed by the fires, as of Saturday, to which Moody’s then applies an average insured loss per structure of $802,000 (which is based on historical Cali wildfire loss estimates).
Moody’s says that using the above estimations and data, suggests “losses would be close to $4.6 billion and growing as the fire continues.”
For the re/insurance and possibly insurance-linked securities (ILS) markets the wildfires will likely drive additional pressure to 2017 profits, after extremely costly events in the third-quarter, namely hurricanes Harvey, Irma, Maria, and the Mexico earthquakes, drove increased catastrophe losses for companies....
The Federal Reserve is raising interest rates and the European Central Bank is considering buying fewer bonds, but the Bank of Japan will stick to its easing guns forever, right?
Wrong. In fact, Japan’s central bank’s latest moves make it seem like it is tightening policy, too.
The BOJ bought just ¥7.7 trillion ($68.8 billion) worth of Japanese government bonds in September, according to J.P. Morgan. The figure represents its smallest monthly amount of outright buying, which doesn’t account for maturing bonds, since October 2014.
That looks a lot like tapering.
Since the rollout of the BOJ’s “yield curve control” policy last year, the central bank has been able to buy as many or as few bonds as it needs to keep its 10-year government-bond yield near zero, which investors have interpreted as a range between negative 0.1% and 0.1%. So when the 10-year yield doesn’t move dramatically, the central bank doesn’t have to buy as many bonds
That’s what happened last month, according to Noriko Miyoshi, head of fixed income at Simplex Asset Management in Tokyo. “The market has understood the intention of the BOJ,” she said.
While the BOJ has slowed its bond buys this year, its leader has been quick to dispel any chatter about a shift in policy. Gov. Haruhiko Kuroda said Sunday that aggressive easing will remain in place as Japan’s inflation rate is still a long way from reaching the central bank’s 2% target.
The numbers, however, tell a different story. The central bank is currently on pace to buy some ¥60 trillion worth of bonds this year after adjusting for about ¥40 trillion worth of maturing JGBs, according to J.P. Morgan. Officially, though, the Bank of Japan continues to pledge it will buy government bonds at an annual rate of ¥80 trillion....MORE
In 2013, a reclusive New York tycoon and his wife began buying up expensive Palm Beach real estate—lots of it. First they bought seven mansions for a total of $23 million. Then another four “moderately priced” homes for $8.4 million. Then five more for $23 million. None of them were purchased in the tycoon’s name. They weren’t purchased in his wife’s name, either. Instead, the homes were deeded to limited-liability companies, including L. Jakes LLC and 124 Coconut Row LLC. Think of those luxury homes as the shuttered offices and fired workers of hometown newspapers across the United States, because gutting those newspapers helped make spending $57.2 million on 16 Palm Beach mansions a trifling expense for the tycoon.
His spending spree began after the tycoon acquired two firms, the Journal-Register and MediaNews Group, which would merge into one of America’s largest newspaper chains, Digital First Media. It continued under the veil of yet more limited-liability companies that likewise owned luxury homes. The only thing linking all these purchases was the same postal address in Manhattan’s glamorous Lipstick Building. There, within the tycoon’s privately held investment firm, his personal real-estate deals were commingled with the sales of scores of newsrooms, printing plants, and office buildings that previously belonged to small hometown newspapers across the United States.
The tycoon continued to finance his lavish lifestyle by purchasing and then destroying newspapers. His henchmen—young executives in expensive suits with no experience in the news business—laid off hundreds of journalists and other news workers. They ultimately closed or radically downsized such venerable papers as the Oakland Tribune, the San Jose Mercury News, the St. Paul Pioneer Press, and The Denver Post. At the Mercury News, the newspaper’s printing press was literally dismantled and carted away, which one staff reporter likened to “watching a heart being ripped out.”
The tycoon behind all this private profit and public destruction is Randall D. Smith, a seasoned Wall Street operator in his mid-70s who shuns publicity. Smith is the founder and chief of investments at Alden Global Capital, which manages $2 billion worth of assets. He has no experience with actually managing a newspaper, and his professional history reflects no interest in journalism beyond profiteering. Rather, he is what is known on Wall Street as a “vulture capitalist.” Or, as he prefers to phrase it in one of the company’s brochures, Smith invests in “distress.”
“Distress” is an apt word for the current state of America’s newspapers, and Smith isn’t the only financial mogul gobbling them up. On September 4, the New York Daily News was purchased by Tronc, the media conglomerate whose majority shareholder is Michael W. Ferro, the business magnate who founded the investment firm Merrick Ventures.
The shrinking and disappearing of hometown newspapers has done incalculable damage to Americans’ knowledge of the world around them. Democratic self-governance presumes an informed public, but the -hollowing-out of America’s newspapers, in both their online and print versions, leaves citizens increasingly ignorant of vital public matters. It also undermines the press’s ability to hold elected officials and powerful interests to account. When vulture capitalism eliminates reporters and closes hometown papers, where can citizens turn for in-depth local news? Who will cover City Council meetings, school-board decisions, election campaigns, and other staples of civic life? And who will call out corruption and incompetence on the part of local officials or private companies?
The most commonly cited culprit for the decline of America’s newspapers is the Internet and the assumption that no one needs to pay for news anymore. But simple capitalist greed is also to blame. Since 2004, speculators have bought and sucked dry an estimated 679 hometown newspapers that reached a combined audience of 12.8 million people....MUCH MORE
Unlike large corporate owners in the past, the stated goal of the investment firms is not to keep struggling newspapers alive; it is to siphon off the assets and profits, then dispose of what little remains. Under this strategy, America’s newsrooms shriveled from 46,700 full-time journalists in 2009 to 32,900 in 2015—a loss of roughly one journalist out of every three. The American Society of Newspaper Editors stopped trying to estimate the number of working journalists in 2016 because “layoffs, buyouts, and restructuring are a norm.”...
Intel is ready to ship its long awaited computer chip used to power artificial intelligence projects by the end of the year.
Intel CEO Brian Krzanich explained the chip-maker’s foray into the red-hot field of artificial intelligence Tuesday and said that Facebook (FB, +0.89%) has assisted the company in prelude to its new chip’s debut.
“We are thrilled to have Facebook in close collaboration sharing its technical insights as we bring this new generation of AI hardware to market,” Krzanich wrote. An Intel spokesperson wrote to Fortune in an email that while the two companies are collaborating, they do not have a formal partnership.
The genesis of the Intel Nervana Neural Network Processor comes from Intel’s acquisition of the chip startup Nervana Systems in 2016. That acquisition was intended to help Intel create its own semiconductor technology tailored for tasks like deep learning that require a lot of heavy computer processing to create software that can spot and react to patterns in enormous quantities of data.
In the absence of AI-optimized chips, companies like Walmart looking to power deep learning tasks in their internal data centers have been turning to rival chip makers like Nvidia (NVDA, +0.32%) that build graphical processing units (GPUs).NVIDIA is still the class of the field but going forward it is not going to be as easy as it has been.
With so much hype around artificial intelligence and its potential to become a big business, Intel’s new chip represents a key moment for the company that has missed out on previous technology trends like mobile computing.
“A company like Intel doesn’t announce a new class of products very often,” said Intel’s leader of the new chip project Naveen Rao. “This is really a historical point in the history of computation.”...MORE
One of SoftBank’s favorite pastimes seems to be buying up other companies, so it’s not really much of a surprise to hear that it might be close to striking a deal with Uber. This doesn’t appear to be a Sprint-scale buyout, where SoftBank is looking to acquire a majority stake in Uber. Instead, it could own as much as 20% of the company by the time everything is said and done
Uber board member Arianna Huffington said last night at The Wall Street Journal’s D.Live conference that an agreement between the two companies might be on the way. As Recode reports, that deal may materialize in as little as week. SoftBank is said to be looking at acquiring at least 14% of Uber, though that percentage may go up if the price is right.
Price is currently the sticking point for some of Uber’s shareholders. It sounds like the only reason a deal hasn’t been announced yet is because Uber and SoftBank haven’t found a price that satisfies Uber shareholders enough to surrender their stake. While SoftBank may be looking at a valuation of $50 billion, shareholders may want a valuation closer to $68 billion.
There’s always the possibility of the deal falling apart if shareholders aren’t happy with the price SoftBank is willing to buy at, so while the two may be close to striking a deal, it isn’t necessarily a sure thing. Still, Huffington said an agreement is “very likely” to materialize in the next week, but whether that means shareholders will compromise and accept a lower price is up in the air....MORE
The calorie count shows up on the map if the driving directions you've requested cover a short distance. If you already have walking selected, the calories are shown in the step-by-step directions, but not the map.
Basically, it wants to try to encourage you to walk instead of driving by showing you how many calories you'd burn if you walked:
The calorie count shows up on the map if the driving directions you've requested cover a short distance. If you already have walking selected, the calories are shown in the step-by-step directions, but not the map.
Basically, it wants to try to encourage you to walk instead of driving by showing you how many calories you'd burn.
And then...it tells you how many "mini cupcakes" the calories you'd burn would add up to.
I have no idea why I still use Google Maps pic.twitter.com/OE6J3gqX0c— kadhim (＾ｰ^)ノ (@kadhimshubber) December 2, 2016
Oh yes good perfect thanks pic.twitter.com/SsthmaGoU9— kadhim (＾ｰ^)ノ (@kadhimshubber) March 7, 2017
The other Musk is leading a band of hipster Brooklyn farmers on a mission to overthrow Big Ag.
Farmers have always had a tough time. They have faced rapacious bankers, destructive pests, catastrophic weather, and relentless pressure to cut prices to serve huge grocery suppliers.
And now they must compete with Brooklyn hipsters. Hipsters with high-tech farms squeezed into 40-foot containers that sit in parking lots and require no soil, and can ignore bad weather and even winter.
No, the 10 young entrepreneurs of the “urban farming accelerator” Square Roots and their ilk aren’t going to overthrow big agribusiness — yet. Each of them has only the equivalent of a two-acre plot of land, stuffed inside a container truck in a parking lot. And the food they grow is decidedly artisanal, sold to high-end restaurants and office workers who are amenable to snacking on Asian Greens instead of Doritos. But they are indicative of an ag tech movement that’s growing faster than Nebraska corn in July. What’s more, they are only a single degree of separation from world-class disrupters Tesla and SpaceX: Square Roots is co-founded by Kimbal Musk, sibling to Elon and board member of those two visionary tech firms.
Kimbal’s passion is food, specifically “real” food — not tainted by overuse of pesticides or adulterated with sugar or additives. His group of restaurants, named The Kitchen after its Boulder, Colorado, flagship, promotes healthy meals; a sister foundation creates agricultural classrooms that center a teaching curriculum around modular gardens that allow kids to experience and measure the growing process. More recently, he has been on a crusade to change the eating habits of the piggiest American cities, beginning with Memphis.
“This is the dawn of real food,” says Musk. “Food you can trust. Good for the body. Good for farmers.”
Square Roots is one more attempt to extend the “impact footprint” of The Kitchen, says its CEO and co-founder Tobias Peggs, a longtime friend of Musk’s. (Musk himself is executive chair.) Peggs is a lithe Brit with a doctorate in AI who has periodically been involved in businesses with Musk, along with some other ventures, and wound up working with him on food initiatives. Both he and Musk claim to sense that we’re at a moment when a demand for real food is “not just a Brooklyn hipster food thing,” but rather a national phenomenon rising out of a deep and wide distrust of the industrial food system, a triplet that Peggs enunciates with disdain. People want local food, he says. And when he and Musk talk about this onstage, there are often young people in the audience who agree with them but don’t know how to do something about it. “In tech, if I have an idea for a mobile app, I get a developer in the Ukraine, get an angel investor to give me 100k for showing up, and I launch a company,” Peggs says. “In the world of real food, there’s no easy path.”
The company is headquartered in the Brooklyn neighborhood of Bedford-Stuyvesant, right next to the Marcy Projects, which were the early stomping grounds of Jay Z. It’s one of over 40 food-related startups housed in a former Pfizer chemicals factory, which at one time produced a good chunk of the nation’s ammonia. (Consider its current role as a hub of crunchy food goodness as a form of penance.) Though Peggs’ office and a communal area and kitchen are in the building, the real action at Square Roots is in the parking lot. That’s where the company has plunked down ten huge shipping containers, the kind you try to swerve around when they’re dragged by honking 18-wheel trucks.
These are the farms: $85,000 high-tech growing chambers pre-loaded with sensors, exotic lighting, precision plumbing for irrigation, vertical growing towers, a climate control system, and, now, leafy greens....MORE
Tesla Inc.’s former director of battery technology has joined Plenty Inc. to lead the vertical farming startup’s plan to build indoor growing rooms around the world.Be right back with an added twist to the story.
Kurt Kelty, who joined Tesla in 2006 and left earlier this year, was one of the longest-serving executives at the carmaker led by Elon Musk. He joins SoftBank Group Corp.-backed Plenty as the senior vice president of operations and market development. Kelty had previously spent more than 14 years at Panasonic Corp.
"At Tesla I was employee number fifty or sixty,” Kelty said in an interview. “It’s a very different company from when I joined. I wanted to figure out where I would contribute to the next big wave. I see my next 10-year-run as growing Plenty."
Japanese telecommunications giant SoftBank led a $200 million investment in Plenty in July....MORE
A CNN story late last year declared 2016 “a tipping point for excitement in self-driving cars.” In fact, the year saw reports of major investments by Ford, Mercedes, Apple, Intel, Delphi Automotive, and venture capitalists, among numerous players jockeying for position in the emerging market for autonomous vehicles. There may be debate about the time it will take the technology to adapt enough for wide deployment, but there is consensus that this time will come in a matter of years rather than decades.
To date, there are no public estimates of how large this surge in investment in autonomous vehicle technology is in the aggregate. PWC’s 2016 Connected Car Study, its fourth annual report on the sector, says the top five original equipment manufacturers spent $46 billion in research and development in 2015, and there are numerous reports that catalogue investments, acquisitions, and other activities in the growing ecosystem that supports self-driving cars. We set out to estimate the aggregate investment across this entire area.
The inquiry provides a useful window onto the state of play in development of autonomous vehicle technology, as entities from the major auto manufacturers to startups scramble to take the lead. It is also a measure of what it takes to reach a tipping point in development of sophisticated artificial (or augmented) intelligence. As Qi Lu, the star Microsoft engineer who moved to Beijing to become Baidu’s chief operation officer, recently put it, “In autonomous systems, the car is the first major commercial application that is going to land.”
Investment in self-driving cars appears to be the leading edge for AI development. Indeed, as progress accelerates on vehicles interest is increasing in broader artificial intelligence. A Forrester Research report predicted that 2017 will the year that “Artificial intelligence (AKA cognitive computing) technologies will be rapidly assimilated into analytics practices,” driving new insights from big data, and investment in artificial intelligence has been touted by popular investor news sites like The Motley Fool and a column on Fortune.com. If 2016 was a tipping point for investment in self-driving cars, 2017 could turn out to be a tipping point for investment in artificial intelligence more generally.
The Autonomous Vehicle Landscape
The ecosystem for self-driving cars has numerous layers. It encompasses not just the machine-learning that operates the vehicles, but also the array of sensor and navigation technology needed, from refinements to the advanced braking or lane-keeping assistance common in today’s vehicles, advances in more granular and adaptive mapping, and vehicle-to-vehicle and vehicle-to-infrastructure communications systems.
Because lives and safety are at stake, autonomous vehicles require a high degree of reliability and need to adapt to widely diverse contexts. In Austin, for example, one of Google’s autonomous vehicles was befuddled by a cyclist rocking back and forth in a track stand at a red light and, in Australia, Volvo engineers found it a challenge to predict the bounding of kangaroos. As a result, in order to train self-driving artificial intelligence systems, the players involved are stretching to accumulate vehicle miles and investing untold hours of engineering time.
Every major car manufacturer in the world wants to be an early mover – or at least to avoid competitive disadvantage. They are joined by automotive suppliers like Bosch and Delphi Automotive. The technology-intensive aspect has drawn in tech companies that see their core competencies involved; Google’s self-driving cars have been the poster children of the sector for several years, but Apple, Microsoft, Alibaba, and Baidu are increasingly involved and players such as Intel, NVIDIA, and Qualcomm are investing in making the microprocessors required. Fleet operators are also involved, including rideshare and logistics companies that are likely to be the earliest adopters and have a ready supply of vehicle miles to supply data for machine learning.
In addition to these established players, startups are playing an increasing role: Nutonomy, a Massachusetts outgrowth of MIT, received a $20 million in investments investment and many of the investments by established players have involved the acquisitions or stakes in startups, such as Ford’s $1 billion investment in ArgoAI. This startup activity has attracted the attention of venture capitalists; venture leader Andreesen Horowitz announced investments in advanced mapping, driver assistance systems, and delivery robots, and several funds have been set up to invest in autonomous vehicle technologies. The venture firm Comet Labs mapped startups working on pieces of the autonomy puzzle and found some 263 firms involved. ...MUCH MORE
Middle America is under financial stress.
Over the past year, US casinos (ex-Vegas) have enjoyed only 1 month of positive growth.
Las Vegas is doing a bit better thanks to the return of Chinese gamblers: YTD gaming revenues are up 3.5%.
This reflects the bifurcated US economy: middle America is pulling back on frivolous activities while upper income consumers continue to spend.
(NOTE: Will higher spending by the 1% be enough to offset a spending pullback by the bottom 99%? We’ll have to find out…)
Beer consumption fell in 2016 for the first time since 2011 (per IWSR).
Several reasons are possible:HT: ZeroHedge
But maybe cannabis legalization is also playing a role....MORE
- Changes in drinking habits: Millenials are socializing differently: it’s popular to hang out at home with friends and “Netflix and chill.” And that’s driving a shift: drinking at bars (on-premise) has dropped while drinking at home (off-premise) has grown.
- Shifts in taste: Beer is down, but distilled alcohol is up. That’s largely from new styles of tequila and bourbons being released, and the big marketing campaigns pushing them.
Addressing a gathering of news executives and propaganda officials in February 2016, President Xi Jinping laid out the core qualities that must define the media professional in China. Politics, he said, must always come first. Beyond professional expertise, journalists and media leaders “must have the heads of politicians.”
This injunction, echoing that of Mao Zedong, who said in the midst of a brutal crackdown on intellectuals 60 years ago that “politicians must run the newspapers,” has taken on fresh meaning for Chinese Internet firms this month. The government is now pushing to acquire “special management shares” in some of China’s largest tech companies -- including Tencent and Weibo -- giving it direct power over corporate decisions that have a bearing on the leadership's larger media control agenda.
These “special management shares,” generally between 1 and 2%, to be held by official agencies or trusted state media, would allow the government to influence company behavior from inside the boardroom, and to have direct access to innovative technologies.
Centralized Control Of Information
However worrying, this development should come as no surprise. China’s government, obsessed about maintaining centralized control of information, has been talking openly for several years now about creating a “special management share system” for media to improve ideological controls and reinforce Chinese Communist Party rule.
Control over mobile and online media especially has growing urgency for China’s leaders in a world increasingly shaped, and periodically disrupted, by advances in digital technology. The rules of that world are determined not by central planners, but by technology trailblazers responding to investors and consumers. Chinese tech companies, some listed on overseas exchanges, have never been wired into the Party-run media system, in which newspapers and traditional broadcasters are defined to this day as “mouthpieces,” or tools for Party propaganda -- and are overseen directly by Party committees.
Over the past two decades, the old media system, its controls exercised through the Party's Central Propaganda Department, has been seriously challenged by digital and commercial developments -- with profound ramifications for the government’s efforts to achieve what its calls “public opinion guidance,” or dominance of social and political agendas in order to maintain stability and Party legitimacy. To cite one of countless examples, Chinese media broadly ignored initial bans on coverage of a tragic high-speed rail collision in July 2011, as live accounts proliferated on the Internet and social media provided a release valve for widespread public outrage.
Since coming into office in November 2012, Xi has been far more resolute than his predecessors in breaking the cycle of media defiance. Recognising the new centrality of digital, he launched a leading group on cyberspace, with himself as chairman, and a new enforcement body, the Cyberspace Administration of China (CAC), that made a sustained attack on social media and its most influential voices its first item of business. In his February 2016 address on media policy, Xi reaffirmed and re-consolidated the CCP’s paternalistic hold on the media, saying that they must all "be surnamed Party." And he made it very clear that this demand extended to commercial media and social networks.
The question, though, was how to bring Chinese tech firms, these new players, into the old family of trust and self-discipline. This is where the “special management share system” now comes in....MORE
A new paper has troubling implications.
Protesters regularly wear disguises like bandanas and sunglasses to prevent being identified, either by law enforcement or internet sleuths. Their efforts may be no match for artificial intelligence, however.
A new paper to be presented at the IEEE International Conference on Computer Vision Workshops (ICCVW) introduces a deep-learning algorithm—a subset of machine learning used to detect and model patterns in large heaps of data—that can identify an individual even when part of their face is obscured. The system was able to correctly identify a person concealed by a scarf 67 percent of the time when they were photographed against a "complex" background, which better resembles real-world conditions.
The deep-learning algorithm works in a novel way. The researchers, from Cambridge University, India's National Institute of Technology, and the Indian Institute of Science, first outlined 14 key areas of the face, and then trained a deep-learning model to identify them. The algorithm connects the points into a "star-net structure," and uses the angles between the points to identify a face. The algorithm can still identify those angles even when part of a person's mug is obscured, by disguises including caps, scarves, and glasses.
The research has troubling implications for protestors and other dissidents, who often work to make sure they aren't ID'd at protests and other demonstrations by covering their faces with scarves or by wearing sunglasses. "To be honest when I was trying to come up with this method, I was just trying to focus on criminals," Amarjot Singh, one of the researchers behind the paper and a Ph.D student at Cambridge University, told me on a phone call.HT: Marginal Revolution
Singh said he isn't sure how to prevent the technology from being used by authoritarian regimes in the future. "I actually don't have a good answer for how that can be stopped," he said. "It has to be regulated somehow … it should only be used for people who want to use it for good stuff." How to guarantee algorithms like the one Singh developed don't get into nefarious hands is an ongoing problem.
Zeynep Tufekci, a professor at the University of North Carolina, Chapel Hill, and a writer at The New York Times, discussed the dubious implications of the algorithm described in the paper on Twitter: "too many worry about what AI—as if some independent entity—will do to us. Too few people worry what *power* will do *with* AI," she wrote in a tweet.
Don't fret yet, though. While the algorithm described in the paper was fairly impressive, it's definitely not reliable enough to be used by law enforcement or anyone else. But the researchers behind the paper have provided future academics with an important gift to do their work. One of the problems with training machine learning models is that there simply aren't enough quality databases out there to train them on. But this paper provides researchers in the field with two different databases to train algorithms to do similar tasks, each with 2,000 images.
"This is a minor paper; narrow, conditional results. But it's the direction & this will be done with nation-state data—not by grad students," Tufecki wrote in a followup tweet.
The system described in the paper isn't capable of identifying people wearing all types of disguises. Singh pointed out to me that the rigid Guy Fawkes masks often donned by members of hacking collective Anonymous would be able to evade the algorithm, for example. He hopes one day though to be able to ID people even wearing rigid masks. "We are trying to find ways to explore that problem," he told me over the phone. It's worth noting that experimental algorithms can already identify people with 99 percent accuracy based on how they walk....MORE