Thursday, August 16, 2018

Ahead of NVIDIA Earnings: The Last of the "Easy" Comparisons (NVDA)

The Yahoo Finance forecast and actual for the last four quarters:

Beating that Q3 2017 EPS, 90 cents, by double i.e. $1.80 or more, is doable but AI and data centers will have to pick up the slack from the Q1 and Q2 cryptocurrency bump that started declining with Bitmain and other miners use of ASICs rather than GPU's.

Going forward the trend toward specialist proprietary chips, see Tesla's development of their own chips etc, etc will leave NVIDIA with a couple holes in the potential addressable markets they will want to fill.

Additionally, the smaller pups, some still in stealth, are nipping at the big dog's heels, making it more expensive for NVIDIA to maintain their edge in architecture.

This week's announcement of the Turing chips received a surprisingly muted reaction from the stock. We'll see what the after hours beauty contest movement  is with everyone focusing on guidance over performance.

The stock is trading flat ahead of the release, $258.65 down 43 cents.
Here's more from TheStreet:

Nvidia Top Risks to Watch for Ahead of Earnings on Thursday
Most on Wall Street agree Nvidia Corp. (NVDA - Get Report) has the expertise, technology, and cash-flow to compete devastatingly well in all of its business segments.

TheStreet will be live-blogging Nvidia's report and call on Thursday after the close. Please check our home page then for more details. 

Just look at its price-to-earnings ratio of 44, compared to the tech-heavy Nasdaq's 23. And look at the analysts' consensus price target of $283.52, roughly 10% above its current levels.
And while Nvidia could beat second-quarter Wall Street estimates on Thursday Aug. 16, buying the stock for a post-earnings pop isn't necessarily a sure thing. Shares of the hot chipmaker are already up 34% year to date and 55% over the last 12 months.

Although Wall Street expects positive results on Thursday, there are a few notable risks to the earnings print. First, the law of large numbers may weigh on the extent of data center revenue growth. Second, Nvidia may see a slight growth pause in some of its products as it introduces a new and better chip that won't be out for a few months. Plus, the cryptocurrency market could weigh on results. But some still very much see upside to the earnings report, and the arguments are certainly aplenty.

Here's a closer look at some key areas of risk for the chipmaker:

Data Center Revenue
Analysts are expecting data center revenue to be $740 million for the quarter, and RBC Capital Markets analyst Mitch Steves, who has a $310 price target on the chipmaker, said he thinks it could come in even higher. MKM Partners analyst Ruben Roy, who has a price target of $255 on Nvidia, told TheStreet that "the law of large numbers" will likely put a damper on Nvidia's chances of beating data center revenue estimates.

The chip giant saw data center revenue growth of 71% year-over-year in its April quarter. And while expectations are still for strong growth, it would be hard for any company to sustain such a rapid growth rate. "They've been blowing out the data center numbers," Roy said. "The rate of growth is coming off a little bit."

Steves, however, told TheStreet that "I think they're set up well for a beat." While power and speed improvements to Nvidia's GPU's are driving growth, it is Nvidia's CUDA software platform that will propel sales of data center GPU's. "It's coming down to which software is the best, and CUDA is the best software out there," Steves said.

Gaming Revenue
There are two main risks to Nvidia's gaming GPU revenue. Firstly, the cryptocurrency market hasn't performed well of late, which means that demand amongst crypto miners may have come down, meaning that Nvidia will have sold fewer of those chips. "A lot of the currencies have really deteriorated," Roy said. "They're not making money anymore, so there's less consumption of those GPU's," he added.

An Alliance Bernstein note out to clients on Wednesday morning said "We have been somewhat reticent about crypto, and note the company filled the channel in first-quarter; to that end we model gaming revenues well below seasonal through the year as a result."

The second risk to gaming revenue for the quarter is wrapped inside of an overall positive development for Nvidia. Its soon-to-be-released Turing chip architecture may have paused sales on existing products. Turing doesn't get released until the fourth quarter, which means "there's a risk that customers are going to wait for Turing," Roy said, which is why Nvidia's total revenue guidance for the second-quarter was $3.01 billion, slightly down from its first-quarter revenue of $3.2 billion.
While Steves certainly acknowledged the Turing risk, he also agreed with a Wells Fargo note out on Wednesday morning that not only said Nvidia could see some market share gains in gaming GPUs, but upgraded Nvidia from 'underperform' to outperform.'

"Nvidia's competitive positioning in gaming" was a major driver of its upgrade, Wells Fargo wrote, adding that "while concerns over gaming GPU channel inventory levels vis-à-vis weakening crypto-currency mining demand could persist...Nvidia is well-positioned to continue to leverage and expand its platform story -- working on CUDA programming for gaming," the note added....MORE

"What Makes Paris Look Like Paris?"

From a source we don't link to often enough, Communications of the Association for Computing Machinery. Thanks to a friend:

Given a large repository of geo-tagged imagery, we seek to automatically find visual elements, for example windows, balconies, and street signs, that are most distinctive for a certain geo-spatial area, for example the city of Paris. This is a tremendously difficult task as the visual features distinguishing architectural elements of different places can be very subtle. In addition, we face a hard search problem: given all possible patches in all images, which of them are both frequently occurring and geographically informative? To address these issues, we propose to use a discriminative clustering approach able to take into account the weak geographic supervision. We show that geographically representative image elements can be discovered automatically from Google Street View imagery in a discriminative manner. We demonstrate that these elements are visually interpretable and perceptually geo-informative. The discovered visual elements can also support a variety of computational geography tasks, such as mapping architectural correspondences and influences within and across cities, finding representative elements at different geo-spatial scales, and geographically informed image retrieval.
 Figure 1. These two photos might seem nondescript, but each contains hints about which city it might belong to. Given a large image database of a given city, our algorithm is able to automatically discover the geographically informative elements (patch clusters to the right of each photo) that help in capturing its "look and feel." On the left, the emblematic street sign, a balustrade window, and the balcony support are all very indicative of Paris, while on the right, the neoclassical columned entryway sporting a balcony, a Victorian window, and, of course, the cast-iron railings are very much features of London.
1. Introduction
Consider the two photographs in Figure 1, both downloaded from Google Street View. One comes from Paris, the other one from London. Can you tell which is which? Surprisingly, even for these nondescript street scenes, people who have been to Europe tend to do quite well on this task. In an informal survey, we presented 11 subjects with 100 random Street View images of which 50% were from Paris, and the rest from eleven other cities. We instructed the subjects (who have all been to Paris) to try and ignore any text in the photos, and collected their binary forced-choice responses (Paris/Not Paris). On average, subjects were correct 79% of the time (std = 6.3), with chance at 50% (when allowed to scrutinize the text, performance for some subjects went up as high as 90%). What this suggests is that people are remarkably sensitive to the geographically informative features within the visual environment. But what are those features? In informal debriefings, our subjects suggested that for most images, a few localized, distinctive elements "immediately gave it away." For example for Paris, things like windows with railings, the particular style of balconies, the distinctive doorways, the traditional blue/green/white street signs, etc. were particularly helpful. Finding those features can be difficult though, since every image can contain more than 25,000 candidate patches, and only a tiny fraction will be truly distinctive.

In this work, we want to find such local geo-informative features automatically, directly from a large database of photographs from a particular place, such as a city. Specifically, given tens of thousands of geo-localized images of some geographic region R, we aim to find a few hundred visual elements that are both: (1) repeating, that is, they occur often in R, and (2) geographically discriminative, that is, they occur much more often in R than in RC. Figure 1 shows sample output of our algorithm: for each photograph we show three of the most geo-informative visual elements that were automatically discovered. For the Paris scene (left), the street sign, the window with railings, and the balcony support are all flagged as informative.

But why is this topic important for modern computer graphics? (1) Scientifically, the goal of understanding which visual elements are fundamental to our perception of a complex visual concept, such as a place, is an interesting and useful one. Our paper shares this motivation with a number of other recent works that do not actually synthesize new visual imagery, but rather propose ways of finding and visualizing existing image data in better ways, be it selecting candid portraits from a video stream,5 summarizing a scene from photo collections,19 finding iconic images of an object,1 etc. (2) More practically, one possible future application of the ideas presented here might be to help CG modelers by generating the so-called "reference art" for a city. For instance, when modeling Paris for PIXAR'S Ratatouille, the co-director Jan Pinkava faced exactly this problem: "The basic question for us was: 'what would Paris look like as a model of Paris?', that is, what are the main things that give the city its unique look?"14 Their solution was to "run around Paris for a week like mad tourists, just looking at things, talking about them, and taking lots of pictures" not just of the Eiffel Tower but of the many stylistic Paris details, such as signs, doors, etc.14 (see photos on pp. 120–121). But if going "on location" is not feasible, our approach could serve as basis for a detail-centric reference art retriever, which would let artists focus their attention on the most statistically significant stylistic elements of the city. (3) And finally, more philosophically, our ultimate goal is to provide a stylistic narrative for a visual experience of a place. Such narrative, once established, can be related to others in a kind of geo-cultural visual reference graph, highlighting similarities and differences between regions. For example, one could imagine finding a visual appearance "trail" from Greece, through Italy and Spain and into Latin America. In this work, we only take the first steps in this direction—connecting visual appearance across cities, finding similarities within a continent, and differences between neighborhoods. But we hope that our work might act as a catalyst for research in this new area, which might be called computational geo-cultural modeling.

2. Prior Work
In the field of architectural history, descriptions of urban and regional architectural styles and their elements are well established. Such local elements and rules for combining them have been used in computer systems for procedural modeling of architecture to generate 3D models of entire cities in an astonishing level of detail, for example, Mueller et al.,12 or to parse images of facades, for example, Teboul et al.22 However, such systems require significant manual effort from an expert to specify the appropriate elements and rules for each architectural style.

At the other end of the spectrum, data-driven approaches have been leveraging the huge datasets of geo-tagged images that have recently become available online. For example, Crandall et al.2 use the GPS locations of 35,000 consumer photos from Flickr to plot photographer-defined frequency maps of cities and countries. Geotagged datasets have also been used for place recognition8, 17 including famous landmarks.10, 11 Our work is particularly related to Schindler et al.17 and Knopp et al.,8 where geo-tags are also used as a supervisory signal to find sets of image features discriminative for a particular place. While these approaches can work very well, their image features typically cannot generalize beyond matching specific buildings imaged from different viewpoints. Alternatively, global image representations from scene recognition, such as GIST descriptor13 have been used for geolocalization of generic scenes on the global Earth scale.6, 7 There, too, reasonable recognition performance has been achieved, but the use of global descriptors makes it hard for a human to interpret why a given image gets assigned to a certain location....MUCH MORE

The Irreconcilable Conflict At the Heart Of Bitcoin (plus some other stuff on cryptocurrencies, blockchains, and smart contracts)
"Ethical Theories Spotted in Silicon Valley"
A Major Flaw: "Ethical Trap: Robot Paralyzed by Choice of Who to Save"
You don't want hesitation in your robotrader.

From New Scientist via Communications of the ACM:
Bristol Robotics Laboratory's Alan Winfield and colleagues recently tested an ethical challenge for a robot, programming it to prevent other automatons--representing humans--from falling into a hole.

When researchers used two human proxies, the robot was forced to choose which to save. In some cases, it saved one proxy while letting the other perish, while in others, it saved both. However, in 14 out of 33 trials, the robot spent so much time making its decision that both proxies fell into the hole.

Winfield describes his robot as an "ethical zombie" that has no choice but to behave as it does....MORE
At the same time you want the computer to discriminate between the command "Execute the trade" and the command "Execute the trader".

"Fintech Crowd Dives Into Subprime Credit-Card Lending"

This isn't "disruption", this is hustling a buck in one of the more predatory niches of low finance.
See also "Potemkin AI".
From the Wall Street Journal, Aug. 13:

Financial-technology startups are stepping into a void increasingly left by credit-card-issuing banks: lending to customers with poor credit histories
LendUp Global Inc. and Fair Square Financial LLC, which focus more heavily on riskier borrowers, mailed out roughly 35 million credit-card offers during the first half of the year, according to market-research firm Competiscan, up from 7 million during the same period last year.

CreditShop LLC, a specialist in personal loans to risky borrowers that was acquired last year by investment firm Värde Partners, rolled out a credit card earlier this year. Elevate Credit Inc., ELVT -0.11% which specializes in high-cost installment loans, launched one in July.

Subprime lending can be lucrative. Most of these cards carry interest rates north of 20%, significantly higher than the average credit card interest rate of 14.1%, according to the Federal Reserve. Rewards programs, one of the biggest costs for large card issuers chasing creditworthy customers, are rare.
But risks abound: Facing rising loan losses, especially among the riskiest borrowers, banks are reining in their growth in this sector. Subprime credit-card balances at seven large U.S. banks rose 3% in the first half of the year from a year prior, down from a 13% increase in the year-earlier period, according to Autonomous Research. Capital One Financial Corp.’s subprime balances accounted for 32% of its domestic credit-card balances in the first half of 2018 compared with 36% in the same period a year earlier

The new lenders are getting help from some industry stalwarts. The Orogen Group, an investment firm headed by former Citigroup Inc. chief Vikram Pandit, said in May it was committing $100 million in equity to Fair Square, which distributes cards to borrowers with less-than-pristine credit scores. LendUp recently announced that Capital One co-founder Nigel Morris and former Capital One chief credit officer Frank Rotman were joining its board of directors.

The population of subprime borrowers “is nigh on half of america, and there’s enormous opportunity for others to be able to offer a great product with great sophistication to compete in this space,” Mr. Morris said in an interview.

Fintech startups still account for a relatively small slice of the subprime-card market. At the end of 2017, Fair Square had 124,000 open accounts, more than half of which went to customers which prime credit scores at the time they were opened, and just shy of $95 million in balances for its Ollo card holders, according to people familiar with the matter.

Capital One has around $32 billion in subprime credit-card balances on its books....

"The Very Model of a Modern-Age Millennial"

Catchy but not easy to dance to, I give it an 84.
From Meg Elison at McSweeny's:
I am the very model of a modern-age millennial,
I’ve got no cash, no house, no kids, and student debt perennial,
I know the rules of Tinder, and I’m not sold on monogamy
(For what it’s worth I think that stems from troubles ‘tween my mom and me)

I’m very well acquainted, too, with matters on the gender front
Myself, I am nonbinary; your labels I so do not want
Been disillusioned by my expectations with a lot o’ stuff,
The skills with which I am equipped for life are frankly not enough

My job prospects are hobbled by insistence on a living wage
Compete at entry level with some washed-up folks at twice my age
In matters of identity, employment and such petty ills
I am the very model of a modern-age millennial

Media: "With Felix Salmon, Axios Continues Its Push to Commandeer the Bloomberg Set"

I wouldn't be so sure about that, Mr. Salmon is not the draw he once was.

The financial journalist-as-brand seems to have already come and gone, with a few exceptions, Matt Levine at Bloomberg, Kara Swisher at recode and Izabella Kaminska at the FT are headliners that can bring in the crowd (+cognoscenti?) but even in the latter case, under Izabella and Dan McCrum, Alphaville seems to be even more of an ensemble than it already was.

Anyhoo, here's Vanity Fair's thinking:

Axios is hiring Felix Salmon and Courtenay Brown to spearhead its foray into coverage of the public markets.
Since it launched in early 2017, Axios has had a clear vision of its identity. After witnessing the historic success of reporter Mike Allen’s Politico Playbook, the company sought to hire uniquely wired reporters who could communicate direct, brief news and insights to an obsessive, busy audience—a post-narrative form that they termed “smart brevity.” Almost instantly, the plan worked. Allen successfully reprised and renewed his newsletter into the highly successful Axios A.M., while simultaneously helping to transform a young reporter named Jonathan Swan into a scoop machine with a newsletter of his own, Sneak Peek. Dan Primack, the obsessive cataloguer of private equity and venture funding, left behind his Term Sheet newsletter at Fortune to start Pro Rata. Ina Fried and Sara Fischer were hired to oversee newsletters on technology and media trends, respectively.

By late 2017, Axios had added to its initial $10 million fund-raising round with an additional haul of $20 million—backers from both rounds included Greycroft, Lerer Hippeau, NBC Universal, Laurene Powell Jobs’s Emerson Collective, and Jeffrey Katzenberg’s WndrCo. At the time, the company said it would use the extra dough for a major expansion of its newsroom, which has grown to about 50 journalists since the company’s debut. The latest beneficiary of this hiring spree, I can reveal, is Felix Salmon, the longtime financial columnist and inveterate provocateur. Salmon’s musings on Wall Street, money, and media have made him a hot get for past employers ranging from Condé Nast Portfolio, to Reuters, to Fusion, where he was reportedly being generously compensated before he resigned, rather cryptically, at the beginning of the year, as Fusion Media Group was beginning to implode.

Most recently, Salmon has been hosting Slate’s Money podcast, a gig he’ll maintain as he launches a Sunday newsletter in the style of Swan’s Sneak Peek. The new tip sheet will be called Axios Edge, with “a focus on market trends, business, and economics,” as a spokesperson put it to me. With Axios Edge, the company is signaling an expansion of its coverage of markets and finance, an effort that also will include CNBC alumna Courtenay Brown, another fresh recruit....MORE 
Previously on Felix:
Catfishin': Creating Felix Salmon and the What, Why and How He Got Paid What He Got Paid
Reuters' Felix Salmon is the Columbia Journalism Review’s New Peter G. Peterson Fellow
Felix Salmon Reviews Martin Wolf's "The Shifts and Shocks"
Tart, bordering on acidic.  

And from a 2010 post:

...For those who don't follow this stuff [he means people who have a life -ed] there was a dust-up between Felix and Henry a couple months ago.*
*Felix: "Kicked out of finance, and into journalism"
Henry: "Felix Salmon: Henry Blodget Should Be Banned From The Industry"
Felix: "Disclosing journalists’ pasts"
New York Magazine: "Financial Bloggers Felix Salmon and Henry Blodget Have a Fight, Make Up"

Commodity traders superior to chimpanzees, research shows

Just a reminder.
A repost from 2008.

I made a serious career track mistake.
Years ago a counselor pointed out that I seemed to have an affinity for animals (It's true. Kids and dogs like me. So do drunks and and folks suffering from various psychopathologies).
Had I followed up on her thinking I would now be tenured, trading outside my species and living the grant-proposal dream.

From RISK Over the Counter:
In a radical overturning of conventional wisdom, scientists in Georgia and California have found significant differences between commodity traders and chimpanzees. Chimps are, in fact, not very good at commodity trading:
the researchers found that chimpanzees often did not spontaneously barter food items, but needed to be trained to engage in commodity barter. Moreover, even after the chimpanzees had been trained to do barters with reliable human trading partners, they were reluctant to engage in extreme deals in which a very good commodity (apple slices) had to be sacrificed in order to get an even more preferred commodity (grapes)...
The report becomes particularly readable when it speculates on the reasons why:  
because of their lack of property ownership norms...

...or, for that matter, pockets.... ...chimpanzees in nature do not store property and thus would have little opportunity to trade commodities....MORE
See also:
Jim Cramer beats Monkey in Stock Picking Contest!
(for one week only, the monkey is ahead on performance: see below)
"...You know what – I don’t work for Murdoch”

Cramer; Aug. 20 show. Then he said let Cramer be Cramer or something, I wasn't paying attention, I was reading Warren Buffett's story about arbitraging cocoa beans against an equity....
Regarding the importance of pockets storage see:

To Create A "1%" In A Social Hierarchy You Don't Need An Economic Surplus, Just A Storable Form Of Wealth

Re/insurance: "First-half disaster losses below average at $20bn: Swiss Re"

With the forecast calmer than average Atlantic hurricane season the trend is likely to continue.

However coming into the peak of the season we have opposing forces in play, on the one hand we have slightly El Niño conditions, leading to wind shear that rips storms apart if they form.
On the other hand, the Saharan Air Layer was especially dusty this year, inhibiting cyclone genesis in the first place, but that is changing so those long-haul Cape Verde storms have a better chance to get organized:
And the headline story from Artemis:
Losses from natural catastrophes and man-made disaster events are estimated to have cost the insurance and reinsurance industry $20 billion in the first-half of 2018, well down on the $35 billion average, according to global reinsurer Swiss Re.

Economic losses from major natural catastrophe and man-made loss events came out at $36 billion for the first-half of the year, which is 44% down on the prior years $64 billion and significantly below the long-term average of $125 billion.

On the insured side, last year saw $30 billion of first-half disaster losses for the insurance and reinsurance industry to deal with.

The first six months of 2018 have resulted in an estimated $18 billion of insured natural catastrophe losses, well below the average of $30 billion, and just $2 billion of man-made disaster insured losses, well down on the average of $5 billion.
Catastrophe-related losses in USD billion (2008 – 2018)
Catastrophe-related losses in USD billion (2008 – 2018)

Swiss Re’s figures come in between other estimates already released.

Previously, Aon estimated $21 billion insured disaster losses and $45 billion economic disaster losses in H1 2018, and Munich Re estimated $17 billion insured loss and $33 billion economic.

Commenting on the disaster activity in the first-half, Swiss Re said, “A series of winter storms in Europe and in the US caused the largest losses in the first half of 2018. Globally, around 3 900 people lost their lives or went missing in disaster events during the first six months of 2018, compared to approximately 4 600 for the same period in 2017.”

It was a relatively high percentage of economic losses that were insured in this half-year, as almost 56% fell to insurance and reinsurance given most major catastrophe events occurred in areas with high insurance penetration.

Winter storm Friederike in Europe was the costliest event of the period, and Swiss Re Institute’s sigma estimates the total economic losses from this European windstorm at $2.7 billion, with roughly $2.1 billion insured....

Wednesday, August 15, 2018

"Shipbuilding Contract Signed for Unmanned, Zero-Emission Container Ship ‘Yara Birkeland’"

From gCaptain"
The world’s first autonomous and electric container ship is one step closer to reality with a shipbuilding contract now in signed and sealed for the vessel.
Norwegian technology firm Kongsberg, who is partnering with Yara on the project, announced today that Yara has signed a deal worth NOK 250 million ($25.9 million) with VARD to build the vessel with launch scheduled for early 2020.
The vessel will initially start out with manned operation but quickly move to fully autonomous operation by 2022.

In May 2017, YARA and Kongsberg announced a partnership to build the world’s first autonomous, electric containership.

The vessel, named Yara Birkeland, will replace 40,000 truckloads per year, reducing NOx and CO2 emissions in the process. With the shipyard, selected, construction is now set to begin.
“A vessel like Yara Birkeland has never been built before, and we rely on teaming up with partners with an entrepreneurial mindset and cutting edge expertise. VARD combines experience in customized ship building with leading innovation, and will deliver a game-changing vessel which will help us lower our emissions, and contribute to feeding the world while protecting the planet,” says Svein Tore Holsether, President and CEO of YARA....
...MUCH MORE (it's a kinda big deal)

"From laboratory in far west, China's surveillance state spreads quietly"

From Reuters:

Filip Liu, a 31-year-old software developer from Beijing, was traveling in the far western Chinese region of Xinjiang when he was pulled to one side by police as he got off a bus.
 FILE PHOTO: SenseTime surveillance software identifying details about people and vehicles runs as a demonstration at the company's office 
in Beijing, China, October 11, 2017. REUTERS/Thomas Peter/File Photo
The officers took Liu’s iPhone, hooked it up to a handheld device that looked like a laptop and told him they were “checking his phone for illegal information”.

Liu’s experience in Urumqi, the Xinjiang capital, is not uncommon in a region that has been wracked by separatist violence and a crackdown by security forces.

But such surveillance technologies, tested out in the laboratory of Xinjiang, are now quietly spreading across China.

Government procurement documents collected by Reuters and rare insights from officials show the technology Liu encountered in Xinjiang is encroaching into cities like Shanghai and Beijing.
Police stations in almost every province have sought to buy the data-extraction devices for smartphones since the beginning of 2016, coinciding with a sharp rise in spending on internal security and a crackdown on dissent, the data show.

The documents provide a rare glimpse into the numbers behind China’s push to arm security forces with high-tech monitoring tools as the government clamps down on dissent.

The Ministry of Industry and Information Technology and the Public Security Bureau, which oversee China’s high-tech security projects, did not respond to requests for comment.

The scanners are hand-held or desktop devices that can break into smartphones and extract and analyze contact lists, photos, videos, social media posts and email.

Hand-held devices allow police to quickly check the content of phones on the street. Liu, the Beijing software developer, said the police were able to review his data on the spot. They apparently didn’t find anything objectionable as he was not detained.

The data Reuters analyzed includes requests from 171 police stations across 32 out of 33 official mainland provinces, regions and municipalities, and appears to show only a portion of total spending.
The data shows over 129 million yuan ($19 million) in budgeting or spending on the equipment since the beginning of 2016, with amounts accelerating in 2017 and 2018....MUCH MORE
Previously on SenseTime, the most valuable A.I. startup in the world:
March 8
Facial recognition In China
March 23 
"China’s Surveillance State: AI Startups, Tech Giants Are At The Center Of The Government’s Plans"
April 17
ICYMI: The Most Valuable AI Start-up Inthe World Does Facial Recognition
July 24 
"The 10 Biggest Artificial Intelligence Startups in The World"

"I want bad news and I want it fast: That’s the business model for Factal, a business-focused company from the founders of Breaking News"

Following on yesterday's link to NiemanLab: "Facebook’s message to media: 'We are not interested in talking to you about your traffic…That is the old world and there is no going back'”.
Probably not related.

From NiemanLab
A consumer product is on the roadmap, but for now, Factal is aimed at businesses and will cost several thousand dollars a month.
Breaking News, which sent out news alerts from around the globe 24 hours a day, was beloved, but that wasn’t enough to save it. The company, consisting of a Twitter feed (with 9.1 million followers), app, and website, was shut down by its owner, NBC News, at the end of 2016. From a memo to employees at the time, in part:
Breaking News has built up a large following among journalists, government workers, industries whose success depends on accurate and fast news, and news junkies of all types from around the globe. Unfortunately, despite its consumer appeal, Breaking News has not been able to generate enough revenue to sustain itself.
A little under two years later, the founders of Breaking News think they’ve found a way to bring back the product (sort of) while making money. Cory Bergman announced Tuesday that he and Ben Tesch are launching Factal, with former MSNBC Interactive president Charlie Tillinghast as CEO. The service will cost a few thousand dollars a month (via yearly subscription) and, for now, is aimed solely at businesses who need to make quick decisions (whether to close a store, for instance) in emergencies; while a consumer product is in the works, it’s a “next-year project,” Bergman told me....MUCH MORE

Metals Meltdown: Copper Crashes, Silver Slammed, Palladium Plunges

Someone's into a little alliteration over at ZeroHedge. First up: 

Metal Meltdown: Palladium Plunges, Copper Crashes Into Bear Market
While traders are keeping a close eye on developed markets to see if the risk from from Turkey’s financial crisis or China’s trade war is spreading, the emerging-market contagion has slammed the base metal markets. Hard.

Base metal markets plunged on Wednesday, with most contracts falling more than 2% in London: the broader base metals spot price index was down over 4%, and is now just shy of a bear market.
The components were all ugly: Copper sank 2.3% to 5,903 a metric, the lowest since July 2017 and is set to enter a bear market. Aluminum slumped 2.1% to $2,027 a tone, while palladium plunged 5% and the FTSE 350 Mining Index sank to a four-month low, while Zinc plunged 3.1% and was trading at a two year low of 2,377/ton. Not even gold, the usual safe haven, was spared from the selloff, sliding as low at 1,180, and was down 0.9%.

Putting today's plunge in context, today alone, the six LME base metals have in aggregate dropped more than 25%, and are headed for the biggest collective loss since 2011.
Commodities have been hammered by growing fear that problems in China and Turkey will lead to weaker global economic growth, and eventually hurt demand for raw materials. Losses on Wednesday were triggered by a broad retreat in China as the yuan weakened and recent data showed the economy hit a rough patch. Earlier this week Beijing reported an across the board miss for all key economic categories: industrial output, fixed-asset investment and retail sales.
Amid the slowdown, copper has once again emerged as the true barometer of China's economy, with its price tracking the slide in the offshore yuan almost tick for tick.


And (this headline is in error. it's the lowest ratio of silver to gold, not lowest price see below)

Silver Slammed To Lowest Since April 2009
As the dollar surges, precious metals are under notable pressure, but silver is dramatically underperforming gold as its industrial-usage weighs it down to its lowest price since April 2009...
Gold is back at its lowest since Dec 2016 in USDollars...
And has broken down out of its recent tight range in Yuan...
But silver is getting monkeyhammered in USDollars...


As noted in a December '16 post: was last down $10.90 an ounce at $1,131.80. March Comex silver was last down $0.289 at $15.80 an ounce....

...Throwing numbers around, the December 2015 low in silver was $13.90-something so we have a couple bucks to go before that multi-year support.
On gold we still think the the 1980 highs-Hong Kong $875; New York $850-are achievable. An equivalent 23% decline in silver gets you down to the low $12's.

That Polish Olympian who sold his silver medal last August said he did it to save a kid with eye cancer but I'm starting to think he was using some black-box market timing. 
Gold  $1184.80 down $15.90
Silver $14.45 down 60 cents

Yesterday, our caution on the short side was early:
Gold, Silver Looking At Two-Year Lows

Ethanol: "Bribes, Backdoor Deals, and Pay to Play: How Bad Rosé Took Over"

May I be so bold as to recommend a 'big-brand pink swill?'

From Bon Appetit, August 13:

A sommelier opens up about the shady business practices that are behind the rise of watery, terrible rosé.
Here’s a snippet of an email I got last week:
FROM: Wine account salesperson
TO: Victoria James, Cote Korean Steakhouse
Subject: Opportunity?
...I wanted to see if you were around next week to try some wines and see if there is any opportunity for our brands in Cote. It would be my pleasure to put together a proposal for you that includes funding for the location…
Do you see what’s happening here? If not, it’s okay. The world of wines in restaurants is not a very public one. This dude is slyly offering me the possibility to get paid to place his company’s wines on our restaurant wine list. It should be the other way around: I pay the winery for cases of wines I can’t wait to share with diners. But when it comes to rosé, sneaky deals like this have become par for the course as the wine becomes more and more popular. We call it pay to play, and it’s caused an outbreak of shitty rosé on wine lists everywhere. Specifically on by-the-glass lists, which sell the highest quantity and where diners are more likely to order based on name-brand recognition.

As a sommelier, I know I am spoiled, but when I see big-brand pink swill on otherwise nice restaurant menus, I get furious. You might know which brands I am talking about, the ones that sponsor huge parties in the Hamptons. They masquerade as luxury goods, with fun bottle shapes and cutesy names, but are simply bulk wines.

But Wait, What Is Bulk Wine?
When I say “bulk,” I mean rosé that might be made from rotten or low-quality grapes, underripe fruit, or red wine by-products. It relies on mass-produced laboratory yeast that's advertised as “full bodied, fruit/lush blush wines, to enhance white country fruit and flower in wines.” (Yeast not only converts sugar to alcohol but also contributes to the final flavors. These commercial yeast strains attempt to mask subpar grapes by adding unnatural aromas to the wine and speeding along fermentation.) Bulk wine is often treated like a lab formula, with chemicals, dyes, and additives that chase that desired light salmon color. Since an ingredient list isn’t required on wine labels, the average shopper might not realize that their go-to grocery store wine has up to 75 ingredients other than grapes. These wines come from huge swaths of land, particularly in California and Provence, with “terroir” barely suitable for even vegetables. Bulk wines—and there are hundreds of them—are owned by large companies with deep pockets, with big marketing budgets. Money is channeled away from the high-quality grape production and toward massive advertising campaigns coupled with paid inclusion on hot restaurant menus.

The Shady Dealings...

Okay, Here's the Plan: We Turn All Our Health Information Over to Google, Amazon and Microsoft and Then... (AMZN; GOOG; MSFT)

...and then, I'm not sure what.
Good idea? Bad idea?

From CNBC, August 13:

Amazon, Alphabet, Microsoft and other tech giants want to fix one of the most broken things about health care
There are many broken things about the U.S. health care system. But one of the biggest and most overlooked problems is that patients still find it too hard to share their medical information between doctors, especially those working in different hospitals.

It's a huge problem for many reasons: It makes it harder for consumers to access the highest-quality care, and new patients who walk into a hospital are like strangers — care-givers won't know if they have an allergy or a chronic disease.

Some of the largest technology companies in the world are undertaking a new effort to fix that. And they have a good reason to do it, as the lack of open standards around health data is a huge barrier for them to get into the $3 trillion health system.

On Monday, Alphabet, Amazon, IBM, Microsoft and Salesforce spoke out at an event in Washington D.C. called the Blue Button 2.0 Developer conference. These companies are rivals in some important ways, so it's a strong signal that they came together on this issue.
Here's the joint statement:
We are jointly committed to removing barriers for the adoption of technologies for healthcare interoperability, particularly those that are enabled through the cloud and AI. We share the common quest to unlock the potential in healthcare data, to deliver better outcomes at lower costs.
To address the problem, these tech companies are proposing to build tools for the health community around a set of common standards for exchanging health information electronically, called "FHIR."
Resistant to change
The government and the private sector have tried to fix this problem for decades, spending billions in the process. Unfortunately, the bulk of that funding was spent on moving doctor's offices from paper-based systems to electronic ones, and not on data sharing....MUCH MORE
On the one hand, if providers are going to have to compete—and as a step in that direction Medicare in the U.S. will be requiring hospitals to post prices online—records portability will be paramount.
On the other, centralizing records and handing them over to the tech giants will have ramifications we can't even foresee at the moment.

Your call.

"China is building coal power again"

This morning's FT Alphaville Further Reading post has an interesting link to a story on energy policy in Australia: "Coalition votes to kill renewables, encourage new coal generation".

It's not just Australia though. In June EurActiv published "Germany pours cold water on EU’s clean energy ambitions".

And then there's this, from China Dialogue, August 3:

Experts are calling for the government to return to cutting capacity after policy reversal, reports Feng Hao
Satellite imagery reveals that many coal-fired power projects that were halted by the Chinese government have quietly restarted.

Analysis by CoalSwarm estimates that 46.7 gigawatts of new and restarted coal-fired power construction is visible based on satellite imagery supplied by Planet Labs. The coal-fired power plants are either generating power or will soon be operational. If all the plants reach completion they would increase China’s coal-fired power capacity by 4%.

One of the biggest issues facing China’s coal sector since 2016 has been too much generating capacity, not too little. So what changed?

Demand for coal-power rebounds

Recently published economic data for the first half of 2018, along with the latest policy adjustments, indicate that China’s power demand is rebounding.

Li Fulong, head of the department of development and planning at the National Energy Administration, said at a press conference on July 30 that coal consumption in China increased about 3.1% in the first half of 2018 compared with the same period last year. The main driver of that was coal-fired power generation. Figures from the National Bureau of Statistics show a leap of 9.4% in electricity use across the same period.

Meanwhile, the arrival of summer has led to temporary electricity shortages in many regions, with reports of power demand outstripping supply in Shandong, Henan, Hunan, Hubei and Zhejiang provinces. In Shandong the shortfall was estimated at three gigawatts.

This has resulted in a loosening of policy-level restrictions on the coal power sector. In May 2018 the National Energy Administration permitted Shaanxi, Hubei, Jiangxi and Anhui to restart construction of coal-fired power stations. Restrictions were also relaxed to some degree in four other provinces.

“A rebound in industrial demand for electricity seems to have shifted attitudes among policy-makers, who are now more accepting of overcapacity,” said Lauri Myllyvirta, energy analyst with Greenpeace.

Yuan Jiahai, a professor at North China Electric Power University, said that some plants are almost complete but not generating power or making money, while loans taken out still need repaying. This has led companies and local governments, which are under pressure to get projects operational, to lobby for a change in policy.

A lack of policy focus

The focus of the past two years has been on cutting capacity in the coal sector prompted by concerns about its rapid expansion and contribution to air pollution.

Power-hungry sectors such as construction grew rapidly early in the century, and by 2013 China had experienced 12 years of breakneck growth in consumption of coal and power. This led to overinvestment in coal power throughout the country and ultimately overcapacity and financial risk.

That blind expansion also worsened air pollution, and in some regions caused water shortages. The Chinese government was forced, for both economic and environmental reasons, to rein in the coal-power sector.

In April 2016 the National Development and Reform Commission and the National Energy Administration – the country’s top economic planning and energy regulation authorities respectively – issued a joint document instructing provinces to limit total coal-fired power capacity. Almost half of all China’s provinces were told to postpone the construction of new coal-fired power projects. In 2017 the State Energy Administration again halted work on over 100 plants that were under construction....

Even for a big country, 46.7 gigawatts of coal-fired electricity is a lot of power. In the U.S. the rule of thumb is 1 gigawatt can power around 700,000 homes  with the U.S. EIA saying the average home uses around 900 kwh per month. Divide by hours in a month, blah, blah blah and you get the U.S. 700K houses figure. Multiply by 46.7 and you have enough additional coal power for 32 million houses.

And that's on top of record solar installations.
However, China has dropped its 2020 minimum goals for solar power to 110 GW (from 150 GW originally), says that a gigawatt is 3.125 million 320 watt nameplate-capacity solar PV Panels, which you should discount for intermittency.

Throw in the collapse of the U.N.'s Green Climate Fund:
Green Climate Fund ‘a laughing stock’, say poor countries
—Climate Home News, April 4, 2017

UN climate fund chief resigns for personal reasons while board meeting collapses
—Climate Home News, July 4, 2018

8 takeaways from the Green Climate Fund meltdown
—July 6, 2018

At the UN's Green Climate Fund, the honeymoon is over
—DevEx, July 10, 2018
The Fund has set itself a goal of raising $100 billion a year by 2020. It is at $3.5 billion total so far.

And honestly you start to wonder if the whole Paris Climate Accord wasn't just for show.

"Beneficiaries Of A Strengthening Dollar"

From StockCharts:

Tuesday, August 14th
...Yesterday, I wrote about the recent breakout in the U.S. Dollar Index ($USD).  I'd like to take that a step further today.  The USD rose sharply off the January through March lows and with it was a surge in small cap stocks vs. their larger cap counterparts.  During the USD consolidation that took place over the past 2-3 months, we saw small caps pull back on a relative basis.  Now that the USD has broken out again, you can already see the relative strength in small caps returning:
Although the USD did consolidate for those 2-3 months, it did so in very bullish ascending triangle fashion.  Those patterns typically result in a breakout to the upside and that's exactly what we've seen.  Furthermore, the 10 year U.S. treasury yield ($UST10Y) continues to push higher relative to the 10 year German treasury yield ($DET10Y).  That normally is accompanied by a rising dollar....

Bill Gates: "Not enough people are paying attention to this economic trend"

From Gates Notes:

Capitalism Without Capital
By the second semester of my freshman year at Harvard, I had started going to classes I wasn’t signed up for, and had pretty much stopped going to any of the classes I was signed up for—except for an introduction to economics class called “Ec 10.” I was fascinated by the subject, and the professor was excellent. One of the first things he taught us was the supply and demand diagram. At the time I was in college (which was longer ago than I like to admit), this was basically how the global economy worked:
Capitalism Without Capital book review
There are two assumptions you can make based on this chart. The first is still more or less true today: as demand for a product goes up, supply increases, and price goes down. If the price gets too high, demand falls. The sweet spot where the two lines intersect is called equilibrium. Equilibrium is magical, because it maximizes value to society. Goods are affordable, plentiful, and profitable.

Everyone wins.
The second assumption this chart makes is that the total cost of production increases as supply increases. Imagine Ford releasing a new model of car. The first car costs a bit more to create, because you have to spend money designing and testing it. But each vehicle after that requires a certain amount of materials and labor. The tenth car you build costs the same to make as the 1000th car. The same is true for the other things that dominated the world’s economy for most of the 20th century, including agricultural products and property.

Software doesn’t work like this. Microsoft might spend a lot of money to develop the first unit of a new program, but every unit after that is virtually free to produce. Unlike the goods that powered our economy in the past, software is an intangible asset. And software isn’t the only example: data, insurance, e-books, even movies work in similar ways.

The portion of the world's economy that doesn't fit the old model just keeps getting larger. That has major implications for everything from tax law to economic policy to which cities thrive and which cities fall behind, but in general, the rules that govern the economy haven’t kept up. This is one of the biggest trends in the global economy that isn’t getting enough attention.

If you want to understand why this matters, the brilliant new book Capitalism Without Capital by Jonathan Haskel and Stian Westlake is about as good an explanation as I’ve seen. They start by defining intangible assets as “something you can’t touch.” It sounds obvious, but it’s an important distinction because intangible industries work differently than tangible industries. Products you can’t touch have a very different set of dynamics in terms of competition and risk and how you value the companies that make them.

Haskel and Westlake outline four reasons why intangible investment behaves differently:
  1. It’s a sunk cost. If your investment doesn’t pan out, you don’t have physical assets like machinery that you can sell off to recoup some of your money.
  2. It tends to create spillovers that can be taken advantage of by rival companies. Uber’s biggest strength is its network of drivers, but it’s not uncommon to meet an Uber driver who also picks up rides for Lyft.
  3. It’s more scalable than a physical asset. After the initial expense of the first unit, products can be replicated ad infinitum for next to nothing.
  4. It’s more likely to have valuable synergies with other intangible assets. Haskel and Westlake use the iPod as an example: it combined Apple’s MP3 protocol, miniaturized hard disk design, design skills, and licensing agreements with record labels.
None of these traits are inherently good or bad. They’re just different from the way manufactured goods work.

Haskel and Westlake explain all this in a straightforward way—the book is almost written like a textbook without a lot of commentary. They don’t act like there’s something evil about the trend or prescribe hard policy solutions. Instead they take the time to convince you why this transition is important and offer broad ideas about what countries can do to keep up in a world where the “Ec 10” supply and demand chart is increasingly irrelevant.....MORE

Capital Markets: "Lira Rallies on Cut in Swaps, but Fails to Dent Dollar Demand"

From Marc to Market:
The Turkish lira is extending yesterday's recovery today on the back of actions by officials that are aimed at limiting foreign access to the lira to short. Without introducing new capital controls, regulators halved the amount of swap transactions banks can do to 25% of shareholder equity. This is meant to make it more difficult to access lira in the offshore swaps market, which is an important channel. The US dollar fell to TRY5.8830 before recovering toward TRY6.16. At the time of this writing, it is near TRY6.10, nearly four percent lower on the day after the 7.75% pullback yesterday.

If the lira's dramatic plunge hit other markets, its recovery appears to have gone largely unnoticed. Most other emerging market currencies are weaker, led by the South African rand (-0.6%), Polish zloty (-0.4%) and Russian ruble (-0.4%). That said, Indonesia hiked rates by 25 bp, making it a cumulative 125 bp increases since the end of Q1. Officials are thought to have intervened yesterday, and the currency strengthened slightly. The Hong Kong Monetary Authority intervened today to defend the lower end of its currency band and bought HKD2.158 bln. They had intervened (~HKD70 bln) in similar defensive posture by in early Q2 in almost 20 operations.

In terms of flows, we note two developments.
First, an estimated $90.4 mln flowed into the MSCI iShare Turkey ETF yesterday, which appears to be the most in at least a year, and was the third consecutive session of inflows (totaling a modest $161 mln) or around 5.7% of assets. Second, despite (or maybe partly because of) steep losses in South African bonds and the rand recently, investors flocked to yesterday's debt auction. The ZAR2.4 bln of paper was oversubscribed by a factor of four, the most in nearly five months.

The US dollar remains firm against the major currencies. The euro made new lows for the move near $1.1315. The next target is the 61.8% retracement objective of last year's rally that is found a little below $1.1190. The measuring objective of head and shoulder pattern on the weekly bar charts is near $1.05. It finished yesterday outside its Bollinger Band for the third consecutive session. The lower band is near $1.1345 today.

The UK reports an uptick in headline CPI to 2.5% from 2.4%, albeit as expected, and sterling remains heavy. It traded below $1.27 for the first time since June 2017. The core rate was steady at 1.9%, the lowest since March 2017. There is an expiring $1.2750 option (~GBP400 mln) today that could come into play if North America takes some profits on long dollar positions. Note that there is a GBP1.4 bln option at $1.27 that expires tomorrow.

The dollar extended its recovery against the yen. At the start of the week, the dollar has tested support near JPY110.00 and today's gains lifted it to almost JPY111.45. The long-term trendline comes in near JPY111.55. However, we suspect North American operators will be reluctant to push it through the trendline today, preferring perhaps to follow Tokyo's lead....

Tuesday, August 14, 2018

"Azealia Banks exposing Elon Musk for tweeting while on Acid.. while she was waiting for Grimes at her home ... whewwww lord"

Oops, almost forgot:

First up, Business Insider:
Rapper Azealia Banks claims she was at Elon Musk’s house over the weekend as he was ’scrounging for investors’
Now, this time I mean it, Helen, Knighty and I really gotta go.
Note to self: acid tweeting no good.

"Here’s the Secret to My Recent Trading Prowess"

This is pretty funny. A recent string of headlines/posts from The Fly at iBankCoin:

August 9 
You Cannot Stop the Madness: Bought $NTNX
If you want the short version of how Fly is doing these days, look no further than my triple sized HUBS position and doubled sized TEAM and ZEN holdings. I don’t think I’ve done something this bold in quite some time. I am literally invincible, unable to lose money if I wanted to....

August 10 

August 13  
“The Fly” Is Invincible

August 14 
Here’s the Secret to My Recent Trading Prowess

Unfortunately for the narrative, for some time now I see the word 'invincible' (Aug. 9 post, 13 headline) and one of two things comes to mind:
1) Helen Reddy sings the line "I am invincible" followed by "I am woomaaan"
2) ...Monty Python Does Finance
Black Knight:     I am invincible! 
King Arthur:      You're a loony!  
Black Knight:  The Black Knights always triumph!
Neither of which, I am sure, is the connotation The Fly wants to suggest.

So, as we bid adieu to The Fly and wish him continued trading success, I will also exit, accompanied by Helen and the knight.

Funding Secured: "Chinese Tesla rival Nio files to raise $1.8 billion in US IPO"

Goldman (Asia) JPM and Morgan Stanley (left lead) are top of the bulge underwriters, I didn't look for bookrunners.
From TechCrunch:
Tesla may be looking to go private, but Chinese rival Nio is going the other way after it filed to raise $1.8 billion in an IPO on the New York Stock Exchange.

Nio was started in 2014, initially as NextCar, by Bin Li, an entrepreneur who founded online automotive services platform Bitauto. The company is backed by Chinese internet giants Baidu and Tencent among others, and it has developed two vehicles so far: the EP9 supercar and ES8.

The former is really a concept/racer car — it broke the electric vehicle speed record last year — but the ES8, pictured above, is a car designed for the masses which is priced at 448,000 RMB, or around $65,000.

Nio opened sales for the ES8 last year but it only began shipping in June. Thus, to date, it has fulfilled just 481 orders, although it claims that there are 17,000 customers who put down reservations waiting in the wings.

That means that, essentially, it is pre-revenue at this point.

The company reported revenue of $6.9 million as of the end of June — so one month of deliveries — with a total loss of $502 million for 2018 to date. Last year, Nio lost $759 million in 2017, that included no revenue and nearly $400 million spent on R&D.....MUCH MORE

Avoid the Middleman: "This app lets consumers sell their data directly to brands"

From AdAge:
Although it's early days, brands such as McDonald's, Staples and GM are paying cash and purchasing data direct from consumer, giving literal meaning toward the notion that "data is the new currency."
Between regulation such as GDPR and scandals like those plaguing Facebook, consumers are aware more than ever of the so-called value exchange when using online services. At the same time, they're also tuning in on how companies such as Cambridge Analytica are plundering their data without their consent.

To that end, Freckle IoT recently launched Killi, an app that makes explicit value of data by actually paying consumers with cash for sharing their data, location, or providing insight about what ads they'd like to see. Even more money is on the table if users scan the back of their driver's license with their phones, for example.

Killi has so far lined up McDonald's, GM, Danone and Staples as participating brands, it says.
"This is not something people in the industry should ignore," says Sargi Mann, exec VP and head of digital strategy and investments at Havas Media Group.

Ad Age reached out for a comment from the brands involved but did not get a response by press time.
"People are excited about this idea and the technology; it's something consumers have been requesting: 'How do I control my data?'" Mann adds. "Data privacy has huge momentum right now and innovations like Killi are certainly a big step … this can take off in a few hours, weeks or months."

As Mann points out, ad blocking was a consumer created solution for bad ads, and when it took off, it caught the entire industry off guard. The notion of consumers controlling which brands can or cannot access their data is perhaps the next evolution, she says.

"Consumers want control of their data and marketers need to be compliant with regulation, but there are zero tools for that," says Neil Sweeney, founder and CEO of Freckle IoT. "When the Cambridge Analytica news hit, everyone did '#DeleteFacebook,' but that was an emotional reaction."


Disrupting Surveillance Capitalism

A good overview of some of the techniques we've looked at over the years and following up on yesterday's "Potemkin AI: Many instances of 'artificial intelligence' are artificial displays of its power and potential", demystifying AI and showing how it is still pretty dumb—compared to what's coming.
From Logic Magazine:

Monkeywrenching the Machine
Silicon Valley’s surveillance-based business model relies heavily on machine learning. But with the right techniques, we can resist the enclosure of our lives for profit and disrupt the disruptors.
This piece is also available in audio from our friends at
Joseph Redmon, "YOLO: Real-Time Object Detection."
Machine learning is the practice of training algorithms to classify and predict in order to support decision-making. In recent years, it has skyrocketed in popularity and ubiquity. It's no stretch to say that most services we use now incorporate machine learning in one way or another. In its pervasiveness, machine learning is becoming infrastructural. And, like all infrastructure, once it matures it will become invisible.

Before that happens, we should develop a way to disrupt it.

A relatively nascent field called "adversarial machine learning" provides a starting point. Described as the intersection of cybersecurity and machine learning, this field studies how these algorithms can be systematically fooled, with or without knowledge of the algorithm itself—an ideal approach, since the specifics of many algorithms are trade secrets. And given the fact that the machine learning regime effectively makes all of us its workers—most of our online activity is in fact labor towards the improvement of these systems—we as individual users have an opportunity to inflict major sabotage.

Consider an early and now-ubiquitous application of machine learning: the everyday spam filter. The job of the spam filter is to categorize an email as either "spam"—junk—or "ham"—non-spam. The simplest case of adversarial machine learning in this context is constructing an email that is spam—a pitch for a pharmaceutical product, for example—but in such a way that the spam filter misclassifies it as ham, thus letting it through to the recipient.

There are a variety of strategies you might employ to accomplish this. A relatively simple one is swapping out the name of "Viagra" for something more obscure to a machine but equally readable to a human: "Vi@gr@", for example.

Today, most spam filters are resistant to this basic obfuscation attack. But we could consider more sophisticated approaches, such as writing a longer, professional-looking email that hints at the product without ever explicitly mentioning it. The hint may be strikingly obvious to a human, but incomprehensible to a spam filter.

A spam filter is less insidious than many other applications of machine learning, of course. But we can generalize from this example to develop techniques for disrupting other applications more worthy of sabotage.
Poisoning the Well
Most machine learning models are constructed according to the following general procedure:
  1. Collect training data.
  2. Run a machine learning algorithm, such as a neural network, over the training data to learn from it.
  3. Integrate the model into your service.
Many websites collect training data with embedded code that tracks what you do on the internet. This information is supposed to identify your preferences, habits, and other facets of your online and offline activity. The effectiveness of this data collection relies on the assumption that browsing habits are an honest portrayal of an individual.

A simple act of sabotage is to violate this assumption by generating "noise" while browsing. You can do this by opening random links, so that it's unclear which are the "true" sites you've visited—a process automated by Dan Schultz's Internet Noise project, available at Because your data is not only used to make assumptions about you, but about other users with similar browsing patterns, you end up interfering with the algorithm's conclusions about an entire group of people.

Of course, the effectiveness of this tactic, like all others described here, increases when more people are using it. As the CIA's Simple Sabotage Field Manual explains, "Acts of simple sabotage, multiplied by thousands of citizens, can be an effective weapon...[wasting] materials, manpower, and time. Occurring on a wide scale, simple sabotage will be a constant and tangible drag on...the enemy."

Attacks of this sort—where we corrupt the training data of these systems—are known as "poisoning" attacks.

The Pathological and the Perturbed
The other category of adversarial machine learning attacks are known as "evasion.” This strategy targets systems that have already been trained. Rather than trying to corrupt training data, it tries to generate pathological inputs that confuse the model, causing it to generate incorrect results.
The spam filter attack, where you trick an algorithm into seeing spam as ham, is an example of evasion. Another is "Hyperface," a collaboration between Hyphen Labs and Adam Harvey, a specially designed scarf engineered to fool facial recognition systems by exploiting the heuristics these systems use to identify faces. Similarly, in a recent study, researchers developed a pair of glasses that consistently cause a state-of-the-art facial recognition system to misclassify faces it would otherwise identify with absolute certainty....MORE

"Russia doubles its production of liquified natural gas from the Arctic as a second plant in the Yamal LNG is launched"

They've only just begun.
From The Barents Observer:

A historic shipment from Sabetta points at global advance of Arctic LNG 
Tanker «Pskov» on 9th August sailed out of the Gulf of Ob with about 170,000 tons of liquified natural gas on board. The historic shipment started in Sabetta, the seaport and terminal on the Yamal Peninsula, where LNG was loaded from Novatek’s second train of the Yamal LNG project.
Plant workers had pushed the button on the 12th of July. Natural gas started to pour into the pipes of the new plant and 8,5 days later it had turned liquified. By 9th August, about 250,000 tons had been produced, more than enough to fill up a LNG carrier.

The new plant was launched more than half a year ahead of schedule. It enables Novatek, the project operator, to double its Yamal LNG output from 5,5 million tons to 11 million tons per year.
The opening of the new plant - the second of a total of three - comes less than eight months after Novatek opened the first train. Since President Putin on 8th December 2017 came to attend the grand opening ceremony, Novatek and partners have produced 3,5 million tons of liquified natural gas on site. A total of 47 shipments have been made from the project terminal of Sabetta, several of them eastwards along the Northern Sea Route to asian buyers.

With the two plants in operation, the Yamal LNG will have a 3,5 percent share of the global LNG market. And far more is to come. According to company CEO Leonid Mikhelson, Novatek will by year 2030 produce up to 60 million tons of liquified natural gas, all of it from Arctic projects. The company is already with full steam developing its next projects, the Arctic LNG 2 and the Arctic LNG 3....

"Facebook’s message to media: 'We are not interested in talking to you about your traffic…That is the old world and there is no going back'”

From NiemanLab, Aug. 13:

That firehose isn’t opening up again anytime soon.
The Australian — the Murdoch-owned national paper — has an interesting (and aggressively paywalled) scoop about Facebook today, based on comments Campbell Brown, the company’s global head of news partnerships, allegedly made during a meeting with Australian media executives in Sydney last week.

Here are the quotes attributed to Brown in the story:
“Mark [Zuckerberg] doesn’t care about publishers but is giving me a lot of leeway and concessions to make these changes,” Ms Brown said.
“We will help you revitalise journalism … in a few years the ­reverse looks like I’ll be holding your hands with your dying ­business like in a hospice.”
I should note that Brown denied making the comments to The Australian (“These quotes are simply not accurate and don’t reflect the discussion we had in the meeting”); I should also note that The Australian has five people in the meeting corroborating them.

Much of the attention given to this story by Media Twitter has focused on the “doesn’t care about publishers” bit and the work-with-us-or-die implication of the second quote. But the story has an attached illustration that includes an alleged Brown quote that didn’t make it into the final story, and in some ways that’s really the most important one:
“We are not interested in talking to you about your traffic and referrals any more. That is the old world and there is no going back.”
That’s the big reversal here, given that “traffic and referrals” were roughly 99 percent of what Facebook had to offer publishers over the past half-decade or so. It was that firehose of eyeballs that led to new editorial strategies designed for share-friendly content, as well as the thought that maybe digital advertising could pay the bills after all.

Facebook has spent most of the last year reducing the amount of traffic it sends publishers, first through unspoken tweaks in 2017 and then with a series of announced changes in early 2018....