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Heavy-handed corruption favoring power company in Nevada: A state-government bait-and-switch scheme

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In the NY Times Jacques Leslie describes a jaw-dropping instance of corruption and a financial attack on the public:

WHEN President Obama proclaimed in his State of the Union address last month that “solar is saving Americans tens of millions of dollars a year on their energy bills,” he clearly wasn’t talking about Nevada.

In late December, the state’s Public Utilities Commission, which regulates Nevada’s energy market, announced a rate change drastic enough to kill Nevada’s booming rooftop solar market and drive providers out of the state. Effective Jan. 1, the new tariffs will gradually increase until they triple monthly fees that solar users pay to use the electric grid and cut by three-quarters users’ reimbursements for feeding electricity into it.

More startlingly, the commission made its decision retroactive. That means that the 17,000 Nevada residents who were lured into solar purchases by state-mandated one-time rebates of up to $23,000 suddenly discovered that they were victims of a bait-and-switch. They made the deals assuming that, allowing for inflation, their rates would stay constant over their contracts’ 20- to 30-year lifetimes; instead, they face the prospect of paying much more for electricity than if they had never made the change, even though they’re generating almost all their electricity themselves.

The commission justified its decision by citing grid construction and maintenance costs that rooftop solar users haven’t been charged for, but circumstantial evidence suggests that other factors played a role. All three commission members were appointed or reappointed by Gov. Brian Sandoval, a Republican, whose two election campaigns have received a total of $20,000, the maximum allowed donation under Nevada law, from NV Energy, the Berkshire Hathaway-owned utility that is a major beneficiary of the rate changes. Two of Mr. Sandoval’s closest informal advisers, Pete Ernaut and Gregory W. Ferraro, are NV Energy lobbyists.

The American Legislative Exchange Council, which drafts model bills for right-wing state legislators and receives financial support from fossil fuel interests, has campaigned for rates like those the commission adopted, and, according to Greenpeace, NV Energy was at one time an ALEC member.

The outcry among solar users and providers has been so vehement that the commission has agreed to hold a hearing next week to reconsider imposing the new rates on existing solar users, and NV Energy announced a week ago that it would not insist on the retroactive provision. But even if limited to future customers, the rate changes will almost certainly decimate one of the largest residential solar markets in the nation. As a result, residential consumers will have little alternative to NV Energy, which uses fossil fuels to generate more than 80 percent of the state’s electricity.

The decision is likely to have national repercussions. One indication is that the stock price of SolarCity, which served 60 percent of Nevada’s rooftop market, has dropped 30 percent since the commission’s decision was announced Dec. 22. Investors in solar companies now have reason to fear that retroactive provisions will spread to other states, no matter what happens in Nevada.

All of this amounts to the most consequential skirmish so far in the struggle over the future of utilities. In fact, it reflects utilities’ defensiveness, as modular devices located close to consumers are undermining their monopolies. Cleaner, more energy-efficient and potentially cheaper than fossil fuels, these technologies include solar, wind, batteries, microturbines, microgrids and smart appliances. As they spread, they strike at the heart of utilities’ business models: To increase profits, utilities must expand operations, but the emergence of distributed energy reduces the need for expansion.

Three years ago, the Edison Electric Institute, the utilities’ trade group, published a report called “Disruptive Challenges” that became famous in the utilities sector for its seeming candor. It describes how distributed forms of energy could send the industry into what has become known as the “utility death spiral.” As more and more consumers switch to distributed energy, the utilities’ costs must be shared among a dwindling number of customers, whose rates therefore increase, causing more of them to shift to distributed energy. The report may be exaggerating when it says that if 10 percent of utility customers switch to distributed energy, utilities’ rates would rise by 20 percent, but even a smaller shift might devastate utilities’ business models.

The industry’s response has been . . .

Continue reading.

So much for the free market ideal that the GOP claims to respect. Obviously, what the GOP respects is greed.

Written by LeisureGuy

1 February 2016 at 11:02 am

Perhaps Spiderman could

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That from this post on Motherboard, by Louise Matsakas. More info at the link.

I do wish the guy had known to look at the camera with the little red light… see that? Look at that.

Written by LeisureGuy

30 January 2016 at 12:34 pm

Creating graphic images using Excel: Advanced division

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You—or, more precisely, Tatsuo Horiuchi, age 73—can use Excel to make images such as this:

tatsuo-horiuchi-1

Here’s another of his Excel spreadsheets:

tatsuo-horiuchi-2

Here’s the story, and at the link you can download the Excel files for the above.

A photo of the artist and some of his work. The four-panel image is impressive, eh?

horiuchi-tatsuo-ph2_px420

Written by LeisureGuy

29 January 2016 at 1:07 pm

Posted in Art, Technology

Wow! Computer running AlphaGo program defeats a professional Go player!

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The video is from the post “AlphaGo: Mastering the ancient game of Go with Machine Learning” on the Google Research blog.

David Silver and Demis Hassabis, of Google DeepMind project, write:

Games are a great testing ground for developing smarter, more flexible algorithms that have the ability to tackle problems in ways similar to humans. Creating programs that are able to play games better than the best humans has a long history – the first classic game mastered by a computer was noughts and crosses (also known as tic-tac-toe) in 1952 as a PhD candidate’s project. Then fell checkers in 1994. Chess was tackled by Deep Blue in 1997. The success isn’t limited to board games, either – IBM’s Watson won first place on Jeopardy in 2011, and in 2014 our own algorithms learned to play dozens of Atari games just from the raw pixel inputs.

But one game has thwarted A.I. research thus far: the ancient game of Go. Invented in China over 2500 years ago, Go is played by more than 40 million people worldwide. The rules are simple: players take turns to place black or white stones on a board, trying to capture the opponent’s stones or surround empty space to make points of territory. Confucius wrote about the game, and its aesthetic beauty elevated it to one of the four essential arts required of any true Chinese scholar. The game is played primarily through intuition and feel, and because of its subtlety and intellectual depth it has captured the human imagination for centuries.

But as simple as the rules are, Go is a game of profound complexity. The search space in Go is vast — more than a googol times larger than chess (a number greater than there are atoms in the universe!). As a result, traditional “brute force” AI methods — which construct a search tree over all possible sequences of moves — don’t have a chance in Go. To date, computers have played Go only as well as amateurs. Experts predicted it would be at least another 10 years until a computer could beat one of the world’s elite group of Go professionals.

We saw this as an irresistible challenge! We started building a system, AlphaGo, described in a paper in Nature this week, that would overcome these barriers. The key to AlphaGo is reducing the enormous search space to something more manageable. To do this, it combines a state-of-the-art tree search with two deep neural networks, each of which contains many layers with millions of neuron-like connections. One neural network, the “policy network”, predicts the next move, and is used to narrow the search to consider only the moves most likely to lead to a win. The other neural network, the “value network”, is then used to reduce the depth of the search tree — estimating the winner in each position in place of searching all the way to the end of the game.

AlphaGo’s search algorithm is much more human-like than previous approaches. For example, when Deep Blue played chess, it searched by brute force over thousands of times more positions than AlphaGo. Instead, AlphaGo looks ahead by playing out the remainder of the game in its imagination, many times over – a technique known as Monte-Carlo tree search. But unlike previous Monte-Carlo programs, AlphaGo uses deep neural networks to guide its search. During each simulated game, the policy network suggests intelligent moves to play, while the value network astutely evaluates the position that is reached. Finally, AlphaGo chooses the move that is most successful in simulation.

We first trained the policy network on 30 million moves from games played by human experts, until it could predict the human move 57% of the time (the previous record before AlphaGo was 44%). But our goal is to beat the best human players, not just mimic them. To do this, AlphaGo learned to discover new strategies for itself, by playing thousands of games between its neural networks, and gradually improving them using a trial-and-error process known as reinforcement learning. This approach led to much better policy networks, so strong in fact that the raw neural network (immediately, without any tree search at all) can defeat state-of-the-art Go programs that build enormous search trees.

These policy networks were in turn used to train the value networks, again by reinforcement learning from games of self-play. These value networks can evaluate any Go position and estimate the eventual winner – a problem so hard it was believed to be impossible.

Of course, all of this requires a huge amount of compute power, so we made extensive use ofGoogle Cloud Platform, which enables researchers working on AI and Machine Learning to access elastic compute, storage and networking capacity on demand. In addition, new open source libraries for numerical computation using data flow graphs, such as TensorFlow, allow researchers to efficiently deploy the computation needed for deep learning algorithms across multiple CPUs or GPUs.

So how strong is AlphaGo? To answer this question, we played a tournament between AlphaGo and the best of the rest – the top Go programs at the forefront of A.I. research. Using a single machine, AlphaGo won all but one of its 500 games against these programs. In fact, AlphaGo even beat those programs after giving them 4 free moves headstart [i.e., a four-stone handicap, roughly equivalent to Queen odds in chess – LG] at the beginning of each game. A high-performance version of AlphaGo, distributed across many machines, was even stronger. . .

Continue reading.

Here’s the paper (“Mastering the game of Go with deep neural networks and tree search”)
in Nature. The abstract:

The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.

Written by LeisureGuy

27 January 2016 at 12:26 pm

Posted in Games, Go, Software, Technology

What is the real legacy of Steve Jobs?

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Steve Jobs had a repellent personality, although in material terms he was quite successful. Sue Halpern in the NY Review of Books reviews two movies and a book about Steve Jobs:

Steve Jobs: The Man in the Machine
a documentary film directed by Alex GibneySteve Jobs
a film directed by Danny Boyle

Becoming Steve Jobs: The Evolution of a Reckless Upstart into a Visionary Leader
by Brent Schlender and Rick Tetzeli
Crown Business, 447 pp., $30.00

Partway through Alex Gibney’s earnest documentary Steve Jobs: The Man in the Machine, an early Apple Computer collaborator named Daniel Kottke asks the question that appears to animate Danny Boyle’s recent film about Jobs: “How much of an asshole do you have to be to be successful?” Boyle’s Steve Jobs is a factious, melodramatic fugue that cycles through the themes and variations of Jobs’s life in three acts—the theatrical, stage-managed product launches of the Macintosh computer (1984), the NeXT computer (1988), and the iMac computer (1998). For Boyle (and his screenwriter Aaron Sorkin) the answer appears to be “a really, really big one.”

Gibney, for his part, has assembled a chorus of former friends, lovers, and employees who back up that assessment, and he is perplexed about it. By the time Jobs died in 2011, his cruelty, arrogance, mercurial temper, bullying, and other childish behavior were well known. So, too, were the inhumane conditions in Apple’s production facilities in China—where there had been dozens of suicides—as well as Jobs’s halfhearted response to them. Apple’s various tax avoidance schemes were also widely known. So why, Gibney wonders as his film opens—with thousands of people all over the world leaving flowers and notes “to Steve” outside Apple Stores the day he died, and fans recording weepy, impassioned webcam eulogies, and mourners holding up images of flickering candles on their iPads as they congregate around makeshift shrines—did Jobs’s death engender such planetary regret?

The simple answer is voiced by one of the bereaved, a young boy who looks to be nine or ten, swiveling back and forth in a desk chair in front of his computer: “The thing I’m using now, an iMac, he made,” the boy says. “He made the iMac. He made the Macbook. He made the Macbook Pro. He made the Macbook Air. He made the iPhone. He made the iPod. He’s made the iPod Touch. He’s made everything.”

Yet if the making of popular consumer goods was driving this outpouring of grief, then why hadn’t it happened before? Why didn’t people sob in the streets when George Eastman or Thomas Edison or Alexander Graham Bell died—especially since these men, unlike Steve Jobs, actually invented the cameras, electric lights, and telephones that became the ubiquitous and essential artifacts of modern life?* The difference, suggests the MIT sociologist Sherry Turkle, is that people’s feelings about Steve Jobs had less to do with the man, and less to do with the products themselves, and everything to do with the relationship between those products and their owners, a relationship so immediate and elemental that it elided the boundaries between them. “Jobs was making the computer an extension of yourself,” Turkle tells Gibney. “It wasn’t just for you, it was you.”

In Gibney’s film, Andy Grignon, the iPhone senior manager from 2005 to 2007, observes that

Apple is a business. And we’ve somehow attached this emotion [of love, devotion, and a sense of higher purpose] to a business which is just there to make money for its shareholders. That’s all it is, nothing more. Creating that association is probably one of Steve’s greatest accomplishments.

Jobs was a consummate showman. It’s no accident that Sorkin tells his story of Jobs through product launches. These were theatrical events—performances—where Jobs made sure to put himself on display as much as he did whatever new thing he was touting. “Steve was P.T. Barnum incarnate,” says Lee Clow, the advertising executive with whom he collaborated closely. “He loved the ta-da! He was always like, ‘I want you to see the Smallest Man in the World!’ He loved pulling the black velvet cloth off a new product, everything about the showbiz, the marketing, the communications.”

People are drawn to magic. Steve Jobs knew this, and it was one reason why he insisted on secrecy until the moment of unveiling. But Jobs’s obsession with secrecy went beyond his desire to preserve the “a-ha!” moment. Is Steve Jobs “the most successful paranoid in business history?,” The Economist asked in 2005, a year that saw Apple sue, among others, a Harvard freshman running a site on the Internet that traded in gossip about Apple and other products that might be in the pipeline. Gibney tells the story of Jason Chen, a Silicon Valley journalist whose home was raided in 2010 by the California Rapid Enforcement Allied Computer Team (REACT), a multi-agency SWAT force, after he published details of an iPhone model then in development. A prototype of the phone had been left in a bar by an Apple employee and then sold to Chen’s employer, the website Gizmodo, for $5,000. Chen had returned the phone to Apple four days before REACT broke down his door and seized computers and other property. Though REACT is a public entity, Apple sits on its steering committee, leaving many wondering if law enforcement was doing Apple’s bidding.

Whether to protect trade secrets, or sustain the magic, or both, Jobs was adamant that Apple products be closed systems that discouraged or prevented tinkering. This was the rationale behind Apple’s lawsuit against people who “jail-broke” their devices in order to use non-Apple, third-party apps—a lawsuit Apple eventually lost. And it can be seen in Jobs’s insistence, from the beginning, on making computers that integrated both software and hardware—unlike, for example, Microsoft, whose software can be found on any number of different kinds of PCs; this has kept Apple computer prices high and clones at bay. An early exchange in Boyle’s movie has Steve Wozniak arguing for a personal computer that could be altered by its owner, against Steve Jobs, who believed passionately in end-to-end control. “Computers aren’t paintings,” Wozniak says, but that is exactly what Jobs considered them to be. The inside of the original Macintosh bears the signatures of its creators.

The magic Jobs was selling went beyond the products his company made: . . .

Continue reading.

Later in the review:

. . . As Gibney puts it, “More than a CEO, he positioned himself as an oracle. A man who could tell the future.”

And he could—some of the time. It’s important to remember, though, that when Jobs was forced out of Apple in 1985, the two computer projects into which he had been pouring company resources, the Apple 3 and another computer called the Lisa, were abject failures that nearly shut the place down. A recurring scene in Boyle’s fable is Jobs’s unhappy former partner, the actual inventor of the original Apple computer, Steve Wozniak, begging him to publicly recognize the team that made the Apple 2, the machine that kept the company afloat while Jobs pursued these misadventures, and Jobs scornfully blowing him off.

Jobs’s subsequent venture after he left Apple, a workstation computer aimed at researchers and academics, appropriately called the NeXT, was even more disastrous. The computer was so overpriced and underpowered that few were sold. Boyle shows Jobs obsessing over the precise dimensions of the black plastic cube that housed theNeXT, rather than on the computer’s actual deficiencies, just as Jobs had obsessed over the minute gradations of beige for the Apple 1. Neither story is apocryphal, and both have been used over the years to illustrate, for better and for worse, Jobs’s preternatural attention to detail. (Jobs also spent $100,000 for the NeXT logo.)

Sorkin’s screenplay claims that the failure of the NeXT computer was calculated—that it was designed to catapult Jobs back into the Apple orbit. Fiction allows such inventions, but as the business journalists Brent Schlender and Rick Tetzeli point out in their semipersonal recounting, Becoming Steve Jobs, “There was no hiding NeXT’s failure, and there was no hiding the fact that NeXT’s failure was primarily Steve’s doing.”

Still, Jobs did use the NeXT’s surviving asset, its software division, as the wedge in the door that enabled him to get back inside his old company a decade after he’d been pushed out. NeXT software, which was developed by Avie Tevanian, a loyal stalwart until Jobs tossed him aside in 2006, became the basis for the intuitive, stable, multitasking operating system used by Mac computers to this day. At the time, though, Apple was in free fall, losing $1 billion a year and on the cusp of bankruptcy. The graphical, icon-based operating system undergirding the Macintosh was no longer powerful or flexible enough to keep up with the demands of its users. Apple needed a new operating system, and Steve Jobs just happened to have one. Or, perhaps more accurately, he had a software engineer—Tevanian—who could rejigger NeXT’s operating system and use it for the Mac, which may have been Jobs’s goal all along. Less than a year after Jobs sold the software to Apple for $429 million and a fuzzily defined advisory position at the company, the Apple CEO was gone, and the board of directors was gone, and Jobs was back in charge. . .

Written by LeisureGuy

24 January 2016 at 2:00 pm

Rubik’s-cube-solving machine can solve scramble cube in < 2 seconds

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Take a look. The article contains this video:

Written by LeisureGuy

23 January 2016 at 3:53 pm

What a Million Syllabi Can Teach Us Gray Matter

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A very clever idea: to build a database of the syllabi used for college courses. Joe Karaganis and David McClure report in the NY Times:

College course syllabuses [I prefer the more traditional plural form, “syllabi.” – LG] are curious documents. They represent the best efforts by faculty and instructors to distill human knowledge on a given subject into 14-week chunks. They structure the main activity of colleges and universities. And then, for the most part, they disappear.

Some schools archive them, some don’t. Some syllabus archives are public, some aren’t. Some faculty members treat their syllabuses as trade secrets, others are happy to post them online. Despite the bureaucratization of higher education over the past few decades, syllabuses have escaped systematic treatment.

Until now. Over the past two years, we and our partners at the Open Syllabus Project (based at the American Assembly at Columbia) have collected more than a million syllabuses from university websites. We have also begun to extract some of their key components — their metadata — starting with their dates, their schools, their fields of study and the texts that they assign.

This past week, we made available online a beta version of our Syllabus Explorer, which allows this database to be searched. Our hope and expectation is that this tool will enable people to learn new things about teaching, publishing and intellectual history.

At present, the Syllabus Explorer is mostly a tool for counting how often texts are assigned over the past decade. There is something for everyone here. The traditional Western canon dominates the top 100, with Plato’s “Republic” at No. 2, “The Communist Manifesto” at No. 3, and “Frankenstein” at No. 5, followed by Aristotle’s “Ethics,” Hobbes’s “Leviathan,” Machiavelli’s “The Prince,” “Oedipus” and “Hamlet.”

“The Communist Manifesto” ranks as high as it does (for those wondering) because, like “The Republic,” it is frequently taught in multiple fields — notably in history, sociology and political science. Writing guides are also well represented, with “The Elements of Style” by William Strunk Jr. and E. B. White at No. 1, as are major textbooks, led by Neil Campbell’s “Biology” at No. 4.

What about fiction from the past 50 years? Toni Morrison’s “Beloved” ranks first, at No. 43, followed by William Gibson’s “Neuromancer,” Art Spiegelman’s “Maus,” Ms. Morrison’s “The Bluest Eye,” Sandra Cisneros’s “The House on Mango Street,” Anne Moody’s “Coming of Age in Mississippi,” Leslie Marmon Silko’s “Ceremony” and Alice Walker’s “The Color Purple.”

Top articles? . . .

Continue reading.

In particular, take at the Syllabus Explorer.

Written by LeisureGuy

23 January 2016 at 10:52 am

Posted in Books, Education, Technology

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