Schlagwort-Archive: Elon Musk

These Technologies May Actually Deliver Elon Musk’s Dream of Changing the World

As Tesla founder Elon Musk promises to change the world, starting with a giant battery factory in the Nevada desert, investors from Toronto to Tokyo are quietly developing the next-generation technologies that may actually get him there.

Batteries, especially the lithium-ion variety used in mobile phones and electric cars, are likely to dominate the $44 billion or more spent on energy storage by 2024, according to Bloomberg New Energy Finance. Trouble is, they’re not the solution to all needs.

As well as the environmental impact of mining lithium, which has been blamed for starving flamingos in northern Chile, batteries lose their charge over time. They can balance minute-to-minute shifts in supply. But they can’t absorb solar power generated in summer, say, and deliver it in winter.

“We’re going to need a whole range of solutions to keep the lights on,” said Michael Liebreich, founder of Bloomberg New Energy Finance. “If your problem is that the sun doesn’t shine in winter, are you really going to buy a battery, charge it once a year during summer and use it once a year in winter? I don’t think so. You can’t just jump to batteries as the single solution.”

Storage devices are crucial to expanding the wind and solar industries and curtailing pollution because they allow what’s generated now to be consumed later. Just as refrigeration changed the way we handled food in the 20th century, energy storage will give grid operators and rooftop-solar consumers flexibility about when to use the power they produce — reducing the number of big power plants the world needs.

Here’s the leading energy storage projects on the drawing board that go beyond lithium-ion batteries:

Hydropower

Long before batteries, electricity was stored through plants that pump water uphill to a reservoir and release it through turbines when it’s needed. It’s long-lived enough to be hold solar power generated in the summer for use in the winter. Hydropower is renewable energy’s oldest technology and accounts for well over 90 percent of energy storage, according to the U.S. Department of Energy.

As well as classic hydroelectric stations, tidal lagoons may also offer energy storage in a similar way by holding water for short periods, according to Tidal Lagoon Power Ltd., which is planning to build six lagoons around the U.K. coast line.

Railpower

Trains can double as storage. In April, Advanced Rail Energy Storage won approval from the Nevada Bureau of Land Management for a $55 million project using rail locomotives.

ARES will build a 6-mile uphill rail corridor involving heavily-loaded trains. When power’s cheap, trains will be pushed up a hill. When the power is needed, they’ll be released down, supplying energy back to the grid through an overhead wire.

Chief Executive Officer Jim Kelly reckons the system can be deployed at about 60 percent of the cost of an equivalent pumped-hydro facility. The nine-month construction program is expected to start in the second quarter of 2017. Once complete, it could run for 40 years.

Air storage

Compressed air storage sequesters a gas underground so it can be released later to drive a generation turbine whenever needed.

One project in Toronto sends the air underwater where it’s stored in balloons. When demand for power rises, the air comes back to the surface through a pipe, where it’s converted into electricity.

Compressed air storage requires a specific type of rock formation. The world has a handful of existing projects — one in Huntorf, Germany and another in McIntosh, Alabama. Several large scale projects have been put on ice, including the Iowa Stored Energy Plant near Des Moines and Dresser-Rand Group’s 317-megawatt Apex Bethel Energy Center in Anderson County, Texas.

Power-to-gas

Companies including carmaker Audi are developing power-to-gas technology that turns excess energy into hydrogen using electrolysis. The hydrogen can be directly injected into a gas network, or “upgraded” into methane and used as a substitute for natural gas.

Siemens, the world’s biggest power-equipment maker, is working on an approach that turns hydrogen into a clean ammonia, that could potentially provide emissions-free fertilizer that could be used by farmers everywhere.

Advocates say it can deliver both long and short-term back up power since the gas can be trapped indefinitely. That means it can shift electricity made in summer for use in the winter. It isn’t yet clear whether the economics will stack up.

Flywheels

Flywheels look nothing like a traditional battery. Think of a spinning drum that stores the kinetic energy in a way that can be made into electricity. Power is used to start the wheel turning. Then when electricity is in short supply, the flywheel turns a motor that generates electricity. They can deliver either short bursts or for longer periods.

Railway Technical Research Institute, a Tokyo-based developer of railroad technologies, is working on a flywheel that uses superconducting magnetic bearings that allow the wheel to spin with less friction. Its system also uses a plastic that’s reinforced with carbon fiber, making the flywheel stronger and faster. The bearings allow the flywheel to float without making contact with its housing, reducing energy lost through friction.

Railway Technical is developing the flywheel technology with Furukawa Electric Co. and Mirapro Co. They have set up a flywheel system at a 1-megawatt solar park in Japan’s Yamanashi prefecture. Temporal Power Ltd. and Beacon Power Corp. are also pursuing flywheel systems.

https://www.bloomberg.com/news/articles/2016-09-08/these-technologies-may-actually-deliver-elon-musk-s-dream-of-changing-the-world

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8 Powerful Lessons You Can Learn From the Career of Elon Musk

Elon Musk, in the words of one blogger who did a series of in-depth interviews with the Tesla and SpaceX founder, is, basically, „the raddest man alive.“ Who could fail to be impressed by a single entrepreneur who has set his sights on both getting humans to Mars and revolutionizing our energy economy?

Because Musk is so obviously extraordinary, it could be easy to feel like his career is a world apart — the efforts of a visionary that mere mortals like us could never emulate. But while it’s probably true that, for most of us, the ship has sailed on leading the way to interplanetary travel, that doesn’t mean folks with more down-to-earth careers have nothing to learn from the mogul.

When a user of question-and-answer site Quora asked the simple question, „What can we learn from Elon Musk?“ a host of devoted Musk watchers offered thoughtful answers. Among the best was a reply from blogger (and recent New York Times profile-ee) James Altucher, who took the time to listen „to every interview [Musk] ever did and compiled what I think are the most inspirational quotes.“

Here are a few of the 22 essential takeaways he extracted from all that research:

1. Focus on the impact of your dreams, not the odds.

Maybe, like Altucher, your initial reaction to this principle is to worry that your particular dreams might just be impossible. But, as Altucher reminds readers, this advice is coming from a man who wants to colonize Mars. Are you dreams really more of a long shot than that?

2. No one does amazing things for the money.

„I’ve interviewed over 100 people now on my podcast. Each of the 100 have achieved amazing results in their life,“ notes Altucher. „But none of them have done if for the money.“ Neither did Musk, who Altucher quotes as saying: „Going from PayPal, I thought: ‚Well, what are some of the other problems that are likely to most affect the future of humanity?‘ Not from the perspective, ‚What’s the best way to make money?'“

The takeaway: if you want to do great things, focus on the difference you’ll make in the world (or to yourself), not the financial rewards (or the glory).

3. Reason from first principles.

A lot has been written about Musk’s mindset, but Altucher sums up his unusual and incredibly effective approach with this quote: „Boil things down to their fundamental truths and reason up from there.“ In short, to improve your thinking, set received wisdom aside and try to look at the world with fresh eyes, using objective data and clear-headed observation.

4. Persistence pays.

Not all of the lessons of Musk’s career are off the wall and unexpected. Sometimes, he proves that conventional wisdom is right. Like with this quote: „Persistence is very important. You should not give up unless you are forced to give up.“

5. In hiring, talent beats numbers.

Some entrepreneurs tackle difficult problems by trying to throw a whole lot of warm bodies at them. Not Musk.

„It is a mistake to hire huge numbers of people to get a complicated job done. Numbers will never compensate for talent in getting the right answer (two people who don’t know something are no better than one), will tend to slow down progress, and will make the task incredibly expensive,“ Altucher quotes him as saying.

So next time you need to hire your way out of jam, spare a thought for this bit of wisdom and take the time to find the right talent rather than just hoping that brute numbers will save you.

6. Talent can’t compensate for a lousy personality.

According to Altucher, Musk is a late but fervent convert to the idea that great ability can’t compensate for a lousy personality.

Here’s the quote: „My biggest mistake is probably weighing too much on someone’s talent and not someone’s personality. I think it matters whether someone has a good heart.“ So, once more with feeling: don’t hire jerks!

7. Constantly question yourself.

You’d think that someone with Musk’s achievements might be satisfied with his efforts, but that’s not the case. Musk claims he constantly strives to improve himself.

„It’s very important to have a feedback loop, where you’re constantly thinking about what you’ve done and how you could be doing it better. I think that’s the single best piece of advice: constantly think about how you could be doing things better and questioning yourself,“ he said. If Musk isn’t resting on his laurels, neither should you.

8. Finding the right questions is most of the battle.

Apparently, Musk’s favorite book as a teenager was The Hitchhiker’s Guide to the Galaxy. Here’s the biggest lesson he took away from it: „It taught me that the tough thing is figuring out what questions to ask, but that once you do that, the rest is really easy.“

 

http://www.inc.com/jessica-stillman/8-powerful-lessons-you-can-learn-from-the-career-of-elon-musk.html

What can we learn from Elon Musk?

The three fundamentals to Elon Musks success.

1. UPDATING YOUR SOFTWARE

How to constantly build your knowledge and understanding.

An oft asked question of Musk – ‘How did he learn so much?’

Since childhood, he has been a tireless self learner. At the age of 10 he resorted to reading Encyclopedia Britannica after devouring every other book at home.

From interviews and discussions with Musk, its becomes apparent that he views people as computer systems, being made up of hardware (body) and software (mind). Recognizing that your software is one of the most powerful tools that you possess, Musk works tirelessly on updating his, feeding it with more knowledge and information when he wants to understand a problem.

Jim Cantrell, one of the founding team members of SpaceX comments on Musk’s incredibly fast learning ability:

“He literally sucks the knowledge and experience out of people that he is around. He borrowed all of my college texts on rocket propulsion when we first started working together in 2001.”

In 2000, before Musk had even set up SpaceX, he began devouring books on propulsion, avionics and aeronautical engineering. He already knew that his goal was landing people on Mars, now he just needed to upgrade his software with the information and tools on how to accomplish it.

A trait that underpins Musk’s model of thinking is being able to quickly consume and understand complex information, then plan with clarity how to apply it in making progress towards his goal. People are impressed with his deep knowledge across a wide range of technical subjects, from electrical, structural, mechanical, aeronautical, and software engineering through to business strategy and more.

“I think most people can learn a lot more than they think they can. They sell themselves short without trying.

One bit of advice: it is important to view knowledge as sort of a semantic tree — make sure you understand the fundamental principles, i.e. the trunk and big branches, before you get into the leaves/details or there is nothing for them to hang on to.”

Elon Musk

This habit of self learning and forcing himself to understand new concepts, gives him a huge internal database of knowledge that he is then able to run through his internal problem solving tool.

2. REASONING FROM FIRST PRINCIPLE

How to get to the nucleus of a problem and understand the facts.

Aristotle described a first principle as, “[the] first basis from which a thing is known”.

It means basing conclusions on fundamental truths, not on assumption or analogy.

Reasoning from first principles requires mental effort. It means boiling things down to their most basic truths, and reasoning up from those truths. It requires you to actively engage your brain and work ideas through.

The alternative to this is reasoning by analogy. Assuming something is true or correct because it’s similar to something else that has been done before.

Musk is a master of using the scientific method of first principle reasoning, and applying it to problem solving scenarios. Here is one example;

“Historically, all rockets have been expensive, so therefore, in the future, all rockets will be expensive. But actually that’s not true. If you say, what is a rocket made of? It’s made of aluminium, titanium, copper, carbon fiber. And you can break it down and say, what is the raw material cost of all these components? If you have them stacked on the floor and could wave a magic wand so that the cost of rearranging the atoms was zero, then what would the cost of the rocket be? And I was like, wow, okay, it’s really small—it’s like 2% of what a rocket costs. So clearly it would be in how the atoms are arranged—so you’ve got to figure out how can we get the atoms in the right shape much more efficiently.

And so I had a series of meetings on Saturdays with people, some of whom were still working at the big aerospace companies, just to try to figure out if there’s some catch here that I’m not appreciating. And I couldn’t figure it out. There doesn’t seem to be any catch. So I started SpaceX.”

Elon Musk

In our day to day, we make most decisions based on analogy. It would simply take too much mental time and capacity to question every single small decision during the day.

But when it comes to big decisions, it’s important to reason from first principle. Make sure you know the facts, data and figures, don’t just follow the crowd and assume.

3. HARD WORK

How to give your ideas the best chance of success.

Highly intelligent, fast learning, dynamic problem solving ability and lots of money, they’ve all contributed to the success of Musk’s endeavours. But there’s another key character trait to the man which has been critical to his success – an incredible and highly efficient work ethic.

“Work like hell. I mean you just have to put in 80 to 100 hour weeks every week. [This] improves the odds of success. If other people are putting in 40 hour work weeks and you’re putting in 100 hour work weeks, then even if you’re doing the same thing you know that… you will achieve in 4 months what it takes them a year to achieve.”

Elon Musk

The fact is that Elon Musk gets a lot done. Running two separate billion dollar companies requires making a lot of decisions and having eyes on many moving parts. Here are some of the key aspects to Musk’s working process that make him so efficient.

– 100 hours a week – has noted many times that at critical periods in the lifespan of his companies, he has gone from working 80-90 hour weeks up to doing 100 hours a week. It is not unusual for him to work seven days a week, normally rising at 7am and getting to bed around 1am.

– Batching – or multitasking, he combines multiple tasks which can be done together effectively e.g. Emailing while reviewing spreadsheets, meetings over lunch, etc.

– Scheduling – A man as busy as Musk needs to run to a tight schedule to be efficient. He spends Monday and Thursday at SpaceX in LA, Tuesday and Wednesday at Tesla in the Bay Area, and splits Friday between both. His assistant has his planner broken down into five minute slots, and there’s a long line of people trying to get ahold of him for that time. Efficient scheduling is a behaviour pattern seen in many highly successful people.

– Feedback loop – Musk is a strong believer in constructive criticism. He constantly bounces ideas off colleagues and advisors to sense check them. Open and honest criticism should be encouraged to help improve an idea or product. “Constantly think about how you could be doing things better and questioning yourself.” – Elon Musk

– Caffeine – „To get through the day, Musk relies on two stimulants: caffeine and a desire to help humanity colonize Mars. Until he recently started cutting back on the former, Musk consumed eight cans of Diet Coke a day, as well as several large cups of coffee. „I got so freaking jacked that I seriously started to feel like I was losing my peripheral vision,“ he says. If he realizes how crazy this sounds, he doesn’t let on.” – from Inc Magazine.

 

https://www.quora.com/What-can-we-learn-from-Elon-Musk

How Hyperloop (Elon Musk) will change how we travel

Hyperloop one Pod in motionHyperloop One

If you are like most, you probably find air travel to be a stressful experience.

There’s the commute to the airport, the long lines to check your bag, the security check, and then once you’re finally on the plane, there’s the tight squeeze of sitting for several hours with barely any legroom.

And yet, air travel is really our only option for traveling hundreds of miles quickly. But the Hyperloop could change that.

The Hyperloop is a tubular transport system that carries passengers in capsules at speeds reaching more than 700 miles per hour. Tesla and SpaceX CEO Elon Musk first proposed the idea in a white paper published in 2013 and made his research public so others could pursue developing the concept. The LA-based startup Hyperloop One is doing just that.

But Hyperloop One doesn’t just want to build a system that is as fast as a plane. The company wants to create an entirely new travel experience, one that is a lot less stressful and a lot more convenient.

„It’s not about getting somewhere, it’s about being somewhere. We’re not trying to optimize the transportation experience, we are trying to eliminate it,“ Brogan BamBrogan, Hyperloop One’s co-founder and chief technology officer, said at a company event earlier this month.

How exactly does it plan on doing this?

BamBrogan shared with Tech Insider four ways the Hyperloop will revolutionize all aspects of transportation.

It will be more accessible and more efficient.

It will be more accessible and more efficient.

Hyperloop One

For starters, Hyperloop One wants to put stations in the middle of cities so that there’s no annoying commute to an airport-like hub outside metropolitan areas, Brogan BamBrogan, Hyperloop One’s co-founder, told Tech Insider.

“Effectively, the Hyperloop will move people about the speed of an airplane. But we can do it city center to city center as we integrate ourselves into tunneling, so that’s really a value add,” BamBrogan said.

That’s right, effectively you’d get to say goodbye to that $30 cab ride to and from the airport outside the city.

What’s more, because the Hyperloop is in a controlled environment and is completely autonomous, you will never be delayed because of weather or because of an operator’s error.

No more ticket lines.

No more ticket lines.

Hyperloop One

BamBrogan also said the company wants to introduce a streamlined ticketing system so that lines are a thing of the past.

“Certainly, as we move forward, there’s going to be autonomous ticketing systems and you’re going to have an absolute elevator experience that is going to seamlessly deliver you to your destination.”

BamBrogan didn’t elaborate on how exactly this would work, but he did mention that part of the process could be through your smartphone.

In Musk’s white paper, he stated that all ticketing and baggage tracking would be handled electronically, effectively doing away with printing boarding passed and luggage labels.

The seats will actually be comfortable.

As for the seating, BamBrogan said the company is working to design passenger pods that are not only comfortable, but also spacious enough to allow people to keep their luggage with them at all times.

The company aims to share some renderings of potential pod designs sometime during the next three months, BamBrogan said.

It will be affordable.

In addition to being comfortable and convenient, the company also wants to make the Hyperloop affordable.

“Any Hyperloop form of transportation is going to be extremely low cost,” BamBrogan said.

“All the value the Hyperloop brings isn’t worth it at a very high price. So our goal is to make Hyperloop very inexpensive to deploy relative to other forms of transportation, so that on top of that low cost you would also get the high-speed extremely safe and energy efficient.”

While BamBrogan would not share a specific price point, Musk’s original white paper suggested a $20 one-way ticket.

http://www.businessinsider.com/4-ways-the-hyperloop-will-change-how-we-travel-2016-5

Elon Musk: we’ll ultimately be in the position where almost everyone will be able to afford a Tesla

The Internet is still waking up from the madness that was the Model 3 unveiling, but Tesla CEO is – as always – looking towards the future. While in Norway recently, Musk talked about Tesla’s upcoming EV. No, not the 3, but the even cheaper and smaller electric vehicle that will be coming out after the 3 debuts. Musk said the following, talking about the Model 3 (to start):

I’m super excited about being able to produce a car that most people can afford. And there will be future cars that are even more affordable down the road, but, with something like the Model 3, it’s designed such that roughly half of the people will be able to afford the car. Then, with fourth generation and smaller cars, we’ll ultimately be in the position where almost everyone will be able to afford the car.

You can hear it for yourself at about 12 minutes into the video above. It’s worth watching the whole thing, because Musk also mentions fossil fuel subsidies, that mysterious mass transit solution thing and
dying on Mars.

What’s most interesting about Musk’s comments about Tesla’s future is that he may not be around to steer the ship when this next EV arrives. Musk has said that he will remain the Tesla CEO at least until the Model 3 production has ramped up, but after that, who knows. As he said a year and a half ago, „I will never leave Tesla forever, but I may not be CEO forever. Nobody should be CEO forever.“

The brightest minds in AI research – Machine Learning

In AI research,  brightest minds aren’t driven by the next product cycle or profit margin – They want to make AI better, and making AI better doesn’t happen when you keep your latest findings to yourself.

http://www.wired.com/2016/04/openai-elon-musk-sam-altman-plan-to-set-artificial-intelligence-free/

Inside OpenAI, Elon Musk’s Wild Plan to Set Artificial Intelligence Free

ElonMusk201604

THE FRIDAY AFTERNOON news dump, a grand tradition observed by politicians and capitalists alike, is usually supposed to hide bad news. So it was a little weird that Elon Musk, founder of electric car maker Tesla, and Sam Altman, president of famed tech incubator Y Combinator, unveiled their new artificial intelligence company at the tail end of a weeklong AI conference in Montreal this past December.

But there was a reason they revealed OpenAI at that late hour. It wasn’t that no one was looking. It was that everyone was looking. When some of Silicon Valley’s most powerful companies caught wind of the project, they began offering tremendous amounts of money to OpenAI’s freshly assembled cadre of artificial intelligence researchers, intent on keeping these big thinkers for themselves. The last-minute offers—some made at the conference itself—were large enough to force Musk and Altman to delay the announcement of the new startup. “The amount of money was borderline crazy,” says Wojciech Zaremba, a researcher who was joining OpenAI after internships at both Google and Facebook and was among those who received big offers at the eleventh hour.

How many dollars is “borderline crazy”? Two years ago, as the market for the latest machine learning technology really started to heat up, Microsoft Research vice president Peter Lee said that the cost of a top AI researcher had eclipsed the cost of a top quarterback prospect in the National Football League—and he meant under regular circumstances, not when two of the most famous entrepreneurs in Silicon Valley were trying to poach your top talent. Zaremba says that as OpenAI was coming together, he was offered two or three times his market value.

OpenAI didn’t match those offers. But it offered something else: the chance to explore research aimed solely at the future instead of products and quarterly earnings, and to eventually share most—if not all—of this research with anyone who wants it. That’s right: Musk, Altman, and company aim to give away what may become the 21st century’s most transformative technology—and give it away for free.

Zaremba says those borderline crazy offers actually turned him off—despite his enormous respect for companies like Google and Facebook. He felt like the money was at least as much of an effort to prevent the creation of OpenAI as a play to win his services, and it pushed him even further towards the startup’s magnanimous mission. “I realized,” Zaremba says, “that OpenAI was the best place to be.”

That’s the irony at the heart of this story: even as the world’s biggest tech companies try to hold onto their researchers with the same fierceness that NFL teams try to hold onto their star quarterbacks, the researchers themselves just want to share. In the rarefied world of AI research, the brightest minds aren’t driven by—or at least not only by—the next product cycle or profit margin. They want to make AI better, and making AI better doesn’t happen when you keep your latest findings to yourself.

OpenAI is a billion-dollar effort to push AI as far as it will go.
This morning, OpenAI will release its first batch of AI software, a toolkit for building artificially intelligent systems by way of a technology called “reinforcement learning”—one of the key technologies that, among other things, drove the creation of AlphaGo, the Google AI that shocked the world by mastering the ancient game of Go. With this toolkit, you can build systems that simulate a new breed of robot, play Atari games, and, yes, master the game of Go.

But game-playing is just the beginning. OpenAI is a billion-dollar effort to push AI as far as it will go. In both how the company came together and what it plans to do, you can see the next great wave of innovation forming. We’re a long way from knowing whether OpenAI itself becomes the main agent for that change. But the forces that drove the creation of this rather unusual startup show that the new breed of AI will not only remake technology, but remake the way we build technology.

AI Everywhere
Silicon Valley is not exactly averse to hyperbole. It’s always wise to meet bold-sounding claims with skepticism. But in the field of AI, the change is real. Inside places like Google and Facebook, a technology called deep learning is already helping Internet services identify faces in photos, recognize commands spoken into smartphones, and respond to Internet search queries. And this same technology can drive so many other tasks of the future. It can help machines understand natural language—the natural way that we humans talk and write. It can create a new breed of robot, giving automatons the power to not only perform tasks but learn them on the fly. And some believe it can eventually give machines something close to common sense—the ability to truly think like a human.

But along with such promise comes deep anxiety. Musk and Altman worry that if people can build AI that can do great things, then they can build AI that can do awful things, too. They’re not alone in their fear of robot overlords, but perhaps counterintuitively, Musk and Altman also think that the best way to battle malicious AI is not to restrict access to artificial intelligence but expand it. That’s part of what has attracted a team of young, hyper-intelligent idealists to their new project.

OpenAI began one evening last summer in a private room at Silicon Valley’s Rosewood Hotel—an upscale, urban, ranch-style hotel that sits, literally, at the center of the venture capital world along Sand Hill Road in Menlo Park, California. Elon Musk was having dinner with Ilya Sutskever, who was then working on the Google Brain, the company’s sweeping effort to build deep neural networks—artificially intelligent systems that can learn to perform tasks by analyzing massive amounts of digital data, including everything from recognizing photos to writing email messages to, well, carrying on a conversation. Sutskever was one of the top thinkers on the project. But even bigger ideas were in play.

Sam Altman, whose Y Combinator helped bootstrap companies like Airbnb, Dropbox, and Coinbase, had brokered the meeting, bringing together several AI researchers and a young but experienced company builder named Greg Brockman, previously the chief technology officer at high-profile Silicon Valley digital payments startup called Stripe, another Y Combinator company. It was an eclectic group. But they all shared a goal: to create a new kind of AI lab, one that would operate outside the control not only of Google, but of anyone else. “The best thing that I could imagine doing,” Brockman says, “was moving humanity closer to building real AI in a safe way.”

Musk is one of the loudest voices warning that we humans could one day lose control of systems powerful enough to learn on their own.
Musk was there because he’s an old friend of Altman’s—and because AI is crucial to the future of his various businesses and, well, the future as a whole. Tesla needs AI for its inevitable self-driving cars. SpaceX, Musk’s other company, will need it to put people in space and keep them alive once they’re there. But Musk is also one of the loudest voices warning that we humans could one day lose control of systems powerful enough to learn on their own.

The trouble was: so many of the people most qualified to solve all those problems were already working for Google (and Facebook and Microsoft and Baidu and Twitter). And no one at the dinner was quite sure that these thinkers could be lured to a new startup, even if Musk and Altman were behind it. But one key player was at least open to the idea of jumping ship. “I felt there were risks involved,” Sutskever says. “But I also felt it would be a very interesting thing to try.”

Breaking the Cycle
Emboldened by the conversation with Musk, Altman, and others at the Rosewood, Brockman soon resolved to build the lab they all envisioned. Taking on the project full-time, he approached Yoshua Bengio, a computer scientist at the University of Montreal and one of founding fathers of the deep learning movement. The field’s other two pioneers—Geoff Hinton and Yann LeCun—are now at Google and Facebook, respectively, but Bengio is committed to life in the world of academia, largely outside the aims of industry. He drew up a list of the best researchers in the field, and over the next several weeks, Brockman reached out to as many on the list as he could, along with several others.

Many of these researchers liked the idea, but they were also wary of making the leap. In an effort to break the cycle, Brockman picked the ten researchers he wanted the most and invited them to spend a Saturday getting wined, dined, and cajoled at a winery in Napa Valley. For Brockman, even the drive into Napa served as a catalyst for the project. “An underrated way to bring people together are these times where there is no way to speed up getting to where you’re going,” he says. “You have to get there, and you have to talk.” And once they reached the wine country, that vibe remained. “It was one of those days where you could tell the chemistry was there,” Brockman says. Or as Sutskever puts it: “the wine was secondary to the talk.”

By the end of the day, Brockman asked all ten researchers to join the lab, and he gave them three weeks to think about it. By the deadline, nine of them were in. And they stayed in, despite those big offers from the giants of Silicon Valley. “They did make it very compelling for me to stay, so it wasn’t an easy decision,” Sutskever says of Google, his former employer. “But in the end, I decided to go with OpenAI, partly of because of the very strong group of people and, to a very large extent, because of its mission.”

The deep learning movement began with academics. It’s only recently that companies like Google and Facebook and Microsoft have pushed into the field, as advances in raw computing power have made deep neural networks a reality, not just a theoretical possibility. People like Hinton and LeCun left academia for Google and Facebook because of the enormous resources inside these companies. But they remain intent on collaborating with other thinkers. Indeed, as LeCun explains, deep learning research requires this free flow of ideas. “When you do research in secret,” he says, “you fall behind.”

As a result, big companies now share a lot of their AI research. That’s a real change, especially for Google, which has long kept the tech at the heart of its online empire secret. Recently, Google open sourced the software engine that drives its neural networks. But it still retains the inside track in the race to the future. Brockman, Altman, and Musk aim to push the notion of openness further still, saying they don’t want one or two large corporations controlling the future of artificial intelligence.

The Limits of Openness
All of which sounds great. But for all of OpenAI’s idealism, the researchers may find themselves facing some of the same compromises they had to make at their old jobs. Openness has its limits. And the long-term vision for AI isn’t the only interest in play. OpenAI is not a charity. Musk’s companies that could benefit greatly the startup’s work, and so could many of the companies backed by Altman’s Y Combinator. “There are certainly some competing objectives,” LeCun says. “It’s a non-profit, but then there is a very close link with Y Combinator. And people are paid as if they are working in the industry.”

According to Brockman, the lab doesn’t pay the same astronomical salaries that AI researchers are now getting at places like Google and Facebook. But he says the lab does want to “pay them well,” and it’s offering to compensate researchers with stock options, first in Y Combinator and perhaps later in SpaceX (which, unlike Tesla, is still a private company).

Brockman insists that OpenAI won’t give special treatment to its sister companies.
Nonetheless, Brockman insists that OpenAI won’t give special treatment to its sister companies. OpenAI is a research outfit, he says, not a consulting firm. But when pressed, he acknowledges that OpenAI’s idealistic vision has its limits. The company may not open source everything it produces, though it will aim to share most of its research eventually, either through research papers or Internet services. “Doing all your research in the open is not necessarily the best way to go. You want to nurture an idea, see where it goes, and then publish it,” Brockman says. “We will produce lot of open source code. But we will also have a lot of stuff that we are not quite ready to release.”

Both Sutskever and Brockman also add that OpenAI could go so far as to patent some of its work. “We won’t patent anything in the near term,” Brockman says. “But we’re open to changing tactics in the long term, if we find it’s the best thing for the world.” For instance, he says, OpenAI could engage in pre-emptive patenting, a tactic that seeks to prevent others from securing patents.

But to some, patents suggest a profit motive—or at least a weaker commitment to open source than OpenAI’s founders have espoused. “That’s what the patent system is about,” says Oren Etzioni, head of the Allen Institute for Artificial Intelligence. “This makes me wonder where they’re really going.”

The Super-Intelligence Problem
When Musk and Altman unveiled OpenAI, they also painted the project as a way to neutralize the threat of a malicious artificial super-intelligence. Of course, that super-intelligence could arise out of the tech OpenAI creates, but they insist that any threat would be mitigated because the technology would be usable by everyone. “We think its far more likely that many, many AIs will work to stop the occasional bad actors,” Altman says.

But not everyone in the field buys this. Nick Bostrom, the Oxford philosopher who, like Musk, has warned against the dangers of AI, points out that if you share research without restriction, bad actors could grab it before anyone has ensured that it’s safe. “If you have a button that could do bad things to the world,” Bostrom says, “you don’t want to give it to everyone.” If, on the other hand, OpenAI decides to hold back research to keep it from the bad guys, Bostrom wonders how it’s different from a Google or a Facebook.

If you share research without restriction, bad actors could grab it before anyone has ensured that it’s safe.
He does say that the not-for-profit status of OpenAI could change things—though not necessarily. The real power of the project, he says, is that it can indeed provide a check for the likes of Google and Facebook. “It can reduce the probability that super-intelligence would be monopolized,” he says. “It can remove one possible reason why some entity or group would have radically better AI than everyone else.”

But as the philosopher explains in a new paper, the primary effect of an outfit like OpenAI—an outfit intent on freely sharing its work—is that it accelerates the progress of artificial intelligence, at least in the short term. And it may speed progress in the long term as well, provided that it, for altruistic reasons, “opts for a higher level of openness than would be commercially optimal.”

“It might still be plausible that a philanthropically motivated R&D funder would speed progress more by pursuing open science,” he says.

Like Xerox PARC
In early January, Brockman’s nine AI researchers met up at his apartment in San Francisco’s Mission District. The project was so new that they didn’t even have white boards. (Can you imagine?) They bought a few that day and got down to work.

Brockman says OpenAI will begin by exploring reinforcement learning, a way for machines to learn tasks by repeating them over and over again and tracking which methods produce the best results. But the other primary goal is what’s called “unsupervised learning”—creating machines that can truly learn on their own, without a human hand to guide them. Today, deep learning is driven by carefully labeled data. If you want to teach a neural network to recognize cat photos, you must feed it a certain number of examples—and these examples must be labeled as cat photos. The learning is supervised by human labelers. But like many others researchers, OpenAI aims to create neural nets that can learn without carefully labeled data.

“If you have really good unsupervised learning, machines would be able to learn from all this knowledge on the Internet—just like humans learn by looking around—or reading books,” Brockman says.

He envisions OpenAI as the modern incarnation of Xerox PARC, the tech research lab that thrived in the 1970s. Just as PARC’s largely open and unfettered research gave rise to everything from the graphical user interface to the laser printer to object-oriented programing, Brockman and crew seek to delve even deeper into what we once considered science fiction. PARC was owned by, yes, Xerox, but it fed so many other companies, most notably Apple, because people like Steve Jobs were privy to its research. At OpenAI, Brockman wants to make everyone privy to its research.

This month, hoping to push this dynamic as far as it will go, Brockman and company snagged several other notable researchers, including Ian Goodfellow, another former senior researcher on the Google Brain team. “The thing that was really special about PARC is that they got a bunch of smart people together and let them go where they want,” Brockman says. “You want a shared vision, without central control.”

Giving up control is the essence of the open source ideal. If enough people apply themselves to a collective goal, the end result will trounce anything you concoct in secret. But if AI becomes as powerful as promised, the equation changes. We’ll have to ensure that new AIs adhere to the same egalitarian ideals that led to their creation in the first place. Musk, Altman, and Brockman are placing their faith in the wisdom of the crowd. But if they’re right, one day that crowd won’t be entirely human.

Tesla’s Model 3 Reservations Rise to 400,000

Eager Tesla customers continue to reserve the Model 3, despite the ballooning wait times.

Reservations for Tesla’s recently unveiled, mainstream electric car, the Model 3, continue to climb.

According to a speech from Tesla’s Vice President of Business Development, Diarmuid O’Connell, this week, reservations for the car are now approaching 400,000.

That’s an eye-popping figure for an electric car that’s only been available to reserve for about two weeks and won’t start shipping until the end of 2017. Many of those reservations were made before the car was even unveiled on March 31. Now Tesla needs to figure out how to make and deliver those cars on time and budget.

Many of the later orders of the Model 3 likely won’t be fulfilled until 2019, or even into 2020 (four years from now).That’s assuming Tesla will remain on track to start shipping the car at the end of next year, too.

A driveable prototype of Tesla's Model 3.
A driveable prototype of Tesla’s Model 3. Katie Fehrenbacher/Fortune

To get that volume of cars made and delivered on time, Tesla TSLA -2.56% could have to change the way it makes its cars considerably. Tesla has only delivered a little over 100,000 cars in total over its lifetime. During O’Connell’s speech at a conference in Amsterdam, he said the rapid reservation rate gives Tesla the “visibility” and “confidence” into what it would take to build the car.

Tesla CEO Elon Musk tweeted the day after revealing the Model 3 for the first time (when the car had close to 200,000 reservations) that Tesla is “definitely going to need to rethink production planning.” Tesla will likely have to expand production at both its Fremont, Calif. factory more quickly than expected, and it will soon have to start producing a greater number of batteries at its massive battery factory still under construction outside of Reno, Nevada.

O’Connell said that Tesla is “looking at ways to amplify early production.” The company is investigating possible ways to scale up initial investments and ramp up more quickly than previously anticipated. Tesla plans to use lessons learned from the difficulties it had with manufacturing the Model X, Tesla’s SUV electric car.

That car was delayed for years, and it faced slow production at the end of 2015 and into early 2016. The company has admitted hubris for the Model X in trying to fit in too many complex features into the first version of the car.

According to estimates from Cairn Energy Research Advisors, Tesla could ship a little over 400,000 of its Model 3 cars by the end of 2020. But before 2020, production of Model 3 could likely be constrained. For example, Tesla could ship 12,200 Model 3 cars in its first production year in 2017, and another 64,660 Model 3 cars in 2018.

During O’Connell’s speech, he boasted reservations for the Model 3 “have exceeded all of our expectations as far as the rate at which we received reservations,” further describing the Model 3 as “the car for which the company was really set up to build.”

O’Connell suggested that the great demand for the Model 3 delivers a message to the rest of the auto industry that there is “incredible demand” for great electric vehicles out there. In addition, the massive demand refutes the point that other automakers have made that no one wants electric cars, he argued.

To make a reservation for a Model 3 car, Tesla customers only have to put down a fully refundable deposit of $1,000. So it’s unclear how many of the reservation holders would turn into Model 3 buyers.

If all 400,000 reservation holders bought $35,000 Model 3 cars, Tesla would have booked $14 billion in orders. That’s an unprecedented sum—not just in the auto industry, but for a launch of a product in general.

Source: http://fortune.com/2016/04/15/tesla-model-3-reservations-400000/