Why Elon Musk’s SpaceX launch is utterly depressing

Elon Musk is right: silly and fun things are important. But some of them are an indefensible waste of resources


Falcon Heavy, world’s most powerful rocket, successfully launches – video

On Wednesday, two things happened. In Syria, 80 people were killed by government airstrikes. Meanwhile in Florida, Elon Musk fired a sports car into space. Guess which story has dominated mainstream news sites?

The much-anticipated launch of Musk’s Falcon Heavy rocket, the most powerful every launched by a private company, went off without a hitch. Musk successfully sent his cherry-red Tesla roadster hurtling toward Mars, launching what a CNN commentator called “a new space age”.

Musk expects the rocket and car to orbit the sun for hundreds of millions of years, though some experts have speculated that it will disintegrate within a year. The event attracted phenomenal publicity: at one point, 2.3 million viewers were watching the event’s livestream, making the rocket launch a masterstroke of advertising for Tesla.

Meanwhile, in Syria, where hundreds of thousands of refugees may be forced to return to unsafe homes amid “global anti-refugee backlash”, an anti-government activist said despondently that he is no longer sure why he bothers to videotape the effects of bombing, since nobody ever pays attention: “I don’t know what the point is.” The UN human rights coordinator for Syria pondered what level of violence it would take to make the world care, saying that they are “running out of words” with which to try to describe the crisis.

There is, perhaps, no better way to appreciate the tragedy of 21st-century global inequality than by watching a billionaire spend $90m launching a $100,000 car into the far reaches of the solar system.

Musk said he wants to participate in a space race because “races are exciting” and that while strapping his car to a rocket may be “silly and fun … silly and fun things are important.” Thus, anyone who mentions the colossal waste the project involves, or the various social uses to which these resources could be put, can be dismissed as a killjoy.

But one doesn’t have to hate fun to question the justification for pursuing a costly new space race at exactly this moment. If we examine the situation honestly, and get past our natural (and accurate) feeling that rockets are really cool, it becomes hard to defend a project like this.

A mission to Mars does indeed sound exciting, but it’s important to have our priorities straight. First, perhaps we could make it so that a child no longer dies of malaria every two minutes. Or we could try to address the level of poverty in Alabama that has become so extreme the UN investigator did not believe it could still occur in a first-world country. Perhaps once violence, poverty and disease are solved, then we can head for the stars.

Many might think that what Elon Musk chooses to do with his billions is Elon Musk’s business alone. If he wanted to spend all his money on medicine for children, that would be nice, but if he’d like to spend it making big explosions and sending his convertible on a million-mile space voyage, that’s his prerogative.

But Musk is only rich enough to afford these indulgent pet projects because we have allowed gross social inequalities to arise in the first place. If wealth were actually distributed fairly in this country, nobody would be in a position to fund his own private space program.

Yet even on the theory that there’s no moral problem with frittering away hundreds of millions of dollars, and inequality is fine, there’s another reason we are permitted to care about what Musk does. A great deal of his fortune is not actually his own: it’s ours.

Musk’s empire is fueled by billions of dollars in government subsidies. The Los Angeles Times revealed in 2015 that Musk’s companies benefit from “grants, tax breaks, factory construction, discounted loans and environmental credits”, plus the tax credits and rebates that are granted to consumers for buying his products.

The average household income of a Tesla purchaser is in the multiple hundreds of thousands, yet the federal government pays people $7,500 to buy them through tax credits, and many states offer their own cash handouts. Because we’re all giving Elon Musk money, what he chooses to do with that money is very much our business.

Elon Musk is right: silly and fun things are important. But some of them are an indefensible waste of resources. While there are still humanitarian crises such as that in Syria, nobody can justify vast spending on rocketry experiments. That point was made plain in 1970 by poet Gil Scott-Heron, in his record Whitey On The Moon, which criticized the US for spending millions to send men on a pointless moon adventure while the country’s inner cities languished:

“I can’t pay no doctor bills

But whitey’s on the moon

Ten years from now I’ll be payin’ still

While whitey’s on the moon.”

Whitey may not have gone back to the moon recently. But his sports car is now in space.


Never mind the Elon—the forecast isn’t that spooky for AI in business

Don’t fear the machines—AI tech isn’t nearly ready to think for itself.

 Space Imaging’s IKONOS satellite detected this jack-o-lantern corn maze in Bell County, Kentucky. Satellite images are being paired with other data to find a totally different sort of pattern—predicting crop yields and failures.

Despite Elon Musk’s warnings this summer, there’s not a whole lot of reason to lose any sleep worrying about Skynet and the Terminator. Artificial Intelligence (AI) is far from becoming a maleficent, all-knowing force. The only “Apocalypse” on the horizon right now is an over reliance by humans on machine learning and expert systems, as demonstrated by the deaths of Tesla owners who took their hands off the wheel.

Examples of what currently pass for “Artificial Intelligence”—technologies such as expert systems and machine learning—are excellent for creating software that can help in contexts that involve pattern recognition, automated decision-making, and human-to-machine conversations. Both types have been around for decades. And both are only as good as the source information they are based on. For that reason, it’s unlikely that AI will replace human beings’ judgment on important tasks requiring decisions more complex than “yes or no” any time soon.

Expert systems, also known as rule-based or knowledge-based systems, are when computers are programmed with explicit rules, written down by human experts. The computers can then run the same rules but much faster, 24×7, to come up with the same conclusions as the human experts. Imagine asking an oncologist how she diagnoses cancer and then programming medical software to follow those same steps. For a particular diagnosis, an oncologist can study which of those rules was activated to validate that the expert system is working correctly.

However, it takes a lot of time and specialized knowledge to create and maintain those rules, and extremely complex rule systems can be difficult to validate. Needless to say, expert systems can’t function beyond their rules.

One-trick pony

Machine learning allows computers to come to a decision—but without being explicitly programmed. Instead, they are shown hundreds or thousands of sample data sets and told how they should be categorized, such as “cancer | no cancer,” or “stage 1 | stage 2 | stage 3 cancer.”

Sophisticated algorithms “train” on those data sets and “learn” how to make correct diagnoses. Machine learning can train on data sets where even a human expert can’t verbalize how the decision was made. Thanks to the ever-increasing quantity and quality of data being collected by organizations of all types, machine learning in particular has advanced AI technologies into an ever-expanding set of applications that will transform industries—if used properly and wisely.

There are some inherent weaknesses to machine learning, however. For example, you can’t reverse-engineer the algorithm. You can’t ask it how a particular diagnosis was made. And you also can’t ask machine learning about something it didn’t train on.

For instance, a classic example of machine learning is to show it pictures of pets and have it indicate “cat | dog | both | neither.” Once you’ve done that, you can’t ask the resulting machine learning system to decide if an image contains a poodle or a cow—it can’t adapt to the new question without retraining or the addition of one more layer of machine learning.

Viewed as a type of automation, AI techniques can greatly add to business productivity. In some problem areas, AI is doing great, and that’s particularly true when the decision to be made is fairly straightforward and not heavily nuanced.

I’m beginning to see a pattern here

One of the most widely applied types of machine learning is pattern recognition, based on clustering and categorization of data. Amazon customers have already experienced how machine learning-based analytics can be used in sales: Amazon’s recommendation engine uses “clustering” based on customer purchases and other data to determine products someone might be interested in.

Those sorts of analytics have been used in brick-and-mortar stores for years—some groceries place “clustered” products on display near frequently purchased items. But machine learning can automate those sorts of tasks in something approaching real time.

Machine learning excels in all sorts of pattern recognition—in medical imaging, financial services (“is this a fraudulent credit-card transaction?”), and even IT management (“if the server workload is too high, try these things until the problem goes away”).

That sort of automation based on data is being used outside the retail world to drive other routine tasks. The startup Apstra, for example, has tools that use machine learning and real-time analytics to automatically fine tune and optimize data center performance, not only reducing the need for some IT administrative staff but also reducing the need to upgrade hardware.

Another startup, Respond Software, has expert systems that corporate Security Operations Centers (SOCs) can use to automatically diagnose and escalate security incidents. And Darktrace, another security vendor, uses machine learning to identify suspicious behavior on networks—the company’s Enterprise Immune System looks for activities that fall outside of previously observed behaviors, and it alerts SOC staffers to things that may be of interest. And a module called Antigena can automate response to detected problems, disrupting network connections that appear to be malicious.

Human intelligence

Machine learning has also been applied to analysis of more human communications. With a good bit of work by data scientists and developers up front, machine learning algorithms have been able to relatively reliably detect the “sentiment” of a piece of text—determining whether the contents are positive or negative. That has begun to be applied to “text mining” in social media and to image processing as well.

Microsoft’s Project Oxford created an application interface for checking the emotional expression of people in images and also created a text-processing API that detects sentiment. IBM’s Watson also performs this sort of analysis with its Tone Analyzer, which can rank the emotional weight of tweets, e-mails, and other texts.

These types of technologies are being integrated into customer service systems, which identify customer complaints about products or services and prompt a human to respond to them. IBM partnered with Genesys to build Watson into Genesys’ “Customer Experience Platform,” providing a way to respond to customer questions directly and connect people with complaints to employees armed with the best information to resolve them. The system has to learn from humans along the way but gradually improves in responses—though the effectiveness of the system has yet to be fully tested.

Even the ultimate people field—human resources—is benefitting from AI in terms of measuring worker productivity and efficiency, conducting performance reviews, and even deploying intelligent chatbots that can help employees schedule vacations or express concerns to management using plain language. AI startups are optimizing mundane HR tasks: Butterfly offers coaching and mentoring, Entelo helps recruiters scour social media to find employment candidates, and Textio helps with writing more effective job descriptions.

But AI doesn’t do well with uncertainty, and that includes biases in the training data or in the expert rules. Different doctors, after all, might honestly make different diagnoses or recommend different treatments. So, what’s the expert diagnosis system to do?

An often-discussed case of machine learning is screening college admission applications. The AI was trained on several years’ admissions files, such as school report cards, test scores, and even essays and was told whether the student had been admitted or rejected by human admission officers.

The goal was to mimic those admissions officers, and the system worked—but also mimicked their implicit flaws, such as biases toward certain racial groups, socio-economic classes, and even activities like team sports participation. The conclusion: technical success but epic fail otherwise.

Until there are breakthroughs in handling ambiguity or disagreements in rules and implicit or explicit biases in training data, AI will struggle.

Help wanted

To get better, machine learning systems need to be trained on better data. But in order to understand that data, in many cases, humans have to pre-process the information—applying the appropriate metadata and formatting, then directing machine learning algorithms at the right parts of data to get better results.

Many of the advances being made in machine learning and artificial intelligence applications today are happening because of work done by human experts across many fields to provide more and better data.

Cheap historical satellite imagery and improved weather data, for example, make it possible for machine learning engines to forecast crop failures in developing countries. Descartes Labs was able, using LANDSAT 8 satellite data, to build a 3.1 trillion pixel mosaic of the world’s arable land and track changes in plant growth. Combined with meteorological data, the company’s machine learning-based system was able to accurately predict corn and soybean yield in the US, county by county. With the increasingly large volume of low-cost satellite imagery and pervasive weather sensors, forecasting systems will continue to become more accurate—with the help of data scientists and other human experts.

Forecasting of other sorts may well change the shape of businesses. A recent paper by researchers at Nayang Technological University in Singapore demonstrated that machine learning forecasts using neural networks could more accurately forecast manufacturing demand, allowing companies to better plan their inventory than when using expert systems or other forecasting methodologies that rely just on time-series data, particularly in industries with “lumpy” demand—where demand is either high or low but seldom in between—because the systems can find patterns without being told how to model the data in advance.

These sorts of systems, as they grow more complex and apply more types of data, could provide businesses and organizations with the power to find patterns in even more vast datasets. But while we can use AI to help humans make decisions about things we already know how to do, we can’t send AI-based agents into the true unknown without human oversight to provide expert rules or create new training data from scratch.

While some AI systems, like IBM’s Watson or Amazon’s Alexa, can hoover in huge amounts of unstructured data from the Internet and use it for text-based searches and building up a knowledge base to help answer questions, that won’t help in creating new training databases for pattern recognition, at least not yet. The science-fiction trope of computers intelligently autonomously searching for its own data sources (and for some inexplicable reason, flashing black-and-white battlefield pictures on a screen) is beyond today’s AI—and beyond tomorrow’s as well. The decisions—and the questions—will continue to have to be made by humans.

SpaceX’s Secret “Zuma” Mission Is Successful

Fourth time’s a charm: SpaceX’s secret “Zuma” mission finally got underway Sunday night as Elon Musk’s aerospace company completed its first mission of 2018.


Zuma is a Northrop Grumman Corporation-made spacecraft, and it was sent into low-Earth orbit, but that’s all SpaceX or the defense contractor has released about the mission, calling Zuma “restricted payload.” Zuma is the third classified mission SpaceX has performed for the U.S. government. (The first was to launch a spy satellite in May and the second was to launch the X-37B spy plane in September.)

A little after 8 p.m. Sunday, precisely as many Americans might have been sitting down to watch the Golden Globes, this rocket was taking off in Florida:

Northrop Grumman has a long history making technology for the military and NASA. It has also made the B-2 stealth bomber, the Global Hawk surveillance drone, and the James Webb Space Telescope, which is undergoing testing ahead of its launch into space in 2019.

Zuma’s inside this payload fairing, seen here in the pre-launch webcast.

The Falcon 9 launched from the SLC-40 launchpad in Cape Canaveral, Florida, after being moved from its original launch site, Launchpad 39A, which is currently booked with the SpaceX Falcon Heavy, the rocket system that will test launch in late January. The first stage of the rocket separated from the second and headed back to Earth, while the second stage continued on to put the Zuma payload into orbit. The SpaceX webcast cut out video for this part of the mission.

A few minutes later the first stage of the Falcon 9 rocket booster landed safely at LZ-1 near Cape Canaveral, as can be seen in this video from a camera mounted on the rocket:

“And the Falcon has landed,” said Brian Mahlstedt, a SpaceX software engineer who was hosting the Zuma webcast.

The rocket landing was the 21st by SpaceX. The first was on December 21, 2015, also at LZ-1. In addition to LZ-1, SpaceX has landed first-stage boosters on the drone ships Of Course I Still Love You, in the Atlantic Ocean, and Just Read the Instructions, in the Pacific Ocean.

The Zuma mission was first scheduled to launch on November 15 and experienced subsequent delays that led to the launch on Sunday.

China is reportedly building a $2 billion AI park as it looks to become a world leader in the field

xi jinpingXi Jinping, the president of China.
  • China is reportedly building a new artificial intelligence (AI) park in west Beijing.
  • The park is being built by a state-owned developer called Zhongguancun Development Group.
  • Russian president Vladimir Putin believes that in the future, the country that leads in AI could dominate the world.

The Chinese government is building a $2 billion (£1.5 billion) artificial intelligence (AI) research park as it looks to become a world leader in the field by 2025, Reuters reports , citing local news agency Xinhua.

The AI research park – to be located in west Beijing – will reportedly be able to accommodate 400 companies and that are expected to generate 50 billion yuan (£5.6 billion) each year.

The park’s developer, state-owned Zhongguancun Development Group, is hoping to partner with foreign universities and build a “national-level” AI lab in the area, according to Reuters. It will reportedly aim to attract companies working on big data, biometric identification, deep learning, and cloud computing.

The AI race could cause tensions

Russian president Vladimir Putin believes that in the future, the country that leads in AI could dominate the world, while tech billionaire Elon Musk thinks AI will be the most likely cause of WWIII  (although his comments should be taken with a pinch of salt).

Eric Schmidt, the executive chairman of Google parent company Alphabet, warned in November that China is poised to overtake the US in the field of AI if the US government doesn’t act soon.

Speaking at the Artificial Intelligence and Global Security Summit, the former Google CEO said: “Trust me, these Chinese people are good.”

He added: “They are going to use this technology for both commercial as well as military objectives with all sorts of implications.”

China published its AI strategy in July and said that it wanted to be the world leader in AI by 2025.

“It’s pretty simple,” said Schmidt, who claims to have read the report. “By 2020 they will have caught up. By 2025 they will be better than us. And by 2030 they will dominate the industries of AI. Just stop for a sec. The [Chinese] government said that.”

While the US has Google, Facebook, Microsoft, IBM, OpenAI and others, China has its own enormous tech giants aggressively pursuing AI research. Examples include Alibaba, Baidu, and Tencent, to name but a few.

We Asked Experts Whether Elon Musk Can Really Send a Roadster to Space

The First Car in Space

On December 21, SpaceX and Tesla founder Elon Musk posted seven photos to Instagram of his red Tesla roadster being encased within a Falcon Heavy rocket, seemingly confirming rumors (that he had himself started via Twitter) that he plans to make the vehicle the payload for the rocket’s first test. The Falcon Heavy is slated to carry supplies into Mars orbit for future manned missions — but it has to get off the ground first.

After months of delays, the test mission is planned for sometime in January, and will utilize the Roadster as a dummy payload to be delivered into a “billion year elliptic Mars orbit.”

Musk has been very open about the fact that the first Falcon Heavy flight could very likely fail; this test flight will allow the rocket to demonstrate that it can carry real supplies into orbit, but without risking the loss of expensive equipment packed into the first-ever launch.

“There’s a lot of risk associated with Falcon Heavy, a real good chance that that vehicle does not make it to orbit,” he said at a discussion during the International Space Station Research and Development conference. “I want to make sure to set expectations accordingly. I hope it makes it far enough beyond the pad so that it does not cause pad damage. I would consider even that a win, to be honest.”

Whether the launch is successful or not, there are still lots of questions remaining about the specifics of the unusual payload: is it legal to launch a car in the Falcon Heavy? Is it safe? Does it pose the threat of creating space debris? And, since Musk has stated that he loves the idea of “a car drifting apparently endlessly through space and perhaps being discovered by an alien race millions of years in the future,” is a Tesla Roadster really the best legacy we can leave for aliens to discover?

We asked experts about the legal, technical, and extraterrestrial issues that arise from Musk’s planned Roadster launch — and what might happen if that launch fails.

Frans Von der Dunk, Othmer Professor of Space Law, University of Nebraska-Lincoln College of Law

Whether Musk’s plans are feasible and/or advisable is, of course, not a legal question as such — so I’ll limit myself to the legal sides indeed. The short answer is yes. There is law both on the international plane and on the U.S. plane dealing with that.

The 1967 Outer Space Treaty (OST) imputes state responsibility and state liability on relevant states (in this case the U.S.) for private launches into, and activities in, space, such as Musk’s; SpaceX being both a U.S. company and launching from U.S. territory or facilities. It also offers the U.S. the possibility to exercise jurisdiction and control over SpaceX vehicles, so as to effectively make them into vehicles carrying the U.S. flag just like U.S.-registered ships or aircraft. The U.S. is one of the parties to the OST, together with all other important spacefaring nations, including Russia and China.

Other than that, the OST only imposes some rather general limitations to the legitimate use of space, such as the orbiting of weapons of mass destruction or (to some extent only) the wanton harmful interference with other states’ legitimate space activities. But in principle, outer space is a kind of global commons, an area free for exploration and use by all states and their private entities as long as properly authorized and supervised.

The 1972 Liability Convention (LC) has further detailed the liability regime for damage caused by space objects — including anything sent into outer space by Musk — again imputing that liability to the U.S.

Following on from these international obligations resting upon the U.S. (as well as from its own varied interests in a relatively orderly conduct of mankind’s activities in outer space) the U.S. has enunciated a set of laws over the last decades to implement these obligations, notably of authorization and supervision through a complicated licensing system, imposing the obligation to ensure upon licensees.

SpaceX needs to have a license prior to being allowed to launch, whereby the relevant office within the Federal Aviation Administration (FAA) scrutinizes the safety, security and other relevant public interest aspects of the launch, including a payload review addressing, in this case, the Tesla Roadster. In the license will include an obligation to repay the U.S. government the first tier of any international liability claim that the U.S. government would be required to pay out following its OST and LC obligations, a first tier likely amounting to a seven or eight figure dollar amount, against which SpaceX has to then insure itself.

Prof. Joanne Irene Gabrynowicz, Emerita, Editor-in-Chief Emerita, Journal of Space Law

There is clear U.S. law regarding launches and reentries. Therefore, if the launch is from U.S. territory, SpaceX will have to get a U.S. launch license from the FAA. However, the FAA only has authority to license launches and reentries.

As for the payload itself, the FAA does not have authority to license activities of payloads on-orbit or on celestial bodies. It has issued one favorable payload review for a lunar activity. A favorable payload review determination means that a payload does not present any issues affecting public health and safety, the safety of property, U.S. national security or foreign policy interests, or international obligations of the United States. It is not an authorization to operate. Congress would have to pass a law that grants jurisdiction to a federal agency, or agencies, to authorize on-orbit operations.

The FAA has said that any future payload reviews would have to be done on a case-by-case basis. Therefore, the FAA would have to conduct a review to determine if a payload consisting of a Tesla Roadster presents any issues affecting public health and safety, the safety of property, U.S. national security or foreign policy interests, or international obligations of the United States. And if a favorable review were made, there would still have to be a law in place that allows a federal agency to authorize on-orbit activities. In short, the legal environment is currently ambiguous for non-traditional space missions.

Don Kessler, Orbital Debris and Meteoroid Consultant; Retired NASA Senior Scientist for Orbital Debris Research

The issue as to whether Mr. Musk creates a debris hazard depends more on where in Mars orbit he puts the Roadster, rather than what material is in the required payload. However, the international debris community has yet to define the “safe limits” of debris in various regions of Earth orbit, much less in orbit around Mars or in any other planetary orbit.

The international community still needs to address the need for an international long-term management plan, starting with all of Earth orbit and then expanding to a plan for other planets. So far, the only plan that exists is a set of unenforceable guidelines to minimize the accumulation of debris in all Earth orbits below geosynchronous orbit.

Most of space is large enough that any mass we introduce could not cause a significant debris hazard. That is not true of the space around planets. Obviously, the closer an orbit is to a planet, the less available space there is, making collisions between orbiting objects more probable, and any mass in lower orbits becomes more dangerous due to the increase in velocity necessary to stay in orbit.

At this point, the safe option for Musk is to leave the payload in an orbit around the sun rather than in an orbit around Mars. However, we have already introduced artificial objects into Mars orbits and so Musk would not be the first to contribute to a future debris hazard there. By ignoring these issues in Mars orbit, we are beginning a process that created debris issues in Earth orbit that we have not yet adequately explored. We should not make the same mistake again.

Seth Shostak, Chief Astronomer, Search for Extraterrestrial Intelligence (SETI) Institute

Compare this payload to the Voyager pioneer probe, which was created to send messages to aliens. There are two aspects of considering both: one, would aliens understand any of it, would they be appropriate messages? And two, would they ever find it?

Regarding that second thing, the chances that they’ll find the Voyager probe strike me as somewhat less than minuscule. To find this thing, it will have to get by chance maybe within one light year to someone else’s star system. It takes a hundred thousand years for the fastest rockets to go to Alpha Centauri. And that doesn’t matter because it will survive that journey, but in between – it’s very small. It’s the size of a compact car, and space is really big. I did a calculation a few weeks ago, and it would kind of be like locating a bacterium on the Pacific ocean without knowing it’s there! And that’s only a two-dimensional calculation; it’s actually much worse than that because it’s a 3D problem.

Now, if a payload is going into Mars orbit, that changes things. Theoretically, aliens might go into our solar system, look around Mars, [and] might eventually find it.

The first question, of: will they understand it, or be ticked off by the message, send battle wagons to earth to destroy the planet out of pique? [When it comes to the Voyager message], that’s a question about alien sociology more than anything. But I think that probably the most informative thing would be any craft they find. You could think of it as if we had found one of the ships from the great age of exploration from the 1500’s. There might be a Bible or two on board, but the interesting thing would be the ship itself. [We would know], so they steered with this big wooden paddle on the back, they had sails and ropes, and that would tell you something about the technical sophistication of that society. So aliens could probably learn more from the craft itself.

I think, if I’m going to construct a message for the aliens, I would just give them the internet. They could pick up a lot from that. There’s a lot of redundancy there. If you look up “cat,” you don’t just see symbols. Their computers could look through the whole thing, referenced many times, and between images and funny videos they could get an idea of what that meant. I wouldn’t go for a very carefully crafted, specific message: we’re humans and this is what we looked like. I’d just give them lots and lots of stuff.


IF YOU’VE BEEN dying to know what it’s like to ride in a hyperloop at hundreds of miles per hour, read this…

Last week, Virgin Hyperloop One, one of the leading companies in the budding industry dedicated to realizing Elon Musk’s vision of flinging people and stuff through tubes in a near-vacuum and at borderline supersonic speeds, set a new speed record: 240 miles per hour.

At its DevLoop track in the Nevada desert, north of Las Vegas, the company’s engineers loaded their 28-foot-long pod into a 1,600-foot-long concrete test tube. The newly developed airlock system maintained the nearly airless environment, which approximated the air pressure you get at 200,000 feet above sea level (the thinner the air, the less resistance to overcome). Thanks to magnetic levitation, the pod hovered above the test track, knocking off another key source of friction—the deadliest enemy of futuristic, high-speed travel.

And then, whoosh.

Within a few seconds, the pod had hit top speed, beating the existing (publicly known) hyperloop speed record, set by Elon Musk this summer at 220 mph. To cap off the good news, the company also announced that Richard Branson is coming aboard as chairman and that it just raised $50 million. (A badly needed cash infusion, according to Axios.) Shervin Pishevar, the company’s co-founder and co-executive chairman, has temporarily left amidst allegations of sexual misconduct.

Of course, a whole lot of work remains to be done before you get to climb into the tube along with the pod, and then there’s no guarantee hyperloop will work on an economic level. But those are concerns for the future—a future that just got a little bit closer.

Elon Musk Reveals What He Most Wants to Do Next at Tesla in Twitter Chat

Elon Musk spent the day after Christmas checking in with owners of Tesla cars, thanking them for their support and asking what they want his company to get to work on in 2018 and beyond. In the process, Musk dropped some major hints about what we could see next, including a specific kind of vehicle he most wants to make.


“Wanted again to send a note of deep gratitude to Tesla owners [worldwide] for taking a chance on a new company that all experts said would fail,” Musk tweeted Tuesday. “So much blood, sweat & tears from the Tesla team went into creating cars that you’d truly love. I hope you do. How can we improve further?”

Understandably, a lot of the responses focused on immediate, targeted responses to the existing Tesla vehicles, but one response focused on a future car the tweeter — and Musk himself, it turns out — would like to see: an electric pickup truck.

Musk has previously said the Model Y will be the next car the company focuses on developing after the Model 3, which itself still has the company in production hell as it seeks to ramp up production and achieve the scale necessary to fulfill orders. The reported plan for the Model Y is a more cost-efficient crossover utility vehicle with falcon-wing doors, drawing on elements of the Model 3 and the Model X.

Exactly when Tesla would be able to focus on a pickup would likely depend on when it can sort out logistics with the Model 3 and complete development of the Model Y, and there’s not yet a clear timeline for the latter. But consider 2018 to be the official first year of Tesla pickup rumors, even if we’re a ways way from seeing the reality.

Musk dropped one other hint when asked about the size of the truck, saying he wanted to include what he thinks is a “really game-changing feature.”

The rest of the requests were more incremental, with the most exciting being the prospect of intelligent windshield wipers that can respond to rain levels. Musk said this would be coming very soon.

He acknowledged the browser for the Model S and other cars is “terrible” and needs significant upgrades, which should start happening sometime in 2018.

He also promised major improvements for Tesla’s maps and navigation systems. Whether this would be part of the same upgrade that would see the improved browser is an open question, but it sounds like major updates could be coming by next spring.

Throw in some goodies with heated windows and general ability to control internal heat settings with a push of a button.

His other responses were a little less concrete, though at least the “Done” indicates Musk plans to address what sounds like what can be a pretty annoying issue with the bluetooth setup.

This proposed theft response sounds like the sort of clever, next-level automation that Musk and Tesla like to make their trademark. Whether it’s actually something to expect anytime soon is a little hard to parse from a simple “Ok,” but at least it’s on Musk’s radar.

He also said “sure” in response to this suggested feature for the app, which would inform others when the Tesla and its driver can be expected home.

And here’s a whole laundry list of features that Musk signed off on, but it ended up being the last one that really caught his eye.

Honestly, based on everything we know about Elon Musk and his love of whimsical bonus features, we can probably expect the first Tesla upgrade of 2018 to be Disco Mode. Actually, there’s still a few days left in 2017, no reason that can’t be Tesla’s final big splash.

Here’s the Capsule That Will Take Elon Musk’s Tesla Roadster to Mars

SpaceX’s long-awaited Falcon Heavy launch vehicle is perhaps the company’s most enigmatic invention. First there was the series of launch delays, then the question of whether or not Elon Musk was actually going to use the rocket to send a Tesla Roadster into Mars’s orbit. Now that all the pieces of the puzzle are coming together, it seems we’re getting a peek at the Falcon Heavy fairing itself.

On Tuesday, Reddit user St-Jed-of-Calumet posted an image of the Falcon Heavy’s payload fairing — also known as the nose cone — on the SpaceX subreddit. The fairing protects the payload, which in this case, will be a Tesla Roadster “playing Space Oddity, on a billion year elliptic Mars orbit,” according to Elon Musk’s Instagram.

“FH Fairing spotted at the Cape,” the redditor wrote, referring to the Kennedy Space Center at Cape Canaveral in Florida, which is where the Falcon Heavy will launch from.

Falcon Heavy fairing at Cape Canaveral, Flordia

Inverse found the photo’s original poster, which appears to be Emiliano C. Diaz de Leon, who tweeted out the image on Tuesday. It seems he snapped the pic while on a bus tour of NASA’s Kennedy Space Center.

In the lead up to the big launch in early 2018, Elon Musk has been dropping some visual hints about the Falcon Heavy. It started last week, when the SpaceX founder posted a pic of the rocket at Cape Canaveral.

Of course, the follow-up pic of the payload is even better:

The Falcon Heavy’s maiden launch is a big deal for SpaceX, especially since it was originally set to launch in 2013. Better late than never, though, as it’s an absolute beast of a rocket. Its first stage is composed of three Falcon 9 engine cores and 27 Merlin engines. When it launches, this alone will generate more than 5 million pounds of thrust, according to SpaceX.

Although SpaceX plans to phase out the Falcon 9s — and even the Falcon Heavy — in order to focus on its BFR, the Falcon Heavy’s maiden launch will be a major accomplishment that’s at least six years in the making. As always, ad astra, SpaceX.

Elon Musk calls transit expert an ‘idiot,’ and then the ‘idiot’ fires back

Musk got a little defensive about his tunnel vision.

We all have our buttons, and Elon Musk clearly got his pushed earlier this week after a Wired story revealed his serious distaste for public transportation.

A serious, and perhaps irrational, distaste.

“It’s a pain in the ass,” the Tesla TSLA, +0.09%  boss and Boring Co. visionary said at a recent event. “That’s why everyone doesn’t like it. And there’s like a bunch of random strangers, one of who might be a serial killer, OK, great. And so that’s why people like individualized transport, that goes where you want, when you want.”

Read: How Elon Musk and shorter commutes could transform lives.

Transit expert Jarrett Walker took to Twitter to explain why Musk’s vision of a personalized pod speeding through the city’s underbelly may make sense in the gilded fantasies of the 1%, but not in the real world.

Walker probably didn’t expect his little tweet to actually be seen — much less responded to — by Musk. But it was. And he did.

Musk, would later “apologize” and snarkily explain to his 16.7 million followers that he meant “sanctimonious idiot.” Claws out.

The internet immediately took sides. There was a contingent praising Musk for his relentless assault on the status quo, and then there were the likes of Jeff Novich, who made his feelings quite clear:

The debate raged over the weekend, but Musk didn’t linger too long. After all, he’s got cars to build, tunnels to dig and planets to conquer.

Walker, however, continued to milk the Twitter buzz, tweeting and retweeting about the whole interaction several days later. Here he is sharing his latest thoughts, and even invoking the great Yogi Berra:

Meanwhile, Musk’s Boring Co. could potentially start digging tunnels in Baltimore as early as next month, according to published reports.

Jack Ma believes that artificial intelligence can lead to the outbreak of the World War 3.

Chinese billionaire Jack Ma, president of Alibaba, joins the list of personalities including Bill Gates, Elon Musk and Stephen Hawking – who are alarming about the disaster that artificial intelligence can bring.

Jack Ma/Alibaba artificial intelligence
Jack Ma

“The first technological revolution led to the First World War, the second technological revolution led to the Second World War. This is the third technological revolution, “said Jack Ma in an interview with CNBC at Gateway 2017.

To avoid this, I believe that governments need to help citizens adapt to new requirements by investing more in education. People will not be replaced, however, by robots, because they have a characteristic that the machines will lack: wisdom. “I do not think the cars or the artificial intelligence will be able to replace wisdom,” President Alibaba said.

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