A day after SpaceX's Falcon Heavy blasted into space, the private spacefaring company has posted some spectacular images of the launch as well as updating the status of the Tesla Roadster that acted as ballast payload. In a Twitter post, CEO and Founder Elon Musk confirmed that the second stage of what is now the world's most powerful rocket fired successfully, sending the car and its dummy driver into an orbit that will take it well into the Asteroid Belt. 

On February 6 at 3:45 pm EST, the first Falcon Heavy rocket lifted off from Launch Complex 39A (LC-39A) at NASA's Kennedy Space Center, Florida on a demonstration mission. Due to the tremendous complexity of the new booster and the 50/50 chance that the mission might end in failure, no active satellite was carried in the nose cone of the vehicle. Instead, Mr Musk's own Tesla Roadster was used as ballast in a gesture that he characterized at a post-launch press conference as a ridiculous bit of fun.
At the conference, Musk confirmed that the core rocket that made up the center of the three-part first stage of the Heavy did not have enough propellant aboard to make a soft landing, and hit the water at about 300 mph (480 km/h). He went on to say that, if the onboard cameras survived, "There could be some pretty fun footage." 

Musk went on to say that neither of the recovered side boosters that successfully landed at Cape Canaveral, Florida will be reused because they were not built for commercial missions. They are Block Four versions of the Falcon 9, and SpaceX will be using only Block Five versions from now on.
Also, during yesterday's conference, Musk expressed concern about the second stage of the rocket, which spent five hours parked in the Van Allen Belt before it restarted its Merlin engine for the third time to escape Earth's gravity. The Van Allen Belts are regions of intense radiation created by cosmic ray particles captured by the Earth's magnetic field. They act as a protective shield for the planet, but they can be a hazard for spacecraft that do not make a quick transition of the belts.
In this case, the second stage was not adversely affected, and fired as scheduled. It expended its propellant entirely, causing it to not only reach escape velocity, but also to reach an orbit that will circle the Sun at a distance between 91 million mi (150 million km) and 240 million mi (390 million km) – well into the Asteroid Belt between Mars and Jupiter, and almost as far out as the orbit of the dwarf planet Ceres. 

Commercial space travel took a quantum leap forward today as SpaceX's Falcon Heavy rocket made its maiden flight. At 3:45 pm EST, the world's most powerful operational booster lifted off from Launch Complex 39A (LC-39A) at NASA's Kennedy Space Center, Florida on a demonstration mission intended to gather information about the reusable launch system before it goes into service. Today's launch came after a two-hour and 15 minute delay due to high-altitude shear winds that were outside the rocket's safety parameters. During liftoff, the 27 Merlin 1D engines of the Falcon Heavy's three Falcon 9 core boosters generated 5,548,500 lb of thrust as they gulped down supercooled liquid oxygen and kerosene. Two minutes and 29 seconds into the flight, the two outboard boosters shut down and then separated from the central core four seconds later. The latter shutdown at the three minute and four second mark, then the second stage separated three seconds after.The second stage then fired for five minutes and 16 seconds, followed by a second burn for 30 seconds. Once the orbital maneuver was executed, it marked a first for SpaceX as its unique payload separated and was set on an elliptical interplanetary trajectory around the Sun that will send it as far as the orbit of Mars.

Because the Falcon Heavy was only given a 50/50 chance of success, SpaceX decided not to risk a functioning spacecraft. In such a situation, it's usual to swap out the satellite with ballast made of blocks of steel or concrete, but in a whimsical move, the company used SpaceX CEO and Lead Designer Elon Musk's midnight-cherry Tesla Roadster. This marks either the first time that a motor car was sent into space or the fourth, if you count the three Lunar Rovers carried on the last three Apollo lunar landing missions in the early 1970s.

In the driver's seat of the Roadster is a mannequin, named "Starman" after the 1972 David Bowie song, who is dressed in one of the spacesuits that will be worn by the passengers and crew on SpaceX's manned dragon missions. In addition, Bowie's hit "Space Oddity" was playing on the car's soundtrack, though in the vacuum of space, there won't be anyone to hear it. However, in a nod to The Hitchhiker's Guide to the Galaxy, the words "Don't Panic" were displayed on the dashboard screen in large, friendly letters.No details were given about the Roadster itself, but it's likely that it was modified to make it suitable for space travel by removing the lithium ion battery banks or replacing them with dummies. Though the Roadster is headed out toward Mars, its trajectory is such that it has no chance of colliding with the Red Planet.

In keeping with SpaceX's policy of developing a fully reusable launch system, the two side boosters, which had already flown before, executed a reentry and landing maneuver to bring them down on SpaceX's Landing Zones 1 and 2 at Cape Canaveral Air Force Station, Florida to the cheers of the crowd outside of Mission Control. Meanwhile, the center core attempted a sea landing on the company's "Of Course I Still Love You" droneship in the Atlantic Ocean.Update: Musk has confirmed in a press conference following the launch the landing of the middle core was unsuccessful, as two of the three engines failed to fire and the booster slammed into the ocean at 300 mph (482 km/h).

Source: SpaceX, NewAtlas

For much of the 20th century, the game of chess served as a benchmark for artificial intelligence researchers. John McCarthy, who coined the term "artificial intelligence" back in the early 1950s, once referred to chess as the "Drosophila of AI", in a reference to how significant early research on the fruit fly was to the field of genetics.In the late 1990s, IBM's Deep Blue embarked upon a series of chess games against Garry Kasparov, the world champion. In 1997, Deep Blue ultimately beat Kasparov, marking the first time a machine had defeated a world champion in match play. By the early to mid 2000s, the technology had improved to the point where the machines were consistently beating chess grandmasters in almost every game-playing context.
Naturally, AI developers moved onto other, more complex, games to test their increasingly sophisticated algorithms. Over the past 12 months AI crossed a series of new thresholds, finally beating human players in a variety of different games, from the ancient game of Go to the dynamic and interactive card game, Texas Hold-Em Poker.
Going, going, gone
In the late 1990s, after a machine finally and definitively beat a chess grandmaster, an astrophysicist from Princeton remarked, ''It may be a hundred years before a computer beats humans at Go – maybe even longer."
Taking up the challenge, computer scientists turned their attention to this ancient Chinese strategy game, which is both deceptively simple to play, yet extraordinarily complex to master.
It has only been in the last decade that machine learning developments have created truly competitive AI Go players. In 2014, Google commenced working on a deep learning neural network called AlphaGo. After a couple of years of semi-successful Go challenges the development team tried something different.
At the very end of 2016 a mysterious online Go player named "Master" appeared on the popular Asian game server Tygem. Over the next few days this mysterious player dominated in games with many world champions on the system. By the 4th of January the jig was up and it was officially confirmed that the "Master" was in fact the latest iteration of DeepMind's AI AlphaGo.
In May of 2017, AlphaGo "Master" took on Ke Jie, the world's highest ranked Go player. Over three games the machine comprehensively dominated the world champion, but perhaps most startling was the revelation in October that Google had already produced a more sophisticated iteration of AlphaGo that was even better than the "Master"
AlphaGo Zero, revealed in a Nature journal article, was a revolutionary algorithm designed to learn entirely from self-play. The system simply plays against itself, over and over, and learns how to master whatever game it has been programmed to work with. After 21 days of learning, AlphaGo Zero had reached the level of "Master" and by day 40 it had exceeded the skill level of every prior version.
By December 2017, an even newer version of the system was revealed by DeepMind. Called AlphaZero, this new AI could master a variety of games in just hours. After merely eight hours of self training the system could not only beat prior versions of AlphaGo Zero, but it also could become a chess grandmaster and a shogi champion.
Mastering the bluff
While Go offers a game rich in complexity, mastering Poker has proved an entirely different proposition for AI. To win big at Poker one needs to master the art of deception. Bluffing and recognizing when you are being bluffed are key dynamic abilities that need to be mastered to win big in this infamous card game.
After over a decade of attempts 2017 saw two separate studies revealing AI finally beating big-time poker professionals. DeepStack, from the University of Alberta, unveiled an AI system that could comprehensively dominate human poker players using an artificially intelligent form of "intuition".

A team from Carnegie Mellon University put on a more public spectacle in January of 2017 when its Libratus AI system spent 20 days playing 120,000 hands of No Limit Texas Hold'em against four poker professionals. While the pros spent every evening of the challenge discussing amongst themselves what weaknesses they could exploit in the AI, the machine also improved itself every day, patching holes in its gameplay and improving its strategy.
The human brains were no match for the machine and after nearly a month of full-time gameplay Libratus was up by US$1.7 million, with every one of the four professionals having lost thousands of fictional dollars. One of the losing professional players said to Wired halfway through the bruising competition, "I felt like I was playing against someone who was cheating, like it could see my cards. I'm not accusing it of cheating. It was just that good."
Elon Musk's AI experiment
In 2015, Elon Musk and a small group of investors founded a group called OpenAI. The venture was designed to explore the development of artificial intelligence systems with a particular interest in reinforcement learning - systems where a machine teaches itself how to improve at a particular task.
In August 2017, the OpenAI team set its sights on conquering Dota 2, the central game in a giant eSports tournament called The International. Dota 2 is an extremely popular, and complicated, multiplayer online battle arena game and is serious business in the world of competitive gaming.

After just two weeks of learning, the OpenAI bot was unleashed on the tournament and subsequently beat several of the world's top players. The AI system was only trained on a more simplistic one-to-one version of the game, but the OpenAI team is now working on teaching the system how to play "team" games of five-on-five.
Divide and conquer - The Pac-Man challenge
A couple of years ago, Google DeepMind set its AI loose on 49 Atari 2600 games. Provided with the same inputs as any human player the AI figured out how to play, and win, many of the games. Some games proved harder than others for the AI to master though, and the classic, but notoriously difficult, 1980s video game Ms Pac-Man was especially challenging.
In 2017, a deep learning startup called Maluuba was acquired by Google and incorporated into the DeepMind group. Maluuba's novel machine learning method was called "Hybrid Reward Architecture" (HRA). Applying this method to Ms Pac-Man the system created more than 150 individual agents, each tasked with specific goals – such as finding a specific pellet, or avoiding ghosts.

The HRA method generates a top agent, something akin to a senior manager. This top agent evaluates all the suggestions from the lower agents before making the final decision on an individual move. The approach has been euphemistically dubbed "divide-and-conquer," where a complex task is broken up into smaller parts.
After applying the method to Ms Pac-Man the AI quickly figured out how to achieve a top score of 999,990, which no human or AI has managed to achieve previously.
AI will soon make the games
What is the next logical step if AI can beat us in almost every game out there?
A researcher from the University of Falmouth recently revealed a machine learning algorithm that he claims can dream up its own games for us to play from scratch. Called Angelina, this AI system is improving itself from day to day but can currently make games using datasets it scrapes from sources as varied as Wikimedia Commons to online newspapers and social media.
So what does all this mean?
Perhaps the most significant, and potentially frightening, development of 2017 has been the dramatic progress of reinforcement learning systems. These programs can efficiently teach themselves how to master new skills. The most recent AlphaZero iteration, for example, can achieve superhuman skills at some games after just a few days of self-directed learning.
A large survey of more than 350 AI researchers suggested it won't be too long before AI can beat us as pretty much everything. The survey predicted that within 10 years AI will drive better than us, by 2049 it will be able to write a best-selling novel, and by 2053 it will perform better than humans at surgery. In fact, the survey concluded that there is a 50 percent chance that by 2060 AI will essentially be able to do everything we can do, but better.
2017 has undoubtedly been a milestone year in AI beating humans at increasingly complex games, and while this may seem like a trivial achievement the implications are huge. Many of these AI-development companies are quickly turning their sights on real-world challenges. Google DeepMind has already moved the AlphaGo Zero system away from the game and onto a comprehensive study of protein folding in the hopes of revealing a treatment for diseases such as Alzheimer's and Parkinson's.
"Ultimately we want to harness algorithmic breakthroughs like this to help solve all sorts of pressing real world problems," says Demis Hassabis, co-founder and CEO of DeepMind. "If similar techniques can be applied to other structured problems, such as protein folding, reducing energy consumption or searching for revolutionary new materials, the resulting breakthroughs have the potential to drive forward human understanding and positively impact all of our lives."

Soure: NewAtlas

In a seemingly impromptu Twitter Q&A yesterday, Tesla CEO Elon Musk revealed some further details about the company's expansion into larger forms of terrestrial electric vehicles. Promising that a long-awaited pickup truck was indeed on the way, he also hinted at a few upcoming software upgrades to make life easier for existing Tesla owners.
Tesla first signaled its intentions to move into the trucking space when Musk published Master Plan, Part Deux in July of 2016. Musk wrote of the Tesla Semi's potential to "deliver a substantial reduction in the cost of cargo transport," and also listed a new kind of pickup truck as part of its strategy to hit most segments of the consumer market.
Musk then took to Twitter in April last year to state that the pickup unveil was 18 to 24 months away. His latest public statements on the matter don't do much to clarify this timeline, but do suggest he hasn't lost any enthusiasm for the project.
"I promise that we will make a pickup truck right after Model Y," he tweeted. "Have had the core design/engineering elements in my mind for almost 5 years. Am dying to build it."
The Tesla Model Y is Tesla's upcoming all-electric compact SUV, expected to be built on a new vehicle platform and launched in 2019 at the earliest. Musk expanded a little on what the pickup might look like, in response to a Twitter user asking how it would compare to America's best-selling pickup, the Ford F-150."Similar total size," he tweeted. "Maybe slightly bigger to account for a really game-changing (I think) feature I'd like to add."
What that game-changing feature might be is anyone's guess, but Musk did tease a few new bells and whistles for those with a Tesla already parked in the driveway. According to the tech tycoon, new features to be added via software updates in the near future include app-controlled window, seat and steering wheel heaters, an upgraded browser, "vastly better" maps and navigation and windscreen wipers that adjust their speed automatically depending on how heavy the rainfall is.
Musk also appeared to okay a number of other requests from owners, but without specifying a timeline. These include a feature where internal cameras automatically start recording if the windows are smashed in the event of a break-in, the navigation system sending an alert to other family members about what time to expect you home and some kind of Disco Mode, where ambient light changes in time to funky beats.
"Was gonna say we'll do all but the last, but that last one sounds like good, cheesy fun :)," he said.

The Boring Company was dreamt up by serial entrepreneur Elon Musk as a way of solving traffic woes, and the notoriously choked up highways of LA are among its very first targets. With a proof-of-concept tube already underway, The Boring Company has now expanded on its plans for the City of Angels, offering an idea of how a network of high-speed tunnels could take shape.
Musk first started talking about tunnels as a solution to traffic problems last December, and in the space of a year he's made some impressive strides. Earlier this year, he held "promising talks" with the mayor of LA about his tunnel network ideas, and then in October revealed that the company had begun boring its first tube beneath the city. 

These tunnels would essentially function as fast freeways, where vehicles and passenger pods latch onto electric skates and get shuttled along at up to 150 mph (241 km/h). There would on and off ramps every mile or so, each with a dedicated side tunnel to avoid logjams. These tunnels could also form part of a Hyperloop system over larger distances between cities.

While all of that remains a ways off, the company is making progress on its proof-of-concept tunnel. Photos shared by Musk in October showed a fully concreted tunnel complete with tracks and cables that he said at the time measured 500 ft (152.4 m) and within three of four months would stretch to around 2 mi (3.2 km). 

 

In a new map shared by The Boring Company, this tunnel can be seen in red, running alongside the Interstate 405 highway between West LA and Inglewood. The company has filed an excavation permit application for this, which it calls Phase 1 proof-of-concept tunnel and says it would eventually measure 6.5 mi (10.4 km) in length.

This tube will be used for safety testing and demonstrations, but not for public transport until it had been ticked off by the local authorities and The Boring Company itself. The routes shown in blue are potential expansion options, though these are simply conceptual at this point. The company says it would develop these tunnels in collaboration with the authorities and will be seeking input from the general public on station locations and other system improvements.

Source: The Boring Company, NewAtlas