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More than 180 House Democrats joined a nearly united Republican caucus Wednesday night to pass a sweeping $738 billion military spending bill that gives President Donald Trump his long-sought "Space Force," free rein to wage endless wars, and a green light to continue fueling the humanitarian catastrophe in Yemen.

"Every member of Congress who voted to give the most corrupt, unhinged, and unstable president in history $738 billion to fight endless wars...must never tell us that we cannot afford Medicare for All or a Green New Deal." Warren Gunnels, Sanders senior adviser

Just 48 members of the House, including 41 Democrats, voted against the 2020 National Defense Authorization Act (NDAA), which increases the Pentagon budget by $22 billion. The final vote was 377-48.

"This NDAA is atrocious, and it's very depressing that only 48 members of congress voted against it," tweeted anti-war group CodePink.

In a floor speech ahead of Wednesday's vote, Rep. Ro Khanna (D-Calif.), the most vocal opponent of the NDAA in the House, said "there are many things you can call the bill, but it's Orwellian to call it progressive." Khanna was standing across the aisle from Rep. Adam Smith (D-Wash.), who hailed the measure as "the most progressive defense bill we have passed in decades."

"Let's speak in facts," said Khanna. "This defense budget is $120 billion more than what Obama left us with. That could fund free public college for every American. It could fund access to high-speed, affordable internet for every American. But it's worse. The bipartisan amendment to stop the war in Yemen: stripped by the White House. The bipartisan amendment to stop the war in Iran: stripped by the White House."

My friend @RepRoKhanna is right: it is Orwellian for Congress to hand over billions of dollars worth of weapons and bombs to a president waging a horrific, unconstitutional war in Yemenand call that progressive. pic.twitter.com/6SUmUUhv3q

Bernie Sanders (@SenSanders) December 11, 2019

According to the New York Times, Smithchairman of the House Armed Services Committeenegotiated several provisions of the NDAA directly with Jared Kushner, Trump's son-in-law and senior adviser.

"It was Mr. Kushner who helped broker a deal to create the Space Force, a chief priority of the president's, in exchange for the paid parental leave [for federal employees]," the Times reported Wednesday. "It was also Mr. Kushner who intervened on measures targeting Saudi Arabia that would have prohibited arms sales or military assistance to the Saudi-led intervention in Yemen. He said they were nonstarters for the White House."

Sen. Bernie Sanders' (I-Vt.) foreign policy adviser Matt Duss expressed outrage that Democrats allowed Kushnerwho has close ties to Saudi Crown Prince Mohammed bin Salmanto kill an amendment that would have helped end U.S. complicity in the world's worst humanitarian crisis.

Congrats to Democratic leadership on getting outnegotiated by JARED KUSHNER. On a provision that was already passed by bipartisan majorities in Congress. To end US support for a war that has created to the world's worst humanitarian catastrophe.

Great goddam job. https://t.co/JmYd57lgds

Matt Duss (@mattduss) December 11, 2019

Rep. Barbara Lee (D-Calif.), who voted against the NDAA, noted in a statement that the final version also stripped out her House-passed amendment that would have repealed the 2002 Iraq Authorization for Use of Military Force (AUMF).

"With the release of the Afghanistan Papers, it is especially imperative that we take a hard look at our military spending and authorizations," said Lee, the only member of Congress to vote against the war in Afghanistan in 2001. "I can tell you: it is an appalling, but not shocking read for those of us who have been working to stop endless war. It's past time to end the longest war in United States history, withdraw our troops, and bring our servicemembers home."

The 2020 NDAA now heads to the Republican-controlled Senate, where it is expected to pass. In a tweet ahead of the House vote on Wednesday, Trump praised the bill and said he would sign it into law "immediately."

"New rule: Every member of Congress who voted to give the most corrupt, unhinged, and unstable president in history $738 billion to fight endless wars, fund a bogus space force, and put our troops at risk must never tell us that we cannot afford Medicare for All or a Green New Deal," Warren Gunnels, Sanders' senior adviser, tweeted Wednesday night. "Ever."

See the original post:
'Atrocious': 188 Democrats Join GOP to Hand Trump $738 Billion Military Budget That Includes 'Space Force' - Common Dreams

Whether were learning to cook an omelet or drive a car, the path to mastering new skills often begins by watching others. But can artificial intelligence learn the same way? A new challenge teaching AI agents to play Minecraft suggests its much trickier for computers.

Announced earlier this year, the MineRL competition asked teams of researchers to create AI bots that could successfully mine a diamond in Minecraft. This isnt an impossible task, but it does require a mastery of the games basics. Players need to know how to cut down trees, craft pickaxes, and explore underground caves while dodging monsters and lava. These are the sorts of skills that most adults could pick up after a few hours of experimentation or learn much faster by watching tutorials on YouTube.

But of the 660 entries in the MineRL competition, none were able to complete the challenge, according to results that will be announced at the AI conference NeurIPS and that were first reported by BBC News. Although bots were able to learn intermediary steps, like constructing a furnace to make durable pickaxes, none successfully found a diamond.

The task we posed is very hard, Katja Hofmann, a principal researcher at Microsoft Research, which helped organize the challenge, told BBC News. While no submitted agent has fully solved the task, they have made a lot of progress and learned to make many of the tools needed along the way.

This may be a surprise, especially when you think that AI has managed to best humans at games like chess, Go, and Dota 2. But it reflects important limitations of the technology as well as restrictions put in place by MineRLs judges to really challenge the teams.

The bots in MineRL had to learn using a combination of methods known as imitation learning and reinforcement learning. In imitation learning, agents are shown data of the task ahead of them, and they try to imitate it. In reinforcement learning, theyre simply dumped into a virtual world and left to work things out for themselves using trial and error.

Often, AI is only able to take on big challenges by combining these two methods. The famous AlphaGo system, for example, first learned to play Go by being fed data of old games. It then honed its skills and surpassed all humans by playing itself over and over.

The MineRL bots took a similar approach, but the resources available to them were comparatively limited. While AI agents like AlphaGo are created with huge datasets, powerful computer hardware, and the equivalent of decades of training time, the MineRL bots had to make do with just 1,000 hours of recorded gameplay to learn from, a single Nvidia graphics processor to train with, and just four days to get up to speed.

Its the difference between the resources available to an MLB team coaches, nutritionists, the finest equipment money can buy and what a Little League squad has to make do with.

It may seem unfair to hamstring the MineRL bots in this way, but these constraints reflect the challenges of integrating AI into the real world. While bots like AlphaGo certainly push the boundary of what AI can achieve, very few companies and research labs can match the resources of Google-owned DeepMind.

The competitions lead organizer, Carnegie Mellon University PhD student William Guss, told BBC News that the challenge was meant to show that not every AI problem should be solved by throwing computing power at it. This mindset, said Guss, works directly against democratizing access to these reinforcement learning systems, and leaves the ability to train agents in complex environments to corporations with swathes of compute.

So while AI may be struggling in Minecraft now, when it cracks this challenge, itll hopefully deliver benefits to a wider audience. Just dont think about those poor Minecraft YouTubers who might be out of a job.

Read more from the original source:

AI has bested chess and Go, but it struggles to find a diamond in Minecraft - The Verge

12/12/2019 Just imagine you had a chess computer the auto-sensor kind. Would someone who had no knowledge of the game be able to work it out, just by moving pieces. Or imagine you are a very powerful computer. By looking at millions of images of chess games would you be able to figure out the rules and learn to play the game proficiently? The answer is yes because that has just been done by Google's Deep Mind team. For chess and 76 other games. It is interesting, and slightly disturbing. | Graphic: DeepMind

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In 1980 the first chess computer with an auto response board, the Chafitz ARB Sargon 2.5, was released. It was programmed by Dan and Kathe Spracklen and had a sensory board and magnet pieces. The magnets embedded in the pieces were all the same kind, so that the board could only detect whether there was a piece on the square or not. It would signal its moves with LEDs located on the corner of each square.

Chafitz ARB Sargon 2.5 | Photo:My Chess Computers

Some years after the release of this computer I visited the Spracklens in their home in San Diego, and one evening had an interesting discussion, especially with Kathy. What would happen, we wondered, if we set up a Sargon 2.5 in a jungle village where nobody knew chess. If we left the people alone with the permanently switched-on board and pieces, would they be able to figure out the game? If they lifted a piece, the LED on that square would light up; if they put it on another square that LED would light up briefly. If the move was legal, there would be a reassuring beep; the square of a piece of the opposite colour would light up, and if they picked up that piece another LED would light up. If the original move wasnt legal, the board would make an unpleasant sound.

Our question was: could they figure out, by trial and error, how chess was played? Kathy and I discussed it at length, over the Sargon board, and in the end came to the conclusion that it was impossible they could never figure out the game without human instructions. Chess is far too complex.

Now, three decades later, I have to modify our conclusion somewhat: maybe humans indeed cannot learn chess by pure trial and error, but computers can...

You remember how AlphaGo and AlphaZero were created, by Google's DeepMind division. The programs Leela and Fat Fritz were generated using the same principle: tell an AI program the rules of the game, how the pieces move, and then let it play millions of games against itself. The program draws its own conclusions about the game and starts to play master-level chess. In fact, it can be argued that these programs are the strongest entities to have ever played chess human or computer.

Now DeepMind has come up with a fairly atrocious (but scientifically fascinating) idea: instead of telling the AI software the rules of the game, just let it play, using trial and error. Let it teach itself the rules of the game, and in the process learn to play it professionally. DeepMind combined a tree-based search (where a tree is a data structure used for locating information from within a set) with a learning model. They called the project MuZero. The program must predict the quantities most relevant to game planning not just for chess, but for 57 different Atari games. The result: MuZero, we are told, matches the performance of AlphaZero in Go, chess, and shogi.

And this is how MuZero works (description from VenturBeat):

Fundamentally MuZero receives observations images of a Go board or Atari screen and transforms them into a hidden state. This hidden state is updated iteratively by a process that receives the previous state and a hypothetical next action, and at every step the model predicts the policy (e.g., the move to play), value function (e.g., the predicted winner), and immediate reward (e.g., the points scored by playing a move)."

Evaluation of MuZero throughout training in chess, shogi, Go, and Atari the y-axis shows Elo rating| Image: DeepMind

As the DeepMind researchers explain, one form of reinforcement learning the technique in which rewards drive an AI agent toward goals involves models. This form models a given environment as an intermediate step, using a state transition model that predicts the next step and a reward model that anticipates the reward. If you are interested in this subject you can read thearticle on VenturBeat,or visit the Deep Mind site. There you can read this paper on the general reinforcement learning algorithm that masters chess, shogi and Go through self-play. Here's an abstract:

The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go.

That refers to the original AlphaGo development, which has now been extended to MuZero. Turns out it is possible not just to become highly proficient at a game by playing it a million times against yourself, but in fact it is possible to work out the rules of the game by trial and error.

I have just now learned about this development and need to think about the consequences discuss it with experts. My first somewhat flippant reaction to a member of the Deep Mind team: "What next? Show it a single chess piece and it figures out the whole game?"

Link:

MuZero figures out chess, rules and all - Chessbase News

A close-up view of the IBM Q quantum computer. The processor is in the silver-colored cylinder.

Quantum computers are getting a lot more real. No, you won't be playing Call of Duty on one anytime soon. But Google, Amazon, Microsoft, Rigetti Computing and IBM all made important advances in 2019 that could help bring computers governed by the weird laws of atomic-scale physics into your life in other ways.

Google's declaration of quantum supremacywas the most headline-grabbing moment in the field. The achievement -- more limited than the grand term might suggest -- demonstrated that quantum computers could someday tackle computing problems beyond the reach of conventional "classical" computers.

Proving quantum computing progress is crucial. We're still several breakthroughs away from realizing the full vision of quantum computing. Qubits, the tiny stores of data that quantum computers use, need to be improved. So do the finicky control systems used to program and read quantum computer results. Still, today's results help justify tomorrow's research funding to sustain the technology when the flashes of hype inevitably fizzle.

Now playing: Watch this: Quantum computing is the new super supercomputer

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Quantum computers will live in data centers, not on your desk, when they're commercialized. They'll still be able to improve many aspects of your life, though. Money in your retirement account might grow a little faster and your packages might be delivered a little sooner as quantum computers find new ways to optimize businesses. Your electric-car battery might be a little lighter and new drugs might help you live a little longer after quantum computers unlock new molecular-level designs. Traffic may be a little lighter from better simulations.

But Google's quantum supremacy step was just one of many needed to fulfill quantum computing's promise.

"We're going to get there in cycles. We're going to have a lot of dark ages in which nothing happens for a long time," said Forrester analyst Brian Hopkins. "One day that new thing will really change the world."

Among the developments in 2019:

Classical computers, which include everything from today's smartwatches to supercomputers that occupy entire buildings, store data as bits that represent either a 1 or a 0. Quantum computers use a different approach called qubits that can represent a combination of 1 and 0 through an idea called superposition.

Ford and Microsoft adapted a quantum computing traffic simulation to run on a classical computer. The result: a traffic routing algorithm that could cut Seattle traffic congestion by 73%.

The states of multiple qubits can be linked, letting quantum computers explore lots of possible solutions to a problem at once. With each new qubit added, a quantum computer can explore double the number of possible solutions, an exponential increase not possible with classical machines.

Quantum computers, however, are finicky. It's hard to get qubits to remain stable long enough to return useful results. The act of communicating with qubits can perturb them. Engineers hope to add error correction techniques so quantum computers can tackle a much broader range of problems.

Plenty of people are quantum computing skeptics. Even some fans of the technology acknowledge we're years away from high-powered quantum computers. But already, quantum computing is a real business. Samsung, Daimler, Honda, JP Morgan Chase and Barclays are all quantum computing customers. Spending on quantum computers should reach hundreds of millions of dollars in the 2020s, and tens of billions in the 2030s, according to forecasts by Deloitte, a consultancy. China, Europe, the United States and Japan have sunk billions of dollars into investment plans. Ford and Microsoft say traffic simulation technology for quantum computers, adapted to run on classical machines, already is showing utility.

Right now quantum computers are used mostly in research. But applications with mainstream results are likely coming. The power of quantum computers is expected to allow for the creation of new materials, chemical processes and medicines by giving insight into the physics of molecules. Quantum computers will also help for greater optimization of financial investments, delivery routes and flights by crunching the numbers in situations with a large number of possible courses of action.

They'll also be used for cracking today's encryption, an idea spy agencies love, even if you might be concerned about losing your privacy or some snoop getting your password. New cryptography adapted for a quantum computing future is already underway.

Another promising application is artificial intelligence, though that may be years in the future.

"Eventually we'll be able to reinvent machine learning," Forrester's Hopkinssaid. But it'll take years of steady work in quantum computing beyond the progress of 2019. "The transformative benefits are real and big, but they are still more sci-fi and theory than they are reality."

More:

Quantum computing leaps ahead in 2019 with new power and speed - CNET

When asked what invention will be as revolutionary in the 2020s as smartphones were in the 2010s, Bank of America strategist Haim Isreal said, without hesitation, quantum computing.

At the banks annual year ahead event last week in New York, Israel qualified his prediction, arguing in an interview with MarketWatch that the timing of the smartphones arrival on the scene in the mid-2000s, and its massive impact on the American business landscape in the 2010s, doesnt line up neatly with quantum-computing breakthroughs, which are only now being seen, just a few weeks before the start of the 2020s.

The iPhone already debuted in 2007, enabling its real impact to be felt in the 2010s, he said, while the first business applications for quantum computing won't be seen till toward the end of the coming decade.

But, Israel argued, when all is said and done, quantum computing could be an even more radical technology in terms of its impact on businesses than the smartphone has been. This is going to be a revolution, he said.

Quantum computing is a nascent technology based on quantum theory in physics which explains the behavior of particles at the subatomic level, and states that until observed these particles can exist in different places at the same time. While normal computers store information in ones and zeros, quantum computers are not limited by the binary nature of current data processing and so can provide exponentially more computing power.

Quantum things can be in multiple places at the same time, said Chris Monroe, a University of Maryland physicist and founder of IonQ told the Associated Press . The rules are very simple, theyre just confounding.

In October, Alphabet Inc. GOOG, -0.18% subsidiary Google claimed to have achieved a breakthrough by using a quantum computer to complete a calculation in 200 seconds on a 53-qubit quantum computing chip, a task it calculated would take the fastest current super-computer 10,000 years. Earlier this month, Amazon.com Inc. AMZN, +0.03% announced its intention to collaborate with experts to develop quantum computing technologies that can be used in conjunction with its cloud computing services. International Business Machines Corp. IBM, -0.82% and Microsoft Corp. MSFT, +0.84% are also developing quantum computing technology.

Israel argued these tools will revolutionize several industries, including health care, the internet of things and cyber security. He said that pharmaceutical companies are most likely to be the first commercial users of these devices, given the explosion of data created by health care research.

Pharma companies are right now subject to Moores law in reverse, he said. They are seeing the cost of drug development doubling every nine years, as the amount of data on the human body becomes ever more onerous to process. Data on genomics doubles every 50 days, he added, arguing that only quantum computers will be able to solve the pharmaceutical industrys big-data problem.

Quantum computing will also have a major impact on cybersecurity, an issue that effects nearly every major corporation today. Currently cyber security relies on cryptographic algorithms, but quantum computings ability to solve these equations in the fraction of the time a normal computer does will render current cyber security methods obsolete.

In the future, even robust cryptographic algorithms will be substantially weakened by quantum computing, while others will no longer be secure at all, according to Swaroop Sham, senior product marketing manager at Okta.

For investors, Israel said, it is key to realize that the first one or two companies to develop commercially applicable quantum-computing will be richly rewarded with access to untold amounts of data and that will only make their software services more valuable to potential customers in a virtuous circle.

What weve learned this decade is that whoever controls the data will win big time, he said.

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Quantum computing will be the smartphone of the 2020s, says Bank of America strategist - MarketWatch