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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.

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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."

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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

The technology has helped to improve congestion by 73 per cent in scenario-testing

Ford and Microsoft are using quantum-inspired computing technology to reduce traffic congestion. Through a joint research pilot, scientists have used the technology to simulate thousands of vehicles and their impact on congestion in the US city of Seattle.

Ford said it is still early in the project but encouraging progress has been made and it is further expanding its partnership with the tech giant.

The companies teamed up in 2018 to develop new quantum approaches running on classical computers already available to help reduce Seattles traffic congestion.

Writing on a blog post on Medium.com, Dr Ken Washington, chief technology officer, Ford Motor Company, explained that during rush hour, numerous drivers request the shortest possible routes at the same time, but current navigation services handle these requests "in a vacuum": They do not take into consideration the number of similar incoming requests, including areas where other drivers are all planning to share the same route segments, when delivering results.

What is required is a more balanced routing system that could manage all the various route requests from drivers and provide optimised route suggestions, reducing the number of vehicles on a particular road.

These and more are all variables well need to test for to ensure balanced routing can truly deliver tangible improvements for cities.

Traditional computers dont have the computational power to do this but, as Washington explained, in a quantum computer, information is processed by a quantum bit (or a qubit) and can simultaneously exist "in two different states" before it gets measured.

This ultimately enables a quantum computer to process information with a faster speed, he wrote. Attempts to simulate some specific features of a quantum computer on non-quantum hardware have led to quantum-inspired technology powerful algorithms that mimic certain quantum behaviours and run on specialised conventional hardware. That enables organisations to start realising some benefits before fully scaled quantum hardware becomes available."

Working with Microsoft, Ford tested several different possibilities, including a scenario involving as many as 5,000 vehicles each with 10 different route choices available to them simultaneously requesting routes across Metro Seattle. It reports that in 20 seconds, balanced routing suggestions were delivered to the vehicles that resulted in a 73 per cent improvement in total congestion when compared to selfish routing.

The average commute time, meanwhile, was also cut by eight per cent representing an annual reduction of more than 55,000 hours across this simulated fleet.

Based on these results, Ford is expanding its partnership with Microsoft to further improve the algorithm and understand its effectiveness in more real-world scenarios.

For example, will this method still deliver similar results when some streets are known to be closed, if route options arent equal for all drivers, or if some drivers decide to not follow suggested routes? wrote Washington. These and more are all variables well need to test for to ensure balanced routing can truly deliver tangible improvements for cities.

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Could quantum computing be the key to cracking congestion? - SmartCitiesWorld

Quantum is having a moment. In October, Google claimed to have achieved a quantum supremacy milestone. In November, Microsoft announced Azure Quantum, a cloud service that lets you tap into quantum hardware providers Honeywell, IonQ, or QCI. Last week, AWS announced Amazon Braket, a cloud service that lets you tap into quantum hardware providers D-Wave, IonQ, and Rigetti. At the Q2B 2019 quantum computing conference this week, I got a pulse for how the nascent industry is feeling.

Binary digits (bits) are the basic units of information in classical computing, while quantum bits (qubits) make up quantum computing. Bits are always in a state of 0 or 1, while qubits can be in a state of 0, 1, or a superposition of the two. Quantum computing leverages qubits to perform computations that would be much more difficult for a classical computer. Potential applications are so vast and wide (from basic optimization problems to machine learning to all sorts of modeling) that interested industries span finance, chemistry, aerospace, cryptography, and more. But its still so early that the industry is nowhere close to reaching consensus on what the transistor for qubits should look like.

Currently, your cloud quantum computing options are limited to single hardware providers, such as those from D-Wave and IBM. Amazon and Microsoft want to change that.

Enterprises and researchers interested in testing and experimenting with quantum are excited because they will be able to use different quantum processors via the same service, at least in theory. Theyre uneasy, however, because the quantum processors are so fundamentally different that its not clear how easy it will be to switch between them. D-Wave uses quantum annealing, Honeywell and IonQ use ion trap devices, and Rigetti and QCI use superconducting chips. Even the technologies that are the same have completely different architectures.

Entrepreneurs and enthusiasts are hopeful that Amazon and Microsoft will make it easier to interface with the various quantum hardware technologies. Theyre uneasy, however, because Amazon and Microsoft have not shared pricing and technical details. Plus, some of the quantum providers offer their own cloud services, so it will be difficult to suss out when it makes more sense to work with them directly.

The hardware providers themselves are excited because they get exposure to massive customer bases. Amazon and Microsoft are the worlds biggest and second biggest cloud providers, respectively. Theyre uneasy, however, because the tech giants are really just middlemen, which of course poses its own problems of costs and reliance.

At least right now, it looks like this will be the new normal. Even hardware providers that havent announced they are partnering with Amazon and/or Microsoft, like Xanadu, are in talks to do just that.

Overall at the event, excitement trumped uneasiness. If youre participating in a domain as nascent as quantum, you must be optimistic. The news this quarter all happened very quickly, but there is still a long road ahead. After all, these cloud services have only been announced. They still have to become available, gain exposure, pick up traction, become practical, prove useful, and so on.

The devil is in the details. How much are these cloud services for quantum going to cost? Amazon and Microsoft havent said. When exactly will they be available in preview or in beta? Amazon and Microsoft havent said. How will switching between different quantum processors work in practice? Amazon and Microsoft havent said.

One thing is clear. Everyone at the event was talking about the impact of the two biggest cloud providers offering quantum hardware from different companies. The clear winners? Amazon and Microsoft.

ProBeat is a column in which Emil rants about whatever crosses him that week.

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ProBeat: AWS and Azure are generating uneasy excitement in quantum computing - VentureBeat