As r
eported by TechRepublic:
Google has developed a machine learning system capable of mastering Go - an ancient Chinese game whose complexity stumped computers for decades.
Go has 200 moves per turn compared to 20 per turn in Chess. Over the course of a game of Go there are so many possible moves that searching through each of them to identify the best play is too costly from a computational point of view.
Now a system developed by Google DeepMind has beaten European Go champion and elite player Fan Hui. Rather than being programmed in how to play the game, the AlphaGo system learned how to do so using two
deep neural networks and an
advanced tree search.
Go is typically played on a 19-by-19-square board and sees players attempt to capture empty areas and surround an opponent's pieces. To teach the system how to play the game, moves from 30 million Go games played by human experts were fed into AlphaGo's neural networks. The system then used reinforcement learning to work out the type of moves that were most likely to succeed, based on these past matches. This approach allows AlphaGo to restrict the number of possible moves it needs to search through during a game - making the process more manageable.
DeepMind CEO Demis Hassibis
described Go as "probably the most complex game that humans play. There's more configurations of the board then there are atoms in the universe."
It was that complexity that meant the game had been so difficult for machines to master said DeepMind's David Silver. "In the game of Go we need this amazingly complex intuitive machinery, which people previously thought was only available in the human brain, to even have the right idea of who's ahead and what the right move is."
Google has suggested that the approach used by AlphaGo to learn how to master Go could be extended to solving more weighty problems, such as climate change modelling, as well as to improving Google's interactions with users of its services.
For instance, DeepMind's Silver suggests the technology could help personalize healthcare by using a similar reinforcement learning technique to understand which treatments would "lead to the best outcomes for individual patients based on their particular track record and history".
More significantly, Hassabis sees the achievement as progress towards an even grander goal, of building an AI with the same general capabilities and understanding as humans.
"Most games are fun and were designed because they're microcosms of some aspect of life. They might be slightly constrained or simplified in some way but that makes them the perfect stepping stone towards building general artificial intelligence."
Why Google is pursuing narrow, not general, AI
Simon Stringer, director of the Oxford Centre for Theoretical Neuroscience and Artificial Intelligence, said that AlphaGo and other deep learning systems are good at specific tasks - be that spotting objects or animals in photos or mastering a game. But these systems work very differently from the human brain and shouldn't be viewed as representing progress towards developing a general, human-like intelligence - which he believes requires an approach guided by biology.
"If you want to solve consciousness you're not going to solve it using the sorts of algorithms they're using," he said.
"We all want to get to the moon. They've managed to get somewhere up this stepladder, ahead of us, but we're only going to get there by building a rocket in the long term.
"They will certainly develop useful algorithms with various applications but there will be a whole range of applications that we're really interested in that they will not succeed at by going down that route."
In the case of DeepMind, Stringer says the reinforcement learning approach used to teach systems to play classic arcade games and Go has limitations compared to how animals and human acquire knowledge about the world.
Whereas these reinforcement learning algorithms can learn to map which actions lead to the best outcomes they are "model-free", meaning the system "knows nothing about its world".
That approach is very different to how a rat's brain enables it to navigate a maze, he said.
"It's been shown over a half a century ago that what rats do is learn about the structure of their environment, they learn about the spatial structure and the causal relations in their world and then, when they want to get from A to B, they juggle that information to create a novel sequence of steps to get to that reward."
When you teach a system using model-free reinforcement learning, Stringer says it's "behaviorally very limiting".
"As the environment changes, for example one route is blocked off, the system doesn't know anything about its world so it can't say 'This path is blocked, I'm going to take the next shortest one'. It can't adapt but rats can."
"When we look at a cat, we're not just aware there's a cat in the image, we see all of the millions of visual features that make up that cat and how they're related to each other. In other words our visual experience is much richer than one of these deep learning architectures, which simply tells you whether there's a particular kind of feature in an image."
In particular, such systems lack the human ability to bind features together - he said - to comprehensively understand how features in an image are related to one another. Deep learning neural networks also generally don't model biological systems that appear to play a key role in how humans assign meaning to the world. These models typically exclude, for example, feedback in the brain's visual cortex and the precise timings in the electrical pulses between neurons, he said, adding that the centre in Oxford had developed concrete theories about the importance of these features in the visual cortex.
"We bought all of those elements together. At the very least it gives us a deep insight into what is so special about human vision that hasn't been captured in artificial vision systems yet."
This biologically-inspired approach is very different to that taken by DeepMind but Stringer believes it is necessary to have a chance of one day cracking general artificial intelligence.
"If you want to solve consciousness you're not going to solve it using the sorts of algorithms they're using."
The downside is that Stringer believes the ultimate payoff for his research will be a long time coming, a factor he thinks has driven DeepMind's decision to focus on narrow AI that could be applicable in the near-future.
"I have to admit, I'm always a bit surprised, given the resources that DeepMind have, why they don't devote more resources to actually trying to recreate the dynamics of brain function and I think it's because when you're trying to raise funding you need to produce jam today, you need these algorithms to work quickly otherwise that tap gets turned off.
Google plans to use Movidius's flagship MA2450 chip, touted as the only commercial solution on the market today that can perform complex neural network computations in "ultra-compact" form factors.
Stringer says: "My aim is to produce the first prototypical conscious systems, something very simple, somewhere between a mouse and a rat, within the next 20 - 30 years."
The DeepMind software that beat Go champion Hui, in a match that took place last October, was running on Google Cloud Platform and reportedly distributed across about 170 GPUs (graphics processing units) and 1,200 CPUs (central processing units).
Google has also been experimenting with
Cloud Vision Tech, through a mobile accessible API.
The next major challenge for Google's AlphaGo will come in March, when it will play the world's reigning Go champion
Lee Sedol.
DeepMind's Silver is confident AlphaGo has what it takes to beat all comers, at least in the long run.
"A human can perhaps play 1,000 games a year, AlphaGo can play through millions of games every single day. It's at least conceivable that as a result AlphaGo could, given enough processing, given enough training, given enough search power, reach a level that's beyond any human."
Go has 200 moves per turn compared to 20 per turn in Chess. Over the course of a game of Go there are so many possible moves that searching through each of them to identify the best play is too costly from a computational point of view.
It was that complexity that meant the game had been so difficult for machines to master said DeepMind's David Silver. "In the game of Go we need this amazingly complex intuitive machinery, which people previously thought was only available in the human brain, to even have the right idea of who's ahead and what the right move is."
Similar AI initiatives are underway at tech giants across the world, with Facebook recently revealing its deep learning system's ability to recognize people and things in images and to predict real-world outcomes, such as when a tower of blocks will topple.
In the case of DeepMind, Stringer says the reinforcement learning approach used to teach systems to play classic arcade games and Go has limitations compared to how animals and human acquire knowledge about the world.
With that in mind, Google recently announced that it will use Movidius's processors to power its advanced neural computation engine on mobile devices and in turn help the chip maker with it's neural network technology.
Google has also been experimenting with Cloud Vision Tech, through a mobile accessible API.
The automatic monitoring system of a hydrogen maser atomic clock triggered an alarm which reported a deviation of 13.7 microseconds. While this is tiny, it is a sign of a problem somewhere, and does not exclude the possibility of larger timekeeping problems happening. 
Consider:
NASA tried reusability with the space shuttle. But the vehicle had tremendous turnaround costs after each flight. The shuttle had more than 20,000 tiles as part of its heat shield, each individually numbered, each of which had to be checked. As a NASA program, the shuttle relied on multiple large aerospace contractors working on the program. In the end, the reusable vehicle which aimed to slash the cost of access to space to $25 per pound ended up closer to $25,000 per pound.
NASA says it needs this huge rocket to explore Mars, and that only by delivering exceptionally large payloads to space can it stage human missions to Mars. And that may be right. But along with this large rocket, which may cost as much as $2 billion or $3 billion per launch, NASA will need budgets it has not seen since the days of Apollo to get close to Mars. Unfortunately there is no indication that Congress or a new president will be willing to add billions of dollars a year to NASA’s budget. For example, Donald Trump has said he is more interested in potholes than space.
The two dot-com billionaires are working against decades of spaceflight inertia, in both NASA and its mandated civil servant workforce, but even more so in the large, influential aerospace contractors accustomed to large, cost-plus contracts. NASA does amazing things, very hard things like flying past Pluto or building a technical marvel like the International Space Station, but what it has not accomplished during its half century of existence is making space cheap, or fast.
While all the recent attention has been on SpaceX’s Falcon 9 rocket and its landing technology, engineers have also been working hard on developing the latest version of its Dragon capsule.
Cars are getting smarter with the addition of autonomous technologies, and this is how Volvo hopes to reach its goal of zero deaths in its cars. This does not, of course, preclude someone from driving recklessly and getting themselves killed. However, conventional driving should be made much safer with the inclusion of a number of technologies. It starts with making the interior of the car safer with improved airbags and restraints. Then things get more futuristic.
Volvo already has various smart features in its cars, but by combining them all, it becomes much harder to end up in a serious accident. Adaptive cruise control for example, is already available on many cars. It allows you to set a maximum speed, but uses radar to maintain a safe distance from the car in front of you. It can even apply the brakes if need be. This can be taken a step further with full collision avoidance. When a crash is likely, the driver will be warned. If action isn’t taken, the car can begin braking to avoid, or at least minimize the impact.
Automakers like Ford and Tesla are moving quickly toward fully autonomous vehicles. Then there’s Google’s self-driving car program. Volvo too is in the early stages of driverless tech, and handing control over to a computer when it’s clear something is wrong could be a step in that direction. Proving that vehicles can prevent deaths with automated technologies could go a long way toward convincing the public and regulators that self-driving cars are the best option. Volvo thinks these self-driving cars will be the safest of all.
The drone "estimates" its pose by utilizing a 2-D laser scanner, an altimeter, and an Inertia Measurement Unit sensor to navigate autonomously. With the localization result and using a thermal-imaging camera to recognize objects or people inside a building, the FAROS can also detect and find the fire-ignition point by employing dedicated image-processing technology.
