Daily Archives: May 11, 2017

TechCrunch

Cisco acquires conversational AI startup MindMeld for $125 million TechCrunch MindMeld, originally called Expect Labs, was launched on the stage of TechCrunch Disrupt SF 2012. At that time the startup wanted to build an iPad app that could listen in on your conversations and provide relevant contextual information. Since then ... Cisco taps into AI, collaboration with $125M MindMeld buyNetwork World Cisco To Acquire AI Startup MindMeld For $125 Million To Boost Spark UC ProductsCRN Cisco Adds AI in MindMeld BuyNo Jitter The Register -ZDNet -CNET all 16 news articles »

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Cisco acquires conversational AI startup MindMeld for $125 million - TechCrunch

SEATTLEFor the thirdyear in a row, Microsoft is heavily promoting machine-learning services at its Build developer conference. Over the three years, some of the language used around the services has changedthe "machine learning" term seems to have fallen out of favor, being replaced by the better-known "artificial intelligence," and Microsoft has added many more services. But the bigger change is that ubiquitous intelligence now seems a whole lot more feasible than it did three years ago.

Three years ago, the service selection was narrowa language service that identified important elements from natural language, speech-to-text and text-to-speech, an image-recognition service, a facial recognition service. But outside of certain toy applications, such as Microsoft's age-guessing website, the services felt more than a little abstract. They felt disconnected from real-world applications.

Last year, the services took shape a little more. The bot bandwagon was just getting started, with Microsoft offering a framework for developers to build their own chatbots, and the right plumbing components have been published to hook those bots up to things like Skype and Teams. The appeal of the bots seemed perhaps limited, but other components that were displayed, such as a training user interface to help refine the language-understanding service, looked more promising. They showed ways in which a developer who wasn't an expert in machine learning or artificial intelligence could not just build systems that used machine-learning components, but which tailored those components to tackle the specific problem area the developer was interested in.

This year, the machine-learning story is improving once again. More services have been added, to make the platform able to do more things. Some of these are similar to the old services; for example, there's an image recognition service, "Custom Vision." The difference between this and the old vision service is that the new one is trainable. The old service has a corpus of objects that it understands, and if it sees them in a picture, it'll tell you. But if that corpus doesn't match the needs of your application, there's no way to add to it. The new service lets you upload small amounts of training dataabout 20 representations of each object, typicallyto generate a new image recognition model. The model generation itself, however, is entirely handled by the service; developers don't need to understand how it works.

Microsoft also has what it calls "Cognitive Services Labs," where developers can create more experimental AI-like services. The first of these is a gesture-recognizing service.

As well as working to build more trainable services, Microsoft is also working to train its bots to recognize certain standard processes, such as specifying a date or taking payment information.

These various machine-learning components are starting to become versatile enough and useful enough that they can solve problems that couldn't be solved before. Last year, Rolls-Royce, for example, developed a system that takes buzzwords"Internet of Things" and "machine learning"and did something useful with them. Rolls-Royce makes jet engines used in commercial airliners, and its latest jet engines are Internet of Things jet engines: they collect tons of telemetry data about operating conditions and upload them to Azure. The telemetry data is then combined with plane-level information such as altitude and flight plan.

Rolls-Royce has used machine learning to build a model that takes all this data and estimates when engine components will fail. This, in turn, allows preventative maintenance to be performed; the system can make estimates of which components are near the end of their lifetime (even if that lifetime has been prematurely shortened, as would be the case for an engine used on a plane only used for short flights). The system then advises that maintenance be performed to swap out the parts before they actually fail. This is even tied into inventory management, so the system can suggest making a replacement a little sooner than otherwise necessary, if it knows that the plane is flying somewhere that doesn't have the right parts available.

Hand-in-hand with these intelligent services, Microsoft has promoted its bot framework.Many people have misgivings about the industry-wide focus on bots, finding it hard to envisage a world in which we routinely type or talk to computer programs. However, Microsoft says that the bots have been instrumental in letting people learn how to use the cognitive services, and the company has seen substantial growth in developer interest for bots, especially in business-to-consumer roles. Using text chat on the Web to talk to a low-level sales rep or tech support person is a pretty common activity, for example, and some of this workload is a good match for bots with a suitable understanding of the problem domain.

Culture appears to play a significant role. We all remember Microsoft's neo-Nazi chatbot, Tay, but what's often forgotten is that Redmond had a different chatbot, XiaoIce, that spoke Chinese to Chinese users. That chatbot didn't have any of the problems that Tay did, and the Chinese market uses XiaoIce in a very different way; as well as using the bot's interactive or conversational features, Microsoft has found that people will just talk to it, unwinding from the day's stresses or using it as a sounding board of sorts.

Some of these differences are obvious when explained; for example, we were told that adoption of speech-to-text was much higher in China than in other countries because keyboard entry of Chinese text is much more awkward. Others were a little more surprising. Microsoft has found that even when the input modality is the same, audience demographics change the kind of language that's used with bots, and the things people ask the bots to do. While Facebook Messenger and Kik are both text chat, the older audience on Messenger uses bot services differently than the younger Kik crowd.

Even bot-averse users might find that they're more amenable to the concept in, for example, Teams or Slack. The conceptual shift from typing to your colleagues to typing to a bot feels much smaller.

But the cognitive services don't live or die on the success of bots anyway. We're already seeing hints of more subtle interfaces, such as Cortana reading your e-mails and figuring out if you havehave committed to any particular actions within themshe'll remind you to call people if you previously promised to do something by a given date. Doing this effectively requires comparable natural language parsing to a chatbot, but it transforms the intelligence from a system that must be explicitly interacted with into one that's altogether more transparent.

It's still early days for machine learning, and these capabilities are far from ubiquitous. The shift to "artificial intelligence" terminology is also unfortunate, as it sets users up for disappointmentthese systems are still a long way short of rivaling Lt. Cmdr. Data or the Terminator, and these fictional characters arguably define the widespread perception and understanding of "artificial intelligence."

But the overall movement is positive. Over the last couple of years, Microsoft's cognitive services have gone from abstract and somewhat impenetrable to a useful set of tools that developers of all kinds can integrate into their apps, all without having to be experts in machine learning or artificial intelligence.

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Microsoft's bid to bring AI to every developer is starting to make sense - Ars Technica

Investopedia

Facebook's AI Translates 9X Faster Than Rivals Investopedia Facebook Inc. (FB) wants the world to use its social media networks and communicate with users around the globe, and to meet that end it announced it developed an artificial intelligence (AI)-based tool that can translate languages nine times faster ... Facebook's New AI Could Lead to Translations That Actually Make SenseWIRED Facebook's new AI aims to destroy the language barrierEngadget Facebook Is Using AI To Make Language Translation Much FasterFast Company ABC News -Digital Trends all 47 news articles »

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Facebook's AI Translates 9X Faster Than Rivals - Investopedia

World Chess champion Garry Kasparov (left) ponders a chess move during the sixth and final game of his match with IBM's Deep Blue computer on May 11, 1997.

When the IBM computer Deep Blue beat the world's greatest chess player, Garry Kasparov, in the last game of a six-game match on May 11, 1997, the world was astonished. This was the first time any human chess champion had been taken down by a machine.

That win for artificial intelligence was historic, not only for proving that computers can outperform the greatest minds in certain challenges, but also for showing the limitations and shortcomings of these intelligent hunks of metal, experts say.

Deep Blue also highlighted that, if scientists are going to build intelligent machines that think, they have to decide what "intelligent" and "think" mean. [Super-Intelligent Machines: 7 Robotic Futures]

During the multigame match that lasted days at the Equitable Center in Midtown Manhattan, Deep Blue beat Kasparov two games to one, and three games were a draw. The machine approached chess by looking ahead many moves and going through possible combinations a strategy known as a "decision tree" (think of each decision describing a branch of a tree). Deep Blue "pruned" some of these decisions to reduce the number of "branches" and speed the calculations, and was still able to "think" through some 200 million moves every second.

Despite those incredible computations, however, machines still fall short in other areas.

"Good as they are, [computers] are quite poor at other kinds of decision making," said Murray Campbell, a research scientist at IBM Research. "Some doubted that a computer would ever play as well as a top human.

"The more interesting thing we showed was that there's more than one way to look at a complex problem," Campbell told Live Science. "You can look at it the human way, using experience and intuition, or in a more computer-like way." Those methods complement each other, he said.

Although Deep Blue's win proved that humans could build a machine that's a great chess player, it underscored the complexity and difficulty of building a computer that could handle a board game. IBM scientists spent years constructing Deep Blue, and all it could do was play chess, Campbell said. Building a machine that can tackle different tasks, or that can learn how to do new ones, has proved more difficult, he added.

At the time Deep Blue was built, the field of machine learning hadn't progressed as far as it has now, and much of the computing power wasn't available yet, Campbell said. IBM's next intelligent machine, named Watson, for example, works very differently from Deep Blue, operating more like a search engine. Watson proved that it could understand and respond to humans by defeating longtime "Jeopardy!" champions in 2011.

Machine learning systems that have been developed in the past two decades also make use of huge amounts of data that simply didn't exist in 1997, when the internet was still in its infancy. And programming has advanced as well.

The artificially intelligent computer program called AlphaGo, for example, which beat the world's champion player of the board game Go, also works differently from Deep Blue. AlphaGo played many board games against itself and used those patterns to learn optimal strategies. The learning happened via neural networks, or programs that operate much like the neurons in a human brain. The hardware to make them wasn't practical in the 1990s, when Deep Blue was built, Campbell said.

Thomas Haigh, an associate professor at the University of Wisconsin-Milwaukee who has written extensively on the history of computing, said Deep Blue's hardware was a showcase for IBM's engineering at the time; the machine combined several custom-made chips with others that were higher-end versions of the PowerPC processors used in personal computers of the day. [History of A.I.: Artificial Intelligence (Infographic)]

Deep Blue also demonstrated that a computer's intelligence might not have much to do with human intelligence.

"[Deep Blue] is a departure from the classic AI symbolic tradition of trying to replicate the functioning of human intelligence and understanding by having a machine that can do general-purpose reasoning," Haigh said, hence the effort to make a better chess-playing machine.

But that strategy was based more on computer builders' idea of what was smart than on what intelligence actually might be. "Back in the 1950s, chess was seen as something that smart humans were good at," Haigh said. "As mathematicians and programmers tended to be particularly good at chess, they viewed it as a good test of whether a machine could show intelligence."

That changed by the 1970s. "It was clear that the techniques that were making computer programs into increasingly strong chess players did not have anything to do with general intelligence," Haigh said. "So instead of thinking that computers were smart because they play chess well, we decided that playing chess well wasn't a test of intelligence after all."

The changes in how scientists define intelligence also show the complexity of certain kinds of AI tasks, Campbell said. Deep Blue might have been one of the most advanced computers at the time, but it was built to play chess, and only that. Even now, computers struggle with "common sense" the kind of contextual information that humans generally don't think about, because it's obvious.

"Everyone above a certain age knows how the world works," Campbell said. Machines don't. Computers have also struggled with certain kinds of pattern-recognition tasks that humans find easy, Campbell added. "Many of the advances in the last five years have been in perceptual problems," such as face and pattern recognition, he said.

Another thing Campbell noted computers can't do is explain themselves. A human can describe her thought processes, and how she learned something. Computers can't really do that yet. "AIs and machine learning systems are a bit of a black box," he said.

Haigh noted that even Watson, in its "Jeopardy!" win, did not "think" like a person. "[Watson] used later generations of processors to implement a statistical brute force approach (rather than a knowledge-based logic approach) to Jeopardy!," he wrote in an email to Live Science. "It again worked nothing like a human champion, but demonstrated that being a quiz champion also has nothing to do with intelligence," in the way most people think of it.

Even so, "as computers come to do more and more things better than us, we'll either be left with a very specific definition of intelligence or maybe have to admit that computers actually are intelligent, but in a different way from us," Haigh said.

Because humans and computers "think" so differently, it will be a long time before a computer makes a medical diagnosis, for example, all by itself, or handles a problem like designing residences for people as they age and want to remain in their homes, Campbell said. Deep Blue showed the capabilities of a computer geared to a certain task, but to date, nobody has made a generalized machine learning system that works as well as a purpose-built computer.

For example, computers can be very good at crunching lots of data and finding patterns that humans would miss. They can then make that information available to humans to make decisions. "A complementary system is better than a human or machine," Campbell said.

It's also probably time to tackle different problems, he said. Board games like chess or Go allow players to know everything about their opponent's position; this is called a complete information game. Real-world problems are not like that. "A lesson we should have learned by now There's not that much more that we can learn from board games." (In 2017, the artificially intelligent computer program called Libratus beat the best human poker players in a 20-day No-Limit Texas Hold 'em tournament, which is considered a game of incomplete information.)

As for Deep Blue's fate, the computer was dismantled after the historic match with Kasparov; components of it are on display at the National Museum of American History in Washington, D.C., and the Computer History Museum in Mountain View, California.

Original article on Live Science.

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What Is Intelligence? 20 Years After Deep Blue, AI Still Can't Think Like Humans - Live Science

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Research into artificial intelligence is going gangbusters, and the frenetic pace wont let up for about five yearsafter which the industry will concentrate around a handful of core technologies and leaders, the head of Intels new AI division predicts.

Intel is keen to be among them. In March, it formed an Artificial Intelligence Products Group headed byNaveen Rao. He previously was CEO ofNervana Systems, a deep-learning startup Intel acquired in 2016. Rao sees the industry moving at breakneck speed.

Its incredible, he said. You go three weeks without reading a paper and youre behind. Its just amazing.

It wasnt so long ago that artificial intelligence research was solely the domain of university research labs, but tech companies have stormed into the space in the last couple of years and sent technical hurdles tumbling.

Weve hit upon a set of fundamental principles, and now we can really get to that point where we can innovate and iterate quickly on them and build really new cool things, he said.

Rao likened it to the development of concrete. It took a while for humans to invent and perfect concrete, but once that happened, all sorts of things suddenly became possible.

Thats why I think the next five or six years are going to be really, really fast moving. It will stabilize at that point after we figure out what the stack looks like and who the players are in thestack, he said.

Intels new AI group represents its biggest step yet toward being one of those leaders. The group brings together all of the companys hardware and software researchtied to machine learning, algorithms and deep learning.

While Intel is best known as a chip maker, its AI research also includes software packages that help developers add AI capabilities to Intel-based hardware. By doing some of the software work, Intel aims to make it easier for its customers to build AI-based systems. That, in turn, will help it sell hardware.

The company does something similar in other areas of its business.One of the areas its already focused on is self-driving cars. The vehicles use artificial intelligence to make split-second decisions about how to navigate roads and are a good example of a research area thats seen rapid progress.

A car used by Intel to test the companys autonomous driving technology as seen on May 3, 2017 in San Jose, California.

A lot of Intels competition comes from the big tech companies of Silicon Valley. The U.S. is one of the biggest players in AI, thanks to companies like Google and Facebook, and Rao also credits Canada and the U.K. as pioneers. But China is beginning to make its presence felt.

I was in China a few months ago. Its really taking off, he said. The folks there are very hungry to build these kinds of things, and the skill sets are building up really quick, so I think in the next couple of years youll start seeing China be a major player.

Martyn Williams covers general technology news for the IDG News Service and is based in San Francisco. He was previously based in Tokyo.

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The next 5 years in AI will be frenetic, says Intel's new AI chief - PCWorld

Deep learning, self-driving cars, and AI are all huge topics these days, with companies like Nvidia, IBM, AMD, and Intel all throwing their hats into the ring. Now Cray, which helped pioneer the very concept of a supercomputer, is also bringingits own solutions to market.

Cray announced a pair of new systems: the Cray CS-Storm 500GT, and the CS-Storm 500NX. Both are designed to work with Nvidias Pascal-based Tesla GPUs, but they offer different feature sets and capabilities. the CS-Storm 500GT supports up to 8x 450W or 10x 400W accelerators, including Nvidias Tesla P40 or P100 GPU accelerators. Add-in boards like Intels Knights Landing and FPGAs built by Nallatech are also supported in this system, which uses PCI Express for its peripheral interconnect. The 500GT platform uses Intels Skylake Xeon processors.

The Cray CS-Storm 500GT supports up to 10 P40 or P100 GPUs and taps Nvidias NVLink connector rather than PCI Express. Xeon Phi and Nallatech devices arent listed as being compatible with this system architecture. Full specs on each are listed below:

The CS-Storm 500NX uses NVLink, which is why Cray can list it as supporting up to eight P100 SMX2 GPUs, without having eighth PCIe 3.0 slots (just in case that was unclear).

Customer demand for AI-capable infrastructure is growing quickly, and the introduction of our new CS-Storm systems will give our customers a powerful solution for tackling a broad range of deep learning and machine learning workloads at scale with the power of a Cray supercomputer, said Fred Kohout, Crays senior vice president of products and chief marketing officer. The exponential growth of data sizes, coupled with the need for faster time-to-solutions in AI, dictates the need for a highly-scalable and tuned infrastructure.

Nvidias NVLink fabric can be used to attach GPUs without using PCI Express.

The surge in self-driving cars, AI, and deep learning technology could be a huge boon to companies like Cray, which once dominated the supercomputing industry. Cray went from an early leader in the space to a shadow of its former self after a string of acquisitions and unsuccessful products in the late 1990s and early 2000s. From 2004 forwards the company has enjoyed more success, with multiple high-profile design wins using AMD, Intel, and Nvidia hardware.

So far, Nvidia has emerged as the overall leader in HPC workload accelerators. Of the 86 systems listed as using an accelerator at the TOP500 list, 60 of them use Fermi, Kepler, or Pascal (Kepler is the clear winner, with 50 designs). The next-closest hybrid is Intel, which has 21 Xeon Phi wins.

AMD has made plans to enter these markets with deep learning accelerators based on its Polaris and Vega architectures, but those chips havent actually launched in-market yet. By all accounts, these are the killer growth markets for the industry as a whole, and they help explain why even some game developers like Blizzard want to get in on the AI craze. As compute resources shift towards Amazon, Microsoft, and other cloud service providers, the companies that can provide the hardware these workloads run on will be best positioned for the future. Smartphones and tablets didnt really work for Nvidia or Intelmaking AMDs decision to stay out of those markets retrospectively look very, very wisebut both are positioned well to capitalize on these new dense server trends. AMD is obviously playing catch-up on the CPU and GPU front, but Ryzen should deliver strong server performance when Naples launches later this quarter.

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Cray Announces New, AI-Focused Supercomputers - ExtremeTech