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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

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

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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

New York Times

A Trump Dividend for Canada? Maybe in Its AI Industry New York Times The MaRS Discovery District in Toronto is one of the world's largest urban innovation hubs. Canada has well-funded programs to lure A.I. experts and persuade homegrown talent to stay in Canada. Credit Aaron Vincent Elkaim for The New York Times.

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A Trump Dividend for Canada? Maybe in Its AI Industry - New York Times

Executive Summary

In the near future, as Artificial Intelligence (AI) systems become more capable, we will begin to see more automated and increasingly sophisticated social engineering attacks. The rise of AI-enabled cyber-attacks is expected to cause an explosion of network penetrations, personal data thefts, and an epidemic-level spread of intelligent computer viruses. Ironically, our best hope to defend against AI-enabled hacking is by using AI. But this is also very likely to lead to an AI arms race, the consequences of which may be very troubling in the long term, especially as big government actors join in the cyberwars. Business leaders would be well advised to familiarize themselves with the state-of-the-art in AI safety and security research. Armed with more knowledge, they can then rationally consider how the addition of AI to their product or service will enhance user experiences, while weighing the costs of potentially subjecting users to additional data breaches and other possible dangers.

In the near future, as artificial intelligence (AI) systems become more capable, we will begin to see more automated and increasingly sophisticated social engineering attacks. The rise of AI-enabled cyberattacks is expected to cause an explosion of network penetrations, personal data thefts, and an epidemic-level spread of intelligent computer viruses. Ironically, our best hope to defend against AI-enabled hacking is by using AI. But this is very likely to lead to an AI arms race, the consequences of which may be very troubling in the long term, especially as big government actors join the cyber wars.

My research isat the intersection of AI and cybersecurity. In particular, I am researching how we can protect AI systems from bad actors, as well as how we can protect people from failed or malevolent AI. This work falls into a larger framework of AI safety,attempts to create AI that is exceedingly capable but also safe and beneficial.

A lot has been written about problems thatmight arise with the arrival of true AI, either as a direct impact of such inventions or because of a programmers error. However, intentional malice in design and AI hacking have not been addressed to a sufficient degree in the scientific literature. Its fair to say that when it comes to dangers from a purposefully unethical intelligence, anything is possible. According to Bostroms orthogonality thesis, an AI system can potentially have any combination of intelligence and goals. Such goals can be introduced either throughthe initial design or throughhacking, or introduced later, in case of an off-the-shelf software just add your own goals. Consequently, depending on whose bidding the system is doing (governments, corporations, sociopaths, dictators, military industrial complexes, terrorists, etc.), it may attempt to inflict damage thats unprecedented in the history of humankind or thats perhaps inspired by previous events.

Even today, AI can be used to defend and to attack cyber infrastructure, as well as to increase the attack surface that hackers can target,that is, the number of ways for hackers to get into a system. In the future, as AIs increase in capability, I anticipate that they will first reach and then overtake humans in all domains of performance, as we have already seen with games like chessandGoand are now seeing with important human tasks such asinvestinganddriving. Its important for business leaders to understand how that future situation will differ from our current concerns and what to do about it.

If one of todays cybersecurity systems fails, the damage can be unpleasant, but is tolerable in most cases: Someone loses money orprivacy. But for human-level AI (or above), the consequences could be catastrophic. A single failure of a superintelligent AI (SAI) system could cause an existential risk event an event that has the potential to damage human well-being on a global scale. The risks are real, as evidenced by the fact that some of the worlds greatest minds in technology and physics, includingStephen Hawking, Bill Gates, and Elon Musk, have expressed concerns about the potential for AI to evolve to a point where humans could no longer control it.

When one of todays cybersecurity systems fails, you typically get another chance to get it right, or at least to do better next time. But with an SAI safety system, failure or success is a binary situation: Either you have a safe, controlled SAIor you dont. The goal of cybersecurity in general is to reduce the number of successful attacks on a system; the goal of SAI safety, in contrast, is to make sure noattacks succeed in bypassing the safety mechanisms in place. The rise of brain-computer interfaces, in particular, will create a dream target for human and AI-enabled hackers. And brain-computer interfaces are not so futuristic theyre already being used in medical devices and gaming, for example. If successful, attacks onbrain-computer interfaces would compromise not only critical information such as social security numbers or bank account numbers but also our deepest dreams, preferences, and secrets. There is the potential to create unprecedented new dangers for personal privacy, free speech, equal opportunity, and any number of human rights.

Business leaders are advised to familiarize themselves with the cutting edge ofAI safety and security research, which at the moment is sadly similar to the state of cybersecurity in the 1990s, andour current situation with the lack of security forthe internet of things. Armed with more knowledge, leaderscan rationally consider how the addition of AI to their product or service will enhance user experiences, while weighing the costs of potentially subjecting users to additional data breaches and possible dangers. Hiring a dedicated AI safety expert may be an important next step, as most cybersecurity experts are not trained in anticipating or preventing attacks against intelligent systems. I am hopeful that ongoing research will bring additional solutions for safely incorporatingAI into the marketplace.

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AI Is the Future of Cybersecurity, for Better and for Worse - Harvard Business Review

Language translation has typically been done by recurrent neural networks (RNN), which process language one word at a time in a linear order, either right-to-left or left-to-right, depending on the language. This CNN-based architecture pays attention to words farther along in a sentence to help understand the meaning from context farther along the string of words, much like humans do. While the older RNN method has been typically fine for end users in regards to speed and accuracy, there's a functional limit to the tech, one which the parallel processing model of CNNs can address. This is the first time a CNN has outperformed the more traditional RNN techniques. Facebook hopes to use the new methodology to scale its translation efforts to cover "more of the world's 6,500 languages."

Now that the popular social network has chosen CNN translation processing architecture, it will be interesting to see what comes next. Fast, accurate language translation might make our world feel a little smaller and more connected without the barrier of language in the way. The impact of this new technology will likely be felt globally, especially across the many Facebook-owned apps that help connect us all, like Messenger, WhatsApp and Instagram.

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Facebook's new AI aims to destroy the language barrier - Engadget

Forbes

Who's Who: The 6 Top Thinkers In AI And Machine Learning Forbes So, in this post I am going to highlight some of the current movers 'n' shakers, whose breakthroughs in machine learning are proving to be fundamental to developing the digital tools and technologies making AI possible, from social networks to self ... and more »

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Who's Who: The 6 Top Thinkers In AI And Machine Learning - Forbes

Facebook (NASDAQ:FB) has been doubling down on artificial intelligence (AI) to process the large amount of content users post to its platform, trying to make sense of all of that data to make communication easier. For instance, AI helps the social media specialist classify live videos in real time, while also helping in speech and text translations.

One of the ways to take advantage of Facebook's increasing AI adoption is through NVIDIA (NASDAQ:NVDA), as its graphics processing units (GPUs) are playing a mission-critical role in the fast processing of huge data sets.

Back in March, Facebook announced that it is using NVIDIA's GPUs to power its next-generation GPU server -- Big Basin -- so it can train bigger machine learning models for faster processing of photos, text, and videos. NVIDIA supplied eight of its Tesla P100 GPU accelerators for the server, along with its high-speed NVLink technology that enables ultra-fast communication between the GPUs by removing any connection-related bottlenecks.

NVIDIA's Tesla GPUs and the NVLink interconnect technology are allowing Facebook to train 30% larger AI models, thanks to a 33% jump in the bandwidth memory as compared to the previous generation -- Big Sur processor. As it turns out, Big Basin can perform 100% faster than Big Sur in certain scenarios, processing more complex models in a shorter time frame.

Image source: NVIDIA.

What's more, NVIDIA and Facebook have now taken their AI relationship further with the Caffe2, a scalable deep learning AI framework that gives developers more power in training and iterating AI models. Caffe2 connects eight of Facebook's Big Basin servers, giving users the capability of using 64 NVIDIA Tesla GPU accelerators and allowing them to train AI models seven times faster with the help of a supercomputer.

Facebook will need more high-performance servers going forward thanks to booming mobile data traffic and a huge user base. The social media specialist has 1.23 billion daily active users who post 300 million photos a day and 510,000 comments each second. What's more, the company is betting big on video, and its "Live" service has seen a 400% surge in streaming since launch.

Facebook's growth is not going to stop anytime soon as its emerging markets user base is growing at a terrific pace. Research firm eMarketer forecasts that countries such as India, Indonesia, Mexico, and the Philippines will become its fastest-growing markets until 2020, leading to a spurt in content posted onto the platform, especially due to growing smartphone penetration.

Facebook, therefore, will need more capable servers to tackle the growing data volume and complexity. This is good news for NVIDIA's professional visualization business, which houses the Tesla GPU unit. The Tesla GPUs are aimed at accelerating high-performance computing and hyperscale data center workloads -- allowing them to crunch huge amounts of data at a fast pace -- so Facebook is going to need more of them as its workload grows.

As the likes of Facebook and others start using AI to train their analytics models, NVIDIA will find a bigger market to sell its GPU accelerators. Markets and Markets forecasts that the AI chipset market will grow at over 60% a year until 2022, hitting a size of $16 billion. GPU accelerators could make up a big part of this market thanks to the crucial role they play in the AI space.

This should supercharge NVIDIA's professional visualization business, which is already reaping the benefits of growing data center workloads. In fact, the Tesla GPUs are being used by cloud service providers such as Amazon Web Services, Google Cloud, and Microsoft Azure, and Facebook will further boost the segment's growth thanks to its growing AI bets.

Suzanne Frey, an executive at Alphabet, is a member of The Motley Fools board of directors. Teresa Kersten is an employee of LinkedIn and is a member of The Motley Fools board of directors. LinkedIn is owned by Microsoft. Harsh Chauhan has no position in any stocks mentioned. The Motley Fool owns shares of and recommends Alphabet (A shares), Amazon, Facebook, and Nvidia. The Motley Fool has a disclosure policy.

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How Facebook's AI Ambitions Will Boost NVIDIA - Motley Fool

An AI lawyer weighs up a particularly tricky contract law dispute while pondering how to kill Arnie Schwarzenegger

Rise of the Machines Ball-gazers* at Gartner reckon robots could replace doctors, lawyers and IT workers in the next five years. Panic, all ye faithful.

"The economics of AI and machine learning will lead to many tasks performed by professionals today becoming low-cost utilities," said Stephen Prentice, Gartner Fellow and veep.

"AI's effects on different industries will force the organisation to adjust its business strategy," he continued presumably talking about others rather than his outfit of mystic mages. "Many competitive, high-margin industries will become more like utilities as AI turns complex work into a metered service that the enterprise pays for, like electricity."

Inevitably, the semi-mythical beast known as the CIO must prepare for this, apparently by devising Soviet-style five-year plans that "achieve the right balance of AI and human skills".

Prentice intoned: "The CIO should commission the enterprise architecture team to identify which IT roles will become utilities and create a timeline for when these changes become possible."

We are told that machine learning means an expensively trained lawyer could easily be replaced by an AI system capable of learning, which can then be cheaply cloned across law firms looking to create an army of electronic Rumpoles of the Bailey.

Lawyers appear particularly worried that AI and/or robots might replace them, though AI advocates are keen to insist that it will displace them sideways rather than resulting in layoffs. Feisty lawyerly blog Legal Cheek spotted a study earlier this year which reckoned that adoption of AI by law firms would be slow and that it would mainly be focused on "drudgery" such as reviewing documents for disclosure purposes in commercial litigation.

*We are assured that Gartner's balls are crystal, not hairy.

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Rich professionals could be replaced by AI, shrieks Gartner - The Register