Monthly Archives: May 2017

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

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

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

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

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

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

According to NVIDIA, there are already hundreds of millions of surveillance cameras around the globe, with the number expected to rise to the 1 billion mark by 2020. Human beings have a hard time sifting through the flood of moving images, storing the majority of it on hard drives for later viewing. NVIDA thinks that deep learning AI can help video analytics much more accurately than humans or even real-time computer monitoring. The company has partnered with more than 50 companies that make security cameras, including Hikvision. "The benefit of GPU deep learning is that data can be analyzed quickly and accurately to drive deeper insights," said Shiliang Pu, president at the Hikvision Research Institute in China.

A city with cloud-connected, AI-powered surveillance systems in place could find missing persons, notify residents of nearby emergencies, alert police to crimes in progress or even send out traffic congestion warnings. It could also track and monitor our behavior both legal and otherwise along with gathering personal data for advertisers. Tomorrow can be both exciting and scary at the same time. Whether the city of the future keeps us safe, keeps us in line or something in-between will depend on how we implement emerging technology like this now.

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NVIDIA's AI may keep watch over smart cities of the future - Engadget

Nvidia said it plans to train 100,000 developers through its Deep Learning Institute.

For Nvidia, the Deep Learning Institute, an effort to train developers in machine learning and artificial intelligence, is a way to create a well of expertise that can ultimately lead to more sales of GPUs.

The bet for Nvidia is that IDC estimates that 80 percent of all applications will have AI as a component by 2020.

Nvidia's Deep Learning Institute launched a year ago and has held training events at academic institutions, companies and government agencies. So far, Nvidia's efforts have trained more than 10,000 developers who use Amazon Web Services (AWS) EC2 P2 GPU instances.

How to Implement AI and Machine Learning

The next wave of IT innovation will be powered by artificial intelligence and machine learning. We look at the ways companies can take advantage of it and how to get started.

Greg Estes, vice president of developer programs at Nvidia, acknowledged that training 100,000 developers in 2017 is ambitious, but added that there is strong demand and expanded content can broaden the audience.

In an effort to train 100,000 developers in the next year, Nvidia has stepped up its offerings with the following:

Estes told journalists at Nvidia GTC it made sense for the company to partner with larger companies.

"They are going to help us expand our reach ... because these companies are much bigger than we are, and they have a lot of worldwide reach," he said.

"I think most people would agree that we are at the very leading edge of artificial intelligence and deep learning -- so if we take our knowledge and expertise there, and we work with these other companies, they can help bring that out into the community -- it's a win for everybody."

In the coming year, Nvidia is also planning to certify engineer competence.

"Today when you go through and you take these learning courses, we give you a certificate that you have attended the course, but we don't have the testing at the end," he said. "That is on our roadmap, and we plan to do that this year."

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Nvidia aims to train 100000 developers in deep learning, AI technologies - ZDNet

Baidu, Inc. (NASDAQ: BIDU) is the largest online search engine in China, and since its entry into the realm of artificial intelligence (AI), the company has been integrating its AI know-how into nearly every facet of its business. What investors may not know is that this process produces a virtuous cycle that continues to feed itself.

In the case of Baidu, it has been engaged in an area of AI known as deep learning. Algorithms and software models are used to develop artificial neural networks that mimic the human brain's ability to learn. Vast amounts of data are required to train the system, and Baidu's online search engine provides a vast depository of information on which to draw. Once trained, these AI systems are then used to process data at much faster rates than their human counterparts can and are skilled at detecting patterns. One key aspect of deep learning is that these systems become more useful the more they are used.

Baidu is in a virtuous cycle with its AI. Image source: Baidu.

The ability to detect patterns can be used in a wide array of areas, and these systems are particularly skilled at tasks such as image recognition, making more precise online search recommendations, and more accurately predicting traffic conditions for users of its Maps service.

The company has also applied its AI acumen to better estimating delivery times for its Baidu Delivery service and making customized restaurant suggestions for its recommendation platform, Nuomi. It uses AI to recommend content to its millions of users of its iQiyi video streaming service and provide more relevant content for its news feed.

The process of improving products with AI is a two-way street. When it makes recommendations, the AI system receives feedback from these diners and drivers and streamers, which allows the system to make more reliable recommendations in the future. The system improves with each interaction.

Baidu is using its AI system to develop self-driving car technology in China. It recently announced the acquisition of xPerception, a start-up in the field of computer vision. The company focuses on object recognition and depth perception that can be used in the area of autonomous vehicles and can also be used for drones.

This virtuous cycle is the secret sauce of Baidu's AI system. By using AI, it improves its products and recommendations. By integrating the feedback into the AI system, it improves the relevance of future recommendations. This holds true across the plethora of ways that Baidu is using its AI.

Baidu also announced that it would open-source its platform for autonomous driving, in a move that was said to be inspired by Google's open sourcing of its Android platform. The Alphabet Inc. (NASDAQ: GOOGL) (NASDAQ: GOOG) division came to dominate the smartphone market with its Android operating system, by making it available to all comers, thereby maintaining its supremacy in mobile search.

This is a smart move, as the cumulative data that's acquired from self-driving cars will make the systems safer, and Baidu hopes to become the de facto leader of such data in its native China.

Baidu's strategy regarding AI has yet to bear fruit. While the company had been investing large sums to bolster its AI capability, and it has been seeing incremental improvements in a variety of areas, it has yet to produce any significant increase in revenue.

In the most recent quarter, the company marked its third consecutive quarter of declining revenue, though Baidu hopes to return to growth later this year. The revenue from the company's most recent quarter increased 6.8% over the previous year quarter to $2.45 billion, and earnings of $258 million were down by 10.6% year-over-year.

The area of artificial intelligence offers exciting possibilities, but investors would more likely be excited by a return to growth in revenue and earnings.

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Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool's board of directors. Danny Vena owns shares of Alphabet (A shares) and Baidu. Danny Vena has the following options: long January 2018 $640 calls on Alphabet (C shares) and short January 2018 $650 calls on Alphabet (C shares). The Motley Fool owns shares of and recommends Alphabet (A shares), Alphabet (C shares), and Baidu. The Motley Fool has a disclosure policy.

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Baidu is Using AI to Improve Its Products -- and Its Products to Improve Its AI - Madison.com