AI

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Nvidia's (NVDA)  spectacular earnings and guidance last week provided good evidence that the GPU leader is on its way to making the powering of artificial intelligence workloads a 10-figure annual business. Since then, it hasn't wasted time announcing moves that grow its AI ecosystem and could help keep hungry rivals at bay. On Monday, Nvidia and IBM (IBM) announced the latter is rolling out a software toolkit called IBM PowerAI for IBM servers containing Nvidia's Tesla accelerator cards, which are widely used to handle a popular type of AI known as deep learning.

Nvidia's Deals with IBM, Microsoft and Google Strengthen Its Artificial Intelligence Lead - TheStreet

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These projections have led many artificial intelligence stocks to soar to new highs in recent weeks as deal-making in the space accelerates. For instance, International Business Machines (IBM) and Salesforce.com (CRM) announced that Watson and Einstein would join forces to create the biggest AI platform in the space with over a billion users per day. The move could provide the companies with a monopoly of sorts when it comes to enterprise users.

The artificial intelligence industry is rapidly evolving with breakthroughs occurring every day, which provides many catalysts for traders watching these stocks. Investors looking for broader long-term exposure may want to consider AI-focused ETFs, like the Global X Robotics & Artificial Intelligence Thematic ETF (BOTZ), to gain diversified exposure to the space. That ETF has also broken out to new all-time highs given the optimism surrounding the industry.

2 AI Stocks Set to Break Out to New Highs (IBM, CRM) | Investopedia

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Move over, managers, there’s a new boss in the office: artificial intelligence. The same technology that enables a navigation app to find the most efficient route to your destination or lets an online store recommend products based on past purchases is on the verge of transforming the office—promising to remake how we look for job candidates, get the most out of workers and keep our best workers on the job.

How AI Is Transforming the Workplace - WSJ

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Federated Learning enables mobile phones to collaboratively learn a shared prediction model while keeping all the training data on device, decoupling the ability to do machine learning from the need to store the data in the cloud. This goes beyond the use of local models that make predictions on mobile devices (like the Mobile Vision API and On-Device Smart Reply) by bringing model training to the device as well.

Research Blog: Federated Learning: Collaborative Machine Learning without Centralized Training Data

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For the past twenty years, computer interpretation has often been a feature of these systems. The programs that do the work tend to be fairly straightforward. Characteristic waveforms are associated with various conditions—atrial fibrillation, or the blockage of a blood vessel—and rules to recognize these waveforms are fed into the appliance. When the machine recognizes the waveforms, it flags a heartbeat as “atrial fibrillation.”

A.I. Versus M.D. - The New Yorker

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Who has time to read every article they see shared on Twitter or Facebook, or every document that’s relevant to their job? As information overload grows ever worse, computers may become our only hope for handling a growing deluge of documents. And it may become routine to rely on a machine to analyze and paraphrase articles, research papers, and other text for you. An algorithm developed by researchers at Salesforce shows how computers may eventually take on the job of summarizing documents. It uses several machine-learning tricks to produce surprisingly coherent and accurate snippets of text from longer pieces. And while it isn’t yet as good as a person, it hints at how condensing text could eventually become automated.

An Algorithm Summarizes Lengthy Text Surprisingly Well - MIT Technology Review

Improbable just became the UK's latest $1billion tech startup. The inside story of its insanely ambitious plan to built virtual worlds, change the way we make decisions, and maybe one day build the Matrix.

Inside Improbable, the $1billion UK startup building the Matrix | WIRED UK

new smart-home assistant and security monitor can tell the difference between specific adults and spot kids and pets, and send you smartphone alerts about what they’re up to. Lighthouse went on sale on Thursday, though it won’t ship to customers until September. A single Lighthouse device plus a year of service runs $399, and it will cost $10 per month after that. (By comparison, a Nest camera and its service, which together have some similar features, would cost $299 for a camera and a year of service, and $10 per month thereafter.)

Home Monitors Are Getting Smarter (and Creepier) - MIT Technology Review

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One of the most striking debuts in Pichai’s talk was the second version of Google's custom “TPU,” or “tensor processing unit” circuitry, for cloud computing. Those chips had traditionally been used for simpler types of processing, but Google now says it will make them available for both “inference,” the simpler tasks, such as answering a Web query, and now “training,” the more “computationally-intensive" task. Pichai said the chips are the company’s focus on “A.I.-first,” a slogan that is replacing last year’s slogan of “mobile-first.” Pichai unveiled “Google.ai,” an umbrella for several things including “state of the art research” and “applied” artificial intelligence.

Pichai said the TPU boards will be made available to outsiders to use in Google’s cloud computing service. He also made a point of mentioning Google is making available the “great” GPU chips from Nvidia (NVDA), a reference to the “Volta” parts announced by Nvidia last week. “We want Google cloud to be the best cloud for machine learning." You can re-warch yesterday’s keynote on YouTube. In a note this morning, Trip Chowdhry of Global Equities Research, who has written frequently on how the TPU relates to Nvidia GPUs, writes that “Google statements can safely be ignored for now” and Nvidia is in no danger. “Our research is indicating that Google Cloud will continue to grow its GPU farm by >100% this year.""

Alphabet’s Google’s TPUs and All the Rest Leave Street Satisfied - Barron's

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The machine learning algorithms that underpin the AI revolution place extreme demands on conventional computing hardware. At last year’s Google I/O, the search giant announced that it had designed a custom chip called a tensor processing unit for machine learning applications. Tests show that these chips can execute machine learning code up to 30 times faster than conventional computer chips. Over the past year, Google has installed racks and racks of these chips in its vaunted data centers to support the growing AI capabilities of various Google products. On Wednesday, Google announced that it will soon be opening up these chips for anyone to use as part of Google’s cloud computing platform. Google has already released its powerful machine learning software, called TensorFlow, as an open source project so that anyone could use it. Google isn’t just being nice, of course. The larger goal is to establish Google’s AI platform as the industry standard thousands of other companies rely on for their own AI software. Once you build software on top of one platform, it’s very expensive to switch, so becoming an industry standard could make Google billions in the coming years..

Artificial intelligence is getting more powerful, and it's about to be everywhere - Vox

Google showed off a small example of what this might look like with Google’s voice-based assistant. On the I/O stage a Google executive said, “I'd like delivery from Panera,” and this started a conversation with the app that worked a lot like a conversation you’d have with a human Panera cashier. The executive said she wanted to order a sandwich. The virtual assistant asked if she wanted to add a drink. After she chose a drink, the assistant told her the total price and asked if she wanted to place the order. The remarkable thing about this exchange wasn’t so much the ability to carry out a simple conversation — something virtual assistants like Apple’s Siri have been able to do for a few years. It’s the promise that every retail establishment in America could build a similar capability without having to hire a bunch of computer science PhDs.

Artificial intelligence is getting more powerful, and it's about to be everywhere - Vox

But there are a couple of facets to this trend that aren't as well-appreciated: The usefulness of a machine learning algorithm for handling tasks normally done by humans doesn't necessarily improve at a linear pace, but can improve exponentially or close to it when a tipping point is reached due to all of the data that the algorithm has been run against. Machine learning R&D work can often be applied to many different tasks, including some that don't have much in common at first glance. Both of these phenomena work very much in Alphabet/Google's (GOOGL) favor. Though Amazon.com (AMZN), Microsoft (MSFT) and others have also made tremendous progress in delivering AI-powered products and services on a large scale, Google arguably remains a step ahead when it comes to many of the tasks that both Google and rivals are trying to address. And as the announcements made at this week's Google I/O developers conference show, a lot of these offerings seem to be hitting a tipping point in terms of what they can do.

Google Has an AI Lead and Is Putting It to Good Use - TheStreet

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The Accenture report looked at 12 countries and found that AI — or technology that senses the environment, comprehends what's happening and takes action — could increase productivity by up to 40 percent in 2035. The report also forecasts economic growth in the U.S. could increase from 2.6 percent to 4.6 percent over the same period with the adoption of AI technologies. Among the countries that stand to make the largest gains in productivity from AI in 2035 are Sweden, Finland, the U.S. and Japan.

Artificial intelligence will boost US productivity, says report

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Compared to the last few Industrial Revolutions, China has clearly not lagged in the Fourth one. Instead, it plays an important role in it. China is competing head-to-head with the U.S. and other tech powerhouses in the hottest area of technological innovation: Artificial Intelligence. With its big reserve in AI talent, excellent engineering education and massive market for AI adoption, China is poised to be a leader in AI.

How China is leading Artificial Intelligence under the radar - Artificial Intelligence Society of Hong Kong