The Dot Product Engine

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One of the most common computational actions today is matrix multiplication, mathematical functions across columns of figures. One of the most popular expressions of this is object recognition and another is large-scale optimization and the location of global minima. But this action is very compute-intensive. Add to this the data flood from edge devices ranging from smart phones to sensors – which has made data analysis the key to finding signals in that noise, and artificial intelligence the key to data analysis – and you have a perfect storm. Too much data, too little information.

Hewlett Packard Enterprise’s Dot Product Engine (DPE) is a multiple-use accelerator architecture the company has produced to breast that storm. Most accelerators are designed to help solve specific problems. They’re one-offs, their creation is expensive, and they are irreplicable. With the DPE, you can spin up new accelerators with much less effort. And now, in the next step, an HPE team has created a full-stack DPE demo.

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Impossible Problems

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For the last half-century or more, our computers have had pretty much the same classical computing process of zeroes and ones and pretty much the same structure, the von Neumann architecture. For most of this time, that has not just worked well, it has worked better and better. But that era is coming to an end, along with the observation underlying much of that improvement, Moore’s Law.

Practically since the first transistor-based computer, the number of transistors on a chip has doubled every couple of years. That meant the speed and capacity of the computers we use have doubled along with it. But the price of that doubling has grown increasingly dear. It takes more energy and produces more heat to increase computing speed. So alternatives have proliferated as well. 

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AI: promises and perils

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A Q&A with HPE’s Dr. Eng Lim Goh on AI, ethics, and the future

Dr. Eng Lim Goh, vice president and chief technology officer for high-performance computing and artificial intelligence at Hewlett Packard Enterprise, has spent his career considering what machines can do, what they might do, and what they shouldn’t do. As AI has become more prominent, he has been asked to play the role of futurist by the customers and partners he deals with daily.

Goh, like most scientists, is unwilling to roll out any sort of crystal ball. But given his long familiarity with computer graphics, machine learning, analytics, and data, he is in a good position to talk about the different viewpoints on the subject. In this Q&A, he outlines the promises and concerns introduced by the ongoing uptick in AI adoption.

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4 obstacles to ethical AI (and how to address them)

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Data bias, opacity, data monopoly, and job loss are issues that plague the field of artificial intelligence. Here are some simple solutions to these problems.

The past several years have seen a dramatic swell in development of—and discussion about—artificial intelligence. Many of these conversations have a teleological bent: AI will kill us all. Or AI will save us all. But we technologists lack an outline of the ethical obstacles to functioning AI, as well as practical steps to solve these problems so that we control AI instead of the other way around.

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(Series) Toward a New Data-Based Economy

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In part one of this series, “Data is the new currency,” we talked about the shift in technology and business strategy from making things to knowing things.

In part two, “The smart city is the data economy made manifest,” we investigate how a company will have to alter what they do to thrive in this new environment.

As the economy continues to change and as data assumes an even more important place in it, businesses will need to structure themselves in such a way as to locate, collect, and refine data. Companies need to move from a belief that data is a cost – in electricity, space, employee hours, capital outlay, and latency – to an understanding that data is a strategic asset.

The sounds of North Beach

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We recently took a walk through Ready State’s neighborhood, San Francisco’s North Beach, in the morning, at midday, and in the evening, to capture the area’s signature sounds.

Those sounds, in the order you hear them, are the Chinese pop music that tai chi practitioners play in the morning in Washington Square; the bells of Saints Peter and Paul Church; the tsunami siren (technically the emergency siren) that sounds every Tuesday at noon; Army aviators and the Navy’s Blue Angels making survey flights above the city during Fleet Week; and music from the bars on Grant Avenue as night takes over from the concerns of the day.

Listen to the sounds of North Beach

Beyond the greeting card: A visual interview

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The creativity bunker of Jodi Wing, design director here at Ready State, is stuffed with a cityscape’s worth of whirring, sparking, and futuristic technology. Her tools range from a CAT scanner to a 3D printer to an Occulus to a homemade Matrioshka brain.
Tech aside, Jodi has retained one tool from the beginning of her professional life, as a greeting-card illustrator: freehand drawing.

Taking a new approach to an interview, we asked her the following questions using our persistent tool (our babble hole) and received these visual responses via hers (a pen and a sketchbook).

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Hewlett Packard Labs sub-site for The Machine

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Created a dedicated section of Labs’ website to exhaustively detail work on the 160 terabyte, Memory-Driven Computing-focused Machine technology, including overseeing design and implementation, writing and editing copy.

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Conversational AI and the rise of the chatbots

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You can hardly turn on the television news, pull a magazine off a rack in a doctor’s office, or check out your social media without being confronted by a discussion about artificial intelligence. Whether the writer or talking head is decrying the imminent robot apocalypse or celebrating our deep-learning-based salvation, most of the coverage has one thing in common: an imprecise definition of AI. AI is, at its base, nothing more than software that simulates intelligence.

One specific type of AI is cropping up all around the Internet: conversational AI, mostly in the form of chatbots. The most recent and high-profile news about AI was Google’s announcement that its AI, called Google Assistant, beat the Turing test—150 times. The Turing test evaluates a machine’s ability to successfully mimic human intelligence by presenting as indistinguishable from human communication.

Given the fact that we are already interacting with this sort of AI daily—in the form of our phone’s digital assistant or mapping software or a help bot on a website we use, for instance—it’s important to understand what conversational AI is, why it’s become so popular, the obstacles to its adoption, and its likely future.

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