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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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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Exascale computing: The Space Race of our time

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Anyone who remembers the time before human beings stepped on the moon can recall the exhilaration of the challenge President John Kennedy made in 1961: “I believe that this nation should commit itself to achieving the goal, before this decade is out, of landing a man on the moon and returning him safely to Earth.”

Nothing in the interim has come close to the excitement of doing something so impossible on the face of it. But PathForward, the exascale computing challenge, comes close. We are going to make a computer so powerful and so fast that it will alter the way we live. If we succeed, life will be as different afterward as it was when we saw astronaut Neil Armstrong take “a giant leap for mankind.”

Earlier this year, the annual list of the world’s fastest computers came out: The Chinese are responsible for the top two slots, and the third is held by the Swiss, knocking the U.S. down to the fourth spot. Now, the U.S. Department of Energy (DOE) has awarded six American companies shares of a $258 million grant pool in the pursuit of exascale computing. If successful, the PathForward program will put the U.S. at the head of this list.

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How to make the business case for blockchain

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You would be forgiven if you thought the first blockchain to ever be developed was the cryptocurrency Bitcoin. While Bitcoin is the highest profile use case of blockchain—a transparent, immutable ledger platform—it is far from the first.

“Blockchain is just a shared database with time stamping,” says Stuart Haber, the man behind Surety, the first blockchain, which was first published in 1995. “It is a data structure and doesn’t have to be linked to a volatile currency.”

While confusion between the two technologies may still exist, some experts believe it’s only a matter of time before the blockchain ecosystem develops beyond Bitcoin.

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An oral history of The Machine

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The Machine is a computing architecture so radically different than any which has come before that it will affect everything we do in the future. Hewlett Packard Labs has spent the last five years developing the memory-driven computing, photonics, and fabric that has gone into The Machine and which have made the impossible inevitable.

We spoke to several dozen researchers – programmers, architects, open source advocates, optical scientists, and others – to construct a ten-part oral history of the years-long process of innovating the most fundamental change in computing in 70 years.

These men and women are not only scientists, they are also compelling story tellers with an exciting history to relate. If you’re interested in how differently we will be gathering, storing, processing, retrieving, and applying information in the near future, or you just enjoy good stories about science and discovery, read on.

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