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Despite this, a regular ritual of hype and hysteria is now built into the news cycle. Every now and again, at some huge auditorium, a senior staff member at one of the big firms based in northern California – ordinarily a man – will take the stage dressed in box-fresh casualwear, and inform the gathered multitudes of some hitherto unimagined leap forward, supposedly destined to transform millions of lives. (There will be whoops and gasps in response, and a splurge of media coverage – before, in the wider world, a palpable feeling of anticlimax sets in.)

It’s years since Silicon Valley gave us a game-changer. Instead, from curing disease to colonies on Mars, we’re fed overblown promises, says Guardian columnist John Harris.

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Scientists at the University of Alberta have applied a machine learning technique using artificial intelligence to perfect and automate atomic-scale manufacturing, something which has never been done before. The vastly greener, faster, smaller technology enabled by this development greatly reduces impact on the climate while still satisfying the insatiable demands of the information age.

“Most of us thought we’d never be able to automate atomic writing and editing, but stubborn persistence has paid off, and now Research Associate Moe Rashidi has done it,” said Robert Wolkow, professor of physics at the University of Alberta, who along with his Research Associate has just published a paper announcing their findings.

“Until now, we printed with about as efficiently as medieval monks produced books,” explained Wolkow. “For a long while, we have had the equivalent of a pen for writing with atoms, but we had to write manually. So we couldn’t mass produce atom-scale devices, and we couldn’t commercialize anything. Now that has all changed, much like the disruption following the arrival of the printing press for those medieval monks. Machine learning has automated the atom fabrication process, and an atom-scale manufacturing revolution is sure to follow.”

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The Marshall Islands made its own cryptocurrency, doing away with the US dollar. The government has signed the change into law, making the “sovereign” its new official cryptocurrency, as spotted by CNBC Africa cryptocurrency trader host Ran Neuner on Twitter yesterday.

The bill was signed into effect on March 1st, but the news is making waves again this week. The Marshall Islands’ population is 53,066, so the change doesn’t affect many, but it is significant for citizens of the islands because banks and credit card companies will need to begin accepting it. With the recent change, US dollars are still likely to be accepted on the Marshall Islands — the sovereign will just be considered the nation’s official legal tender.

In February, top officials from the Marshall Islands confirmed that the Pacific republic would issue its own cryptocurrency to be circulated as legal tender. The digital coin also received approval from the country’s parliament. “As a country, we reserve the right to issue a currency in whatever form it is, whether in digital or fiat form,” said David Paul, minister-in-assistance to the president of the Marshall Islands, to Reuters at the time.

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SpaceX is taking a commanding role in the rocket business — but Gwynne Shotwell, the company’s president and chief operating officer, expects the satellite business to be more lucrative.

Shotwell sized up SpaceX’s road ahead in a CNBC interview that aired today in connection with the cable network’s latest Disruptor 50 list. For the second year in a row, the space venture founded by billionaire Elon Musk leads the list.

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Over the last few years, Google and Coursera have regularly teamed up to launch a number of online courses for developers and IT pros. Among those was the Machine Learning Crash course, which provides developers with an introduction to machine learning. Now, building on that, the two companies are launching a machine learning specialization on Coursera. This new specialization, which consists of five courses, has an even more practical focus.

The new specialization, called “Machine Learning with TensorFlow on Google Cloud Platform,” has students build real-world machine learning models. It takes them from setting up their environment to learning how to create and sanitize datasets to writing distributed models in TensorFlow, improving the accuracy of those models and tuning them to find the right parameters.

As Google’s Big Data and Machine Learning Tech Lead Lak Lakshmanan told me, his team heard that students and companies really liked the original machine learning course but wanted an option to dig deeper into the material. Students wanted to know not just how to build a basic model but also how to then use it in production in the cloud, for example, or how to build the data pipeline for it and figure out how to tune the parameters to get better results.

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