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Another day, another $50 million ICO exit scam

Savedroid, a German company that purportedly raised $50 million in ICO and direct funding, has exited with a bang. The site is currently displaying the above image and the founder, one – Dr. Yassin Hankir – has posted a tweeted thanking investors and saying “Over and out.”

A reverse image search found Hankir’s photo on this page for Founder Institute and he has pitched his product at multiple events including this one in German:

Savedroid was originally supposed to use AI to manage user investments and promised a crypto-backed credit card, a claim that CCN notes is popular with scam ICOs. It ran for a number of months and was clearly well managed as the group was able to open an office and appear at multiple events.

One Reddit user visit SaveDroid’s offices and recorded this desolate scene:

Still another wrote: “The CEO on their twitter feed posted this several times ‘contribute now to participate in our #Airdrop and become a #Crypto Millionaire.’ Not about technology, its all about GIVE US MONEY AND WE WILL MAKE YOU A MILLIONAIRE. Anyone who fell for this despite all the warning signs can blame no one but themselves.”

The beer Hankir is holding in that image is Egyptian and one can assume that the backdrop is easily recognizable and designed to throw pursuers off the trail… for good reason.

News Source = techcrunch.com

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Artificial Intelligence

Nvidia’s researchers teach a robot to perform simple tasks by observing a human

Industrial robots are typically all about repeating a well-defined task over and over again. Usually, that means performing those tasks a safe distance away from the fragile humans that programmed them. More and more, however, researchers are now thinking about how robots and humans can work in close proximity to humans and even learn from them. In part, that’s what Nvidia’s new robotics lab in Seattle focuses on and the company’s research team today presented some of its most recent work around teaching robots by observing humans at the International Conference on Robotics and Automation (ICRA), in Brisbane, Australia.

Nvidia’s director of robotics research Dieter Fox.

As Dieter Fox, the senior director of robotics research at Nvidia (and a professor at the University of Washington), told me, the team wants to enable this next generation of robots that can safely work in close proximity to humans. But to do that, those robots need to be able to detect people, tracker their activities and learn how they can help people. That may be in small-scale industrial setting or in somebody’s home.

While it’s possible to train an algorithm to successfully play a video game by rote repetition and teaching it to learn from its mistakes, Fox argues that the decision space for training robots that way is far too large to do this efficiently. Instead, a team of Nvidia researchers led by Stan Birchfield and Jonathan Tremblay, developed a system that allows them to teach a robot to perform new tasks by simply observing a human.

The tasks in this example are pretty straightforward and involve nothing more than stacking a few colored cubes. But it’s also an important step in this overall journey to enable us to quickly teach a robot new tasks.

The researchers first trained a sequence of neural networks to detect objects, infer the relationship between them and then generate a program to repeat the steps it witnessed the human perform. The researchers say this new system allowed them to train their robot to perform this stacking task with a single demonstration in the real world.

One nifty aspect of this system is that it generates a human-readable description of the steps it’s performing. That way, it’s easier for the researchers to figure out what happened when things go wrong.

Nvidia’s Stan Birchfield tells me that the team aimed to make training the robot easy for a non-expert — and few things are easier to do than to demonstrate a basic task like stacking blocks. In the example the team presented in Brisbane, a camera watches the scene and the human simply walks up, picks up the blocks and stacks them. Then the robot repeats the task. Sounds easy enough, but it’s a massively difficult task for a robot.

To train the core models, the team mostly used synthetic data from a simulated environment. As both Birchfield and Fox stressed, it’s these simulations that allow for quickly training robots. Training in the real world would take far longer, after all, and can also be more far more dangerous. And for most of these tasks, there is no labeled training data available to begin with.

“We think using simulation is a powerful paradigm going forward to train robots do things that weren’t possible before,” Birchfield noted. Fox echoed this and noted that this need for simulations is one of the reasons why Nvidia thinks that its hardware and software is ideally suited for this kind of research. There is a very strong visual aspect to this training process, after all, and Nvidia’s background in graphics hardware surely helps.

Fox admitted that there’s still a lot of research left to do be done here (most of the simulations aren’t photorealistic yet, after all), but that the core foundations for this are now in place.

Going forward, the team plans to expand the range of tasks that the robots can learn and the vocabulary necessary to describe those tasks.

News Source = techcrunch.com

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With at least $1.3 billion invested globally in 2018, VC funding for blockchain blows past 2017 totals

Although bitcoin and blockchain technology may not take up quite as much mental bandwidth for the general public as it did just a few months ago, companies in the space continue to rake in capital from investors.

One of the latest to do so is Circle, which recently announced a $110 million Series E round led by bitcoin mining hardware manufacturer Bitmain. Other participating investors include Tusk VenturesPantera CapitalIDG Capital PartnersGeneral CatalystAccel PartnersDigital Currency GroupBlockchain Capital and Breyer Capital.

This round vaults Circle into an exclusive club of crypto companies that are valued, in U.S. dollars, at $1 billion or more in their most recent venture capital round. According to Crunchbase data, Circle was valued at $2.9 billion pre-money, up from a $420 million pre-money valuation in its Series D round, which closed in May 2016. According to Crunchbase data, only Coinbase and Robinhood — a mobile-first stock-trading platform which recently made a big push into cryptocurrency trading — were in the crypto-unicorn club, which Circle has now joined.

But that’s not the only milestone for the world of venture-backed cryptocurrency and blockchain startups.

Back in February, Crunchbase News predicted that the amount of money raised in old-school venture capital rounds by blockchain and blockchain-adjacent startups in 2018 would surpass the amount raised in 2017. Well, it’s only May, and it looks like the prediction panned out.

In the chart below, you’ll find worldwide venture deal and dollar volume for blockchain and blockchain-adjacent companies. We purposely excluded ICOs, including those that had traditional VCs participate, and instead focused on venture deals: angel, seed, convertible notes, Series A, Series B and so on. The data displayed below is based on reported data in Crunchbase, which may be subject to reporting delays, and is, in some cases, incomplete.

A little more than five months into 2018, reported dollar volume invested in VC rounds raised by blockchain companies surpassed 2017’s totals. Not just that, the nearly $1.3 billion in global dollar volume is greater than the reported funding totals for the 18 months between July 1, 2016 and New Year’s Eve in 2017.

And although Circle’s Series E round certainly helped to bump up funding totals year-to-date, there were many other large funding rounds throughout 2018:

There were, of course, many other large rounds over the past five months. After all, we had to get to $1.3 billion somehow.

All of this is to say that investor interest in the blockchain space shows no immediate signs of slowing down, even as the price of bitcoin, ethereum and other cryptocurrencies hover at less than half of their all-time highs. Considering that regulators are still figuring out how to treat most crypto assets, massive price volatility and dubious real-world utility of the technology, it may surprise some that investors at the riskiest end of the risk capital pool invest as much as they do in blockchain.

Notes on methodology

Like in our February analysis, we first created a list of companies in Crunchbase’s bitcoin, ethereum, blockchaincryptocurrency and virtual currency categories. We added to this list any companies that use those keywords, as well as “digital currency,” “utility token” and “security token” that weren’t previously included in the above categories. After de-duplicating this list, we merged this set of companies with funding rounds data in Crunchbase.

Please note that for some entries in Crunchbase’s round data, the amount of capital raised isn’t known. And, as previously noted, Crunchbase’s data is subject to reporting delays, especially for seed-stage companies. Accordingly, actual funding totals are likely higher than reported here.

News Source = techcrunch.com

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Artificial Intelligence

AI will save us from yanny/laurel, right? Wrong

If you haven’t taken part in the yanny/laurel controversy over the last couple days, allow me to sincerely congratulate you. But your time is up. The viral speech synth clip has met the AI hype train and the result is, like everything in this mortal world, disappointing.

Sonix, a company that produces AI-based speech recognition software, ran the ambiguous sound clip through Google, Amazon, and Watson’s transcription tools, and of course its own.

Google and Sonix managed to get it on the first try — it’s “laurel,” by the way. Not yanny. Laurel.

But Amazon stumbled, repeatedly producing “year old” as its best guess for what the robotic voice was saying. IBM’s Watson, amazingly, got it only half the time, alternating between hearing “yeah role” and “laurel.” So in a way, it’s the most human of them all.

Top: Amazon; bottom: IBM.

Sonix CEO Jamie Sutherland told me in an email that he can’t really comment on the mixed success of the other models, not having access to them.

“As you can imagine the human voice is complex and there are so many variations of volume, cadence, accent, and frequency,” he wrote. “The reality is that different companies may be optimizing for different use cases, so the results may vary. It is challenging for a speech recognition model to accommodate for everything.”

My guess as an ignorant onlooker is it may have something to do with the frequencies the models have been trained to prioritize. Sounds reasonable enough!

It’s really an absurd endeavor to appeal to a system based on our own hearing and cognition to make an authoritative judgement in a matter on which our hearing and cognition are demonstrably lacking. But it’s still fun.

News Source = techcrunch.com

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