Year: 2018

Electric scooters have become the hot new area for startups and “innovation.” For those who haven’t been keeping track, there are three main players in the Silicon Valley scooter wars: Bird, Lime and Spin. Bird first launched in Venice, Calif. before expanding into San Francisco in March. It’s worth pointing out that Bird, for now, is strictly an electric scooter company. That’s not the case for Lime and Spin, which both have their own bike-share services deployed throughout various parts of the country and world.

That same month — almost in complete lockstep — Lime and Spin deployed their own electric scooters in the city. Fast forward to June and the city of SF has placed a temporary hold on electric scooters until it can review permit applications. As part of a new city law, which went into effect June 4, scooter companies are not able to operate their services in SF without a permit.

Twelve companies (Uber/JUMP, Lyft, Skip, Spin, Lime, Scoot, ofo, Skip, Razor, CycleHop, USSCooter and Ridecell) have applied for permits in SF, but the city’s Municipal Transportation Agency will issue permits for no more than five companies during the 24-month pilot program. The program would grant up to 2,500 scooters to operate in total, but it’s not yet clear how many scooters each company would be allowed to deploy.

Uber and Lyft’s entrance into the electric scooter space was expected, given that Uber CEO Dara Khosrowshahi told me in April that he had his eyes on electric scooters, and Lyft had reportedly been in talks with the SFMTA about its permitting process. But it became more official this past week when both companies applied for permits to operate in SF. Both Uber and Lyft, which have both recently announced public transit integration, are clearly vying to become the one-stop shop for all transportation needs.

The SFMTA said it’s aiming to notify companies of their permit status by the end of June. If issued a permit, companies must then pay an annual permit fee of $25,000, as well as a $10,000 public property repair and maintenance endowment. Companies must also share trip data with the city.

But the scooter moratorium in SF has little effect on the state of scooters as a whole. The last week alone has been filled with multimillion-dollar investments in electric scooter companies like Bird and Lime. Bird authorized a new $200 million funding round that could value the company at around $1 billion post-money, and Bird competitor Lime is also reportedly raising $250 million. 

Below, you can see where some of these newer players stack up in comparison to each other. This is just a look at companies that have deployed electric scooters in the United States.

Where the scooters at

California is the main hot spot for scooters in the U.S., but they have also popped up in Texas, Washington D.C., North Carolina and other states throughout the country. Unsurprisingly, regulation has proved to be an issue for many of these companies. In SF, the MTA is currently reviewing permit applications from electric scooter companies looking to operate in the city. The permit process came as a result of Bird, Lime and Spin deploying their electric scooters without permission in the city in March.

Over in Austin, dockless electric scooter startup GOAT says it’s working with the city to ensure its service meets the criteria laid out by regulators. Moving forward, GOAT says it’s actively working with other cities to pursue additional operating permits. In D.C., Skip, which is trying to differentiate itself by being more heavy-duty, worked with city officials and lawmakers to ensure it had the greenlight before launching.

Here’s an overview of where you can expect to see electric scooters throughout the country.

Outside of the U.S., Bird is looking at deploying scooters throughout Europe, the Middle East and Africa. In February, Bird brought on Patrick Studener, a former international growth product manager at Uber, to serve as head of EMEA at Bird, according to Studener LinkedIn. Earlier this week, TechCrunch also spotted a job posting for a general manager in Europe to lead market management.

Meanwhile, a source sent us a Lime on the streets of Zurich, Switzerland. It turns out Lime is working with the city around some pilot programs with private businesses.

Building scooters

Many companies aren’t actually building their own scooters. Instead, they’re slapping stickers and logos on scooters that have been around for years. Lime, Bird and Spin launched using scooters from Ninebot, a Chinese scooter company that has merged with Segway. Ninebot is backed by investors including Sequoia Capital, Xiaomi and ShunWei. But Lime, Skip, Spin and Bird are looking to change that.

In May, Lime partnered with Segway to launch its next generation of electric scooters. These Segway-powered Lime scooters are designed to be safer, longer-lasting via battery power and more durable for what the sharing economy requires, Lime CEO Toby Sun told TechCrunch last month. Now, instead of a maximum distance of 23 miles or so, Lime scooters can go up to 35 miles.

“A lot of the features in the past on scooters were made for the consumer market,” Sun said. “Not for the shared, heavy-duty markets.”

Bird is also experimenting with some new scooter models, but they seem to modified versions of a Segway ES2. When reached for comment, Bird said it didn’t have many details to provide. Meanwhile, Skip does have plans to build its own custom scooters but currently modifies the Speedway Mini4 63V 21Ah scooters.

With Spin, the company does have plans to build its own scooters but isn’t ready to announce details. What Spin CEO Euwyn Poon would share with me is that the company has spun up a custom production line and supply chain.

GOAT, on the other hand, is deliberately taking the partnership route, having developed GOAT on top of a Segway scooter since the beginning.

Next week professional services firm Accenture will be launching a new tool to help its customers identify and fix unfair bias in AI algorithms. The idea is to catch discrimination before it gets baked into models and can cause human damage at scale.

The “AI fairness tool”, as it’s being described, is one piece of a wider package the consultancy firm has recently started offering its customers around transparency and ethics for machine learning deployments — while still pushing businesses to adopt and deploy AI. (So the intent, at least, can be summed up as: ‘Move fast and don’t break things’. Or, in very condensed corporate-speak: “Agile ethics”.) 

“Most of last year was spent… understanding this realm of ethics and AI and really educating ourselves, and I feel that 2018 has really become the year of doing — the year of moving beyond virtue signaling. And moving into actual creation and development,” says Rumman Chowdhury, Accenture’s responsible AI lead — who joined the company when the role was created, in January 2017.

“For many of us, especially those of us who are in this space all the time, we’re tired of just talking about it — we want to start building and solving problems, and that’s really what inspired this fairness tool.”

Chowdhury says Accenture is defining fairness for this purpose as “equal outcomes for different people”. 

“There is no such thing as a perfect algorithm,” she says. “We know that models will be wrong sometimes. We consider it unfair if there are different degrees of wrongness… for different people, based on characteristics that should not influence the outcomes.”

She envisages the tool having wide application and utility across different industries and markets, suggesting early adopters are likely those in the most heavily regulated industries — such as financial services and healthcare, where “AI can have a lot of potential but has a very large human impact”.

“We’re seeing increasing focus on algorithmic bias, fairness. Just this past week we’ve had Singapore announce an AI ethics board. Korea announce an AI ethics board. In the US we already have industry creating different groups — such as The Partnership on AI. Google just released their ethical guidelines… So I think industry leaders, as well as non-tech companies, are looking for guidance. They are looking for standards and protocols and something to adhere to because they want to know that they are safe in creating products.

“It’s not an easy task to think about these things. Not every organization or company has the resources to. So how might we better enable that to happen? Through good legislation, through enabling trust, communication. And also through developing these kinds of tools to help the process along.”

The tool — which uses statistical methods to assess AI models — is focused on one type of AI bias problem that’s “quantifiable and measurable”. Specifically it’s intended to help companies assess the data sets they feed to AI models to identify biases related to sensitive variables and course correct for them, as it’s also able to adjust models to equalize the impact.

To boil it down further, the tool examines the “data influence” of sensitive variables (age, gender, race etc) on other variables in a model — measuring how much of a correlation the variables have with each other to see whether they are skewing the model and its outcomes.

It can then remove the impact of sensitive variables — leaving only the residual impact say, for example, that ‘likelihood to own a home’ would have on a model output, instead of the output being derived from age and likelihood to own a home, and therefore risking decisions being biased against certain age groups.

“There’s two parts to having sensitive variables like age, race, gender, ethnicity etc motivating or driving your outcomes. So the first part of our tool helps you identify which variables in your dataset that are potentially sensitive are influencing other variables,” she explains. “It’s not as easy as saying: Don’t include age in your algorithm and it’s fine. Because age is very highly correlated with things like number of children you have, or likelihood to be married. Things like that. So we need to remove the impact that the sensitive variable has on other variables which we’re considering to be not sensitive and necessary for developing a good algorithm.”

Chowdhury cites an example in the US, where algorithms used to determine parole outcomes were less likely to be wrong for white men than for black men. “That was unfair,” she says. “People were denied parole, who should have been granted parole — and it happened more often for black people than for white people. And that’s the kind of fairness we’re looking at. We want to make sure that everybody has equal opportunity.”

However, a quirk of AI algorithms is that when models are corrected for unfair bias there can be a reduction in their accuracy. So the tool also calculates the accuracy of any trade-off to show whether improving the model’s fairness will make it less accurate and to what extent.

Users get a before and after visualization of any bias corrections. And can essentially choose to set their own ‘ethical bar’ based on fairness vs accuracy — using a toggle bar on the platform — assuming they are comfortable compromising the former for the latter (and, indeed, comfortable with any associated legal risk if they actively select for an obviously unfair tradeoff).

In Europe, for example, there are rules that place an obligation on data processors to prevent errors, bias and discrimination in automated decisions. They can also be required to give individuals information about the logic of an automated decision that effects them. So actively choosing a decision model that’s patently unfair would invite a lot of legal risk.

 

While Chowdhury concedes there is an accuracy cost to correcting bias in an AI model, she says trade-offs can “vary wildly”. “It can be that your model is incredibly unfair and to correct it to be a lot more fair is not going to impact your model that much… maybe by 1% or 2% [accuracy]. So it’s not that big of a deal. And then in other cases you may see a wider shift in model accuracy.”

She says it’s also possible the tool might raise substantial questions for users over the appropriateness of an entire data-set — essentially showing them that a data-set is “simply inadequate for your needs”.

“If you see a huge shift in your model accuracy that probably means there’s something wrong in your data. And you might need to actually go back and look at your data,” she says. “So while this tool does help with corrections it is part of this larger process — where you may actually have to go back and get new data, get different data. What this tool does is able to highlight that necessity in a way that’s easy to understand.

“Previously people didn’t have that ability to visualize and understand that their data may actually not be adequate for what they’re trying to solve for.”

She adds: “This may have been data that you’ve been using for quite some time. And it may actually cause people to re-examine their data, how it’s shaped, how societal influences influence outcomes. That’s kind of the beauty of artificial intelligence as a sort of subjective observer of humanity.”

While tech giants may have developed their own internal tools for assessing the neutrality of their AI algorithms — Facebook has one called Fairness Flow, for example — Chowdhury argues that most non-tech companies will not be able to develop their own similarly sophisticated tools for assessing algorithmic bias.

Which is where Accenture is hoping to step in with a support service — and one that also embeds ethical frameworks and toolkits into the product development lifecycle, so R&D remains as agile as possible.

“One of the questions that I’m always faced with is how do we integrate ethical behavior in way that aligns with rapid innovation. So every company is really adopting this idea of agile innovation and development, etc. People are talking a lot about three to six month iterative processes. So I can’t come in with an ethical process that takes three months to do. So part of one of my constraints is how do I create something that’s easy to integrate into this innovation lifecycle.”

One specific draw back is that currently the tool has not been verified working across different types of AI models. Chowdhury says it’s principally been tested on models that use classification to group people for the purposes of building AI models, so it may not be suitable for other types. (Though she says their next step will be to test it for “other kinds of commonly used models”.)

More generally, she says the challenge is that many companies are hoping for a magic “push button” tech fix-all for algorithmic bias. Which of course simply does not — and will not — exist.

“If anything there’s almost an overeagerness in the market for a technical solution to all their problems… and this is not the case where tech will fix everything,” she warns. “Tech can definitely help but part of this is having people understand that this is an informational tool, it will help you, but it’s not going to solve all your problems for you.”

The tool was co-prototyped with the help of a data study group at the UK’s Alan Turing Institute, using publicly available data-sets. 

During prototyping, when the researchers were using a German data-set relating to credit risk scores, Chowdhury says the team realized that nationality was influencing a lot of other variables. And for credit risk outcomes they found decisions were more likely to be wrong for non-German nationals.

They then used the tool to equalize the outcome and found it didn’t have a significant impact on the model’s accuracy. “So at the end of it you have a model that is just as accurate as the previous models were in determining whether or not somebody is a credit risk. But we were confident in knowing that one’s nationality did not have undue influence over that outcome.”

A paper about the prototyping of the tool will be made publicly available later this year, she adds.

With Microsoft’s $7.5 billion acquisition of GitHub this week, we can now decisively declare a trend: 2018 is shaping up as a darn good year for U.S. venture-backed M&A.

So far this year, acquirers have spent just over $20 billion in disclosed-price purchases of U.S. VC-funded companies, according to Crunchbase data. That’s about 80 percent of the 2017 full-year total, which is pretty impressive, considering we’re barely five months into 2018.

If one included unreported purchase prices, the totals would be quite a bit higher. Fewer than 20 percent of acquisitions in our data set came with reported prices.¹ Undisclosed prices are mostly for smaller deals, but not always. We put together a list of a dozen undisclosed price M&A transactions this year involving companies snapped up by large-cap acquirers after raising more than $20 million in venture funding.

The big deals

The deals that everyone talks about, however, are the ones with the big and disclosed price tags. And we’ve seen quite a few of those lately.

As we approach the half-year mark, nothing comes close to topping the GitHub deal, which ranks as one of the biggest acquisitions of a private, U.S. venture-backed company ever. The last deal to top it was Facebook’s $19 billion purchase of WhatsApp in 2014, according to Crunchbase.

Of course, GitHub is a unique story with an astounding growth trajectory. Its platform for code development, most popular among programmers, has drawn 28 million users. For context, that’s more than the entire population of Australia.

Still, let’s not forget about the other big deals announced in 2018. We list the top six below:

Flatiron Health, a provider of software used by cancer care providers and researchers, ranks as the second-biggest VC-backed acquisition of 2018. Its purchaser, Roche, was an existing stakeholder who apparently liked what it saw enough to buy up all remaining shares.

Next up is job and employer review site Glassdoor, a company familiar to many of those who’ve looked for a new post or handled hiring in the past decade. The 11-year-old company found a fan in Tokyo-based Recruit Holdings, a provider of recruitment and human resources services that also owns leading job site Indeed.com.

Meanwhile, Impact Biomedicines, a cancer therapy developer that sold to Celgene for $1.1 billion, could end up delivering an even larger exit. The acquisition deal includes potential milestone payments approaching nearly $6 billion.

Deal counts look flat

Not all metrics are trending up, however. While acquirers are doing bigger deals, they don’t appear to be buying a larger number of startups.

Crunchbase shows 216 startups in our data set that sold this year. That’s roughly on par with the pace of dealmaking in the year-ago period, which had 222 M&A exits using similar parameters. (For all of 2017, there were 508 startup acquisitions that met our parameters.²)

Below, we look at M&A counts for the past five calendar years:

Looking at prior years for comparison, the takeaway seems to be that M&A deal counts for 2018 look just fine, but we’re not seeing a big spike.

What’s changed?

The more notable shift from 2017 seems to be buyers’ bigger appetite for unicorn-scale deals. Last year, we saw just one acquisition of a software company for more than a billion dollars — Cisco’s $3.7 billion purchase of AppDynamics — and that was only after the performance management software provider filed to go public. The only other billion-plus deal was PetSmart’s $3.4 billion acquisition of pet food delivery service Chewy, which previously raised early venture funding and later private equity backing.

There are plenty of reasons why acquirers could be spending more freely this year. Some that come to mind: Stock indexes are chugging along, and U.S. legislators have slashed corporate tax rates. U.S. companies with large cash hordes held overseas, like Apple and Microsoft, also received new financial incentives to repatriate that money.

That’s not to say companies are doing acquisitions for these reasons. There’s no obligation to spend repatriated cash in any particular way. Many prefer share buybacks or sitting on piles of money. Nonetheless, the combination of these two things — more money and less uncertainty around tax reform — are certainly not a bad thing for M&A.

High public valuations, particularly for tech, also help. Microsoft shares, for instance, have risen by more than 44 percent in the past year. That means that it took about a third fewer shares to buy GitHub this month than it would have a year ago. (Of course, GitHub’s valuation probably rose as well, but we’ll ignore that for now.)

Paying retail

Overall, this is not looking like an M&A market for bargain hunters.

Large-cap acquirers seem willing to pay retail price for startups they like, given the competitive environment. After all, the IPO window is wide open. Plus, fast-growing unicorns have the option of staying private and raising money from SoftBank or a panoply of other highly capitalized investors.

Meanwhile, acquirers themselves are competing for desirable startups. Microsoft’s winning bid for GitHub reportedly followed overtures by Google, Atlassian and a host of other would-be buyers.

But even in the most buoyant climate, one rule of acquiring remains true: It’s hard to turn down $7.5 billion.

1. The data set included companies that have raised $1 million or more in venture or seed funding, with their most recent round closing within the past five years.
2. For the prior year comparisons, including the chart, the data set consisted of companies acquired in a specified year that raised $1 million or more in venture or seed funding, with their most recent round closing no more than five years before the middle of that year.

For many of us, clean, drinkable water comes right out of the tap. But for billions it’s not that simple, and all over the world researchers are looking into ways to fix that. Today brings work from Berkeley, where a team is working on a water-harvesting apparatus that requires no power and can produce water even in the dry air of the desert. Hey, if a cactus can do it, why can’t we?

While there are numerous methods for collecting water from the air, many require power or parts that need to be replaced; what professor Omar Yaghi has developed needs neither.

The secret isn’t some clever solar concentrator or low-friction fan — it’s all about the materials. Yaghi is a chemist, and has created what’s called a metal-organic framework, or MOF, that’s eager both to absorb and release water.

It’s essentially a powder made of tiny crystals in which water molecules get caught as the temperature decreases. Then, when the temperature increases again, the water is released into the air again.

Yaghi demonstrated the process on a small scale last year, but now he and his team have published the results of a larger field test producing real-world amounts of water.

They put together a box about two feet per side with a layer of MOF on top that sits exposed to the air. Every night the temperature drops and the humidity rises, and water is trapped inside the MOF; in the morning, the sun’s heat drives the water from the powder, and it condenses on the box’s sides, kept cool by a sort of hat. The result of a night’s work: 3 ounces of water per pound of MOF used.

That’s not much more than a few sips, but improvements are already on the way. Currently the MOF uses zicronium, but an aluminum-based MOF, already being tested in the lab, will cost 99 percent less and produce twice as much water.

With the new powder and a handful of boxes, a person’s drinking needs are met without using any power or consumable material. Add a mechanism that harvests and stores the water and you’ve got yourself an off-grid potable water solution.

“There is nothing like this,” Yaghi explained in a Berkeley news release. “It operates at ambient temperature with ambient sunlight, and with no additional energy input you can collect water in the desert. The aluminum MOF is making this practical for water production, because it is cheap.”

He says there are already commercial products in development. More tests, with mechanical improvements and including the new MOF, are planned for the hottest months of the summer.

Just processed by the SEC on this bright Friday afternoon: TraceLink, a software-as-a-service platform for tracking pharmaceuticals and trying to weed out counterfeit prescription drugs in the process, has raised $60 million in Series D funding.

The filing shows that 18 firms participated, including, presumably, Goldman Sachs, whose growth equity arm had led the company’s $51.5 million Series C round roughly 18 months ago. Others of the nine-year-old company’s earlier investors include FirstMark Capital, Volition Capital, and F-Prime Capital.

As TC’s Jordan Crook reported at the time of that last round, TraceLink helps pharma companies comply with country-specific track-and-trace requirements through their supply chain, which has grown increasingly important following the passage of the Drug Supply Chain Security Act in 2013. The consumer-protection measure aims to protect consumers from exposure to drugs that could be counterfeit, stolen, contaminated, or otherwise harmful.

At the time of its enactment, it also gave the industry one decade before unit-level traceability becomes enforced, meaning the clock is ticking.

Also working in the favor of TraceLink: opioids, whose spread has been rising since the late ’90s, creating ever-growing pressure to isolate vulnerabilities in the pharmaceutical supply chain.

Little wonder the company looks to be preparing for life as a publicly traded company, including by releasing quarterly revenue and customer growth numbers. Indeed, according to its “growth highlights,” released just a couple of weeks ago, the company’s first quarter revenue in 2018 was 69 percent higher than it was in the first quarter of 2017.

An optical illusion popular in the 19th century could make trips on the Hyperloop appear to take place in a transparent tube. Regularly spaced, narrow windows wouldn’t offer much of a view individually, but if dozens of them pass by every second an effect would be created like that of a zoetrope, allowing passengers to effectively see right through the walls.

It’s an official concept from Virgin Hyperloop One and design house Bjarke Ingels Group (BIG), and in fact was teased back in 2016. Now the companies have shared a video showing how it would work and what it would look like for passengers — though there’s no indication it would actually be put in place in the first tracks.

A zoetrope is a simple apparatus consisting of a cylinder with slits on the sides and a series of sequential or looping images printed on the inside. When the cylinder is spun, the slits blur together to the eye but have the effect of showing the images on the inside clearly as if they are succeeding one another — an elementary form of animation.

Want an example? Here’s Pixar breaking down a zoetrope it built for Disney’s California Adventure:

The design concept shown is actually a linear zoetrope, in which the images are viewed not as a loop inside a cylinder, but in a long strip. You may have seen these before in the form of animated advertisements visible through the windows of subways.

In the case of the Hyperloop, the tube through which the “pod” moves would have portholes or slit windows placed every 10 meters through which the outside world is visible. At low speeds these would merely zoom by a few per second and might even be unpleasantly strobe-like, but that would smooth out as the pods reach their target speed of 1200 KPH (about 745 MPH).

The team simulated how it would appear in the video below:

Is it really necessary? You could, of course, just provide a faked view of the outside via LCD “portholes” or have people focus on their own little TV screens, like on an airplane. But that wouldn’t be nearly as cool. Perhaps the windows could double as escape or access hatches; as you can see above on the existing test track, there are already such holes, so this may be easier than expected to implement.

Of course, it all seems a little premature, as Hyperloop type transport is still very much in prototype form and existing endeavors to bring it to life may in fact never come to fruition. Nevertheless, it is a clever and interesting way to solve the problem of preventing people from thinking about the fact that they’re traveling at ludicrous speeds down a narrow tube.

Earlier this week, Microsoft announced the second phase of Project Natick, a research experiment that aims to understand the benefits and challenges of deploying large-scale data centers under water. In this second phase, the team sank a tank the size of a shipping container with numerous server racks off the coast of the Orkney islands and plans to keep it there for a few years to see if this is a viable way of deploying data centers in the future.

Computers and water famously don’t mix, as anyone who has ever spilled a cup of water over a laptop, so putting server racks under water sure seems like an odd idea. But as Microsoft Research’s Ben Cutler told me, there are good reasons for why the bottom of the ocean may be a good place for setting up servers.

The vast majority of people live within 200 kilometers of the ocean, Cutler noted, and Microsoft’s cloud strategy has long been about putting its data centers close to major population centers. So with large offshore wind farms potentially providing renewable power and the obvious cooling benefits of being under water (and cooling is a major cost factor for data centers), trying an experiment like this makes sense.

“Within Microsoft, we’ve spent an enormous amount of energy and time on cloud — and obviously money,” Cutler explained when I asked him about the genesis of this project. “So we’re always looking for new ways that we can innovate. And this idea sort of gelled originally with one of our employees who worked on a U.S. Navy submarine and knew something about this technology, and that this could maybe be applied to data centers.”

So back in 2013, the team launched phase one and dropped a small pressure vessel with a few servers into the waters of the Pacific Ocean. That experiment worked out pretty well. Even the local sea life seemed to appreciate it. The team found that the vessel didn’t heat up the water close to it by more than a few thousandths of a degree Celsius warmer than a few feet further away from it. The noise, too, was pretty much negligible. “We found that once we were a few meters away from the vessel, we were drowned out by background noise, which is things like snapping shrimp, which is actually the predominant sound of the ocean,” Cutler told me, and stressed that the team’s job is to measure all of this as the ocean is obviously a very sensitive environment. “What we found was that we’re very well received by wildlife and we’re very quickly colonized by crabs and octopus and other things that were in the area.”

For this second phase, the team decided on the location off the coast of Scotland because it’s also home to the European Marine Energy Center, so the infrastructure for powering the vessel from renewable energy from on- and off-shore sources was already in place.

Once the vessel is in the ocean, maintenance is pretty much impossible. The idea here is to accept that things will fail and can’t be replaced. Then, after a few years, the plan is to retrieve the vessel, refurbish it with new machines and deploy it again.

But as part of this experiment, the team also thought about how to best make these servers last as long as possible — and because nobody has to go replace a broken hard drive inside the vessel, the team decided to fill the atmosphere with nitrogen to prevent corrosion, for example. To measure the impact of that experiment, Microsoft also maintains a similar vessel on land so it can compare how well that system fares over time.

Cutler stressed that nothing here is cutting-edge technology. There are no exotic servers here and both underwater cabling and building vessels like this are well understood at this point.

Over time, Cutler envisions a factory that can prefabricate these vessels and ship them to where they are needed. That’s why the vessel is about the size of a shipping container and the team actually had it fabricated in France, loaded it on a truck and shipped it to England to test this logistics chain.

Whether that comes to pass remains to be seen, of course. The team is studying the economics of Natick for the time being, and then it’s up to Microsoft’s Azure team to take this out of the research labs and put it into more widespread production. “Our goal here is to drive this to a point where we understand that the economics make sense and that it has the characteristics that we wanted it to, and then it becomes a tool for that product group to decide whether and where to use it,” said Cutler.

Uber and Lyft have officially put their respective names into the electric scooter competition. Uber and Lyft are among the eleven companies that applied to operate an electric scooter sharing service within San Francisco city limits. The city, however, will only offer up to five companies permits to operate as part of a one-year test program.

Uber declined to comment but confirmed that it has applied for a permit via JUMP, the bike-share startup Uber acquired for about $200 million in April. Once Uber is cleared to operate electric scooters, the plan is to integrate them into the Uber app and continue fleshing out Uber CEO Dara Khosrowshahi’s vision for a full-fledged multi-modal transportation platform.

Lyft also confirmed to TechCrunch that the company applied for a permit, but declined to share any further details. Here’s the full list of companies that applied, via the SF Chronicle:

1. Bird
2. CycleHop
3. JUMP via Uber
4. Lime
5. Lyft
6. ofo
7. Razor (yes, *that* Razor)
8. Ridecell
9. Scoot
10.  Spin
11.  USSCooter

San Francisco’s permit process came as a result of Bird, Lime and Spin deploying their electric scooters without permission in the city in March. As part of a new city law, which went into effect June 4, scooter companies are not able to operate their services in San Francisco without a permit. The SFMTA said it’s aiming to notify companies of their permit status by the end of June.

For more information about electric scooter regulation in San Francisco, be sure to check out my previous coverage.

IBM and the U.S. Department of Energy’s Oak Ridge National Laboratory (ORNL) today’s unveiled Summit, the department’s newest supercomputer. IBM claims that Summit is currently the world’s “most powerful and smartest scientific supercomputer” with a peak performance of a whopping 200,000 trillion calculations per second. That performance should put it comfortably at the top of the Top 500 supercomputer ranking when the new list is published later this month. That would also mark the first time since 2012 that a U.S.-based supercomputer holds the top spot on that list.

Summit, which has been in the works for a few years now, features 4,608 compute servers with two 22-core IBM Power9 chips and six Nvidia Tesla V100 GPUs each. In total, the system also features over 10 petabytes of memory. Given the presence of the Nvidia GPUs, it’s no surprise that the system is meant to be used for machine learning and deep learning applications, as well as the usual high performance computing workloads for research in energy and advanced materials that you would expect to happen at Oak Ridge.

IBM was the general contractor for Summit and the company collaborated with Nvidia, RedHat and InfiniBand networking specialists Mellanox on delivering the new machine.

“Summit’s AI-optimized hardware also gives researchers an incredible platform for analyzing massive datasets and creating intelligent software to accelerate the pace of discovery,” said Jeff Nichols, ORNL associate laboratory director for computing and computational sciences, in today’s announcement.

Summit is one of two of these next-generation supercomputers that IBM is building for the DEO. The second one is Sierra, which will be housed at the Lawrence Livermore National Laboratory. Sierra, which is also scheduled to go online this year, is less powerful at an expected 125 petaflops, but both systems are significantly more powerful than any other machine in the DoE’s arsenal right now.

Airbnb has been one of the breakthrough stories in the wave of shared-economy startups that have emerged out of Silicon Valley, with a valuation of $30 billion for its travellers platform that lets people book private homes as accommodations, as well as other services. But even so, it’s not immune to the force of regulation and the impact it can have on its business.

Airbnb has had to cancel a swathe of reservations in Japan, after a change in local laws required hosts to have specific licenses, but some have failed to get these ahead of the deadline set by regulators.

It’s not clear how many people or hosts have been impacted — the numbers are shifting as hosts receive their licenses — but Airbnb says that it has set up a fund of $10 million to cover any travellers who might get put out as a result of the rules. Some have estimated that as much as 80 percent of bookings have been impacted by the changes.

As Airbnb notes, the cancellations and its resulting moves are a result of changes to the country’s Japanese Hotels and Inns Act. Modified last year to include people using private homes for tourist accommodation for up to 180 days/year, those hosting now have to register and display a license number alongside their listings. The tourism authority (JTA) had set a deadline of June 15 to do this, and those who hadn’t received a license by June 1 had to cancel reservations booked before June 15, and Airbnb has extended this to cover the gap of other travellers so that they have time to make alternative arrangements:

“Any reservation scheduled for guest arrival between June 15 and June 19 at a listing in Japan that does not currently have a license has been cancelled,” Airbnb writes. “Going forward, unless the government reverses its position, we will automatically cancel and fully refund any reservations at listings in Japan that have not been licensed within 10 days of guest arrival.”

The $10 million fund, Airbnb said, will cover “additional expenses for guests who are scheduled to travel to Japan and have had their plans interrupted due to a cancellation.” Those whose reservations are cancelled on or after June 15 because of the license situation will get a full refund and a coupon worth “at least 100% of the booking value” to use on a future Airbnb trip. They will also receive a $100 coupon for an Airbnb Experience. 

Those who are unable to find alternative Airbnb places to stay for their trip will be put in touch with a travel agency in Japan, JTB, to find alternatives.

For those who are impacted by this news, Airbnb will be sending you step-by-step instructions of what to do next, or you can find them here.

This is not the first time that Airbnb has had a stumble on the heels of regulatory changes. In Amsterdam, regulators are preparing to halve the number of nights a property can be let out to 30 nights per year starting in 2019, from 60 nights currently. Berlin and Barcelona have also tried to limit the platform’s growth with their own regulations.