disrupt sf 2018 Archives - Product Management Confabulation

September 7, 2018

And the winner of Startup Battlefield at Disrupt SF 2018 is… Forethought

At the very beginning, there were 21 startups. After three days of incredibly fierce competition, we now have a winner.

Startups participating in the Startup Battlefield have all been hand-picked to participate in our highly competitive startup competition. They all presented in front of multiple groups of VCs and tech leaders serving as judges for a chance to win $100,000 and the coveted Disrupt Cup.

After hours of deliberations, TechCrunch editors pored over the judges’ notes and narrowed the list down to five finalists: CB Therapeutics, Forethought, Mira, Origami Labs and Unbound.

These startups made their way to the finale to demo in front of our final panel of judges, which included: Cyan Banister (Founders Fund), Roelof Botha (Sequoia Capital), Jeff Clavier (Uncork Capital), Kirsten Green (Forerunner Ventures), Aileen Lee (Cowboy Ventures) and Matthew Panzarino (TechCrunch).

And now, meet the Startup Battlefield winner of TechCrunch Disrupt SF 2018.

Winner: Forethought

Forethought has a modern vision for enterprise search that uses AI to surface the content that matters most in the context of work. Its first use case involves customer service, but it has a broader ambition to work across the enterprise.

Read more about Forethought in our separate post.

Runner-Up: Unbound

Unbound makes fashion-forward vibrators, and their latest is the Palma. The new device masquerades as a ring, offers multiple speeds, and is completely waterproof. And the team plans to add accelerometer features.

Read more about Unbound in our separate post.

September 7, 2018

And the winner of Startup Battlefield at Disrupt SF 2018 is… Forethought

At the very beginning, there were 21 startups. After three days of incredibly fierce competition, we now have a winner.

After hours of deliberations, TechCrunch editors pored over the judges’ notes and narrowed the list down to five finalists: CB Therapeutics, Forethought, Mira, Origami Labs and Unbound.

And now, meet the Startup Battlefield winner of TechCrunch Disrupt SF 2018.

Winner: Forethought

Read more about Forethought in our separate post.

Runner-Up: Unbound

Read more about Unbound in our separate post.

September 7, 2018

How Adidas and Carbon are changing the sneaker supply chain

While the Adidas Futurecraft 4D shoes are cool looking sneakers, the story behind those shoes is even more interesting. The sportswear company has partnered with Carbon to design a new kind of sneakers.

Behind the Futurecraft 4D, you can find a process that is not that new — 3D printing. Many companies promised an industrial revolution by bringing back factories to service-driven countries, such as the U.S. and European countries. But this partnership between Adidas and Carbon could turn that wild dream into a reality.

“What you saw there was basically this integration of hardware, software and chemistry all coming together to take a digital model, print it very fast, but do it out of the materials that have the properties to be final parts,” Carbon co-founder and CEO Joseph DeSimone told TechCrunch’s Matthew Panzarino.

And the secret sauce behind Carbon’s process is its cloud-based software tool. You use a primitive CAD, define some mechanical properties and it gets manufactured in front of your eyes.

It’s quite hard to buy Futurecraft 4D shoes right now because production is still extremely limited. Adidas CMO Eric Liedtke is hopeful that it’s going to change over the coming years.

“Ultimately, we're still ramping up the innovation. It will be faster, more limited material. Ideally, the vision is to build and print on demand,” he said. “Right now, most of our products are made out of Asia and we put them on a boat or on a plane so they end up on Fifth Avenue.”

You could imagine Adidas reducing the stock in its warehouse. “Instead of having some sort of micro-distribution center in Jersey, we can have a micro-factory in Jersey,” Liedtke said. When it comes to material, this manufacturing process lets you partly use corn-based material.

And it’s not just design. Making shoes on demand lets you optimize the structure of the shoe for different sports and bodies.

“In this case, we took 10 years plus — maybe 20 years — of science that we had on foot strikes, and running, and how runners run, and where the impact zones are, and what we need to design into it from a data standpoint. And then, we let the creative takeover,” Liedtke said.

Carbon isn’t just working with Adidas. The company is quite active on the dental market for instance, working on resins. “We now also have the world's first 3D-printed FDA-approved dentures,” DeSimone said.

It’s interesting to see that a simple product, such as a pair of shoes, can become the representation of a long process of research and development, engineering and design.

September 7, 2018

Live from Disrupt SF 2018 day three!

All good things must come to an end, and Disrupt SF is no different.

But, in many ways, we’ve saved the best for last. Today we’ll hear from Silicon Valley creator Mike Judge, DraftKings CEO Jason Robins and Coinbase CEO Brian Armstrong.

And that’s just the main stage.

In the afternoon, the real drama begins. Five finalists have been chosen to compete in the Startup Battlefield Finals. The winner will take home the Disrupt Cup, $100,000 and endless glory.

You can check out the full agenda right here.

Enjoy!

September 7, 2018

Meet the five Startup Battlefield finalists at Disrupt SF 2018

Over the past two days, 21 companies have taken the stage at the Disrupt SF Startup Battlefield. We’ve now taken the feedback from all our expert judges and chosen five teams to compete in the finals.

These teams will all take the stage again tomorrow afternoon to present in front of a new set of judges and answer even more in-depth questions. Then one startup will be chosen as the winner of the Battlefield Cup — and they’ll also take home $100,000.

Here are the finalists. The competition will be livestreamed on TechCrunch starting at 1:35pm on Friday.

CB Therapeutics

CB Therapeutics is a new biotech company that aims to change the game with cannabinoids produced cleanly and cheaply in the lab, out of sugar. What it’s done is bioengineer microorganisms — specifically yeast — to manufacture cannabinoids out of plain-old sugars.

Forethought

Mira

Mira is a new device that aims to help women who are struggling to conceive. The Mira Fertility system offers personalized cycle prediction by measuring fertility hormone concentrations in urine samples, telling women which days they’re fertile.

Origami Labs

Origami Labs wants to bring voice assistants right to your ear without requiring you to wear a device like a Bluetooth headset or Apple AirPods. Instead, the startup is using a ring on your finger combined with bone conduction technology to allow you to use your smartphone’s built-in assistant – whether that’s Google Assistant or Siri – in an all-new way.

Unbound

Vtrus launches drones to inspect and protect your warehouses and factories

Knowing what’s going on in your warehouses and facilities is of course critical to many industries, but regular inspections take time, money, and personnel. Why not use drones? Vtrus uses computer vision to let a compact drone not just safely navigate indoor environments but create detailed 3D maps of them for inspectors and workers to consult, autonomously and in real time.

Vtrus showed off its hardware platform — currently a prototype — and its proprietary SLAM (simultaneous location and mapping) software at TechCrunch Disrupt SF as a Startup Battlefield Wildcard company.

There are already some drone-based services for the likes of security and exterior imaging, but Vtrus CTO Jonathan Lenoff told me that those are only practical because they operate with a large margin for error. If you’re searching for open doors or intruders beyond the fence, it doesn’t matter if you’re at 25 feet up or 26. But inside a warehouse or production line every inch counts and imaging has to be carried out at a much finer scale.

As a result, dangerous and tedious inspections, such as checking the wiring on lighting or looking for rust under an elevated walkway, have to be done by people. Vtrus wouldn’t put those people out of work, but it might take them out of danger.

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The drone, called the ABI Zero for now, is equipped with a suite of sensors, from ordinary RGB cameras to 360 ones and a structured-light depth sensor. As soon as it takes off, it begins mapping its environment in great detail: it takes in 300,000 depth points 30 times per second, combining that with its other cameras to produce a detailed map of its surroundings.

It uses this information to get around, of course, but the data is also streamed over wi-fi in real time to the base station and Vtrus’s own cloud service, through which operators and inspectors can access it.

The SLAM technique they use was developed in-house; CEO Renato Moreno built and sold a company (to Facebook/Oculus) using some of the principles, but improvements to imaging and processing power have made it possible to do it faster and in greater detail than before. Not to mention on a drone that’s flying around an indoor space full of people and valuable inventory.

On a full charge, ABI can fly for about 10 minutes. That doesn’t sound very impressive, but the important thing isn’t staying aloft for a long time — few drones can do that to begin with — but how quickly it can get back up there. That’s where the special docking and charging mechanism comes in.

The Vtrus drone lives on and returns to a little box, which when a tapped-out craft touches down, sets off a patented high-speed charging process. It’s contact-based, not wireless, and happens automatically. The drone can then get back in the air perhaps half an hour or so later, meaning the craft can actually be in the air for as much as six hours a day total.

Probably anyone who has had to inspect or maintain any kind of building or space bigger than a studio apartment can see the value in getting frequent, high-precision updates on everything in that space, from storage shelving to heavy machinery. You’d put in an ABI for every X square feet depending on what you need it to do; they can access each other’s data and combine it as well.

This frequency and the detail which which the drone can inspect and navigate means maintenance can become proactive rather than reactive — you see rust on a pipe or a hot spot on a machine during the drone’s hourly pass rather than days later when the part fails. And if you don’t have an expert on site, the full 3D map and even manual drone control can be handed over to your HVAC guy or union rep.

You can see lots more examples of ABI in action at the Vtrus website. Way too many to embed here.

Lenoff, Moreno, and third co-founder Carlos Sanchez, who brings the industrial expertise to the mix, explained that their secret sauce is really the software — the drone itself is pretty much off the shelf stuff right now, tweaked to their requirements. (The base is an original creation, of course.)

But the software is all custom built to handle not just high-resolution 3D mapping in real time but the means to stream and record it as well. They’ve hired experts to build those systems as well — the 6-person team already sounds like a powerhouse.

The whole operation is self-funded right now, and the team is seeking investment. But that doesn’t mean they’re idle: they’re working with major companies already and operating a “pilotless” program (get it?). The team has been traveling the country visiting facilities, showing how the system works, and collecting feedback and requests. It’s hard to imagine they won’t have big clients soon.

September 7, 2018

Wingly is carpooling for private planes

Don’t call Wingly the “Uber of the Sky” — Wingly co-fonder Emeric de Waziers would like to nip that little misinterpretation in the bud as the French startup looks to expand into the U.S. If anything, the startup’s mission is more akin to carpooling for small aircrafts, helping pilots fill up empty seats in small passenger planes.

The distinction is an important one, with regard to the company’s ability to operate. After all, allowing private pilots to turn a profit changes the math significantly, both with regard to specific licenses and the company’s ability to operate inside different countries. Ninety-five percent of pilots who use the service don’t have a commercial license.

“What often happens with hobby pilots is they set a budget for the year. They’re going to fly as many times as they can with this money. If they can fly four times cheaper, they can fly four times more. We have many pilots posting what we call ‘flexible flights,’ saying, ‘hey, I’m available for a roundtrip from San Francisco to Tahoe.’ The passenger says they’re interested and they book the flight.”

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Founded in July 2015, the company faced regulatory challenges early on in its native France. It was enough to cause Wingly to relocate operations, setting up shop in Germany in February of the following year. That launch was a sort of a proof of concept for the novel flight booking app. It was successful enough to convince Wingly to take on its home country again, pushing back against French regulatory bodies.

These days, it operates in Germany, France and the UK, with those markets composing 45, 30 and 20 percent of the company’s business, respectively (with the other five percent belonging to various parts of Europe). Wingly’s flight matching service currently hosts around 2,000 passengers a month, with each flight averaging about 1.8 passengers.

It’s not a huge number, but, then, these aren’t huge planes, with the prop and twin-engine crafts sporting between two and six seats each. Profitability for Wingly means pushing into high volume numbers, but the current pace has been successful enough for the startup to begin pursuing the U.S. as its next major market — a move the company plans to begin in earnest as a Battlefield contestant at Disrupt today in San Francisco.

Currently, Wingly takes a 15-percent commission on each flight, along with a €5 charge. The company has also raised €2.5 million including a €2 million seed round back in December. It’s been enough funding to help the company thrive in Europe, but coming to the States will require additional cash, particularly its current launch time frame of early 2019. From there, Wingly hopes to reach numbers comparable to the business it’s doing in Europe by August/September of next year.

September 7, 2018

Kinta AI uses artificial intelligence to make factories more efficient

Kinta AI aims to make manufacturers smarter about how they deploy their equipment and other factory resources.

The company, which is presenting today at TechCrunch’s Startup Battlefield in San Francisco, was founded by a team with plenty of experience in finance, tech and AI.

CEO Steven Glinert has held management and AI roles at fintech startups, CTO Rob Donnelly is studying the intersection of machine learning and economics as a Ph.D. candidate at Stanford and VP of Engineering Ben Zax has worked at both Facebook and Google.

Glinert told me that when factory owners are making production decisions, they’re usually relying on “dumb software” to decide which machines should be used when, which can result in machines being deployed at the wrong time or in the wrong sequence, or sitting idle when they shouldn’t be. As a result, he said that scheduling errors account for 45 percent of late manufacturing orders.

So Kinta AI tries to solve this problem with artificial intelligence, specifically reinforcement learning. Glinert said the company will run “millions and millions of factory simulations,” where the system gains “a statistical understanding of how your factory works and learning what actions you do to get what result” — and it can then use those simulations to choose the best schedule.

“We run through, not every possible scenario, but we try to go through some of those,” he said.

Glinert added that Kinta AI works with its customers to understand the nuances of each factory. He also compared the technology to Google’s AlphaGo AI and OpenAI’s Dota 2 neural networks — except that instead of using AI to play Go or Dota 2, Gilnert said Kinta AI is utilizing it “to do these detailed production planning decisions that are being made on the factory floor.”

“Not all factories are that dissimilar from each other,” he said — similar to how “if you learned how to play Go, you can easily teach that neural net how to play chess or other game of that type.”

And Kinta AI already has some customers, including chemical manufacturer BASF and a medical device manufacturer.

Ultimately, Glinert said Kinta AI could become a crucial part of the manufacturing process. He predicted that “in the factory of the future, there will be fewer people and more automation, with a vast environment of Internet of Things devices.”

In that environment, he wants Kinta AI to be “the manufacturer execution system.”

September 7, 2018

Mira launches a device for more accurate fertility testing in the home

Mira, launching today at TechCrunch Disrupt SF 2018, is a new device that aims to help women who are struggling to conceive. The Mira Fertility system offers personalized cycle prediction by measuring fertility hormone concentrations in urine samples, telling women which days they’re fertile. The system is more advanced and accurate than the existing home test kits, the company claims, which can be hard to read and aren’t personalized to the individual.

The company behind Mira, Quanovate, was founded in late 2015 by a group of scientists, engineers, OBGYN doctors, and business execs to solve the problem of the unavailability of advanced home health testing.

“I have a lot of friends who, like me, [prioritized] their career advancement and higher education, and they tended to delay their maternal age,” explains Mira co-founder and CEO Sylvia Kang. “But there’s no education for them about when to try for a baby, and they have no awareness about their fertility health,” she says.

Kang received an MBA at Cornell Johnson, went to Columbia for an MS in Biomedical engineering and received at PhD in Biophysics from University of Pittsburgh, before working as a Business Director at Corning where she was responsible for $100 million in global P&L, which she left to start Mira.

She says that women’s hormones are changing daily, and everyone’s profiles differ due to their lifestyle, stress levels and other factors. The only way to accurately track fertile days, then, is through continuous testing – something that’s been difficult to do at home.

To solve this problem, the team worked to develop the Mira system, which includes a small home analyzer, urine test strips, and an accompanying mobile application. The home analyzer miniaturizes lab equipment for home use, and brings down the cost.

To use the system, the woman places the test strip into the device which then uses immunofluorescence technology to read the results. Currently, the device tests for the presence of luteinizing hormone (LH), which is an indicator of ovulation. However, the company has already has plans to update the device so it can test for other hormones in the near future. (It’s FDA-cleared to detect estrogen, for example, but that won’t be available at launch.)

The system instead is $199 and ships with 10 test strips. After analyzing the strip, information about the hormone levels is displayed on the screen and sent to the Mira app via Bluetooth.

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The app offers women more information about what this data means – like whether they should attempt to conceive today or wait. A subscription service will also offer them access to doctors so they can ask questions, but this will be free at launch.

“This technology is completely different from all the test strips on the market. It’s more accurate, but more importantly, this one is quantitative – that means we give you your actual, formal concentrations,” says Kang. “The [existing] tests strips only give you positive or negative. Since we have your numbers, our A.I. can do pattern recognition. Our algorithm prediction is based on your pattern specifically, not the average of all the population.”

What this means, in practice, is that women struggling to conceive will have more accurate, more actionable, and more personalized results with Mira. During a clinical trial with 400 patient samples, Mira reached 99 percent accuracy, compared with lab equipment, the company says. They also have 18 IPs covering hardware, software, database management and more, including utility patents and models, design patents, trademarks and copyrights.

The company is now working on a portal for doctors, so they could access their own patients’ data for further analysis. Mira may also eventually make its collected data, once anonymized, available to researchers, as well. But Kang says no formal decisions have been made on that front yet.

Longer-term, Kang explains that the same system can be adapted to track pregnancy and menopause, and eventually similar technology could be put to use for analyzing other conditions, like those related to kidney problems or the thyroid.

The Pleasanton, Calif.-based company, is currently a team of 36 and has raised $4.5 million from investors including Gopher Ventures, and two other cross-border investors Mira doesn’t want to disclose publicly.

At Disrupt, the company announced the Mira device is now available for pre-order and will begin shipping in October 2018.

It’s sold online via the Mira website, but is in discussions with doctors and retailers to broaden its availability going forward.

September 7, 2018

Crossing Minds would like to recommend a few entertainment options

Crossing Minds, which is launching in our Disrupt SF 2018 Battlefield today, is an AI startup that focuses on recommendations. The company’s app, Hai, provides you with a wide range of entertainment recommendations, including books, music, shows, video games and restaurants, based on the data it can gather about you from services like Spotify, Netflix, Hulu and your Xbox.

The company’s co-founders Alexandre Robicquet (CEO) and Emile Contal (CTO) tell me that they want Hai, which is available for iOS and on the web, to become people’s central hub for their entertainment needs. Both founders have extensive experience in machine learning and also managed to bring Sebastian Thrun on as an advisor. The team describes Hai as the “first pure cross-domain recommendation engine truly focused on the user.”

Ahead of its launch, Crossing Minds raised $3.5 million from Index Ventures, Sound Ventures and You & Mr Jones Brandtech Ventures.

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As the team told me, the idea for Crossing Minds and Hai came from their own need of wanting a smart recommendation engine that went beyond a single domain. To get started, they downloaded a few data sets and started experimenting. That was 2016. Those first experiments were successful, but to build a full-scale product, the team needed more data and cleaner data sets. That’s what Crossing Minds focused on over the course of the last year or so, which really isn’t a surprise, given that we’re dealing with rather messy data here, yet there’s no way to build a machine learning-based recommendation system without a lot of data.

Then, using techniques like transfer learning and other modern machine learning approaches, the team is able to take what it knows about you and apply that to other domains as well. “For example, when you read a biography of a band’s member, we can extract information that we can then relate to a movie or restaurants and so on,” Contal explained.

The app itself is organized around three tabs: A discovery tab that surfaces its recommendations; the “Ask me” tab for when you are looking for very specific recommendations (a movie on Netflix, maybe); and the training tab that allows you to train Hai’s algorithm. For movies and other content that’s immediately accessible on your phone or on the web, Hai will also show a “Watch Now” button.

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On the technical side, Crossing Minds uses all of the usual machine learning frameworks, but one interesting twist here is that the team decided to build its own hardware infrastructure with off-the-shelf GPUs to train its models and for inference. In part that’s because renting GPUs from a major cloud provider by the hour can quickly get expensive, but the team also noted that owning the hardware allows them to have full control over it and also offers security benefits (though I’m sure the cloud providers would disagree with that last part).

Over the course of the last few months, the team tested Hai with about 1,000 beta testers. The company isn’t quite ready to launch Hai to everybody, but it’s now taking beta sign-ups and plans to open the service to a wider audience over time.