January 17, 2019
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Why Silicon Valley needs more visas

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When I hear protesters shout, “Immigrants are welcome here!” at the San Francisco immigration office near my startup’s headquarters, I think about how simple a phrase that is for a topic that is so nuanced, especially for me as an immigrant entrepreneur.

Growing up in Brazil, I am less familiar with the nuances of the American debate on immigration legislation, but I know that immigrants here add a lot of jobs and stimulate the local economy. As an immigrant entrepreneur, I’ve tried to check all of those boxes, and really prove my value to this country.

My tech startup Brex has achieved a lot in a short period of time, a feat which is underscored by receiving a $1 billion dollar valuation in just one year. But we didn’t achieve that high level of growth in spite of being founded by immigrants, but because of it. The key to our growth and to working towards building a global brand is our international talent pool, without it, we could never have gotten to where we are today.

So beyond Brex, what do the most successful Silicon Valley startups have in common? They’re also run by immigrants. In fact, not only are 57% of the Bay Area’s STEM tech workers immigrants, they also make up 25% of business founders in the US. You can trace the immigrant entrepreneurial streak in Silicon Valley from the founders of SUN Microsystems and Google to the Valley’s most notorious Twitter User, Tesla’s Elon Musk.

Immigrants not only built the first microchips in Silicon Valley, but they built these companies into the tech titans that they are known as today. After all, more than 50% of billion dollar startups are founded by immigrants, and many of those startups were founded by immigrants on H-1B visas.

Photo courtesy of Flickr/jvoves

While it might sound counterintuitive, immigrants create more jobs and make our economy stronger. Research from the National Foundation of American Policy (NFAP) has shown that immigrant-founded billion-dollar companies doubled their number of employees over the past two years. According to the research, “WeWork went from 1,200 to 6,000 employees between 2016 and 2018, Houzz increased from 800 to 1,800 employees the last two years, while Cloudflare went from 225 to 715 employees.”

We’ve seen the same growth at Brex. In just one year we hired 70 employees and invested over $6 million dollars in creating local jobs. Our startup is not alone, as Inc. recently reported, “50 immigrant-founded unicorn startups have a combined value of $248 billion, according to the report [by NFAP], and have created an average of 1,200 jobs each.”

One of the fundamental drivers of our success is our international workforce. Many of our key-hires are from all over Latin America, spanning from Uruguay to Mexico. In fact, 42% of our workforce is made up of immigrants and another 6% are made up of children of immigrants. Plenty of research shows that diverse teams are more productive and work together better, but that’s only part of the reason why you should bet on an international workforce. When you’re working with the best and brightest from every country, it inspires you to bring forth your most creative ideas, collaborate, and push yourself beyond your comfort zone. It motivates you to be your best.

With all of the positive contributions immigrants bring to this country, you’d think we’d have less restrictive immigration policies. However, that’s not the case. One of the biggest challenges that I face is hiring experienced, qualified engineers and designers to continue innovating in a fast-paced, competitive market.

This is a universal challenge in the tech industry. For the past 10 years, software engineers have been the #1 most difficult job to fill in the United States. Business owners are willing to pay 10-20 percent above the market rate for top talent and engineers. Yet, we’re still projected to have a shortage of two million engineering jobs in the US by 2022. How can you lead the charge of innovation if you don’t have the talent to do it?

What makes matters worse is that there are so few opportunities and types of visas for qualified immigrants. This is limiting job growth, knowledge-sharing, and technological breakthroughs in this country. And we risk losing top talent to other nations if we don’t loosen our restrictive visa laws.

H1-B visa applications fell this year, and at the same time, these visas have become harder to obtain and it has become more expensive to acquire international talent. This isn’t the time to abandon the international talent pool, but to invest in highly specialized workers that can give your startup a competitive advantage.

Already, there’s been a dramatic spike in engineering talent moving to Canada, with a 40% uptick in 2017. Toronto, Berlin, and Singapore are fastly becoming burgeoning tech hubs, and many fear (rightfully) that they will soon outpace the US in growth, talent, and developing the latest technologies.

This year, U.S. based tech companies generated $351 billion of revenue in 2018. The U.S. can’t afford to miss out on this huge revenue source. And, according to Harvard Business School Professor William R. Kerr and the author of The Gift of Global Talent: How Migration Shapes Business, Economy & Society, “Today’s knowledge economy dictates that your ability to attract, develop, and integrate smart minds governs how prosperous you will be.”

Immigrants have made Silicon Valley the powerhouse that it is today, and severely limiting highly-skilled immigration benefits no-one. Immigrants have helped the U.S. build one of the best tech hubs in the world— now is the time for startups to invest in international talent so that our technology, economy, and local communities can continue to thrive.

News Source = techcrunch.com

Contentful raises $33.5M for its headless CMS platform

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Contentful, a Berlin- and San Francisco-based startup that provides content management infrastructure for companies like Spotify, Nike, Lyft and others, today announced that it has raised a $33.5 million Series D funding round led by Sapphire Ventures, with participation from OMERS Ventures and Salesforce Ventures, as well as existing investors General Catalyst, Benchmark, Balderton Capital and Hercules. In total, the company has now raised $78.3 million.

It’s only been less than a year since the company raised its Series C round and as Contentful co-founder and CEO Sascha Konietzke told me, the company didn’t really need to raise right now. “We had just raised our last round about a year ago. We still had plenty of cash in our bank account and we didn’t need to raise as of now,” said Konietzke. “But we saw a lot of economic uncertainty, so we thought it might be a good moment in time to recharge. And at the same time, we already had some interesting conversations ongoing with Sapphire [formeraly SAP Ventures] and Salesforce. So we saw the opportunity to add more funding and also start getting into a tight relationship with both of these players.”

The original plan for Contentful was to focus almost explicitly on mobile. As it turns out, though, the company’s customers also wanted to use the service to handle its web-based applications and these days, Contentful happily supports both. “What we’re seeing is that everything is becoming an application,” he told me. “We started with native mobile application, but even the websites nowadays are often an application.”

In its early days, Contentful also focuses only on developers. Now, however, that’s changing and having these connections to large enterprise players like SAP and Salesforce surely isn’t going to hurt the company as it looks to bring on larger enterprise accounts.

Currently, the company’s focus is very much on Europe and North America, which account for about 80% of its customers. For now, Contentful plans to continue to focus on these regions, though it obviously supports customers anywhere in the world.

Contentful only exists as a hosted platform. As of now, the company doesn’t have any plans for offering a self-hosted version, though Konietzke noted that he does occasionally get requests for this.

What the company is planning to do in the near future, though, is to enable more integrations with existing enterprise tools. “Customers are asking for deeper integrations into their enterprise stack,” Konietzke said. “And that’s what we’re beginning to focus on and where we’re building a lot of capabilities around that.” In addition, support for GraphQL and an expanded rich text editing experience is coming up. The company also recently launched a new editing experience.

News Source = techcrunch.com

Agtech startup Imago AI is using computer vision to boost crop yields

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Presenting onstage today in the 2018 TC Disrupt Berlin Battlefield is Indian agtech startup Imago AI, which is applying AI to help feed the world’s growing population by increasing crop yields and reducing food waste. As startup missions go, it’s an impressively ambitious one.

The team, which is based out of Gurgaon near New Delhi, is using computer vision and machine learning technology to fully automate the laborious task of measuring crop output and quality — speeding up what can be a very manual and time-consuming process to quantify plant traits, often involving tools like calipers and weighing scales, toward the goal of developing higher-yielding, more disease-resistant crop varieties.

Currently they say it can take seed companies between six and eight years to develop a new seed variety. So anything that increases efficiency stands to be a major boon.

And they claim their technology can reduce the time it takes to measure crop traits by up to 75 percent.

In the case of one pilot, they say a client had previously been taking two days to manually measure the grades of their crops using traditional methods like scales. “Now using this image-based AI system they’re able to do it in just 30 to 40 minutes,” says co-founder Abhishek Goyal.

Using AI-based image processing technology, they can also crucially capture more data points than the human eye can (or easily can), because their algorithms can measure and asses finer-grained phenotypic differences than a person might pick up on or be easily able to quantify just judging by eye alone.

“Some of the phenotypic traits they are not possible to identify manually,” says co-founder Shweta Gupta. “Maybe very tedious or for whatever all these laborious reasons. So now with this AI-enabled [process] we are now able to capture more phenotypic traits.

“So more coverage of phenotypic traits… and with this more coverage we are having more scope to select the next cycle of this seed. So this further improves the seed quality in the longer run.”

The wordy phrase they use to describe what their technology delivers is: “High throughput precision phenotyping.”

Or, put another way, they’re using AI to data-mine the quality parameters of crops.

“These quality parameters are very critical to these seed companies,” says Gupta. “Plant breeding is a very costly and very complex process… in terms of human resource and time these seed companies need to deploy.

“The research [on the kind of rice you are eating now] has been done in the previous seven to eight years. It’s a complete cycle… chain of continuous development to finally come up with a variety which is appropriate to launch in the market.”

But there’s more. The overarching vision is not only that AI will help seed companies make key decisions to select for higher-quality seed that can deliver higher-yielding crops, while also speeding up that (slow) process. Ultimately their hope is that the data generated by applying AI to automate phenotypic measurements of crops will also be able to yield highly valuable predictive insights.

Here, if they can establish a correlation between geotagged phenotypic measurements and the plants’ genotypic data (data which the seed giants they’re targeting would already hold), the AI-enabled data-capture method could also steer farmers toward the best crop variety to use in a particular location and climate condition — purely based on insights triangulated and unlocked from the data they’re capturing.

One current approach in agriculture to selecting the best crop for a particular location/environment can involve using genetic engineering. Though the technology has attracted major controversy when applied to foodstuffs.

Imago AI hopes to arrive at a similar outcome via an entirely different technology route, based on data and seed selection. And, well, AI’s uniform eye informing key agriculture decisions.

“Once we are able to establish this sort of relation this is very helpful for these companies and this can further reduce their total seed production time from six to eight years to very less number of years,” says Goyal. “So this sort of correlation we are trying to establish. But for that initially we need to complete very accurate phenotypic data.”

“Once we have enough data we will establish the correlation between phenotypic data and genotypic data and what will happen after establishing this correlation we’ll be able to predict for these companies that, with your genomics data, and with the environmental conditions, and we’ll predict phenotypic data for you,” adds Gupta.

“That will be highly, highly valuable to them because this will help them in reducing their time resources in terms of this breeding and phenotyping process.”

“Maybe then they won’t really have to actually do a field trial,” suggests Goyal. “For some of the traits they don’t really need to do a field trial and then check what is going to be that particular trait if we are able to predict with a very high accuracy if this is the genomics and this is the environment, then this is going to be the phenotype.”

So — in plainer language — the technology could suggest the best seed variety for a particular place and climate, based on a finer-grained understanding of the underlying traits.

In the case of disease-resistant plant strains it could potentially even help reduce the amount of pesticides farmers use, say, if the the selected crops are naturally more resilient to disease.

While, on the seed generation front, Gupta suggests their approach could shrink the production time frame — from up to eight years to “maybe three or four.”

“That’s the amount of time-saving we are talking about,” she adds, emphasizing the really big promise of AI-enabled phenotyping is a higher amount of food production in significantly less time.

As well as measuring crop traits, they’re also using computer vision and machine learning algorithms to identify crop diseases and measure with greater precision how extensively a particular plant has been affected.

This is another key data point if your goal is to help select for phenotypic traits associated with better natural resistance to disease, with the founders noting that around 40 percent of the world’s crop load is lost (and so wasted) as a result of disease.

And, again, measuring how diseased a plant is can be a judgement call for the human eye — resulting in data of varying accuracy. So by automating disease capture using AI-based image analysis the recorded data becomes more uniformly consistent, thereby allowing for better quality benchmarking to feed into seed selection decisions, boosting the entire hybrid production cycle.

Sample image processed by Imago AI showing the proportion of a crop affected by disease

In terms of where they are now, the bootstrapping, nearly year-old startup is working off data from a number of trials with seed companies — including a recurring paying client they can name (DuPont Pioneer); and several paid trials with other seed firms they can’t (because they remain under NDA).

Trials have taken place in India and the U.S. so far, they tell TechCrunch.

“We don’t really need to pilot our tech everywhere. And these are global [seed] companies, present in 30, 40 countries,” adds Goyal, arguing their approach naturally scales. “They test our technology at a single country and then it’s very easy to implement it at other locations.”

Their imaging software does not depend on any proprietary camera hardware. Data can be captured with tablets or smartphones, or even from a camera on a drone or using satellite imagery, depending on the sought for application.

Although for measuring crop traits like length they do need some reference point to be associated with the image.

“That can be achieved by either fixing the distance of object from the camera or by placing a reference object in the image. We use both the methods, as per convenience of the user,” they note on that.

While some current phenotyping methods are very manual, there are also other image-processing applications in the market targeting the agriculture sector.

But Imago AI’s founders argue these rival software products are only partially automated — “so a lot of manual input is required,” whereas they couch their approach as fully automated, with just one initial manual step of selecting the crop to be quantified by their AI’s eye.

Another advantage they flag up versus other players is that their approach is entirely non-destructive. This means crop samples do not need to be plucked and taken away to be photographed in a lab, for example. Rather, pictures of crops can be snapped in situ in the field, with measurements and assessments still — they claim — accurately extracted by algorithms which intelligently filter out background noise.

“In the pilots that we have done with companies, they compared our results with the manual measuring results and we have achieved more than 99 percent accuracy,” is Goyal’s claim.

While, for quantifying disease spread, he points out it’s just not manually possible to make exact measurements. “In manual measurement, an expert is only able to provide a certain percentage range of disease severity for an image example; (25-40 percent) but using our software they can accurately pin point the exact percentage (e.g. 32.23 percent),” he adds.

They are also providing additional support for seed researchers — by offering a range of mathematical tools with their software to support analysis of the phenotypic data, with results that can be easily exported as an Excel file.

“Initially we also didn’t have this much knowledge about phenotyping, so we interviewed around 50 researchers from technical universities, from these seed input companies and interacted with farmers — then we understood what exactly is the pain-point and from there these use cases came up,” they add, noting that they used WhatsApp groups to gather intel from local farmers.

While seed companies are the initial target customers, they see applications for their visual approach for optimizing quality assessment in the food industry too — saying they are looking into using computer vision and hyper-spectral imaging data to do things like identify foreign material or adulteration in production line foodstuffs.

“Because in food companies a lot of food is wasted on their production lines,” explains Gupta. “So that is where we see our technology really helps — reducing that sort of wastage.”

“Basically any visual parameter which needs to be measured that can be done through our technology,” adds Goyal.

They plan to explore potential applications in the food industry over the next 12 months, while focusing on building out their trials and implementations with seed giants. Their target is to have between 40 to 50 companies using their AI system globally within a year’s time, they add.

While the business is revenue-generating now — and “fully self-enabled” as they put it — they are also looking to take in some strategic investment.

“Right now we are in touch with a few investors,” confirms Goyal. “We are looking for strategic investors who have access to agriculture industry or maybe food industry… but at present haven’t raised any amount.”

News Source = techcrunch.com

V2X Network gives developers the keys to in-vehicle data

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Data is king. But if there isn’t a way to capture, sort and use it, then there it sits — an untapped resource.

V2X Network, a German-based startup presenting onstage Thursday during Startup Battlefield at TechCrunch Disrupt Berlin, sees opportunity in all the data produced in the modern car. And it’s a hefty sum. A fully connected and automated car loaded with sensors can produce up to 4 terabytes of data per day, the company says. As V2X Network puts it, “cars have basically become rolling data servers.”

This data can provide all kinds of insights, such as driving and road conditions and the locations of charging and fuel stations. Data produced from vehicles, if properly captured and organized, could be used to deliver services to consumers, such as helping to improve driving behavior or handing the information to city planners to better understand traffic patterns.

This isn’t a new opportunity. (After all, Intel calls data the new oil.) And V2X Network is not the first (or the last) company to see gold in the hills of data generated by connected vehicles.

V2X Network, which was founded earlier this year, is taking a carrot-first and blockchain-protected approach. The company, founded by CEO Ahsan Shamim, COO Holger Philipp and CTO Shumail Mohyuddin, has developed what it describes as a decentralized incentivized platform that gives developers access to in-vehicle data, which can be turned into a variety of different apps.

But there’s a moat, and it’s the driver. V2X Network doesn’t allow any application developer to access the data without the driver’s consent, and that can always be revoked, the company’s founders told TechCrunch. 

Here’s how the founders, who have backgrounds in automotive and computer science, envision the system will work.

Giving developers access to in-vehicle data so they can turn it into all kinds of apps delivered to cities, automakers and drivers sounds like a winning idea. The problem is accessing that data. Why would anyone just give it away? And why would automakers hand it over?

V2X Network is taking a dual approach to getting access to that valuable data. The company is collaborating with automakers for direct access. (V2X Network couldn’t say who they were working with; only that they’re starting to work with two OEMs on a proof of concept basis.) The second data source is straight from the car owner through an OBD-II dongle solution used to collect data on older vehicles. A prototype of the V2X Network dongle, a blockchain node that starts sharing information with V2X Network once it’s plugged in, was shown at Disrupt Berlin.  

Once the data is collected, it’s made available to developers who use it to create valuable apps that could be used by drivers, cities and automakers, among others.

Incentivizing the car owner for producing the data lies at the center of the platform. V2X Network isn’t buying the data from the car owner. Instead, the company proposes charging data access fees from the service providers and sharing part of the revenue with the car owners or manufacturers.

The company sees a variety of possible applications developed from the data, from fleet management services and vehicle tracking to traffic congestion control, smart parking and driver coaching.

News Source = techcrunch.com

Apoll01 wants to remake education by decentralizing the diploma

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Dan Genduso spent nearly a decade working in consulting before landing on the Disrupt Berlin stage to launch his first startup, Apoll01 — a small company with a big idea about how to solve America’s expanding education crisis. 

First at Accenture and then at Slalom Consulting in San Francisco, Genduso focused on building out customer engagement platforms that captured the workflows, institutional knowledge and digital assets surrounding the development of customer profiles.

“I was building those out and personalizing products and advertisements to people,” Genduso recalled. “I got kind of tired of doing that and started to notice that there were other applications for this technology to enable people instead of enabling companies.”

That realization started Genduso on the path that would culminate with the launch of Apoll01 and its first product, a digital identity management tool, built on Hyperledger, that the Apoll01 founder hopes will be the first step in the transformation of the American educational system.

There’s no doubt that education in the U.S. is at a tipping point. Whether or not anyone ascribes to the belief of Harvard University Professor Clayton Christensen, the progenitor of the popular theory of disruptive innovation, who predicts that “50 percent of the 4,000 colleges and universities in the U.S. will be bankrupt in 10 to 15 years,” there’s no arguing against the fact that a wave of attrition is coming for higher education in the country.

That statistic is sobering, but debatable. However, even the Department of Education and Moody’s Investor Services predict that the number of college and university closures will triple in the coming years. 

What’s worrying to Genduso is that this thinning of educational opportunity for students is occurring alongside what will be rising demand for new skill sets as automation transforms the workforce.

Longer term, Genduso sees Apoll01 as a new platform for managing labor in the age of automation. In a future where automation has erased traditional notions of work, Genduso sees people operating in a more flexible and attenuated gig economy where workers will be matched with short-term projects in the same way that Uber drivers are now matched with riders. He thinks that Apoll01 will be the ledger that has a full accounting of its users’ skillsets and is able to match them with the jobs that need to be filled.

“The same way I was automating operations of a company by making it so there’s no middle man, I realized I could match people to education and to work without the middleman as well,” Genduso says.

That’s the long-term vision, but the first step is getting an identity management system to store all of the different accreditations, certificates and skills that a person has amassed over their educational career in a single place. And that’s what Genduso is launching on the Disrupt stage.

“Right now, think about how there are online training platforms like Salesforce’s Trailhead,” said Genduso. “There are industry-specific schools like blockchain schools. You have specialized training schools and then you have Coursera and Udacity. There’s nothing that’s pulling those things together to put a school system together. No one is pulling that together to create an accreditation and acknowledge that what you’re learning counts.”

That vision was enough to earn Genduso a finalist slot in the U.S. Department of Education’s “Reimagining the Higher Education Ecosystem Challenge” and garner praise from the country’s controversial Secretary of Education, Betsy DeVos. Apoll01 was among a number of companies including Competency Catalyst, EdRec: Next Gen by Design, and FlexchainEdu, trying to create ways for skills learned outside of the traditional classroom to be acknowledged by employers and traditional universities.

Other companies, like Learning Machine, raised $3 million to pursue putting digital diplomas on the blockchain. In fact, traditional universities have already acknowledged the value of the tools and services that Genduso is hoping to develop. In September, Genduso was accepted into the University of Southern California’s Rossier EdVentures education technology incubator.

“The original use case of this product was to start within universities to better understand their students and personalize online education for their students. [Universities] wanted to better understand what their students had been learning outside of the university system from other online learning platforms,” Genduso says.

However, the entrepreneur soon realized that for Apoll01 to be successful, it would have to be independent from the university system.

“The only way there could be a profile that moves outside of the university and within the university was through an independent profile,” says Genduso. So he developed an identity management tool on top of the Hyperledger Fabric open source blockchain toolkit.

Some universities are already putting diplomas and certifications on the blockchain. Learning Machine is working with MIT to put their certifications and digital diplomas into a cryptographically secured ledger, while Southern New Hampshire University and Central New Mexico Community College, both issue blockchain diplomas.

“I’m trying to get away from this world where everyone is screwing everything up by creating these closed systems for the user,” says Genduso. “I’m trying to get people who run these online institutions to get those pilot programs to get that started. My customer is not a university, my customer is every single person… I’m trying to do what’s best for them.”

Apoll01 already has its first customer, through a pilot with the blockchain based education company Teachur, but the company’s vision resonates with a number of different potential customers.

One of those could be edX, the online portal for massive open online courses (MOOCs) that were the darling of the education set only a few years ago. Writing in Quartz, edX chief executive, Anant Agarwal laid out a compelling rationale for Apoll01’s technology.

Education isn’t static. In this future, traditional degrees themselves may become antiquated, and employers will increasingly look for what multifarious skills learners know versus what degree they possess. Modular credentials will be ideal for working professionals who want to update their skillset to suit the shifting job market, better preparing students and adults alike for an excitingly unpredictable future.

Initially, Genduso sees his company getting traction by charging universities a small fee for access to the profiles that his users are generating. Eventually, Apoll01’s chief executive thinks there’s an opportunity to raise money through the tokenization of the platform, where advertisers, continuing education companies, and other vendors in the education space would pay for access to the profiles created on Apoll01’s platform. The key, for Genduso, is to make the system as accessible as possible for the students.

“In the next 10 to 15 years 50 percent of colleges and universities are going to be bankrupt and we’re also heading to a time when 10 percent to 15 percent of people are going to be out of work. When you look at that trajectory how do you as a person in the labor force properly prepare for that?” Genduso asked. “You can start building a profile where you’re building up a transcript that actually counts for something and you can get it from all of these different sources.”

News Source = techcrunch.com

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