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UK startup Fabula AI reckons it’s devised a way for artificial intelligence to help user generated content platforms get on top of the disinformation crisis that keeps rocking the world of social media with antisocial scandals.

Even Facebook’s Mark Zuckerberg has sounded a cautious note about AI technology’s capability to meet the complex, contextual, messy and inherently human challenge of correctly understanding every missive a social media user might send, well-intentioned or its nasty flip-side.

“It will take many years to fully develop these systems,” the Facebook founder wrote two years ago, in an open letter discussing the scale of the challenge of moderating content on platforms thick with billions of users. “This is technically difficult as it requires building AI that can read and understand news.”

But what if AI doesn’t need to read and understand news in order to detect whether it’s true or false?

Step forward Fabula, which has patented what it dubs a “new class” of machine learning algorithms to detect “fake news” — in the emergent field of “Geometric Deep Learning”; where the datasets to be studied are so large and complex that traditional machine learning techniques struggle to find purchase on this ‘non-Euclidean’ space.

The startup says its deep learning algorithms are, by contrast, capable of learning patterns on complex, distributed data sets like social networks. So it’s billing its technology as a breakthrough. (Its written a paper on the approach which can be downloaded here.)

It is, rather unfortunately, using the populist and now frowned upon badge “fake news” in its PR. But it says it’s intending this fuzzy umbrella to refer to both disinformation and misinformation. Which means maliciously minded and unintentional fakes. Or, to put it another way, a photoshopped fake photo or a genuine image spread in the wrong context.

The approach it’s taking to detecting disinformation relies not on algorithms parsing news content to try to identify malicious nonsense but instead looks at how such stuff spreads on social networks — and also therefore who is spreading it.

There are characteristic patterns to how ‘fake news’ spreads vs the genuine article, says Fabula co-founder and chief scientist, Michael Bronstein.

“We look at the way that the news spreads on the social network. And there is — I would say — a mounting amount of evidence that shows that fake news and real news spread differently,” he tells TechCrunch, pointing to a recent major study by MIT academics which found ‘fake news’ spreads differently vs bona fide content on Twitter.

“The essence of geometric deep learning is it can work with network-structured data. So here we can incorporate heterogenous data such as user characteristics; the social network interactions between users; the spread of the news itself; so many features that otherwise would be impossible to deal with under machine learning techniques,” he continues.

Bronstein, who is also a professor at Imperial College London, with a chair in machine learning and pattern recognition, likens the phenomenon Fabula’s machine learning classifier has learnt to spot to the way infectious disease spreads through a population.

“This is of course a very simplified model of how a disease spreads on the network. In this case network models relations or interactions between people. So in a sense you can think of news in this way,” he suggests. “There is evidence of polarization, there is evidence of confirmation bias. So, basically, there are what is called echo chambers that are formed in a social network that favor these behaviours.”

“We didn’t really go into — let’s say — the sociological or the psychological factors that probably explain why this happens. But there is some research that shows that fake news is akin to epidemics.”

The tl;dr of the MIT study, which examined a decade’s worth of tweets, was that not only does the truth spread slower but also that human beings themselves are implicated in accelerating disinformation. (So, yes, actual human beings are the problem.) Ergo, it’s not all bots doing all the heavy lifting of amplifying junk online.

The silver lining of what appears to be an unfortunate quirk of human nature is that a penchant for spreading nonsense may ultimately help give the stuff away — making a scalable AI-based tool for detecting ‘BS’ potentially not such a crazy pipe-dream.

Although, to be clear, Fabula’s AI remains in development at this stage, having been tested internally on Twitter data sub-sets at this stage. And the claims it’s making for its prototype model remain to be commercially tested with customers in the wild using the tech across different social platforms.

It’s hoping to get there this year, though, and intends to offer an API for platforms and publishers towards the end of this year. The AI classifier is intended to run in near real-time on a social network or other content platform, identifying BS.

Fabula envisages its own role, as the company behind the tech, as that of an open, decentralised “truth-risk scoring platform” — akin to a credit referencing agency just related to content, not cash.

Scoring comes into it because the AI generates a score for classifying content based on how confident it is it’s looking at a piece of fake vs true news.

A visualisation of a fake vs real news distribution pattern; users who predominantly share fake news are coloured red and users who don’t share fake news at all are coloured blue — which Fabula says shows the clear separation into distinct groups, and “the immediately recognisable difference in spread pattern of dissemination”.

In its own tests Fabula says its algorithms were able to identify 93 percent of “fake news” within hours of dissemination — which Bronstein claims is “significantly higher” than any other published method for detecting ‘fake news’. (Their accuracy figure uses a standard aggregate measurement of machine learning classification model performance, called ROC AUC.)

The dataset the team used to train their model is a subset of Twitter’s network — comprised of around 250,000 users and containing around 2.5 million “edges” (aka social connections).

For their training dataset Fabula relied on true/fake labels attached to news stories by third party fact checking NGOs, including Snopes and PolitiFact. And, overall, pulling together the dataset was a process of “many months”, according to Bronstein, He also says that around a thousand different stories were used to train the model, adding that the team is confident the approach works on small social networks, as well as Facebook-sized mega-nets.

Asked whether he’s sure the model hasn’t been trained to identified patterns caused by bot-based junk news spreaders, he says the training dataset included some registered (and thus verified ‘true’) users.

“There is multiple research that shows that bots didn’t play a significant amount [of a role in spreading fake news] because the amount of it was just a few percent. And bots can be quite easily detected,” he also suggests, adding: “Usually it’s based on some connectivity analysis or content analysis. With our methods we can also detect bots easily.”

To further check the model, the team tested its performance over time by training it on historical data and then using a different split of test data.

“While we see some drop in performance it is not dramatic. So the model ages well, basically. Up to something like a year the model can still be applied without any re-training,” he notes, while also saying that, when applied in practice, the model would be continually updated as it keeps digesting (ingesting?) new stories and social media content.

Somewhat terrifyingly, the model could also be used to predict virality, according to Bronstein — raising the dystopian prospect of the API being used for the opposite purpose to that which it’s intended: i.e. maliciously, by fake news purveyors, to further amp up their (anti)social spread.

“Potentially putting it into evil hands it might do harm,” Bronstein concedes. Though he takes a philosophical view on the hyper-powerful double-edged sword of AI technology, arguing such technologies will create an imperative for a rethinking of the news ecosystem by all stakeholders, as well as encouraging emphasis on user education and teaching critical thinking.

Let’s certainly hope so. And, on the educational front, Fabula is hoping its technology can play an important role — by spotlighting network-based cause and effect.

“People now like or retweet or basically spread information without thinking too much or the potential harm or damage they’re doing to everyone,” says Bronstein, pointing again to the infectious diseases analogy. “It’s like not vaccinating yourself or your children. If you think a little bit about what you’re spreading on a social network you might prevent an epidemic.”

So, tl;dr, think before you RT.

Returning to the accuracy rate of Fabula’s model, while ~93 per cent might sound pretty impressive, if it were applied to content on a massive social network like Facebook — which has some 2.3BN+ users, uploading what could be trillions of pieces of content daily — even a seven percent failure rate would still make for an awful lot of fakes slipping undetected through the AI’s net.

But Bronstein says the technology does not have to be used as a standalone moderation system. Rather he suggests it could be used in conjunction with other approaches such as content analysis, and thus function as another string on a wider ‘BS detector’s bow.

It could also, he suggests, further aid human content reviewers — to point them to potentially problematic content more quickly.

Depending on how the technology gets used he says it could do away with the need for independent third party fact-checking organizations altogether because the deep learning system can be adapted to different use cases.

Example use-cases he mentions include an entirely automated filter (i.e. with no human reviewer in the loop); or to power a content credibility ranking system that can down-weight dubious stories or even block them entirely; or for intermediate content screening to flag potential fake news for human attention.

Each of those scenarios would likely entail a different truth-risk confidence score. Though most — if not all — would still require some human back-up. If only to manage overarching ethical and legal considerations related to largely automated decisions. (Europe’s GDPR framework has some requirements on that front, for example.)

Facebook’s grave failures around moderating hate speech in Myanmar — which led to its own platform becoming a megaphone for terrible ethnical violence — were very clearly exacerbated by the fact it did not have enough reviewers who were able to understand (the many) local languages and dialects spoken in the country.

So if Fabula’s language-agnostic propagation and user focused approach proves to be as culturally universal as its makers hope, it might be able to raise flags faster than human brains which lack the necessary language skills and local knowledge to intelligently parse context.

“Of course we can incorporate content features but we don’t have to — we don’t want to,” says Bronstein. “The method can be made language independent. So it doesn’t matter whether the news are written in French, in English, in Italian. It is based on the way the news propagates on the network.”

Although he also concedes: “We have not done any geographic, localized studies.”

“Most of the news that we take are from PolitiFact so they somehow regard mainly the American political life but the Twitter users are global. So not all of them, for example, tweet in English. So we don’t yet take into account tweet content itself or their comments in the tweet — we are looking at the propagation features and the user features,” he continues.

“These will be obviously next steps but we hypothesis that it’s less language dependent. It might be somehow geographically varied. But these will be already second order details that might make the model more accurate. But, overall, currently we are not using any location-specific or geographic targeting for the model.

“But it will be an interesting thing to explore. So this is one of the things we’ll be looking into in the future.”

Fabula’s approach being tied to the spread (and the spreaders) of fake news certainly means there’s a raft of associated ethical considerations that any platform making use of its technology would need to be hyper sensitive to.

For instance, if platforms could suddenly identify and label a sub-set of users as ‘junk spreaders’ the next obvious question is how will they treat such people?

Would they penalize them with limits — or even a total block — on their power to socially share on the platform? And would that be ethical or fair given that not every sharer of fake news is maliciously intending to spread lies?

What if it turns out there’s a link between — let’s say — a lack of education and propensity to spread disinformation? As there can be a link between poverty and education… What then? Aren’t your savvy algorithmic content downweights risking exacerbating existing unfair societal divisions?

Bronstein agrees there are major ethical questions ahead when it comes to how a ‘fake news’ classifier gets used.

“Imagine that we find a strong correlation between the political affiliation of a user and this ‘credibility’ score. So for example we can tell with hyper-ability that if someone is a Trump supporter then he or she will be mainly spreading fake news. Of course such an algorithm would provide great accuracy but at least ethically it might be wrong,” he says when we ask about ethics.

He confirms Fabula is not using any kind of political affiliation information in its model at this point — but it’s all too easy to imagine this sort of classifier being used to surface (and even exploit) such links.

“What is very important in these problems is not only to be right — so it’s great of course that we’re able to quantify fake news with this accuracy of ~90 percent — but it must also be for the right reasons,” he adds.

The London-based startup was founded in April last year, though the academic research underpinning the algorithms has been in train for the past four years, according to Bronstein.

The patent for their method was filed in early 2016 and granted last July.

They’ve been funded by $500,000 in angel funding and about another $500,000 in total of European Research Council grants plus academic grants from tech giants Amazon, Google and Facebook, awarded via open research competition awards.

(Bronstein confirms the three companies have no active involvement in the business. Though doubtless Fabula is hoping to turn them into customers for its API down the line. But he says he can’t discuss any potential discussions it might be having with the platforms about using its tech.)

Focusing on spotting patterns in how content spreads as a detection mechanism does have one major and obvious drawback — in that it only works after the fact of (some) fake content spread. So this approach could never entirely stop disinformation in its tracks.

Though Fabula claims detection is possible within a relatively short time frame — of between two and 20 hours after content has been seeded onto a network.

“What we show is that this spread can be very short,” he says. “We looked at up to 24 hours and we’ve seen that just in a few hours… we can already make an accurate prediction. Basically it increases and slowly saturates. Let’s say after four or five hours we’re already about 90 per cent.”

“We never worked with anything that was lower than hours but we could look,” he continues. “It really depends on the news. Some news does not spread that fast. Even the most groundbreaking news do not spread extremely fast. If you look at the percentage of the spread of the news in the first hours you get maybe just a small fraction. The spreading is usually triggered by some important nodes in the social network. Users with many followers, tweeting or retweeting. So there are some key bottlenecks in the network that make something viral or not.”

A network-based approach to content moderation could also serve to further enhance the power and dominance of already hugely powerful content platforms — by making the networks themselves core to social media regulation, i.e. if pattern-spotting algorithms rely on key network components (such as graph structure) to function.

So you can certainly see why — even above a pressing business need — tech giants are at least interested in backing the academic research. Especially with politicians increasingly calling for online content platforms to be regulated like publishers.

At the same time, there are — what look like — some big potential positives to analyzing spread, rather than content, for content moderation purposes.

As noted above, the approach doesn’t require training the algorithms on different languages and (seemingly) cultural contexts — setting it apart from content-based disinformation detection systems. So if it proves as robust as claimed it should be more scalable.

Though, as Bronstein notes, the team have mostly used U.S. political news for training their initial classifier. So some cultural variations in how people spread and react to nonsense online at least remains a possibility.

A more certain challenge is “interpretability” — aka explaining what underlies the patterns the deep learning technology has identified via the spread of fake news.

While algorithmic accountability is very often a challenge for AI technologies, Bronstein admits it’s “more complicated” for geometric deep learning.

“We can potentially identify some features that are the most characteristic of fake vs true news,” he suggests when asked whether some sort of ‘formula’ of fake news can be traced via the data, noting that while they haven’t yet tried to do this they did observe “some polarization”.

“There are basically two communities in the social network that communicate mainly within the community and rarely across the communities,” he says. “Basically it is less likely that somebody who tweets a fake story will be retweeted by somebody who mostly tweets real stories. There is a manifestation of this polarization. It might be related to these theories of echo chambers and various biases that exist. Again we didn’t dive into trying to explain it from a sociological point of view — but we observed it.”

So while, in recent years, there have been some academic efforts to debunk the notion that social media users are stuck inside filter bubble bouncing their own opinions back at them, Fabula’s analysis of the landscape of social media opinions suggests they do exist — albeit, just not encasing every Internet user.

Bronstein says the next steps for the startup is to scale its prototype to be able to deal with multiple requests so it can get the API to market in 2019 — and start charging publishers for a truth-risk/reliability score for each piece of content they host.

“We’ll probably be providing some restricted access maybe with some commercial partners to test the API but eventually we would like to make it useable by multiple people from different businesses,” says requests. “Potentially also private users — journalists or social media platforms or advertisers. Basically we want to be… a clearing house for news.”

from Startups – TechCrunch https://tcrn.ch/2GedEgz

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There has never been a better time to start, join or fund a startup as a student. 

Young founders who want to start companies while still in school have an increasing number of resources to tap into that exist just for them. Students that want to learn how to build companies can apply to an increasing number of fast-track programs that allow them to gain valuable early stage operating experience. The energy around student entrepreneurship today is incredible. I’ve been immersed in this community as an investor and adviser for some time now, and to say the least, I’m continually blown away by what the next generation of innovators are dreaming up (from Analytical Space’s global data relay service for satellites to Brooklinen’s reinvention of the luxury bed).

Bill Gates in 1973

First, let’s look at student founders and why they’re important. Student entrepreneurs have long been an important foundation of the startup ecosystem. Many students wrestle with how best to learn while in school —some students learn best through lectures, while more entrepreneurial students like author Julian Docks find it best to leave the classroom altogether and build a business instead.

Indeed, some of our most iconic founders are Microsoft’s Bill Gates and Facebook’s Mark Zuckerberg, both student entrepreneurs who launched their startups at Harvard and then dropped out to build their companies into major tech giants. A sample of the current generation of marquee companies founded on college campuses include Snap at Stanford ($29B valuation at IPO), Warby Parker at Wharton (~$2B valuation), Rent The Runway at HBS (~$1B valuation), and Brex at Stanford (~$1B valuation).

Some of today’s most celebrated tech leaders built their first ventures while in school — even if some student startups fail, the critical first-time founder experience is an invaluable education in how to build great companies. Perhaps the best example of this that I could find is Drew Houston at Dropbox (~$9B valuation at IPO), who previously founded an edtech startup at MIT that, in his words, provided a: “great introduction to the wild world of starting companies.”

Student founders are everywhere, but the highest concentration of venture-backed student founders can be found at just 5 universities. Based on venture fund portfolio data from the last six years, Harvard, Stanford, MIT, UPenn, and UC Berkeley have produced the highest number of student-founded companies that went on to raise $1 million or more in seed capital. Some prospective students will even enroll in a university specifically for its reputation of churning out great entrepreneurs. This is not to say that great companies are not being built out of other universities, nor does it mean students can’t find resources outside a select number of schools. As you can see later in this essay, there are a number of new ways students all around the country can tap into the startup ecosystem. For further reading, PitchBook produces an excellent report each year that tracks where all entrepreneurs earned their undergraduate degrees.

Student founders have a number of new media resources to turn to. New email newsletters focused on student entrepreneurship like Justine and Olivia Moore’s Accelerated and Kyle Robertson’s StartU offer new channels for young founders to reach large audiences. Justine and Olivia, the minds behind Accelerated, have a lot of street cred— they launched Stanford’s on-campus incubator Cardinal Ventures before landing as investors at CRV.

StartU goes above and beyond to be a resource to founders they profile by helping to connect them with investors (they’re active at 12 universities), and run a podcast hosted by their Editor-in-Chief Johnny Hammond that is top notch. My bet is that traditional media will point a larger spotlight at student entrepreneurship going forward.

New pools of capital are also available that are specifically for student founders. There are four categories that I call special attention to:

• University-affiliated accelerator programs
• University-affiliated angel networks
• Professional venture funds investing at specific universities
• Professional venture funds investing through student scouts

While it is difficult to estimate exactly how much capital has been deployed by each, there is no denying that there has been an explosion in the number of programs that address the pre-seed phase. A sample of the programs available at the Top 5 universities listed above are in the graphic below — listing every resource at every university would be difficult as there are so many.

One alumni-centric fund to highlight is the Alumni Ventures Group, which pools LP capital from alumni at specific universities, then launches individual venture funds that invest in founders connected to those universities (e.g. students, alumni, professors, etc.). Through this model, they’ve deployed more than $200M per year! Another highlight has been student scout programs — which vary in the degree of autonomy and capital invested — but essentially empower students to identify and fund high-potential student-founded companies for their parent venture funds. On campuses with a large concentration of student founders, it is not uncommon to find student scouts from as many as 12 different venture funds actively sourcing deals (as is made clear from David Tao’s analysis at UC Berkeley).

Investment Team at Rough Draft Ventures

In my opinion, the two institutions that have the most expansive line of sight into the student entrepreneurship landscape are First Round’s Dorm Room Fund and General Catalyst’s Rough Draft Ventures. Since 2012, these two funds have operated a nationwide network of student scouts that have invested $20K — $25K checks into companies founded by student entrepreneurs at 40+ universities. “Scout” is a loose term and doesn’t do it justice — the student investors at these two funds are almost entirely autonomous, have built their own platform services to support portfolio companies, and have launched programs to incubate companies built by female founders and founders of color. Another student-run fund worth noting that has reach beyond a single region is Contrary Capital, which raised $2.2M last year. They do a particularly great job of reaching founders at a diverse set of schools — their network of student scouts are active at 45 universities and have spoken with 3,000 founders per year since getting started. Contrary is also testing out what they describe as a “YC for university-based founders”. In their first cohort, 100% of their companies raised a pre-seed round after Contrary’s demo day. Another even more recently launched organization is The MBA Fund, which caters to founders from the business schools at Harvard, Wharton, and Stanford. While super exciting, these two funds only launched very recently and manage portfolios that are not large enough for analysis just yet.

Over the last few months, I’ve collected and cross-referenced publicly available data from both Dorm Room Fund and Rough Draft Ventures to assess the state of student entrepreneurship in the United States. Companies were pulled from each fund’s portfolio page, then checked against Crunchbase for amount raised, accelerator participation, and other metrics. If you’d like to sift through the data yourself, feel free to ping me — my email can be found at the end of this article. To be clear, this does not represent the full scope of investment activity at either fund — many companies in the portfolios of both funds remain confidential and unlisted for good reasons (e.g. startups working in stealth). In fact, the In addition, data for early stage companies is notoriously variable in quality, even with Crunchbase. You should read these insights as directional only, given the debatable confidence interval. Still, the data is still interesting and give good indicators for the health of student entrepreneurship today.

Dorm Room Fund and Rough Draft Ventures have invested in 230+ student-founded companies that have gone on to raise nearly $1 billion in follow on capital. These funds have invested in a diverse range of companies, from govtech (e.g. mark43, raised $77M+ and FiscalNote, raised $50M+) to space tech (e.g. Capella Space, raised ~$34M). Several portfolio companies have had successful exits, such as crypto startup Distributed Systems (acquired by Coinbase) and social networking startup tbh (acquired by Facebook). While it is too early to evaluate the success of these funds on a returns basis (both were launched just 6 years ago), we can get a sense of success by evaluating the rates by which portfolio companies raise additional capital. Taken together, 34% of DRF and RDV companies in our data set have raised $1 million or more in seed capital. For a rough comparison, CB Insights cites that 40% of YC companies and 48% of Techstars companies successfully raise follow on capital (defined as anything above $750K). Certainly within the ballpark!

Source: Crunchbase

Dorm Room Fund and Rough Draft Ventures companies in our data set have an 11–12% rate of survivorship to Series A. As a benchmark, a previous partner at Y Combinator shared that 20% of their accelerator companies raise Series A capital (YC declined to share the official figure, but it’s likely a stat that is increasing given their new Series A support programs. For further reading, check out YC’s reflection on what they’ve learned about helping their companies raise Series A funding). In any case, DRF and RDV’s numbers should be taken with a grain of salt, as the average age of their portfolio companies is very low and raising Series A rounds generally takes time. Ultimately, it is clear that DRF and RDV are active in the earlier (and riskier) phases of the startup journey.

Dorm Room Fund and Rough Draft Ventures send 18–25% of their portfolio companies to Y Combinator or Techstars. Given YC’s 1.5% acceptance rate as reported in Fortune, this is quite significant! Internally, these two funds offer founders an opportunity to participate in mock interviews with YC and Techstars alumni, as well as tap into their communities for peer support (e.g. advice on pitch decks and application content). As a result, Dorm Room Fund and Rough Draft Ventures regularly send cohorts of founders to these prestigious accelerator programs. Based on our data set, 17–20% of DRF and RDV companies that attend one of these accelerators end up raising Series A venture financing.

Source: Crunchbase

Dorm Room Fund and Rough Draft Ventures don’t invest in the same companies. When we take a deeper look at one specific ecosystem where these two funds have been equally active over the last several years — Boston — we actually see that the degree of investment overlap for companies that have raised $1M+ seed rounds sits at 26%. This suggests that these funds are either a) seeing different dealflow or b) have widely different investment decision-making.

Source: Crunchbase

Dorm Room Fund and Rough Draft Ventures should not just be measured by a returns-basis today, as it’s too early. I hypothesize that DRF and RDV are actually encouraging more entrepreneurial activity in the ecosystem (more students decide to start companies while in school) as well as improving long-term founder outcomes amongst students they touch (portfolio founders build bigger and more successful companies later in their careers). As more students start companies, there’s likely a positive feedback loop where there’s increasing peer pressure to start a company or lean on friends for founder support (e.g. feedback, advice, etc).Both of these subjects warrant additional study, but it’s likely too early to conduct these analyses today.

Dorm Room Fund and Rough Draft Ventures have impressive alumni that you will want to track. 1 in 4 alumni partners are founders, and 29% of these founder alumni have raised $1M+ seed rounds for their companies. These include Anjney Midha’s augmented reality startup Ubiquity6 (raised $37M+), Shubham Goel’s investor-focused CRM startup Affinity (raised $13M+), Bruno Faviero’s AI security software startup Synapse (raised $6M+), Amanda Bradford’s dating app The League (raised $2M+), and Dillon Chen’s blockchain startup Commonwealth Labs (raised $1.7M). It makes sense to me that alumni from these communities that decide to start companies have an advantage over their peers — they know what good companies look like and they can tap into powerful networks of young talent / experienced investors.

Beyond Dorm Room Fund and Rough Draft Ventures, some venture capital firms focus on incubation for student-founded startups. Credit should first be given to Lightspeed for producing the amazing Summer Fellows bootcamp experience for promising student founders — after all, Pinterest was built there! Jeremy Liew gives a good overview of the program through his sit-down interview with Afterbox’s Zack Banack. Based on a study they conducted last year, 40% of Lightspeed Summer Fellows alumni are currently active founders. Pear Ventures also has an impressive summer incubator program where 85% of its companies successfully complete a fundraise. Index Ventures is the latest to build an incubator program for student founders, and even accepts founders who want to work on an idea part-time while completing a summer internship.

Let’s now look at students who want to join a startup before founding one. Venture funds have historically looked to tap students for talent, and are expanding the engagement lifecycle. The longest running programs include Kleiner Perkins’ class=”m_1196721721246259147gmail-markup–strong m_1196721721246259147gmail-markup–p-strong”> KP Fellows and True Ventures’ TEC Fellows, which focus on placing the next generation’s most promising product managers, engineers, and designers into the portfolio companies of their parent venture funds.

There’s also the secretive Greylock X, a referral-based hand-picked group of the best student engineers in Silicon Valley (among their impressive alumni are founders like Yasyf Mohamedali and Joe Kahn, the folks behind First Round-backed Karuna Health). As these programs have matured, these firms have recognized the long-run value of engaging the alumni of their programs.

More and more alumni are “coming back” to the parent funds as entrepreneurs, like KP Fellow Dylan Field of Figma (and is also hosting a KP Fellow, closing a full circle loop!). Based on their latest data, 10% of KP Fellows alumni are founders — that’s a lot given the fact that their community has grown to 500! This helps explain why Kleiner Perkins has created a structured path to receive $100K in seed funding to companies founded by KP Fellow alumni. It looks like venture funds are beginning to invest in student programs as part of their larger platform strategy, which can have a real impact over the long term (for further reading, see this analysis of platform strategy outcomes by USV’s Bethany Crystal).

KP Fellows in San Francisco

Venture funds are doubling down on student talent engagement — in just the last 18 months, 4 funds have launched student programs. It’s encouraging to see new funds follow in the footsteps of First Round, General Catalyst, Kleiner Perkins, Greylock, and Lightspeed. In 2017, Accel launched their Accel Scholars program to engage top talent at UC Berkeley and Stanford. In 2018, we saw 8VC Fellows, NEA Next, and Floodgate Insiders all launch, targeting elite universities outside of Silicon Valley. Y Combinator implemented Early Decision, which allows student founders to apply one batch early to help with academic scheduling. Most recently, at the start of 2019, First Round launched the Graduate Fund (staffed by Dorm Room Fund alumni) to invest in founders who are recent graduates or young alumni.

Given more time, I’d love to study the rates by which student founders start another company following investments from student scout funds, as well as whether or not they’re more successful in those ventures. In any case, this is an escalation in the number of venture funds that have started to get serious about engaging students — both for talent and dealflow.

Student entrepreneurship 2.0 is here. There are more structured paths to success for students interested in starting or joining a startup. Founders have more opportunities to garner press, seek advice, raise capital, and more. Venture funds are increasingly leveraging students to help improve the three F’s — finding, funding, and fixing. In my personal view, I believe it is becoming more and more important for venture funds to gain mindshare amongst the next generation of founders and operators early, while still in school.

I can’t wait to see what’s next for student entrepreneurship in 2019. If you’re interested in digging in deeper (I’m human — I’m sure I haven’t covered everything related to student entrepreneurship here) or learning more about how you can start or join a startup while still in school, shoot me a note at sxu@dormroomfund.com. A massive thanks to Phin Barnes, Rei Wang, Chauncey Hamilton, Peter Boyce, Natalie Bartlett, Denali Tietjen, Eric Tarczynski, Will Robbins, Jasmine Kriston, Alicia Lau, Johnny Hammond, Bruno Faviero, Athena Kan, Shohini Gupta, Alex Immerman, Albert Dong, Phillip Hua-Bon-Hoa, and Trevor Sookraj for your incredible encouragement, support, and insight during the writing of this essay.

from Startups – TechCrunch https://tcrn.ch/2taTc7E

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On the heels of a recently-filed class action lawsuit over wages and tips, as well as drivers and shoppers speaking out about Instacart’s alleged practices of subsidizing wages with tips, Instacart is taking steps to ensure tips are counted separately from what Instacart pays shoppers.

In a blog post today, Instacart CEO Apoorva Mehta said all shoppers will now have a guaranteed higher base compensation, paid by Instacart. Depending on the region, Instacart says it will pay shoppers between $7 to $10 for full-service orders (shopping, picking and delivering) and $5 for delivery-only tasks. The company will also stop including tips in its base pay for shoppers.

“After launching our new earnings structure this past October, we noticed that there were small batches where shoppers weren’t earning enough for their time,” Mehta wrote. “To help with this, we instituted a $10 floor on earnings, inclusive of tips, for all batches. This meant that when Instacart’s payment and the customer tip at checkout was below $10, Instacart supplemented the difference. While our intention was to increase the guaranteed payment for small orders, we understand that the inclusion of tips as a part of this guarantee was misguided. We apologize for taking this approach.”

For the shoppers who were subject that approach, Instacart says it will retroactively pay people whose tips were included in payment minimums.

You can read the full blog post at the bottom of this post. For background, Instacart had previously guaranteed its workers at least $10 per job, but workers said Instacart offsets wages with tips from customers.

The suit alleges Instacart “intentionally and maliciously misappropriated gratuities in order to pay plaintiff’s wages even though Instacart maintained that 100 percent of customer tips went directly to shoppers. Based on this representation, Instacart knew customers would believe their tips were being given to shoppers in addition to wages, not to supplement wages entirely.”

In addition to the lawsuit, workers have taken to Reddit and other online forums to speak out against Instacart’s paying practices. Since introducing a new payments structure in October, which includes things like payments per mile, quality bonuses and customer tips, workers have said the pay has gotten worse — far below minimum wage. In one case, Instacart paid a worker just 80 cents for over an hour of work. Instacart has since said it was a glitch — caused by the fact that the customer tipped $10 — and has introduced a new minimum payment for orders. So, Instacart paid the worker $10.80, but just 80 cents of it came from Instacart.

While Instacart has said this was an edge case, Working Washington says this has happened in other cases. In another case, Instacart paid a worker just $7.26 (including cost of mileage) for over two hour’s worth of work.

“We heard loud and clear the frustration when your compensation didn’t match the effort you put forth,” Mehta wrote in the blog post. “As we looked at some of the extreme examples that have been surfaced by you over the last few days, it’s become clear to us that we can and should do better. Instacart shouldn’t be paying a shopper $0.80 for a batch. It doesn’t matter that this only happens 1 out of 100,000 times – it happened to one shopper and that’s one time too many.”

Here’s the full text of Mehta’s post:

To Our Shopper Community:

Every day, millions of people entrust Instacart to help get the food they need to feed their families and get back valuable time to spend with their loved ones. By delivering to and for our customers, you’ve become household heroes for millions of families across North America. This past week however, it’s become clear, that we’ve fallen short in delivering on our promise to you.

As you know, we’ve made changes to our shopper earnings model over the last year. These changes were designed to increase transparency while also keeping pace with a rapidly-evolving industry. In doing so, we’ve tried, in good faith, to balance those needs, but clearly we haven’t always gotten it right.

As a company, we remain committed to listening and putting our shoppers more at the forefront of our decision making. Based on your feedback, today we’re launching new measures to more fairly and competitively compensate all our shoppers. As part of this, our earnings approach moving forward will adhere to the following:

• Tips should always be separate from Instacart’s contribution to shopper compensation

• All batches will have a higher guaranteed compensation floor for shoppers, paid for by Instacart

• Instacart will retroactively compensate shoppers when tips were included in minimums

Below are details on each new element of shopper earnings, which we will be rolling out in the coming days.

Tips Should Always Be Separate From Instacart’s Contribution to Shopper Compensation – After launching our new earnings structure this past October, we noticed that there were small batches where shoppers weren’t earning enough for their time. To help with this, we instituted a $10 floor on earnings, inclusive of tips, for all batches. This meant that when Instacart’s payment and the customer tip at checkout was below $10, Instacart supplemented the difference. While our intention was to increase the guaranteed payment for small orders, we understand that the inclusion of tips as a part of this guarantee was misguided. We apologize for taking this approach.

All Batches Will Have a Higher Guaranteed Floor for Shoppers, Paid by Instacart – We’re instituting a higher minimum floor payment from Instacart on all batches. Today our minimum batch payment is $3. Depending on the region, our minimum batch payment will increase to between $7 and $10 for full service batches (where a shopper picks, packs and delivers the order) and $5 for delivery only batches (where a shopper delivers the order after a separate person picks the groceries). These increased batch floors will be consistent for all shoppers within a particular geographic area. In addition to the higher guaranteed floors, Instacart will also pay a quality bonus and peak boosts for orders that qualify. Any tips earned by shoppers will be separate and in addition to Instacart’s contribution.

Instacart Will Retroactively Compensate Shoppers When Tips Were Included in Minimums – Over the coming days, as we transition to the new higher minimum floor payments, we will make you whole on the transactions that have occurred since the launch of this feature. Specifically, we will proactively reach out to all shoppers who were adversely affected by instances in which Instacart’s payment was below the $10 threshold. For example, if a shopper was paid $6 by Instacart, to compensate for our mistake, he or she will receive an additional $4 from Instacart.

In creating these changes to improve, enhance and create clarity for shopper compensation, these new measures will do the following:

1. Better protect shoppers from smaller, outlying batches. We heard loud and clear the frustration when your compensation didn’t match the effort you put forth. As we looked at some of the extreme examples that have been surfaced by you over the last few days, it’s become clear to us that we can and should do better. Instacart shouldn’t be paying a shopper $0.80 for a batch. It doesn’t matter that this only happens 1 out of 100,000 times – it happened to one shopper and that’s one time too many. We believe that these new guaranteed floor minimums will better protect our shoppers going forward.

2. Customer tips will no longer have any impact on Instacart’s contribution to shopper earnings. With an average tip of $5, our customers regularly recognize shoppers with tips for the services they provide. We believe that with these changes customers will continue to be able to recognize great service and have full confidence that their tips are going to the shopper who delivered their order, with no impact whatsoever on what the shopper receives from Instacart. As always, shoppers will receive 100% of their tips, regardless of the batch compensation.  

3. These changes will increase Instacart’s overall contribution to our shopper’s earnings and we believe that the change in tip structure will separate Instacart from an industry standard that’s no longer working for our shoppers and our customers.

Finally, I want to thank you for your feedback. It’s our responsibility to change course quickly when we realize we’re on the wrong path and we believe today’s changes are a step in the right direction.

Apoorva Mehta

Founder & CEO of Instacart

from Startups – TechCrunch https://tcrn.ch/2TAyWbk

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Lime just announced it has raised a $310 million Series D round. Led by Bain Capital, Andreessen Horowitz, Fidelity Ventures, GV and IVP, the round values Lime at $2.4 billion.

“This new investment demonstrates the fundamental strength of our business and the increasingly rapid adoption of Lime,” Lime CEO Toby Sun wrote in a blog post. “The new funds will give us the ability to expand into new markets, enhance our technology, strengthen the team and pilot new opportunities. We will also continue investing in two critical areas: rider safety and city collaboration.”

In May, Lime partnered with Segway to launch its next generation of electric scooters. These Segway-powered Lime scooters were designed to be safer, longer-lasting via battery power and more durable for what the sharing economy requires, Sun told TechCrunch last year.

But this partnership hasn’t been without its issues. In October, Lime recalled some of its scooters due to battery fire concerns. The next month, Lime put $3 million toward a new safety initiative called “Respect the Ride.” Safety, in general, is a major concern. In September, someone lost their life after a scooter accident.

This brings Lime’s total funding north of $800 million. Lime, which got its beginnings as a bike-share company, has deployed its scooters in more than 100 cities in the U.S. and 27 international cities. Since June, Lime has more than doubled the number of cities where it operates in the U.S. Lime has also partnered with Uber to offer Lime scooters within the Uber app.

from Startups – TechCrunch https://tcrn.ch/2HWUCwL

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Back in 2016 we had Robin up on stage demonstrating the possibility of a robotic mower as a service rather than just something you buy. They’re still going strong, and just introduced and patented what seems in retrospect a pretty obvious idea: an automatic door for the mower to go through fences between front and back yards.

It’s pretty common, after all, to have a back yard isolated from the front lawn by a wood or chainlink fence so dogs and kids can roam freely there with only light supervision. And if you’re lucky enough to have a robot mower, it can be a pain to carry it from one side to the other. Isn’t the whole point of the thing that you don’t have to pick it up or interact with it in any way?

The solution Justin Crandall and his team at Robin came up with is simple and straightforward: an automatic mower-size door that opens only to let it through.

“In Texas over 90 percent of homes have a fenced in backyard, and even in places like Charlotte and Cleveland it’s roughly 25-30 percent, so technology like this is critical to adoption,” Crandall told me. “We generally dock the robots in the backyard for security. When it’s time to mow the front yard, the robots drive to the door we place in the fence. As it approaches the door, the robot drives over a sensor we place in the ground. That sensor unlocks the door to allow the mower access.”

Simple, right? It uses a magetometer rather than wireless or IR sensor, since those introduced possibilities of false positives. And it costs around $100-$150, easily less than a second robot or base, and probably pays for itself in goodwill around the third or fourth time you realize you didn’t have to carry your robot around.

It’s patented, but rivals (like iRobot, which recently introduced its own mower) could certainly build one if it was sufficiently different.

Robin has expanded to several states and a handful of franchises (its plan from the start) and maintains that its all-inclusive robot-as-a-service method is better than going out and buying one for yourself. Got a big yard and no teenage kids who can mow it for you? See if Robin’s available in your area.

from Startups – TechCrunch https://tcrn.ch/2WKuTv0

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An investigation by Consumer Reports may force electric scooter businesses to double back on safety measures.

The magazine found electric scooters caused 1,545 injuries in the U.S. since late 2017, according to data collected from 110 hospitals and five public agencies in 47 cities where Bird or Lime, the leading tech-enabled scooter-sharing platforms, operate.

The news comes shortly after UCLA published a study finding that 249 people required medical care following scooter accidents, with one-third of that group arriving at the hospital in an ambulance.

“These injuries can be severe,” Tarak Trivedi, an emergency physician at UCLA and the study’s lead author, told CNET. “These aren’t just minor cuts and scrapes. These are legit fractures.”

Despite commentary from scooter CEOs suggesting otherwise, safety doesn’t seem to be a priority for businesses in the space. Given the nature of the industry, taking a ride on an e-scooter or a dockless bike without a helmet is the norm. That, coupled with failed hardware, irresponsible riding practices and access to scooters in the evening, has unsurprisingly led to several accidents and even casualties. Just this past weekend, the city of Austin reported a pedestrian riding a Lime scooter died after being struck by an Uber driver. The Lime scooter rider was traveling the wrong way down an interstate.

Lime, Bird and other leading scooter providers do provide free helmets to riders and don’t encourage poor scooter etiquette, but ensuring riders actually carry helmets or don’t do stupid things like travel the wrong way down a busy road is impossible.

With a fresh $310 million in Series D funding for Lime, announced today, it will be interesting to see how the company ramps up safety efforts.

from Startups – TechCrunch https://tcrn.ch/2Bm4yuf

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An investigation by Consumer Reports may force electric scooter businesses to double back on safety measures.

The magazine found electric scooters caused 1,545 injuries in the U.S. since late 2017, according to data collected from 110 hospitals and five public agencies in 47 cities where Bird or Lime, the leading tech-enabled scooter-sharing platforms, operate.

The news comes shortly after UCLA published a study finding that 249 people required medical care following scooter accidents, with one-third of that group arriving at the hospital in an ambulance.

“These injuries can be severe,” Tarak Trivedi, an emergency physician at UCLA and the study’s lead author, told CNET. “These aren’t just minor cuts and scrapes. These are legit fractures.”

Despite commentary from scooter CEOs suggesting otherwise, safety doesn’t seem to be a priority for businesses in the space. Given the nature of the industry, taking a ride on an e-scooter or a dockless bike without a helmet is the norm. That, coupled with failed hardware, irresponsible riding practices and access to scooters in the evening, has unsurprisingly led to several accidents and even casualties. Just this past weekend, the city of Austin reported a pedestrian riding a Lime scooter died after being struck by an Uber driver. The Lime scooter rider was traveling the wrong way down an interstate.

Lime, Bird and other leading scooter providers do provide free helmets to riders and don’t encourage poor scooter etiquette, but ensuring riders actually carry helmets or don’t do stupid things like travel the wrong way down a busy road is impossible.

With a fresh $310 million in Series D funding for Lime, announced today, it will be interesting to see how the company ramps up safety efforts.

from Startups – TechCrunch https://tcrn.ch/2Bm4yuf