As we enter the 20th year of Salesforce, there’s an interesting opportunity to reflect back on the change that Marc Benioff created with the software-as-a-service (SaaS) model for enterprise software with his launch of Salesforce.com.
This model has been validated by the annual revenue stream of SaaS companies, which is fast approaching $100 billion by most estimates, and it will likely continue to transform many slower-moving industries for years to come.
However, for the cornerstone market in IT — large enterprise-software deals — SaaS represents less than 25 percent of total revenue, according to most market estimates. This split is even evident in the most recent high profile “SaaS” acquisition of GitHub by Microsoft, with over 50 percent of GitHub’s revenue coming from the sale of their on-prem offering, GitHub Enterprise.
Data privacy and security is also becoming a major issue, with Benioff himself even pushing for a U.S. privacy law on par with GDPR in the European Union. While consumer data is often the focus of such discussions, it’s worth remembering that SaaS providers store and process an incredible amount of personal data on behalf of their customers, and the content of that data goes well beyond email addresses for sales leads.
It’s time to reconsider the SaaS model in a modern context, integrating developments of the last nearly two decades so that enterprise software can reach its full potential. More specifically, we need to consider the impact of IaaS and “cloud-native computing” on enterprise software, and how they’re blurring the lines between SaaS and on-premises applications. As the world around enterprise software shifts and the tools for building it advance, do we really need such stark distinctions about what can run where?
In his book, Behind the Cloud, Benioff lays out four primary reasons for the introduction of the cloud-based SaaS model:
These arguments also happen to be, more or less, that same ones made by infrastructure-as-a-service (IaaS) providers such as Amazon Web Services during their early days in the mid-late ‘00s. However, IaaS adds value at a layer deeper than SaaS, providing the raw building blocks rather than the end product. The result of their success in renting cloud computing, storage and network capacity has been many more SaaS applications than ever would have been possible if everybody had to follow the model Salesforce did several years earlier.
Suddenly able to access computing resources by the hour—and free from large upfront capital investments or having to manage complex customer installations—startups forsook software for SaaS in the name of economics, simplicity and much faster user growth.
Fast-forward to today, and in some ways it’s clear just how prescient Benioff was in pushing the world toward SaaS. Of the four reasons laid out above, Benioff nailed the first two:
In other areas, however, things today look very different than they did back in 1999. In particular, Benioff’s other two primary reasons for embracing SaaS no longer seem so compelling. Ironically, IaaS economies of scale (especially once Google and Microsoft began competing with AWS in earnest) and software-development practices developed inside those “web scale” companies played major roles in spurring these changes:
Several other factors have also emerged in the last few years that beg the question of whether the traditional definition of SaaS can really be the only one going forward. Here, too, there’s irony in the fact that many of the forces pushing software back toward self-hosting and management can be traced directly to the success of SaaS itself, and cloud computing in general:
The future of software is location agnostic
As the pace of technological disruption picks up, the previous generation of SaaS companies is facing a future similar to the legacy software providers they once displaced. From mainframes up through cloud-native (and even serverless) computing, the goal for CIOs has always been to strike the right balance between cost, capabilities, control and flexibility. Cloud-native computing, which encompasses a wide variety of IT facets and often emphasizes open source software, is poised to deliver on these benefits in a manner that can adapt to new trends as they emerge.
The problem for many of today’s largest SaaS vendors is that they were founded and scaled out during the pre-cloud-native era, meaning they’re burdened by some serious technical and cultural debt. If they fail to make the necessary transition, they’ll be disrupted by a new generation of SaaS companies (and possibly traditional software vendors) that are agnostic toward where their applications are deployed and who applies the pre-built automation that simplifies management. This next generation of vendors will more control in the hands of end customers (who crave control), while maintaining what vendors have come to love about cloud-native development and cloud-based resources.
So, yes, Marc Benioff and Salesforce were absolutely right to champion the “No Software” movement over the past two decades, because the model of enterprise software they targeted needed to be destroyed. In the process, however, Salesforce helped spur a cloud computing movement that would eventually rewrite the rules on enterprise IT and, now, SaaS itself.
It’s only been a couple months since we reviewed the first season of Netflix’s revival of Queer Eye, but the show’s Fab Five are already back with another eight episodes where they remake the homes, wardrobes and lives.
For season two, however, they mix things up a little — not only does the format feel more varied, but the folks being helped now include a woman and a transgendered man.
On the latest episode of the Original Content podcast, we’re joined by Henry Pickavet (editorial director at TechCrunch and co-host of the CTRL+T podcast) to discuss the show. We’re all fans: Queer Eye has its shortcomings, but it really works for us, with multiple episodes ending with tears, on- and off-screen.
You can listen in the player below, subscribe using Apple Podcasts or find us in your podcast player of choice. If you like the show, please let us know by leaving a review on Apple. You also can send us feedback directly.
People hate hubris and hypocrisy more than they hate evil, which is, I think, why we’re seeing the beginnings of a bipartisan cultural backlash against the tech industry. A backlash which is wrongly conceived and wrongly targeted … but not entirely unfounded. It’s hard to shake the sense that, as an industry, we are currently abdicating some of our collective responsibility to the world.
I don’t want to overstate the case. The tech industry remained the single most trusted entity in America as recently as last year, according to the Edelman Trust Barometer. Jeff Bezos is the wealthiest man in the world, and Elon Musk probably its highest-profile billionaire; of course they’re going to attract flak from all sides.
Furthermore, tech has become enormously more powerful and influential over the last decade. The Big Five tech companies now occupy the top five slots on the Fortune 500, whereas in 2008, Hewlett-Packard was tech’s lone Top Ten representative at #9. Power breeds resentment. Some kind of backlash was inevitable.
And yet — the tech industry is by some distance the least objectionable of the world’s power centers right now. The finance industry has become, to paraphrase Rolling Stone, a vampire squid wrapped around the our collective economic throat, siphoning off a quarter of our lifeblood via increasingly complex financial structures which provide very little benefit to the rest of us. But a combination of learned helplessness and lack of hypocrisy — in that very few hedge fund managers pretend to be making the world a better place for anyone but their clients — shields them from anything like the rancor they deserve.
Meanwhile, we’re in the midst of the worldwide right-wing populist uprising which has led governments around the world to treat desperate refugees like nonhuman scum; turning them away by the boatload in Europe; imprisoning them on a godforsaken remote island in Australia; tearing children from their parents and caging them in America.
Tesla and Amazon’s treatment of factory and warehouse workers is at best questionable and at worst egregiously wrong … though if they were all replaced by robots, that would eliminate those complaints but also all of those jobs, which makes the complaints look pretty short-sighted. But it’s not whataboutism to suggest that outrage should be proportional to the relative scale of the offense in question. If it isn’t, then that indicates some seriously skewed priorities. What is it about the tech industry’s relatively venial sins, compared to those of finance and government, which so sticks in the craw of its critics?
Partly it’s the perceived hubris and hypocrisy — that we talk about “making the world a better place” when in fact we sometimes seem to only be making it a better place for ourselves. Life is pretty nice for those of us in the industry, and keeps getting nicer. We like to pretend that slowly, bit by bit, life is getting better for everyone else, too, while or sometimes even because we focus on our cool projects, and the rest of the world will get to live like us too.
Which is even true, for a lot of people! I was in China a couple of months ago: it has changed almost inconceivably since my first visit two decades ago, and overwhelmingly for the better, despite all of the negative side effects of that change. The same is true for India. That’s 2.6 billion people right there whose lives have mostly been transformed for the better over the last couple of decades, courtesy of capitalism and technology. The same is true for other, smaller populations around the world.
However. There are many, many millions of people, including throngs in our own back yards, for whom the world has gotten decidedly worse over the last ten years, sometimes as a result of those same changes or related ones (such as increasing inequality, which is at least arguably partly driven by technology.) Many more have been kept out of, or driven away from, our privileged little world for no good reason. Why is it somehow OK for us to shrug and turn our backs on them? The tech industry is enormously powerful now, and Peter Parker was on to something when he said: “with great power comes great responsibility.”
So why is it that we’re only willing to work on really cool long-term goals like electric cars and space exploration, and not the messy short-term stuff like inequality, housing, and the ongoing brutal oppression of refugees and immigrants? Don’t tell me it’s because those fields are too regulated and political; space travel and road transportation are heavily regulated and not exactly apolitical in case you haven’t noticed.
That painful, difficult stuff is for governments, we say. That’s for international diplomacy. That’s some one else’s problem. Until recently — and maybe even still, for now — this has been true. But with growing power comes growing responsibility. At some point, and a lot of our critics think we have already passed it, those problems become ours, too. Kudos to people like Salesforce’s Marc Benioff, who says “But we cannot delegate these complex problems off to the government and say, “We’re not all part of it,”” for beginning to tackle them.
Let’s hope he’s only among the first. And let’s hope we find a way for technology to help with the overarching problem of incompetent and/or malevolent governments, while we’re at it.
Everyone knows there are thriving startup communities outside of obvious hubs, like San Francisco, Berlin, Bangalore and Beijing, but they don’t always get the support they deserve. Last year, TechCrunch took a major page from its playbook, the Startup Battlefield competition, and staged the event in Nairobi, Kenya to find the best early stage startup in Sub-Saharan Africa, and also to Sydney, Australia, to find the same for Australia and New Zealand. Both were successes, thanks to talented founders and the hard traveling TechCrunch team. And now we’re pleased to announce that we’re stepping up our commitment to emerging ecosystems.
TechCrunch is once again teaming up with Facebook, our partner for last year’s Nairobi event, to bring the Startup Battlefield to three major cities representing regions with vital, emerging startup communities. In Beirut, TechCrunch’s editors will strive to find the best early stage startup in the Middle East and North Africa. In S
There are plenty of ways to manipulate photos to make you look better, remove red eye or lens flare, and so on. But so far the blink has proven a tenacious opponent of good snapshots. That may change with research from Facebook that replaces closed eyes with open ones in a remarkably convincing manner.
It’s far from the only example of intelligent “in-painting,” as the technique is called when a program fills in a space with what it thinks belongs there. Adobe in particular has made good use of it with its “context-aware fill,” allowing users to seamlessly replace undesired features, for example a protruding branch or a cloud, with a pretty good guess at what would be there if it weren’t.
But some features are beyond the tools’ capacity to replace, one of which is eyes. Their detailed and highly variable nature make it particularly difficult for a system to change or create them realistically.
Facebook, which probably has more pictures of people blinking than any other entity in history, decided to take a crack at this problem.
It does so with a Generative Adversarial Network, essentially a machine learning system that tries to fool itself into thinking its creations are real. In a GAN, one part of the system learns to recognize, say, faces, and another part of the system repeatedly creates images that, based on feedback from the recognition part, gradually grow in realism.
In this case the network is trained to both recognize and replicate convincing open eyes. This could be done already, but as you can see in the examples at right, existing methods left something to be desired. They seem to paste in the eyes of the people without much consideration for consistency with the rest of the image.
Machines are naive that way: they have no intuitive understanding that opening one’s eyes does not also change the color of the skin around them. (For that matter, they have no intuitive understanding of eyes, color, or anything at all.)
What Facebook’s researchers did was to include “exemplar” data showing the target person with their eyes open, from which the GAN learns not just what eyes should go on the person, but how the eyes of this particular person are shaped, colored, and so on.
The results are quite realistic: there’s no color mismatch or obvious stitching because the recognition part of the network knows that that’s not how the person looks.
In testing, people mistook the fake eyes-opened photos for real ones, or said they couldn’t be sure which was which, more than half the time. And unless I knew a photo was definitely tampered with, I probably wouldn’t notice if I was scrolling past it in my newsfeed. Gandhi looks a little weird, though.
It still fails in some situations, creating weird artifacts if a person’s eye is partially covered by a lock of hair, or sometimes failing to recreate the color correctly. But those are fixable problems.
You can imagine the usefulness of an automatic eye-opening utility on Facebook that checks a person’s other photos and uses them as reference to replace a blink in the latest one. It would be a little creepy, but that’s pretty standard for Facebook, and at least it might save a group photo or two.
Here is what your daily menu might look like if recently funded startups have their way.
You’ll start the day with a nice, lightly caffeinated cup of cheese tea. Chase away your hangover with a cold bottle of liver-boosting supplement. Then slice up a few strawberries, fresh-picked from the corner shipping container.
Lunch is full of options. Perhaps a tuna sandwich made with a plant-based, tuna-free fish. Or, if you’re feeling more carnivorous, grab a grilled chicken breast fresh from the lab that cultured its cells, while crunching on a side of mushroom chips. And for extra protein, how about a brownie?
Sound terrifying? Tasty? Intriguing? If you checked tasty and intriguing, then here is some good news: The concoctions highlighted above are all products available (or under development) at food and beverage startups that have raised venture and seed funding this past year.
These aren’t small servings of capital, either. A Crunchbase News analysis of venture funding for the food and beverage category found that startups in the space gobbled up more than $3 billion globally in disclosed investment over the past 12 months. That includes a broad mix of supersize deals, tiny seed rounds and everything in-between.
Spending several hours looking at all these funding rounds leaves one with a distinct sense that eating habits are undergoing a great deal of flux. And while we can’t predict what the menu of the future will really hold, we can highlight some of the trends. For this initial installment in our two-part series, we’ll start with foods. Next week, we’ll zero in on beverages.
For protein lovers disenchanted with commercial livestock farming, the future looks good. At least eight startups developing plant-based and alternative proteins closed rounds in the past year, focused on everything from lab meat to fishless fish to fast-food nuggets.
New investments add momentum to what was already a pretty hot space. To date, more than $600 million in known funding has gone to what we’ve dubbed the “alt-meat” sector, according to Crunchbase data. Actual investment levels may be quite a bit higher since strategic investors don’t always reveal round size.
In recent months, we’ve seen particularly strong interest in the lab-grown meat space. At least three startups in this area — Memphis Meats, SuperMeat and Wild Type — raised multi-million dollar rounds this year. That could be a signal that investors have grown comfortable with the concept, and now it’s more a matter of who will be early to market with a tasty and affordable finished product.
Makers of meatless versions of common meat dishes are also attracting capital. Two of the top funding recipients in our data set include Seattle Food Tech, which is working to cost-effectively mass-produce meatless chicken nuggets, and Good Catch, which wants to hook consumers on fishless seafoods. While we haven’t sampled their wares, it does seem like they have chosen some suitable dishes to riff on. After all, in terms of taste, both chicken nuggets and tuna salad are somewhat removed from their original animal protein sources, making it seemingly easier to sneak in a veggie substitute.
Another trend we saw catching on with investors is robot chefs. Modern cooking is already a gadget-driven process, so it’s not surprising investors see this as an area ripe for broad adoption.
Pizza, the perennial takeout favorite, seems to be a popular area for future takeover by robots, with at least two companies securing rounds in recent months. Silicon Valley-based Zume, which raised $48 million last year, uses robots for tasks like spreading sauce and moving pies in and out of the oven. France’s EKIM, meanwhile, recently opened what it describes as a fully autonomous restaurant staffed by pizza robots cooking as customers watch.
Salad, pizza’s healthier companion side dish, is also getting roboticized. Just this week, Chowbotics, a developer of robots for food service whose lineup includes Sally the salad robot, announced an $11 million Series A round.
Those aren’t the only players. We’ve put together a more complete list of recently launched or funded robot food startups here.
Sugar substitutes aren’t exactly a new area of innovation. Diet Rite, often credited as the original diet soda, hit the market in 1958. Since then, we’ve had 60 years of mass-marketing for low-calorie sweeteners, from aspartame to stevia.
It’s not over. In recent quarters, we’ve seen a raft of funding rounds for startups developing new ways to reduce or eliminate sugar in many of the foods we’ve come to love. On the dessert and candy front, Siren Snacks and SmartSweets are looking to turn favorite indulgences like brownies and gummy bears into healthy snack options.
The quest for good-for-you sugar also continues. The latest funding recipient in this space appears to be Bonumuse, which is working to commercialize two rare sugars, Tagatose and Allulose, as lower-calorie and potentially healthier substitutes for table sugar. We’ve compiled a list of more sugar-reduction-related startups here.
It’s tough to tell which early-stage food startups will take off and which will wind up in the scrap bin. But looking in aggregate at what they’re cooking up, it looks like the meal of the future will be high in protein, low in sugar and prepared by a robot.
DeepMind’s foray into digital health services continues to raise concerns. The latest worries are voiced by a panel of external reviewers appointed by the Google-owned AI company to report on its operations after its initial data-sharing arrangements with the U.K.’s National Health Service (NHS) ran into a major public controversy in 2016.
The DeepMind Health Independent Reviewers’ 2018 report flags a series of risks and concerns, as they see it, including the potential for DeepMind Health to be able to “exert excessive monopoly power” as a result of the data access and streaming infrastructure that’s bundled with provision of the Streams app — and which, contractually, positions DeepMind as the access-controlling intermediary between the structured health data and any other third parties that might, in the future, want to offer their own digital assistance solutions to the Trust.
While the underlying FHIR (aka, fast healthcare interoperability resource) deployed by DeepMind for Streams uses an open API, the contract between the company and the Royal Free Trust funnels connections via DeepMind’s own servers, and prohibits connections to other FHIR servers. A commercial structure that seemingly works against the openness and interoperability DeepMind’s co-founder Mustafa Suleyman has claimed to support.
“There are many examples in the IT arena where companies lock their customers into systems that are difficult to change or replace. Such arrangements are not in the interests of the public. And we do not want to see DeepMind Health putting itself in a position where clients, such as hospitals, find themselves forced to stay with DeepMind Health even if it is no longer financially or clinically sensible to do so; we want DeepMind Health to compete on quality and price, not by entrenching legacy position,” the reviewers write.
Though they point to DeepMind’s “stated commitment to interoperability of systems,” and “their adoption of the FHIR open API” as positive indications, writing: “This means that there is potential for many other SMEs to become involved, creating a diverse and innovative marketplace which works to the benefit of consumers, innovation and the economy.”
“We also note DeepMind Health’s intention to implement many of the features of Streams as modules which could be easily swapped, meaning that they will have to rely on being the best to stay in business,” they add.
However, stated intentions and future potentials are clearly not the same as on-the-ground reality. And, as it stands, a technically interoperable app-delivery infrastructure is being encumbered by prohibitive clauses in a commercial contract — and by a lack of regulatory pushback against such behavior.
The reviewers also raise concerns about an ongoing lack of clarity around DeepMind Health’s business model — writing: “Given the current environment, and with no clarity about DeepMind Health’s business model, people are likely to suspect that there must be an undisclosed profit motive or a hidden agenda. We do not believe this to be the case, but would urge DeepMind Health to be transparent about their business model, and their ability to stick to that without being overridden by Alphabet. For once an idea of hidden agendas is fixed in people’s mind, it is hard to shift, no matter how much a company is motivated by the public good.”
“We have had detailed conversations about DeepMind Health’s evolving thoughts in this area, and are aware that some of these questions have not yet been finalised. However, we would urge DeepMind Health to set out publicly what they are proposing,” they add.
DeepMind has suggested it wants to build healthcare AIs that are capable of charging by results. But Streams does not involve any AI. The service is also being provided to NHS Trusts for free, at least for the first five years — raising the question of how exactly the Google-owned company intends to recoup its investment.
Google of course monetizes a large suite of free-at-the-point-of-use consumer products — such as the Android mobile operating system; its cloud email service Gmail; and the YouTube video sharing platform, to name three — by harvesting people’s personal data and using that information to inform its ad targeting platforms.
Hence the reviewers’ recommendation for DeepMind to set out its thinking on its business model to avoid its intentions vis-a-vis people’s medical data being viewed with suspicion.
The company’s historical modus operandi also underlines the potential monopoly risks if DeepMind is allowed to carve out a dominant platform position in digital healthcare provision — given how effectively its parent has been able to turn a free-for-OEMs mobile OS (Android) into global smartphone market OS dominance, for example.
So, while DeepMind only has a handful of contracts with NHS Trusts for the Streams app and delivery infrastructure at this stage, the reviewers’ concerns over the risk of the company gaining “excessive monopoly power” do not seem overblown.
They are also worried about DeepMind’s ongoing vagueness about how exactly it works with its parent Alphabet, and what data could ever be transferred to the ad giant — an inevitably queasy combination when stacked against DeepMind’s handling of people’s medical records.
“To what extent can DeepMind Health insulate itself against Alphabet instructing them in the future to do something which it has promised not to do today? Or, if DeepMind Health’s current management were to leave DeepMind Health, how much could a new CEO alter what has been agreed today?” they write.
“We appreciate that DeepMind Health would continue to be bound by the legal and regulatory framework, but much of our attention is on the steps that DeepMind Health have taken to take a more ethical stance than the law requires; could this all be ended? We encourage DeepMind Health to look at ways of entrenching its separation from Alphabet and DeepMind more robustly, so that it can have enduring force to the commitments it makes.”
Responding to the report’s publication on its website, DeepMind writes that it’s “developing our longer-term business model and roadmap.”
“Rather than charging for the early stages of our work, our first priority has been to prove that our technologies can help improve patient care and reduce costs. We believe that our business model should flow from the positive impact we create, and will continue to explore outcomes-based elements so that costs are at least in part related to the benefits we deliver,” it continues.
So it has nothing to say to defuse the reviewers’ concerns about making its intentions for monetizing health data plain — beyond deploying a few choice PR soundbites.
On its links with Alphabet, DeepMind also has little to say, writing only that: “We will explore further ways to ensure there is clarity about the binding legal frameworks that govern all our NHS partnerships.”
“Trusts remain in full control of the data at all times,” it adds. “We are legally and contractually bound to only using patient data under the instructions of our partners. We will continue to make our legal agreements with Trusts publicly available to allow scrutiny of this important point.”
“There is nothing in our legal agreements with our partners that prevents them from working with any other data processor, should they wish to seek the services of another provider,” it also claims in response to additional questions we put to it.
“We hope that Streams can help unlock the next wave of innovation in the NHS. The infrastructure that powers Streams is built on state-of-the-art open and interoperable standards, known as FHIR. The FHIR standard is supported in the UK by NHS Digital, NHS England and the INTEROPen group. This should allow our partner trusts to work more easily with other developers, helping them bring many more new innovations to the clinical frontlines,” it adds in additional comments to us.
“Under our contractual agreements with relevant partner trusts, we have committed to building FHIR API infrastructure within the five year terms of the agreements.”
Asked about the progress it’s made on a technical audit infrastructure for verifying access to health data, which it announced last year, it reiterated the wording on its blog, saying: “We will remain vigilant about setting the highest possible standards of information governance. At the beginning of this year, we appointed a full time Information Governance Manager to oversee our use of data in all areas of our work. We are also continuing to build our Verifiable Data Audit and other tools to clearly show how we’re using data.”
So developments on that front look as slow as we expected.
The Google-owned U.K. AI company began its push into digital healthcare services in 2015, quietly signing an information-sharing arrangement with a London-based NHS Trust that gave it access to around 1.6 million people’s medical records for developing an alerts app for a condition called Acute Kidney Injury.
It also inked an MoU with the Trust where the pair set out their ambition to apply AI to NHS data sets. (They even went so far as to get ethical signs-off for an AI project — but have consistently claimed the Royal Free data was not fed to any AIs.)
However, the data-sharing collaboration ran into trouble in May 2016 when the scope of patient data being shared by the Royal Free with DeepMind was revealed (via investigative journalism, rather than by disclosures from the Trust or DeepMind).
None of the ~1.6 million people whose non-anonymized medical records had been passed to the Google-owned company had been informed or asked for their consent. And questions were raised about the legal basis for the data-sharing arrangement.
Last summer the U.K.’s privacy regulator concluded an investigation of the project — finding that the Royal Free NHS Trust had broken data protection rules during the app’s development.
Yet despite ethical questions and regulatory disquiet about the legality of the data sharing, the Streams project steamrollered on. And the Royal Free Trust went on to implement the app for use by clinicians in its hospitals, while DeepMind has also signed several additional contracts to deploy Streams to other NHS Trusts.
More recently, the law firm Linklaters completed an audit of the Royal Free Streams project, after being commissioned by the Trust as part of its settlement with the ICO. Though this audit only examined the current functioning of Streams. (There has been no historical audit of the lawfulness of people’s medical records being shared during the build and test phase of the project.)
Linklaters did recommend the Royal Free terminates its wider MoU with DeepMind — and the Trust has confirmed to us that it will be following the firm’s advice.
“The audit recommends we terminate the historic memorandum of understanding with DeepMind which was signed in January 2016. The MOU is no longer relevant to the partnership and we are in the process of terminating it,” a Royal Free spokesperson told us.
So DeepMind, probably the world’s most famous AI company, is in the curious position of being involved in providing digital healthcare services to U.K. hospitals that don’t actually involve any AI at all. (Though it does have some ongoing AI research projects with NHS Trusts too.)
In mid 2016, at the height of the Royal Free DeepMind data scandal — and in a bid to foster greater public trust — the company appointed the panel of external reviewers who have now produced their second report looking at how the division is operating.
And it’s fair to say that much has happened in the tech industry since the panel was appointed to further undermine public trust in tech platforms and algorithmic promises — including the ICO’s finding that the initial data-sharing arrangement between the Royal Free and DeepMind broke U.K. privacy laws.
The eight members of the panel for the 2018 report are: Martin Bromiley OBE; Elisabeth Buggins CBE; Eileen Burbidge MBE; Richard Horton; Dr. Julian Huppert; Professor Donal O’Donoghue; Matthew Taylor; and Professor Sir John Tooke.
In their latest report the external reviewers warn that the public’s view of tech giants has “shifted substantially” versus where it was even a year ago — asserting that “issues of privacy in a digital age are if anything, of greater concern.”
At the same time politicians are also gazing rather more critically on the works and social impacts of tech giants.
Although the U.K. government has also been keen to position itself as a supporter of AI, providing public funds for the sector and, in its Industrial Strategy white paper, identifying AI and data as one of four so-called “Grand Challenges” where it believes the U.K. can “lead the world for years to come” — including specifically name-checking DeepMind as one of a handful of leading-edge homegrown AI businesses for the country to be proud of.
Still, questions over how to manage and regulate public sector data and AI deployments — especially in highly sensitive areas such as healthcare — remain to be clearly addressed by the government.
Meanwhile, the encroaching ingress of digital technologies into the healthcare space — even when the techs don’t even involve any AI — are already presenting major challenges by putting pressure on existing information governance rules and structures, and raising the specter of monopolistic risk.
Asked whether it offers any guidance to NHS Trusts around digital assistance for clinicians, including specifically whether it requires multiple options be offered by different providers, the NHS’ digital services provider, NHS Digital, referred our question on to the Department of Health (DoH), saying it’s a matter of health policy.
The DoH in turn referred the question to NHS England, the executive non-departmental body which commissions contracts and sets priorities and directions for the health service in England.
And at the time of writing, we’re still waiting for a response from the steering body.
Ultimately it looks like it will be up to the health service to put in place a clear and robust structure for AI and digital decision services that fosters competition by design by baking in a requirement for Trusts to support multiple independent options when procuring apps and services.
Without that important check and balance, the risk is that platform dynamics will quickly dominate and control the emergent digital health assistance space — just as big tech has dominated consumer tech.
But publicly funded healthcare decisions and data sets should not simply be handed to the single market-dominating entity that’s willing and able to burn the most resource to own the space.
Nor should government stand by and do nothing when there’s a clear risk that a vital area of digital innovation is at risk of being closed down by a tech giant muscling in and positioning itself as a gatekeeper before others have had a chance to show what their ideas are made of, and before even a market has had the chance to form.
If you’re already resentful of online dating culture and how it turned finding companionship into a game, you may not be quite ready for this: Crown, a new dating app that actually turns getting matches into a game. Crown is the latest project to launch from Match Group, the operator of a number of dating sites and apps including Match, Tinder, Plenty of Fish, OK Cupid, and others.
The app was thought up by Match Product Manager Patricia Parker, who understands first-hand both the challenges and the benefits of online dating – Parker met her husband online, so has direct experience in the world of online dating.
Crown won Match Group’s internal “ideathon,” and was then developed in-house by a team of millennial women, with a goal of serving women’s needs in particular.
The main problem Crown is trying to solve is the cognitive overload of using dating apps. As Match Group scientific advisor Dr. Helen Fisher explained a few years ago to Wired, dating apps can become addictive because there’s so much choice.
“The more you look and look for a partner the more likely it is that you’ll end up with nobody…It’s called cognitive overload,” she had said. “There is a natural human predisposition to keep looking—to find something better. And with so many alternatives and opportunities for better mates in the online world, it’s easy to get into an addictive mode.”
Millennials are also prone to swipe fatigue, as they spend an average of 10 hours per week in dating apps, and are being warned to cut down or face burnout.
Crown’s approach to these issues is to turn getting matches into a game of sorts.
While other dating apps present you with an endless stream of people to pick from, Crown offers a more limited selection.
Every day at noon, you’re presented with 16 curated matches, picked by some mysterious algorithm. You move through the matches by choosing who you like more between two people at a time.
That is, the screen displays two photos instead of one, and you “crown” your winner. (Get it?) This process then repeats with two people shown at a time, until you reach your “Final Four.”
Those winners are then given the opportunity to chat with you, or they can choose to pass.
In addition to your own winners, you may also “win” the crown among other brackets, which gives you more matches to contend with.
Of course, getting dubbed a winner is a stronger signal on Crown than on an app like Tinder, where it’s more common for matches to not start conversations. This could encourage Crown users to chat, given they know there’s more of a genuine interest since they “beat out” several others. But on the flip side, getting passed on Crown is going to be a lot more of an obvious “no,” which could be discouraging.
“It’s like a ‘Bachelorette’-style process of elimination that helps users choose between quality over quantity,” explains Andy Chen, Vice President, Match Group. “Research shows that the human brain can only track a set number of relationships…and technology has not helped us increase this limit.”
Chen is referring to the Dunbar number, which says that people can only really maintain a max of some 150 social relationships. Giving users a never-ending list of possible matches on Tinder, then, isn’t helping people feel like they have options – it’s overloading the brain.
While turning matchmaking into a game feels a bit dehumanizing – maybe even more so than on Tinder, with its Hot-or-Not-inspired vibe – the team says Crown actually increases the odds, on average, of someone being selected, compared with traditional dating apps.
“When choosing one person over another, there is always a winner. The experience actually encourages a user playing the game to find reasons to say yes,” says Chen.
Crown has been live in a limited beta for a few months, but is now officially launched in L.A. (how appropriate) with more cities to come. For now, users outside L.A. will be matched with those closet to them.
There are today several thousand users on the app, and it’s organically growing, Chen says.
Plus, Crown is seeing day-over-day retention rates which are “already as strong” as Match Group’s other apps, we’re told.
As humans, we’ve gotten pretty good at shaping the world around us. We can choose the molecular design of our fruits and vegetables, travel faster and farther and stave off life-threatening diseases with personalized medical care. However, what continues to elude our molding grasp is the airy notion of “time” — how to see further than our present moment, and ultimately how to make the most of it. As it turns out, robots might be the ones that can answer this question.
Computer scientists from the University of Bonn in Germany wrote this week that they were able to design a software that could predict a sequence of events up to five minutes in the future with accuracy between 15 and 40 percent. These values might not seem like much on paper, but researcher Dr. Juergen Gall says it represents a step toward a new area of machine learning that goes beyond single-step prediction.
Although Gall’s goal of teaching a system how to understand a sequence of events is not new (after all, this is a primary focus of the fields of machine learning and computer vision), it is unique in its approach. Thus far, research in these fields has focused on the interpretation of a current action or the prediction of an anticipated next action. This was seen recently in the news when a paper from Stanford AI researchers reported designing an algorithm that could achieve up to 90 percent accuracy in its predictions regarding end-of-life care.
When researchers provided the algorithm with data from more than two million palliative-care patient records, it was able to analyze patterns in the data and predict when the patient would pass with high levels of accuracy. However, unlike Gall’s research, this algorithm focused on a retrospective, single prediction.
Accuracy itself is a contested question in the field of machine learning. While it appears impressive on paper to report accuracies ranging upwards of 90 percent, there is debate about the over-inflation of these values through cherry-picking “successful” data in a process called p-hacking.
In their experiment, Gall and his team used hours of video data demonstrating different cooking actions (e.g. frying an egg or tossing a salad) and presented the software with only portions of the action and tasked it with predicting the remaining sequence based on what it had “learned.” Through their approach, Gall hopes the field can take a step closer to true human-machine symbiosis.
“[In the industry] people talk about human robot collaboration but in the end there’s still a separation; they’re not really working close together,” says Gall.
Instead of only reacting or anticipating, Gall proposes that, with a proper hardware body, this software could help human workers in industrial settings by intuitively knowing the task and helping them complete it. Even more, Gall sees a purpose for this technology in a domestic setting, as well.
“There are many older people and there’s efforts to have this kind of robot for care at home,” says Gall. “In ten years I’m very convinced that service robots [will] support care at home for the elderly.”
The number of Americans over the age of 65 today is approximately 46 million, according to a Population Reference Bureau report, and is predicted to double by the year 2060. Of that population, roughly 1.4 million live in nursing homes according to a 2014 CDC report. The impact that an intuitive software like Gall’s could have has been explored in Japan, where just over one-fourth of the country’s population is elderly. From PARO, a soft, robotic therapy seal, to the sleek companion robot Pepper from SoftBank Robotics, Japan is beginning to embrace the calm, nurturing assistance of these machines.
With this advance in technology for the elderly also comes the bitter taste that perhaps these technologies will only create further divide between the generations — outsourcing love and care to a machine. For a yet mature industry it’s hard to say where this path with conclude, but ultimately that is in the hands of developers to decide, not the software or robots they develop. These machines may be getting better at predicting the future, but even to them their fates are still being coded.
Elizabeth Holmes has left her role as CEO of Theranos and has been charged with wire fraud, CNBC and others report. The company’s former president, Ramesh “Sunny” Balwani, was also indicted today by a grand jury.
These criminal charges are separate from the civil ones filed in March by the SEC and already settled. There are 11 charges; two are conspiracy to commit wire fraud (against investors, and against doctors and patients) and the remaining nine are actual wire fraud, with amounts ranging from the cost of a lab test to $100 million.
Theranos’s general counsel, David Taylor, has been appointed CEO. What duty the position actually entails in the crumbling enterprise is unclear. Holmes, meanwhile, remains chairman of the board.
The FBI Special Agent in Charge of the case against Theranos, John Bennett, said the company engaged in “a corporate conspiracy to defraud financial investors,” and “misled doctors and patients about the reliability of medical tests that endangered health and lives.”
This story is developing. I’ve asked Theranos for comment and will update if I hear back; indeed I’m not even sure anyone is there to respond.
Machine translation of foreign languages is undoubtedly a very useful thing, but if you’re going for anything more than directions or recommendations for lunch, its shallowness is a real barrier. And when it comes to the law and constitutional rights, a “good enough” translation doesn’t cut it, a judge has ruled.
The ruling (PDF) is not hugely consequential, but it is indicative of the evolving place in which translation apps find themselves in our lives and legal system. We are fortunate to live in a multilingual society, but for the present and foreseeable future it seems humans are still needed to bridge language gaps.
The case in question involved a Mexican man named Omar Cruz-Zamora, who was pulled over by cops in Kansas. When they searched his car, with his consent, they found quite a stash of meth and cocaine, which naturally led to his arrest.
But there’s a catch: Cruz-Zamora doesn’t speak English well, so the consent to search the car was obtained via an exchange facilitated by Google Translate — an exchange that the court found was insufficiently accurate to constitute consent given “freely and intelligently.”
The fourth amendment prohibits unreasonable search and seizure, and lacking a warrant or probable cause, the officers required Cruz-Zamora to understand that he could refuse to let them search the car. That understanding is not evident from the exchange, during which both sides repeatedly fail to comprehend what the other is saying.
Not only that, but the actual translations provided by the app weren’t good enough to accurately communicate the question. For example, the officer asked “
Computer systems are getting quite good at understanding what people say, but they also have some major weak spots. Among them is the fact that they have trouble with words that have multiple or complex meanings. A new system called ELMo adds this critical context to words, producing better understanding across the board.
To illustrate the problem, think of the word “queen.” When you and I are talking and I say that word, you know from context whether I’m talking about Queen Elizabeth, or the chess piece, or the matriarch of a hive, or RuPaul’s Drag Race.
This ability of words to have multiple meanings is called polysemy. And really, it’s the rule rather than the exception. Which meaning it is can usually be reliably determined by the phrasing — “God save the queen!” versus “I saved my queen!” — and of course all this informs the topic, the structure of the sentence, whether you’re expected to respond, and so on.
Machine learning systems, however, don’t really have that level of flexibility. The way they tend to represent words is much simpler: it looks at all those different definitions of the word and comes up with a sort of average — a complex representation, to be sure, but not reflective of its true complexity. When it’s critical that the correct meaning of a word gets through, they can’t be relied on.
ELMo (“Embeddings from Language Models”), however, lets the system handle polysemy with ease; as evidence of its utility, it was awarded best paper honors at NAACL last week. At its heart it uses its training data (a huge collection of text) to determine whether a word has multiple meanings and how those different meanings are signaled in language.
For instance, you could probably tell in my example “queen” sentences above, despite their being very similar, that one was about royalty and the other about a game. That’s because the way they are written contain clues to your own context-detection engine to tell you which queen is which.
Informing a system of these differences can be done by manually annotating the text corpus from which it learns — but who wants to go through millions of words making a note on which queen is which?
“We were looking for a method that would significantly reduce the need for human annotation,” explained Mathew Peters, lead author of the paper. “The goal was to learn as much as we can from unlabeled data.”
In addition, he said, traditional language learning systems “compress all that meaning for a single word into a single vector. So we started by questioning the basic assumption: let’s not learn a single vector, let’s have an infinite number of vectors. Because the meaning is highly dependent on the context.”
ELMo learns this information by ingesting the full sentence in which the word appears; it would learn that when a king is mentioned alongside a queen, it’s likely royalty or a game, but never a beehive. When it sees pawn, it knows that it’s chess; jack implies cards; and so on.
An ELMo-equipped language engine won’t be nearly as good as a human with years of experience parsing language, but even working knowledge of polysemy is hugely helpful in understanding a language.
Not only that, but taking the whole sentence into account in the meaning of a word also allows the structure of that sentence to be mapped more easily, automatically labeling clauses and parts of speech.
Systems using the ELMo method had immediate benefits, improving on even the latest natural language algorithms by as much as 25 percent — a huge gain for this field. And because it is a better, more context-aware style of learning, but not a fundamentally different one, it can be integrated easily even into existing commercial systems.
In fact, Microsoft is reportedly already using it with Bing. After all, it’s crucial in search to determine intention, which of course requires an accurate reading of the query. ELMo is open source, too, like all the work from the Allen Institute for AI, so any company with natural language processing needs should probably check this out.
The paper lays down the groundwork of using ELMo for English language systems, but because its power is derived by essentially a close reading of the data that it’s fed, there’s no theoretical reason why it shouldn’t be applicable not just for other languages, but in other domains. In other words, if you feed it a bunch of neuroscience texts, it should be able to tell the difference between temporal as it relates to time and as it relates to that region of the brain.
This is just one example of how machine learning and language are rapidly developing around each other; although it’s already quite good enough for basic translation, speech to text and so on, there’s quite a lot more that computers could do via natural language interfaces — if they only know how.
Apple announced today a multi-year content partnership with Oprah Winfrey to produce programs for the tech company’s upcoming video-streaming service. Apple didn’t provide any specific details as to what sort of projects Winfrey would be involved in, but there will be more than one it seems.
Apple shared the news of its deal with Winfrey in a brief statement on its website, which read:
Apple today announced a unique, multi-year content partnership with Oprah Winfrey, the esteemed producer, actress, talk show host, philanthropist and CEO of OWN.
Together, Winfrey and Apple will create original programs that embrace her incomparable ability to connect with audiences around the world.
Winfrey’s projects will be released as part of a lineup of original content from Apple.
The deal is a significant high-profile win for Apple, which has been busy filing out its lineup with an array of talent in recent months.
The streaming service also will include a reboot of Steven Spielberg’s Amazing Stories, a Reese Witherspoon- and Jennifer Aniston-starring series set in the world of morning TV, an adaptation of Isaac Asimov’s Foundation books, a thriller starring Octavia Spencer, a Kristen Wiig-led comedy, a Kevin Durant-inspired scripted basketball show, a series from “La La Land’s” director and several other shows.
Winfrey, however, is not just another showrunner or producer. She’s a media giant who has worked across film, network and cable TV, print and more as an actress, talk show host, creator and producer.
She’s also a notable philanthropist, having contributed more than $100 million to provide education to academically gifted girls from disadvantaged backgrounds, and is continually discussed as a potential presidential candidate, though she said that’s not for her.
On television, Winfrey’s Harpo Productions developed daytime TV shows like “Dr. Phil,” “The Dr. Oz Show” and “Rachael Ray.” Harpo Films produced several Academy Award-winning movies, including “Selma,” which featured Winfrey in a starring role. She’s also acted in a variety of productions over the years, like “The Color Purple,” which scored her an Oscar nom, “Lee Daniels’ The Butler,” “The Immortal Life of Henrietta Lacks” and Disney’s “A Wrinkle in Time.”
Winfrey also founded the cable network OWN in 2011 in partnership with Discovery Communications, and has exec produced series including “Queen Sugar,” “Oprah’s Master Class” and the Emmy-winning “Super Soul Sunday.”
The latter has a connection with Apple as it debuted as a podcast called “Oprah’s SuperSoul Conversations” and became a No. 1 program on Apple Podcasts.
Winfrey recently extended her contract with OWN through 2025, so it’s unclear how much time she’ll devote specifically toward her Apple projects.
Apple also didn’t say if Winfrey will star or guest in any of the programs themselves, but that’s always an option on the table with a deal like this. CNN, however, is reporting that Winfrey “is expected to have an on-screen role as a host and interviewer.”
The CRM industry is now estimated to be worth some $4 billion annually, and today a startup has announced a round of funding that it hopes will help it take on one aspect of that lucrative pie, customer support. Kustomer, a startup out of New York that integrates a number of sources to give support staff a complete picture of a customer when he or she contacts the company, has raised $26 million.
The funding, a series B, was led by Redpoint Ventures (notably, an early investor in Zendesk, which Kustomer cites as a key competitor), with existing investors Canaan Partners, Boldstart Ventures, and Social Leverage also participating.
Cisco Investments was also a part of this round as a strategic investor: Cisco (along with Avaya) is one of the world’s biggest PBX equipment vendors, and customer support is one of the biggest users of this equipment, but the segment is also under pressure as more companies move these services to the cloud (and consider alternative options). Potentially, you could see how Cisco might want to partner with Kustomer to provide more services on top of its existing equipment, and potentially as a standalone service — although for now the two have yet to announce any actual partnerships.
Given that Kustomer has been approached already for potential acquisitions, you could see how the Ciscos of the world might be one possible category of buyers.
Kustomer is not discussing valuation but it has raised a total of $38.5 million. Kustomer’s customers include brands in fashion, e-commerce and other sectors that provide customer support on products on a regular basis, such as Ring, Modsy, Glossier, Smug Mug and more.
When we last wrote about Kustomer, when it raised $12.5 million in 2016, the company’s mission was to effectively turn anyone at a company into a customer service rep — the idea being that some issues are better answered by specific people, and a CRM platform for all employees to engage could help them fill that need.
Today, Brad Birnbaum, the co-founder and CEO, says that this concept has evolved. He said that “half of its business model still involves the idea of everyone being on the platform.” For example, an internal sales rep can collaborate with someone in a company’s shipping department — “but the only person who can communicate with the customer is the full-fledged agent,” he said. “That is what the customers wanted so that they could better control the messaging.”
The collaboration, meanwhile, has taken an interesting turn: it’s not just related to employees communicating better to develop a more complete picture of a customer and his/her history with the company; but it’s about a company’s systems integrating better to give a more complete view to the reps. Integrations include data from e-commerce platforms like Shopify and Magento; voice and messaging platforms like Twilio, TalkDesk, Twitter and Facebook Messenger; feedback tools like Nicereply; analytics services like Looker, Snowflake, Jira and Redshift; and Slack.
Birnbaum previously founded and sold Assistly to Salesforce, which turned it into Desk.com — (his co-founder in Kustomer, Jeremy Suriel, was Assistly’s chief architect), and between that and Kustomer he also had a go at building out Airtime, Sean Parker’s social startup. Kustomer, he says, is not only competing against Salesforce but perhaps even more specifically Zendesk, in offering a new take on customer support.
Zendesk, he said, had really figured out how to make customer support ticketing work efficiently, “but they don’t understand the customer at all.”
“We are a much more modern solution in how we see the world,” he continued. “No one does omni-channel customer service properly, where you can see a single threaded conversation speaking to all of a customer’s points.”
Going forward, Kustomer will be using the funding to expand its platform with more capabilities, and some of its own automations and insights (rather than those provided by way of integrations). This will also see the company expand into other kinds of services adjacent to taking inbound customer requests, such as reaching out to the customers, potentially to seel to them. “We plan to go broadly with engagement as an example,” Birnbaum said. “We already know everything about you so if we see you on a website, we can proactively reach out to you and engage you.”
“It is time for disruption in customer support industry, and Kustomer is leading the way,” said Tomasz Tunguz, partner at Redpoint Ventures, in a statement. “Kustomer has had impressive traction to date, and we are confident the world’s best B2C and B2B companies will be able to utilize the platform in order to develop meaningful relationships, experiences, and lifetime value for their customers. This is an exciting and forward-thinking platform for companies as well as their customers.”
When you spend time with a lot of BMW folks, as I did during a trip to Germany earlier this month, you’ll regularly hear the word “heritage.” Maybe that’s no surprise, given that the company is now well over 100 years old. But in a time of rapid transformation that’s hitting every car manufacturer, engineers and designers have to strike a balance between honoring that history and looking forward. With the latest version of its BMW OS in-car operating system and its accompanying design language, BMW is breaking with some traditions to allow it to look into the future while also sticking to its core principles.
If you’ve driven a recent luxury car, then the instrument cluster in front of you was likely one large screen. But at least in even the most recent BMWs, you’ll still see the standard round gauges that have adorned cars since their invention. That’s what drivers expect and that’s what the company gave them, down to the point where it essentially glued a few plastic strips on the large screen that now makes up the dashboard to give drivers an even more traditional view of their Autobahn speeds.
With BMW OS 7.0, which I got some hands-on time with in the latest BMW 8-series model that’s making its official debut today (and where the OS update will also make its first appearance), the company stops pretending that the screen is a standard set of gauges. Sure, some of the colors remain the same, but users looking for the classic look of a BMW cockpit are in for a surprise.
“We first broke up the classic round instruments back in 2015 so we could add more digital content to the middle, including advanced driving assistance systems,” one of BMW’s designers told me. “And that was the first break [with tradition]. Now in 2018, we looked at the interior and exterior design of our cars — and took all of those forms — and integrated them into the digital user interface of our cars.”
The overall idea behind the design is to highlight relevant information when it’s needed but to let it fade back when it’s not, allowing the driver to focus on the task at hand (which, at least for the next few years, is mostly driving).
So when you enter the car, you’ll get the standard BMW welcome screen, which is now integrated with your digital BMW Connected profile in the cloud. When you start driving, the new design comes to life, with all of the critical information you need for driving on the left side of the dashboard, as well as data about the state of your driving assistance systems. That’s a set of digital gauges that remains on the screen at all times. On the right side of the screen, though, you’ll see all of the widgets that can be personalized. There are six of those, and they range from G meters for when you’re at a track day to a music player that uses the space to show album art.
The middle of the screen focuses on navigation. But as the BMW team told me, the idea here isn’t to just copy the map that’s traditionally on the tablet-like screen in the middle of the dashboard. What you’ll see here is a stripped-down map view that only shows you the navigational data you need at any given time.
And because the digital user interface isn’t meant to be a copy of its analog counterpart from yesteryear, the team also decided that it could play with more colors. That means that as you move from sport to eco mode, for example, the UI’s primary color changes from red to blue.
The instrument cluster is only part of the company’s redesign. It also took a look at what it calls the “Control Display” in the center console. That’s traditionally where the company has displayed everything from your music player to its built-in GPS maps (and Apple CarPlay, if that’s your thing). Here, BMW has simplified the menu structure by making it much flatter and also made some tweaks to the overall design. What you’ll see is that it also went for a design language here that’s still occasionally playful but that does away with many of the 3D effects, and instead opted for something that’s more akin to Google’s Material Design or Microsoft’s Fluent Design System. This is a subtle change, but the team told me that it very deliberately tried to go with a more modern and flatter look.
This display now also offers more tools for personalization, with the ability to change the layout to show more widgets, if the driver doesn’t mind a more cluttered display, for example.
Thanks to its integration with BMW Connect, the company’s cloud-based tools and services for saving and syncing data, managing in-car apps and more, the updated operating system also lays the foundation for the company’s upcoming e-commerce play. Dieter May, BMW’s VP for digital products and services, has talked about this quite a bit in the past, and the updated software and fully digital cockpit is what will enable the company’s next moves in this direction. Because the new operating system puts a new emphasis on the user’s digital account, which is encoded in your key fob, the car becomes part of the overall BMW ecosystem, which includes other mobility services like ReachNow, for example (though you obviously don’t need to have a BMW Connect account just to drive the car).
Unsurprisingly, the new operating system will launch with a couple of the company’s more high-end vehicles like the 8-series car that is launching today, but it will slowly trickle down to other models, as well.
Disrupt SF is just a few months away (September 5-7 at Moscone Center West) and we’re looking for delegations of international startup groups, government innovation centers, incubators and accelerators to organize a country, state or regional pavilion in Startup Alley. Are you ready to step on a world stage, show off your emerging companies and be recognized as a leader in tech innovation?
Startup Alley is prime real estate, where hundreds of founders from everywhere in the world — and investors looking to fund them — gather to meet, connect and network. And maybe even produce a unicorn or two.
If you want to exhibit in Startup Alley as part of a country, state or region, your delegation startups must meet one requirement only: they must be Pre-Series A startups. If so, shoot our Startup Alley manager, Priya, an email at firstname.lastname@example.org. Tell us about your delegation and where you’re from, and we’ll provide more information about the application process.
Regions that have participated in previous TechCrunch events include St. Louis, Argentina, Austria, Belgium, Brazil, the Caribbean, Catalonia, the Czech Republic, Germany, Hungary, Hong Kong, Korea, Japan, Lithuania, Taiwan, Ukraine and Uruguay. We believe that innovation and great ideas know no geographical boundaries, and we strive to increase the diversity within our regional pavilions at every Disrupt.
Organize a minimum of eight (8) startups in your region and you’ll receive a discount off each Startup Alley company’s exhibitor package — and you’ll get organizer passes to the event. Plus, if you book your pavilion before July 25, your startups will receive one additional Founder ticket to attend Disrupt SF. Email email@example.com for more pricing information.
The complaint, filed in the U.S. District Court Southern District of NY, alleges that wefox reverse engineered Lemonade to create ONE, infringing Lemonade’s intellectual property, violating the Computer Fraud and Abuse Act, and breaching its contractual obligations to Lemonade not to “copy content… to provide any service that is competitive…or to…create derivative works.”
In the filing (which you can see on Pacer or here), Lemonade alleges that Teicke repeatedly registered for insurance on Lemonade under various names and for various addresses, some of which do not exist. Teicke also allegedly filed claims in what appeared to be an attempt to assess and copy the arrangement of those flows.
Lemonade’s counsel says Teicke started seven claims over the course of 20 days, prompting Lemonade to cancel his policy.
Alongside Teicke, a number of other executives and members of leadership at wefox also filed fake claims, says the complaint, despite having opted in to Lemonade’s user agreement and taking an honesty pledge, which is required of all Lemonade users.
This, according to Lemonade, violates the Computer Fraud and Abuse act. Lemonade also alleges that the ONE app infringes Lemonade’s IP, and that in assessing the Lemonade app and building a competitor, Teicke also violated Lemonade’s TOS.
Lemonade has changed the insurance business in two key ways: First, it made the process of actually buying insurance as easy as a few clicks on your smartphone. Digitizing the process makes the issue of getting home or renters insurance far less daunting and more approachable to consumers. Secondly, Lemonade rethought the business model of insurance.
Normally, insurance providers charge you a certain monthly rate based on the value of the property/items looking to be insured. But at the end of the year, the money remaining in that policy becomes profit, putting the insurance company in direct opposition to the consumer any time a claim is filed.
Lemonade takes its profit directly out of each payment, and if a file isn’t claimed, it sends the rest of the leftover money to the charity of your choice, ensuring that Lemonade and the consumer are on the same page when a claim is filed.
In keeping with that thesis, any proceeds generated from this lawsuit will go directly to Code.org.
“We’re not trying to enrich ourselves by poking another startup,” said Lemonade CEO Daniel Schreiber . “We’re not anti-competition. We’re just saying ‘Play by the rules, play fair and square.'”
Update: A wefox spokesperson offered up the following statement:
At wefox Group, we have 160 talented people whose hard work has created a unique business that is challenging the status quo every day. These allegations have no merit and ultimately appear to be an attempt to disrupt our business rather than a serious dispute. Lemonade actually raised these questions with us nine months ago, and – as we explained at the time – the concerns are meritless and we further received no answer. We have not been served any paper from Lemonade: if we are, we intend to defend ourselves vigorously. This lawsuit appears to be an attempt to bait the media into covering a non-issue.
Welcome back to CTRL+T, the TechCrunch podcast where Megan Rose Dickey and I talk about stories from the week that we either found interesting or hated and had more to say about.
This week we talked about Uber . Uber, Uber, Uber. This company wants everything. The rideshare market! Autonomous vehicles! Flying vehicles! And now? Scooters. And to be able to detect inebriation in passengers! This week, we found out that Uber filed for a patent for tech to be able to tell whether a potential passenger is drunk.
And regular listeners know how we at CTRL+T feel about scooters, but we have to keep talking about them because the companies that facilitate that mode of transportation keep getting funded. Thanks, funders. And Uber is taking its place in the scooter racket. I mean, market.
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Start: 09 Aug 2017 | End: 01 May 2018