Researchers detail the evolution of the world’s strangest fish, and describe how it could be a potentially powerful tool for scientists to study ocean life.
Despite the repeated privacy lapses, Facebook offers a fairly robust set of tools to control who knows what about you.
Zagster, the bike-share startup that raised a $15 million round last month, has laid off some employees, TechCrunch has learned. Zagster has since confirmed the layoffs, but has yet to comment on the number of those affected.
“Coming off the heels of our recent financing, we’ve re-structured to accelerate expansion of Pace, with a heavier focus on building in-market teams to greatly expand bike fleets, drive higher ridership, and partner with local businesses to sponsor Pace parking,” Zagster CEO Tim Ericson said in a statement to TechCrunch. “We did have to let a number of people go with roles not aligned to our Pace strategy. We’re extremely appreciative of their contributions and are helping them through the transition.”
The shift from docked to dockless bike-sharing is what prompted the layoffs, Ericson said. Within the U.S. dockless market, Ericson said he sees two models emerging: free-floating and lock-to.
“We believe the U.S. market will move to lock-to, with cities regulating and enforcing lock-to parking within three years,” Ericson said. “Zagster has a strong lead in lock-to, with exclusive rights to operate in more than 100 cities and colleges.”
Zagster’s Pace is one of the newer entrants to the bike-share space, which consists of a number of startups and larger companies battling for contracts with cities all over the world. Pace, which launched just a few months ago (in December), currently operates in Tallahassee, Florida and Knoxville, Tennessee. With the funding, Zagster plans to launch Pace in additional cities this year.
Zagster also operates a bike-share solution for municipalities looking to offer their own city-specific services. Zagster, which launched in 2007, operates more than 200 bike-shares across 35 states in the U.S.
Zagster’s plan, Ericson said, is to convert its bike-shares to the Pace brand and model, with the ultimate goal of creating a nationwide dockless system across 35 states by the end of 2019. Over the next three months, Zagster plans to quadruple the Pace footprint by launching in six new cities.
This year has been full of bike-share news, from JUMP scoring an exclusive contract to operate its stationless bike-share service in San Francisco to both LimeBike and Spin unveiling their own take on e-bikes.
Clari — a startup that has built a predictive sales tool that provides just-in-time assistance for sales people close deals and for those who work in the bigger chain of command to monitor the progress of the sales operation — is capitalising on the big boom in interest for all things AI in the business world. The company is today announcing that it has closed a Series B round of $35 million, funding that it will be using to build out its own sales and marketing team and expand its platform capabilities.
The round was led by Tenaya Capital, the VC fund that started its life as a part of Lehman Brothers, along with participation from other new investors Thomvest Ventures and Blue Cloud Ventures, and previous investors Sequoia Capital, Bain Capital Ventures and Northgate Capital. It brings the total raised by Clari to $61 million.
Andy Byrne, the founder and CEO who is a repeat entrepreneur and has been involved in several exits, said the funding closed “definitely at an upround, and much bigger than we thought it was going to be,” but declined to give a number. For some context, Clari, according to Pitchbook, had a relatively modest post-money valuation of $83.5 million in its last round in 2014, so my guess is that it’s now comfortably into hundred-million territory, once you add in this latest $35 million.
The funding comes at an interesting time for AI startups, particularly those aimed at enterprise IT.
When Clari first emerged from stealth in April 2014, the idea of applying AI to solve pain points for non-technical people in organizations was a fairly nascent and still-novel concept.
Fast forward to today, things have moved very fast, as is often the case in the tech world. Now, you can’t seem to move for all the enterprise IT startups that are either using or claiming to use AI in their solutions. There are so many startup hopefuls, and so many organizations looking for the best way to use AI to improve their business and operations, that there are even startups being founded to manage that opportunity of connecting the two pieces together, such as Element AI.
“I’m not saying we were clairvoyant for targeting the idea of using AI for sales in 2013,” Byrne said. “There has been a large macro trend and if you happen to be a small company that is along for the ride. When we first launched, we had this thesis about AI for sales. Now it’s not the number three or two priority for sales teams, it’s number one. It’s everywhere. Businesses want to invest and spend more money on AI and making things more efficient.”
Clari says that its customer base has tripled in the last year, with customers including Adobe, Audi, Check Point Software, Equinix, Epicor Software Corporation, GE, and PerkinElmer.
Clari’s approach for using AI for the sales team comes in two main areas. First, the company’s system is aimed to reduce some of the busywork that salespeople have in maintaining and updating files on people, by bringing in a number of different data sources and using them to provide composite pictures of target companies that salespeople might have had to otherwise compile with more manual means. Second, Clari puts a lot of focus on its “Opportunity-to-Close (OTC) solutions” — a type of risk-analysis for salespeople and their managers to help them figure out which leads and strategic directly would be the most likely to produce sales.
“Working with Clari since inception, we have been impressed with its growth and strong execution,” said Aaref Hilaly, Partner at Sequoia Capital, in a statement. “Clari has fast become indispensable to many of the most successful sales teams, giving them visibility into their most important metrics: rep productivity, pipeline health, and forecast accuracy.”
Indeed, risk and outcome is a smart area to be in: using AI to help model this is a key area of focus in enterprise IT at the moment, according to feedback I’ve had from a number of others in the enterprise world.
“If you have 150 opportunities presented to you as a salesperson, how do you choose 10 where you should spend your time?” Byrne asked. “A more traditional CRM platform has never showcased your risk and outcomes.”
While up to now Clari has focused on providing intelligence on what is already in a company’s account database, the next step, Byrne noted, is to draw on data from around the web, providing completely new business leads to the sales team.
When we last covered a funding round for Clari, we noted that the company’s laser focus on sales was something that made the company stand out for investors: nailing one aspect of a business’s operations without distractions from other parts of the organization and what it could be spending time solving elsewhere (in fact, when you think about it, the very goal that Clari has been aiming to achieve for salespeople through its product).
But four years on, the company is now widening that ambition. It’s applying its AI engine now to help marketeers weigh up the best opportunities for reaching out to prospective customers; and interestingly it sounds like it will also be applying its engine to product development and specifically supply chain management.
Byrne described one customer, a medical device maker, that was encountering “inefficiencies” around what they should build and when to meet market demand. “Now that they can predict and forecast order bookings and revenue targets, and what’s happened is that their supply chain has become more efficient,” he said. “It is great example of how our AI is now being expanded.”
“The Clari team has leveraged its deep AI expertise to build a unique platform that surfaces predictive insights for sales reps, managers, and execs during the opportunity-to-close process,” said Brian Paul, MD at Tenaya Capital, in a statement. “We see a massive opportunity for AI to transform how sales teams operate which is clearly validated by Clari’s customers and the impressive growth the team has achieved.”
If all goes well, some GIF creators may start seeing their GIFs show up in augmented reality experiences, based on a new deal that’s happening with Gfycat this morning.
Gfycat said it would be working with a company called Metaverse that, like many tools of its kind, is looking to make it easier to build applications in a more plug-and-play matter — this time for building augmented reality apps. Gfycat has more than 130 million monthly active users and in particular gears its tools toward creators, and this could be another step in helping those creators get their content out to the masses as activity in augmented reality starts to continue to pick up. It’s certainly not that pretty right now, but these small agreements can sometimes be the start of increasingly robust toolsets for developers.
To be sure, there’s a number of caveats. The most obvious one is that the GIFs created by those creators have to have a transparent background. After all, it would be weird for them to show up in the real world with a weird kind of background that blocks off the rest of reality and kind of sack the whole “augmented reality” concept. But at the same time, it does start to offer a kind of pseudo-home for creators that are looking to crack into AR as well as also offering developers looking to build games or other apps and opportunity to have easy access to content to get started.
We’ve seen from the explosion of games like Pokémon Go and others that augmented reality games are increasingly going to be A Thing. Niantic may have created a pivotal use case for that with a strong brand, but while looking a bit janky right now, it’s possible that a simple game developer might figure out some niche use case in AR that will actually blow up. That starts with having access to good content, and something like this would help get them started.
All this might be completely moot if Apple and others roll out an increasingly simple interface for AR app development like more robust tools in ARkit, where developers would just sidestep platforms like Metaverse in order to just build their own interfaces. But having a hub of content to start from is also an important step in figuring out where to even begin.
The GIF space is increasingly blowing up. We’ve already talked about how a bunch of these major platforms are continuing to grow with Giphy saying it has 300 million daily active users. Tenor, another GIF platform, meanwhile nets around 12 billion searches a month for its own GIFs.
Over the past year, Cardiogram and UC San Francisco (UCSF) have presented a series of findings on how well consumer wearables like the Apple Watch and Android Wear can detect medical conditions in their users, including diabetes as well as hypertension and sleep apnea.
Now, the startup is reaching a new milestone, this morning publishing the first large-N peer-reviewed clinical study showing that the Apple Watch and other wearables can detect atrial fibrillation with a high degree of accuracy.
The study, published in JAMA Cardiology, included 9,750 participants who used Cardiogram while enrolled in UCSF’s Health eHeart Study. The company collected more than one hundred million heart rate and step counts from users, and that data was fed into a deep learning model to determine whether a particular user had atrial fibrillation. Results from the study show that the condition can be detected at 97% accuracy (c statistic), with a sensitivity (true positive rate) of 98%, and a specificity (true negative rate) of 90%. The study is a continuation of earlier work that Cardiogram had previously presented.
One of the major aspects of the study that Cardiogram is highlighting is that their deep learning model, named DeepHeart, required significantly less training data than comparable models targeting medical conditions. Only 6,338 electrocardiograms (ECGs) were required to build the model, which was 8 layers. This is an important development, since ECGs are both expensive and time consuming to perform at scale. The company has published the methodology of their deep learning model on arXiv.
Discussing the study, Brandon Ballinger, a co-founder of Cardiogram, explained to me that “This is super important. Every healthcare company needs to be built on a foundation of hard, clear evidence.” Ballinger noted that medical journal articles like the one published today are the only mechanism for building trust among health care professionals. “So we are super excited to reach this milestone.”
One caveat of the study is that it focused on patients with a known risk of atrial fibrillation, and further research needs to be conducted to determine how well the company’s deep learning model can prospectively detect the condition in patients with no treatment history. A second exploratory analysis on self-reported patients had an accuracy (c-statistic) of 71%.
Cardiogram will continue to develop more studies going forward. “Just like Google invests in search quality, we are always going to be investing in clinical research,” Ballinger said. He said that the company is developing random control trials — the gold standard in healthcare clinical studies — as well as launching an economic analysis to evaluate whether consumer wearables may improve the cost structure of health care diagnostics.
A broader challenge is what to do with these results. Ballinger said that “consumer wearables can be used for accurate detection of these conditions, but we need to figure out the workflow.” If Cardiogram detects atrial fibrillation for instance, what should happen next for the patient? Should they go to a cardiologist and get follow-up tests, should they be sent an at-home detection kit? At scale, those decisions will have staggering consequences for patient outcomes as well as health care costs, and more work has to be done to properly and rigorously develop these workflows.
Cardiogram, which was founded by Ballinger and Jonathan Hsieh in 2016, has raised $2 million in venture capital from A16Z’s Bio Fund. The app works both on Apple Watch as well as Android Wear watches with a heart rate sensor such as the Huawei Watch. The lead authors of the study were UCSF physicians Gregory Marcus, who is Director of Clinical Research in the Division of Cardiology, José Sanchez, and Geoff Tison.
Updated to include the c-statistic for the second analysis.
Stanford’s John Hennessy, now chair of Google parent Alphabet, and Berkeley’s David Patterson developed the Reduced Instruction Set Computer in the 1980s.