Technology

Valimail helps businesses ensure that nobody can impersonate them over email. Thatnot a sexy business to be in, but very much a necessary one. The companyemail authentication service, which uses standards like SPF, DKIM and DMARC, is currently in use by the likes of Yelp, Uber, Fanni Mae and WeWork. Today, the company announced that it has raised a $25 million Series B round led by Tenaya Capital, with participation from Shasta Ventures, Flybridge Capital Partners and Bloomberg Beta.

This round brings Valimail total amount of funding to $38.5 million, including the company$12 million Series A round in 2016.

&Authentication is at the root of trusted communications,& Valimail CEO and co-founder Alexander Garcia-Tobar told me. &You must be able to trust the authenticity of who/what is on the other side, or the communication is meaningless. For example, no retailer would never accept a credit card without swiping it first (either physically or virtually). What happens in the credit card world needs to happen for communications.&

The funding round is coming at an important time for email authentication. The DMARC standard is now supported by over 5 billion inboxes, according to Valimail. Over the course oft he last six month, domain owners have also published more DMARC records than in the five previous years combined. In addition, all federal agencies must implement this standard, too.

Valimail promises to take care of all the hassles of setting up support for these authentication standards.

&After attempting email authentication with other solutions, I was amazed at the level of automation Valimail provides,& said JJ Agha, VP of information security at WeWork. &It eliminates the need for two FTEs, so my staff can focus on other key priorities. I consider it a ‘set it and forget it& solution for ensuring that our employees and executives can&t be impersonated and that our email is trusted.&

Madrona Venture Group typically flies under the radar of Silicon Valley reporters, partly because itin Seattle. But the 23-year-old, early-stage venture firm has been having a pretty good run of late — success it just used to close its seventh fund with $300 million, the same amount it raised for its sixth fund in 2015.

Among its investors: Bezos Expeditions, Vulcan Capital, and billionaire John Stanton, who is the chairman of the board of Trilogy International Partners (as well as the majority owner of the Major League Baseball team the Seattle Mariners).

Madronamomentum didn&t build overnight. Four Madrona portfolio companies that have IPO&d over the last 20 months — the cloud software companies Smartsheet, Apptio, the real estate site Redfin, and the RFID chip maker Impinj — took on average 12 years to get into the hands of public market investors.

Madrona, the firm is quick to note, was there from the start, writing seed and Series A checks that today range from $200,000 to upwards of $5 million to $7 million. (The firm has, on rare occasion, invested upwards of$30 million in a single company over the life of its investment.)

Yet those four now-public companies share another trait in common; they&re all based in the Pacific Northwest, which includes greater Seattle but also cities like Portland, Ore.; Vancouver, British Columbia; and Spokane, Wa.

Thatno accident. About 90 percent of Madronadeals are local, where the startup scene has seemingly expanded dramatically in recent years. In addition to Madrona and other local venture shops, Google, Facebook, Alibaba and Snowflake Computing have each opened engineering offices. Meanwhile, the University of Washington Computer Science Department — last year renamed the Allen School — is finishing another major building to expand its ability to graduate more CSE students. (A $40 million gift from Paul Allen, who cofounded Microsoft before establishing Vulcan Capital, helped toward that end.) .

Asked how fundraising went this time around, Madronateam describes it as smooth, crediting those IPOs as well as investors& greater appreciation that two of the four largest companies in the world — Amazon and Microsoft — are based in its backyard.

As for how many startups the firm will look to back with its new fund, it suggests the number will be around 40, which is how many startups it backed from its fourth fund.

One of those earlier startups isPulse Labs, a Seattle-based startup that helps brands understand how real people interact with their voice apps and raised $2.5 million in January, led by Madrona.Another is XNOR, a two-year-old, Seattle-based artificial intelligence startup that announced $12 million in Series A funding just last week led by Madrona. We wrote about it here.

Generally speaking, it tells us, when Madrona invests, itlooking to either leverage its growing expertise in cloud computing and machine learning, or itlooking to leverage its relationships with previous Madrona entrepreneurs and partners.

Given how many years it has been in business at this point, thatnow a sizable circle.

A new U.K. self-driving car startup founded by Amar Shah and Alex Kendall, two machine learning PhDs from University of Cambridge, is de-cloaking today. Wayve — backed by New York-based Compound, EuropeFly Ventures, and Brent HobermanFirstminute Capital — is building what it describes as &end-to-end machine learning algorithms& to make autonomous vehicles a reality, an approach it claims is different to much of the conventional thinking on self-driving cars.

Specifically, as Wayve CEO Shah explained in a call last week, the young company believes that the key to making an autonomous vehicle that is truly just that (i.e. able to drive safely in any environment it is asked to), is a much greater emphasis on the self-learning capability of its software. In other words, self-driving cars is an AI problem first and foremost, and one that he and co-founder Kendall argue requires a very specific machine-learning development skill set.

&Wayve is building intelligent software to decide how to control a vehicle on all public roads,& he tells me. &Rather than hand-engineering our solution with heavily rule-based systems, we aim to build data-driven machine learning at every layer of our system, which would learn from experience and not simply be given if-else statements. Our learning-based system will be safer in unfamiliar situations than a rule-based system which would behave unpredictably in a situation it has not seen before&.

To explain his thinking in laymenterms, Shah points to the way a human who is relatively proficient in driving in one city can quickly adapt to the differences in a completely new city, without having to be given extra training or instruction beforehand. It may take around 30 minutes or even a few hours to become fully climatized to new driving conditions or environment, but humans don&t need very much new data to do so.

&Humans have a fascinating ability to perform complex tasks in the real world, because our brains allow us to learn quickly and transfer knowledge across our many experiences,& he says. &We want to give our vehicles better brains, not more hardware&.

To problem, thus far, the pair argue, is that companies like Google and Uber are throwing an engineering mindset at making vehicles autonomous, in the sense of designing rule-based systems that try to pre-empt and deal with every edge case, whilst in tandem adding more sensors and capturing more data. This might produce encouraging results in the specific, narrow setting it has been engineered for, but won&t have maximum payoff longer term.

&Right now, big tech companies have cars with many different sensors of a handful of different types. Their attitude is to have more and more sensors to do more and more difficult driving tasks,& says Shah. &If I ask you to do a difficult athletic obstacle course, something like Ninja warrior, having more eyes isn&t really going to help you much. What you need is better coordination & itthe mind-muscle connection thatthe limiting factor. In driving, itreally the way you use your sensory information thatkey (the AI-wheel connection in the car), not the number of cameras and radars and LIDARs&.

But if a more sophisticated machine-learning approach is the correct one, surely Google (which has several AI efforts under its parent company, including being the owner of DeepMind), would already be going down that avenue, too

&The big teams are distracted by getting something working because they have stakeholders who have been investing for a decade into autonomous driving. They are getting impatient,& the Wayve co-founder pushes back. &How will Alphabet tell their shareholders ‘we&ve invested X billion USD into Waymo and its predecessor with a team of 1,000s, but we are now throwing that approach all down the drain and hiring more AI people to solve driving&. Ita hard sell having spent billions and when they are close to a simple product. Same reason politicians make bad long-term decisions… their output is only short-term&.

&Wayve has a very differentiated technical approach versus most other autonomous vehicle startups,& echoes Fly Ventures& Gabriel Matuschka. &Ita 10x improvement over the rules-based approach taken from legacy robotics to hard-code the driving actions that the vehicle takes once it understands what it sees. Wayve uses end-to-end machine learning to drive cars autonomously, with little data, in novel environments. This means that their software enables a car to drive itself using only understanding of what it can see, just like humans do&.

To that end, the ten-person Wayve is said to be made up of experts in robotics, computer vision and artificial intelligence from both Cambridge and Oxford universities, who have previously worked at the likes of NASA, Google, Facebook, Skydio and Microsoft. Their work ranges from using deep learning for visual scene understanding to autonomous decision-making in uncertain environments. Noteworthy also is that Professor Zoubin Ghahramani, Chief Scientist of Uber, is an investor in Wayve.

&There are very few teams out there with the academic background and technical capabilities to at all have a credible shot at this. Wayve is one of them,& adds Matuschka. &Some people in the industry question if Wayvenovel approach will work. You only stand a chance to compete against Google, Uber, et al. if you try, and are able to do something that the large players haven&t done so far or don&t believe in yet. Then you can have a head start&.