News & Views

 

Madrona Awards the Madrona Prize to the Backscatter Team at the UW Industrial Affiliates Day

Every year, Madrona MDs, Investment Professionals and other staff look forward to the UW CSE Industrial Affiliates day.  Graduate students present posters on their research, which ranges from Robotics to Machine Learning to Databases.  At the end of the day, Madrona awards a Madrona Prize and recognizes runner up teams for the most commercially viable technology.  Last night, Madrona  awarded the cash prize to a team of graduate students from two schools at the University of Washington –Computer Science & Engineering and the Department of Electrical Engineering – working to make battery free communications systems work over long distances.

2016 marks the 11th year of the Madrona Prize which is awarded to a ground breaking and commercially viable technology developed at the University of Washington.  Since Madrona’s inception, more than two decades ago, Madrona has funded 16 companies out of the University of Washington.  These companies include Impinj (NAS:PI), Farecast (acquired by Microsoft) and Turi (acquired by Apple.)

This year the Madrona Prize went to a project building on the Backscatter work at the University of Washington.  In the latest iteration, the team shows that backscatter can work over longer distances than previously thought – up to 1 km – and provide precision communication for agriculture, home sensing and for medical devices such as smart contact lens and flexible epidermal patch sensors.  The team on this particular backscatter project are Vamsi Talla, CSE postdoc; Mehrdad Hessar and Bryce Kellogg, UW Electrical Engineering; Shyam Gollakota, CSE faculty; Josh Smith, CSE and EE faculty.  The prize is a cash award that goes directly to the graduate students involved in directing and conducting the research.

“The University of Washington is one of the top five computer science schools in the nation.  Not only does the university fuel this region and beyond with research on key areas like machine learning, the Internet of Things, and robotics, but it also instills an entrepreneurial spirit – from students to professors – and we want to foster that as much as possible!” said Matt McIlwain, managing director, Madrona Venture Group. “We are excited about the plan to double enrollment, and graduates, over the coming years as the new CSE2 building gets underway with gifts from the core technology tenants of the region – Amazon and Microsoft.  The UW is an invaluable resource to our technology ecosystem.”

Madrona has deep ties with the University of Washington.  Several of the managing directors and investment professionals teach courses on entrepreneurship in different schools and colleges within the university. Dan Weld, the Thomas J. Cable / WRF Professor at UWCSE, is a Venture Partner at Madrona and works hand in hand with investing managing directors to source and evaluate investments as well as provide technological guidance to portfolio companies.

The Madrona Prize comes at the end of the Industrial Affiliates day, when companies, large and small, gather at the UW to hear about new technologies and projects underway.

“It’s wonderful to be in a region where local companies support both our educational and research missions.  We really appreciate Madrona’s long-term support for our students with the Madrona Prize, as well as their commitment to helping us transform and transfer CSE technologies into companies, where they can have major external impact,” said Hank Levy, Chairman of UWCSE.

Each year, the Madrona committee also awards Runner up prizes.  This year the Runners up were:

Runner up: PipeGen: Data Pipe Generator for Hybrid Analytics
(Brandon Haynes, CSE Ph.D. student; Alvin Cheung and Magda Balazinska, UW CSE faculty)

Runner up: Just Say NO to Paxos Overhead: Replacing Consensus with Network Ordering
(Jialin Li, Ellis Michael, Naveen Kr. Sharma, and Adriana Szekeres, CSE Ph.D. students; Dan R. K. Ports, UW CSE faculty)

Runner up: Programming by Examples for Industrial Data Wrangling
(Alex Polozov, CSE Ph.D. student; Sumit Gulwani, Microsoft; the Microsoft PROSE team)

For past winners visit https://www.cs.washington.edu/industrial_affiliates/madrona

POSTED IN: Madrona News

A Call to Action for Angels in Cloud City

As recent entrants into the Venture Capital world, we continue to be positively surprised by the vibrancy of the start-up activity, the quality of tech talent and the richness of the innovation ecosystem in Seattle.  Neither of us are new to the technology scene. Soma has been with Microsoft for a couple of decades since its early years, and Linda was previously at a security start-up in San Francisco. However, VC is a different game and we didn’t know quite what to expect from the Rainy City. It was not long before we discerned that unlike the Bay Area, the Seattle start-up community is tight knit and perhaps quieter, but no less innovative.

Much has been made in the past regarding Seattle’s lack of available early stage angel investments especially relative to the size of the ecosystem and the depth of the city’s talent pool. However, recent data suggests this is changing.

Seattle angel deals grew a whopping 80% (in 2015)

Linda Lian

According to Pitchbook, angel-backed deals grew only 10.6% in California in 2015, compared to a 58.1% increase in the Pacific Northwest. Within the core tech verticals of information and B2B, Seattle angel deals grew a whopping 80%.

Angel investing chart

 

Data: Pitchbook

This clearly indicates that angel funding growth in the Pacific Northwest is not only accelerating, but bucking national growth trends.

The city’s resistance against boom-and-bust investment cycles certainly lies in its strength within the fast-growing cloud space. However, there is a lot more in Seattle’s wheelhouse than just cloud. Seattle is also a burgeoning worldwide hub for VR technology and space exploration in addition to pioneers in ML/AI, chatbots, and natural user interfaces.

 

. . . if Seattle is to fully capitalize on its talent and resources, more of the area’s successful technology execs and veterans must leave the sidelines and get in the game

S. Somasegar

While Seattle’s growth has been remarkable and talent is in no shortage, there is still much to be done before the city’s full potential can be realized. According to a recent BCG report, there are 1.4x the number of angel investors relative to ultra high net worth individuals in the Bay Area. In Seattle, that same ratio is half. The extremity of the comparison must be taken with a grain of salt, as San Francisco’s core cultural identity cannot be separated from the reputation of the city’s startup ecosystem. The age distribution of SF’s wealthy is also likely to skew younger than Seattle. However, this does indicate that if Seattle is to fully capitalize on its talent and resources, more of the area’s successful technology execs and veterans must leave the sidelines and get in the game.

As an angel investor himself prior to joining Madrona, Soma believes angel investing is one of the best ways for a technologist to give back to the innovation ecosystem. This comes not only in the form of capital, but perhaps more importantly in mentorship, guidance and valuable relationships.

somasagar-grid

For angels that are already actively investing, they can also make a more substantial impact by getting involved with great entrepreneurs or products earlier in the funding cycle and not wait for somebody else to “bell the cat”.  While Seattle’s strengths in B2B and enterprise software are indisputable, the willingness for the community to step out of its comfort zone to help nurture and develop consumer-facing companies that sometimes have the characteristic of viral growth before monetization kicks in could be the key to Seattle’s next big win.

It is amazing to see the wonderful, vibrant start-up activity that is getting bigger and broader every day.  Seattle and the Pacific Northwest have an amazing amount of potential to be a phenomenal technology and innovation hub and we are excited to be a part of that journey.

POSTED IN: Madrona News

Introducing Essential: The Latest Madrona Labs Spin-Out

We are thrilled to announce Essential (essential.to), which is the third spin-out from Madrona Labs.  Essential provides intelligent messaging experiences for businesses and was born from our real-world experience building and working with companies that have figured out how to build large, transactional businesses on top of messaging platforms. The team is led by CEO, Mike McMurray, VP of Engineering, Daniel Pirone and VP Product, Zoe Schagrin. Essential’s early customers leverage the platform to drive millions of customer interactions every month and, as importantly, generate millions of dollars in revenue. With the spin-out complete, the Essential founding team is focused on closing their Seed round, making a few key hires, and building the sales pipeline.

Why are we excited about Essential?

Messaging is coming of age. We are reminded constantly of these facts, but it doesn’t make them any less true. There is broad scale consumer adoption of messaging platforms, four billion global SMS users and one billion Facebook Messenger users. The ability for brands to engage with customers via messaging platforms is powerful – the average individual takes 90 minutes to respond to email, and 90 seconds to respond to a text message.

Mobile Apps are not the solution; mobile app fatigue is real and growing. 95% of mobile apps are abandoned within a month. Over the past several months, many of the most prominent players in tech have jumped into the fray.  Microsoft’s CEO Satya Nadella stated recently that “chatbots are the new apps…People-to-people conversations, people-to-digital assistants, people-to-bots and even digital assistants-to-bots. That’s the world you’re going to get to see in the years to come.” These prognostications, while bold, are also resonant; and they are prompting executive discussions in every boardroom in the U.S., as companies grapple with how they can build stronger connections and deeper relationships with their customers through messaging platforms.

Messaging applications are becoming the new operating systems. As covered extensively by the media, following the model of WeChat in Asia, U.S.-based tech behemoths Facebook, Microsoft, Apple and others are expanding their messaging capabilities, including payments and enhanced user interactions, and recruiting businesses to their platforms. Over-hyped or not, we believe we are in the early innings of a massive shift and natural evolution from web to mobile to a new frontier of “messaging first” user experiences. The market is early and undeveloped, but has endless potential.

Why, even with all the hype and new entrants, do we believe Essential can win?

Essential’s aim is to deliver on the opportunity by making it easy for businesses to provide intelligent messaging experiences for their customers, where their customers live, across multiple messaging channels (SMS, Facebook, Slack, etc.).  Unlike Twilio and other underlying providers, which provide the “ingredients for developers” to build bots, Essential is providing full solutions for businesses and delivering material results.  This includes vertically optimized messaging bots out-of-the-box, multi-channel messaging capability, highly-reliable messaging delivery, and conversational machine intelligence for automated customer interactions.  Essential is proud to be working with its innovative early customers like SeatGeek, Peach, and ReplyYes.

The Labs team is incredibly excited to be working with the Essential founding team. CEO Mike McMurray has a strong product and marketing background, previous roles include SVP at PointInside and GM at RealNetworks. He shares our conviction that not only is a major shift underway, but that a focused vertical solutions approach is what customers in this nascent market are asking for and need. VP Engineering Daniel Pirone previously held Senior Engineering roles at Madrona-backed Qumulo and was a former Senior Principle Engineer at Nuance Communications. VP Product Zoe Schagrin was previously a Senior Product Manager at Amazon and a founder and CEO of an early-stage startup.

We can’t wait for the journey ahead and hope you visit essential.to to learn more!

POSTED IN: Madrona News

Need Some AI? Yeah, There’s a Marketplace for That

POSTED IN: Portfolio Company News

Autonomous Vehicle Plan for the I-5 Seattle/Vancouver B.C. Corridor

Executive Summary

Download this report

Seattle and Vancouver have a huge opportunity to reduce congestion, improve the travel experience, reclaim productive hours and reduce accidents on the I-5 Cascadia Corridor by implementing a plan over the next decade that accelerates the introduction of autonomous vehicles on the corridor.  Committing to this vision would not only benefit all who use this corridor but would also demonstrate to the world our Cascadia region’s status as a leading global center of innovation where governments and private enterprises can work in partnership to solve human problems.

Leading technology companies, such as Tesla and Uber, and traditional auto companies, such as Ford and GM, are rapidly developing and testing new technologies in sensors and software that will make fully autonomous vehicles feasible and safe within the next five to ten years.  Governments from Pittsburgh to Singapore, plus the U.S. Department of Transportation, are authorizing street trials and encouraging and even mandating that vehicles be equipped with autonomous technologies.  The governments of the Cascadia Corridor would dramatically seize a leadership position on autonomous vehicles by committing to an innovative autonomous vehicle plan for I-5.

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An autonomous vehicle plan for I-5 could initially allow autonomous vehicles to share the HOV lanes.  Over time, with more and more autonomous vehicles on the road, this would evolve into HOV lanes being exclusively for autonomous vehicles.  The final step as autonomous vehicles largely replace existing vehicles would be to exclude non-autonomous vehicles from I-5 except for certain defined times when highways are not congested such as most of weekends and 8:00 p.m. to 4:00 a.m. on weekdays.  The first phase of this plan could begin to be implemented immediately and the final phase could occur in ten to fifteen years.

I-5 has a minimum of eight lanes (and sometimes 10 lanes) from downtown Seattle to the northern boundary of Everett and then six lanes to the southern boundary of Mount Vernon, all of which could accommodate dedicated lanes for autonomous vehicles.  North from Mount Vernon I-5 is four lanes up to the border-crossing in Blaine, WA, where it becomes Highway 99 with four lanes in British Columbia.  Traffic planners in the future may want to add additional lanes to the four lane portions from Mount Vernon to Vancouver (82 miles) to support dedicated autonomous vehicle lanes.

The last eight miles on Highway 99 from the Vancouver airport into downtown Vancouver present a challenge for any intercity travel because it consists of city streets with traffic lights.  This could be alleviated when travelling by autonomous vehicle from Seattle by having your autonomous vehicle drop you off at the SkyTrain Bridgeport Station in Richmond near the airport and go park itself at the nearby park and ride lot or elsewhere or pick up another passenger.  The SkyTrain departs every 6 minutes most of the day and takes 18 minutes to downtown.  SkyTrains in Vancouver are fully autonomous without drivers.

There are many benefits from autonomous vehicles, but the principal benefit is that it allows drivers to recapture all the time otherwise spent behind the wheel.  This is at least two and one half hours from Seattle to Vancouver.  Imagine being able to watch a video or sporting event, prepare for a business meeting, work on your novel or plan a game with your children.  It is difficult to place a dollar value on this but one source has estimated this at more than $1 trillion a year in the U.S.  Because of wireless and software technologies we can be entertained or productively engaged wherever, whenever.

Other very significant benefits from autonomous vehicles include substantial reductions in vehicle accidents and deaths, less environmental damage, increased capacity of existing roads, reduction of the need for more freeways and lanes, increased use of shared vehicles, reduced congestion and lower transportation costs for consumers.

Although accidents have occurred in the early use of autonomous vehicles, in the longer term the number of accidents and deaths will be reduced. U.S. Transportation Secretary Anthony Foxx recently said that as many as 25,000 road deaths could have been prevented last year if driverless cars were in operation.  Annual cost savings for the United States from reduced traffic collisions, including medical costs, have been estimated at several hundred billion dollars.

With autonomous vehicles, the capacity of roads is increased by closer spacing and platooning of vehicles, narrower lanes, reduction in the wave effect of braking, faster average speeds and fewer accidents.  Major and minor accidents cause substantial traffic tie-ups.

The availability of autonomous vehicles will likely cause more people to travel in vehicles, including the elderly and infirm,  but we expect this will be offset by more vehicle sharing by individuals and through commercial services.  Using apps, mobile devices, data analytics, mapping technologies and the cloud, new ride sharing services are already becoming available through companies such as Uber and Lyft.  With travel times shortened and the cost of drivers eliminated, buses will be more attractive and the introduction of new autonomous mini-bus and van services would likely occur. Autonomous vehicles will also include trucks of all kinds. When trucks are autonomous, there will be more flexibility on scheduling and incentive structures could be created to encourage trucks to travel in non-congestion time periods.

Although not the focus of this paper, all the benefits of autonomous vehicles on I-5 also apply to commuting in the major metropolitan areas on the corridor including Seattle and Vancouver.  Moreover, this plan should be extended to serve drivers on the I-5 Corridor between Seattle, Tacoma, and Portland.

This proposal will initially be highly controversial because of the public’s natural concern about the likelihood and timing of autonomous vehicles, initial accidents and failure to recognize the benefits.  All of the fundamental technologies required for autonomous vehicles, however, are available and only require refinement which are occurring at a rapid rate.  Compared to the cost of improved and high speed rail, estimated by others at upwards of $30 billion, the  cost of this plan would be orders of magnitude less and consumers would begin to benefit decades earlier.

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New technologies that benefit consumers tend to be adopted quickly once made widely available.  Automobile ownership in the U.S. went from 10% of households to 67% of households in 14 years – and since then, adoption rates have accelerated. To reach 90% penetration in the U.S., wired phones took 70 years, cell phones 15 years and smart phones 8 years.  App-based rideshare services only started 4 years ago, and they are already ubiquitous in most major cities across the globe.  We cannot predict the specific adoption rate for autonomous vehicles but with many major vehicle manufacturers announcing that they will be selling autonomous vehicles within five years and the advantages of autonomous vehicles, we expect very significant penetration in ten to fifteen years.

Accordingly, we recommend that our local and regional governmental entities along with private companies form a joint commission to develop a plan for accelerating the introduction of autonomous vehicles for I-5.  They could engage the University of Washington’s new Mobility Innovation Center and a comparable group from the University of British Columbia to assist in developing recommendations.

Tom Alberg, Managing Director, Madrona Venture Group, Craig Mundie, former Chief Research and Strategy Officer, Microsoft Corporation, Daniel Li, Associate, Madrona Venture Group, Connor Raikes, Consultant

Section 1: Background

 Seattle and Vancouver: Economic Partners

Seattle and Vancouver, BC have had a long complementary economic relationship. Situated only 120 miles as the bird flies from each other, they share an environmental and cultural heritage. They have two of the largest ports in North America, and as West Coast cities both Seattle and Vancouver are important gateways to and from the Asian continent. Their cooperation with a touch of rivalry has made the Pacific Northwest a vibrant international hub in a globalized world, and their relationship continues to grow even as they  transition away from the harbor and resource industries that were once their bedrock, and towards technology and service economies.

This is especially true of the tech industry. Seattle has long been a leading tech hub in the United States, boasting the headquarters of Amazon and Microsoft.  Vancouver is a new and growing tech hub; the tech industry is the second fastest growing industry in British Columbia, according to KPMG. In 2015, Seattle and Vancouver both ranked in the Top 20 world’s leading startup cities according to Compass, as they did in 2012.

Seattle and Vancouver do not only have a similar heritage of technology and entrepreneurship; they also work with one another. In 2007, Microsoft opened a large office in Vancouver, Canada, which has since been expanded, and they have considered moving their Canadian headquarters to Vancouver from Mississauga, ON. Amazon has also had a presence in Vancouver as early as 2008; they opened an official Amazon office in 2011 and expanded in 2013 to accommodate up to 1,000 employees.

This is in part in order to attract talent and to keep it near to them in the Pacific Northwest. Vancouver and Seattle boast some of the best supply of tech talent in their respective industries; three of the top five computer programming universities in Canada are located around Vancouver and Seattle’s University of Washington was named the most innovative public university in the world by Reuters. Their historical ties to Asia are also very important; they both have had a long presence of Asian immigrants, which makes Seattle and Vancouver attractive to Asian and Indian tech talent.

When Microsoft opened its Vancouver office in 2007, it emphasized that the company was motivated by frustrations with U.S. immigration and visa restrictions, particularly pertaining to high-skilled labor and the H-1B Visa cap, and so it hired in Canada where immigration restrictions were more relaxed. Hiring in Vancouver meant that Microsoft could locate talent just a short distance from their headquarters without the headache of U.S. restrictions, while taking advantage of the tech workers that were already there. Amazon seems to have been motivated in part by the same reasons.

Notwithstanding these connections between Vancouver and Seattle, a recent study of LinkedIn data surprisingly indicated that connectivity of business people between the two cities is low relative to connectivity with other cities.  “Among the cities with the strongest connection to Vancouver, Seattle ranks #11, behind three other US cities.  Similarly, Seattle has stronger connections with 26 other cities, compared to Vancouver.”  Overall cross-border talent flow is also limited even though Microsoft and Amazon have large offices in Vancouver.

Transportation Problem

Seattle’s and Vancouver’s tech and startup companies would benefit greatly from greater ease of intercity transportation. The connectivity of the digital era has not diminished but seemingly has rejuvenated the value of physical location and meeting in person. Improved transportation from Seattle to Vancouver is not merely about leisure and travel; it’s about making sure Seattle and Vancouver maintain and improve their competitive edge in the modern economy.

Yet in spite of the need for high speed, convenient transportation, the options available have not kept pace with the economic growth in Seattle and Vancouver. The Amtrak Cascades trains are renowned for their views but take up a four-hour trip, at a $40-$70 standard ticket. Greyhound buses are cheaper but take at least as long.  Air Canada and Alaska Airlines offer flights between Seattle and Vancouver that cost hundreds of dollars per trip for an hour in the air, but with travel to and from the airport and the additional hassle of check-in and airport security, the total time spent can be three hours or more.

Beyond that, there is driving. And here is where Seattle’s infamous congestion comes into view. A 140-mile commute which could take 2 hours and 20 minutes is stretched an extra 30 minutes to 90 minutes during working and rush hours – a delay that is costly in both gas and lost productive working hours.  The present difficulties of driving between the two cities significantly reduces tourist and business travel and interchange.

The Rise of Autonomous Vehicles and Services

 A few years ago, it was still a major question whether autonomous car technology would be feasible and even if feasible it was not considered likely for 30 or more years.  But today we already have self-driving cars from Google, Tesla, and Uber driving on our roads.  Although initially led by these tech companies, all of the major auto companies have joined in to develop autonomous vehicles. Seemingly every day we read news articles about auto manufacturers announcing plans to introduce autonomous cars or pilot projects being planned in various places.  Here are some of the companies involved with autonomous vehicles.

In August 2016, Uber announced that their first fleet of self-driving vehicles would be launched in Pittsburgh. Home to the National Robotics Engineering Center at Carnegie Mellon University, Pittsburgh will host Uber’s most ambitious step yet to integrate fully autonomous vehicles into their service. The custom-built Volvos will be supervised by humans in the driver’s seat for now, but if the experiment is successful, Uber aims to gradually replace their 1 million human drivers with autonomous systems.

Transportation in cities is on the verge of large-scale transformation, according to the President’s Council of Advisors on Science and Technology (PCAST), through the effort to develop connected and fully autonomous vehicles. New technologies that benefit consumers tend to be adopted quickly once made widely available.  We cannot predict the specific adoption rate for autonomous vehicles but we believe that widespread adoption of autonomous vehicles is inevitable and will be here sooner than most observers expect.

Ride Sharing 

Uber and Lyft are introducing ride sharing services in many cities using innovations in mobile and cellular technologies.  Consumers are responding favorable to the lower prices and convenience and in some cities in California, Uber and Lyft report that more than 50% of rides are shared.

Ride sharing by individuals, commercial companies and transit authorities will be further stimulated by the introduction of autonomous vehicles.  Entrepreneurial individuals will be able to rent their autonomous vehicle to others or share a ride with them.  New operators of autonomous mini-bus and van services can be launched.

Section 2:  Our Vision

 We propose that local, state and provincial governments on both sides of the border collaborate on a plan to accelerate the introduction of autonomous vehicles on I-5. Initially autonomous vehicles should be authorized to share the HOV lanes. Just as traffic planners incentivized carpooling this would incent the purchase of autonomous vehicles and use of autonomous vehicle services. We recognize this would require a sizeable collaboration between several governmental agencies.  But doing this sooner rather than later would not only allow residents of the Cascadia Corridor to reap the direct benefits sooner it would better connect the two cities and send a message that Seattle and Vancouver embrace new ideas and new ways of thinking, further cementing a reputation for innovation in the Cascadia region.

If phased in with the growth of the number of autonomous vehicles being purchased, this plan will be less disruptive of existing usage than might be feared..  At the first stage, autonomous vehicles would simply join in use of the HOV lanes.  I-5 from downtown Seattle to Everett is at least eight lanes and could accommodate a shared HOV lane.  This is also likely true north of Everett to Mount Vernon which has six lanes.  As more autonomous vehicles are introduced, this shared lane could become exclusively for autonomous vehicles.  At a later stage, transportation authorities could consider building additional lanes in sections of I-5 north of Everett.  Ultimately, I-5 could become exclusively for autonomous vehicles except during certain low traffic times at night and on weekends.  Taking on this project, even though ambitious, would set Seattle and Vancouver on the path to be the example for the future of transportation, and to set the standard for major cities and corridors in North America.

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Section 3: Benefits and Risks of Autonomous Vehicles

When discussing autonomous vehicles, there are different levels of autonomy with anywhere from a single function being automated, such as automatic braking, to the highest level where the car can drive itself without a person supervising or even present in the vehicle. It was not long ago when even the first level seemed like a major innovation, but R&D have pushed us to the point where full unsupervised autonomy will be in mass produced vehicles as soon as five years from now. We are focused on this highest levels of vehicle autonomy – effectively self-driving cars.

Benefits

 What would the greatest benefit be of having your own personal chauffeur? Sure, that chauffeur might be a better driver; you might get to your destination more quickly, and safely. But for many people, the greatest benefit of all would be a better riding experience and a recapture of lost time. Maneuvering in traffic behind the wheel takes time away from your work life and your personal life, and replaces it with anxiety and frustration. That is bad for business and health. But if you were driven around, it would not only reduce the time spent in traffic; it would give the time spent on the road back to you.

You could relax. Would you use that time to do work in the car? Catch up on a TV show? Safely take a phone call or read and send texts? Play Angry Birds? You decide. You are freed up from being cramped behind the wheel, worrying about gridlock. That’s the benefit of being chauffeured from place to place, and that is what autonomous vehicles will provide. The only difference is, this chauffeur is built into the car.

There are many social benefits from road safety to reduced congestion and energy use. According to the Insurance Institute for Highway Safety, up to a third of traffic fatalities could be reduced with forward collision prevention and side view assists alone, and greater automation could reduce the United States’ yearly 32,000 traffic fatalities even further by replacing the primary cause of road accidents: human error. Furthermore, automated vehicles can accelerate and decelerate more quickly, which improves fuel economy, and would likely greatly enable the use of alternative fuel sources. At a possible future high level of the technology, the disadvantages of electric power and fuels cells could be mitigated by allowing fully autonomous self-driving cars to drop off their passengers and automatically find a station to refuel.

Autonomous vehicles also facilitate and optimize connectivity.  Put simply, autonomous vehicles can join a network and coordinate with each other. This increases travel lane capacity, reduces fuel waste, and reduces travel delays by avoiding quick unexpected stops that cascade through traffic.

The potential cost savings from autonomous vehicles are very significant – and not just by reducing delays, improving fuel economy and facilitating alternative energy sources. Autonomous vehicles will also reduce the number of accidents. Nationwide, the cost of traffic collisions is approximately $300 billion a year. Vehicular congestion costs about $124 billion per year in the U.S., as well as tens of billions of associated healthcare costs.  These do not include the opportunity cost of productive hours spent in traffic, which is estimated at $1.2 trillion per year, or the costs of parking spaces.  31% of the space in central business districts of major cities currently is devoted to parking.  Autonomous vehicles will be able drop off their passenger, and immediately pick up a new person or find a place to park – which need not be close to the destination. If the car is low on fuel, it can drive itself to a station and fuel up.

It is also useful to point out that dedicated autonomous lanes multiply the benefits associated particularly with connectivity. They facilitate larger convoys of closely spaced autonomous vehicles – caravans” or “trains” of sorts – which enable road efficiency, higher effective speeds and fewer accidents. 

Risks

 As with most beneficial innovations, there are risks.  For example, autonomous vehicles will make it possible for people incapable of driving because of age or infirmities to use vehicles thereby increasing the total number of vehicle miles traveled.  Of course, providing a means for these people to travel or visit friends and doctors is itself a social benefit.  Such usage will also be offset by increases in ride sharing in private autos, Uber-type services, and mini-buses and buses which would reduce the number of vehicles on the road.

Also, given that autonomous vehicles depend on network systems that would presumably be standardized, this might make them vulnerable to computer crashes and hacking although mechanical breakdowns and malicious hacking are already a risk for standard vehicles.  Also, autonomous vehicles will cause economic disruption in manufacturing and employment, as disruptive technologies have done in the past.

Autonomous vehicles may not be as affordable for all classes of people. Tesla and Ford, however, are working on launched autonomous vehicles that cost less than $35,000.  Policy makers could also provide subsidies through vouchers for low income groups for them to use autonomous vehicle services.

We hope policy makers will recognize the benefits far outweigh the risks.

Section 4: Comparison of the Alternatives

 There is some movement to make better and faster rail options. Notably, the Washington State Department of Transportation (WSDOT) is using funds from the American Recovery and Reinvestment Act (ARRA) to improve Amtrak service from Portland, OR to Vancouver, BC.  One of their goals is to increase maximum speeds from 79 mph to 110 mph but because there is only a single track from Everett to Vancouver and it is shared with freight trains, their plans would only reduce trip times by five percent.  New overpasses are also needed in Everett, Marysville, Mt Vernon and Bellingham.

For several decades, many local officials and economic development organizations have advocated proposals for true high speed rail from Portland to Seattle to Vancouver, BC.  This would provide fast service between train stations.

According to various estimates. high speed rail costs between $125 million and $1 billion per mile, depending on the surface and location. Using these numbers, there is a projected a cost of $20-30 billion total for a high speed rail between Seattle and Vancouver, which may still be optimistic.

Large-scale transportation infrastructure projects typically take decades to envision, plan, and build and have traditionally taken much longer than originally projected. For example, in 2012,

Not only does this delay the economic benefits, but it also exposes the project to greater economic risks. The challenges of regulatory and public approvals, construction funding and likely needed operating subsidies even with one-way fares exceeding $100 per person raise questions as to the feasibility and desirability of high speed rail. By the time the high speed rail is completed, new technology might completely change the transportation paradigm.

This is not to reject the promise of high-speed rail. The dream of one-hour travel by rail between downtown Seattle and Vancouver is worthy of considering. We welcome consideration and discussion of such a proposal. However, we should not limit ourselves to conventional prescriptions to transit needs. Policymakers have to think beyond 20th century solutions to new solutions being made possible by rapid innovation.

By way of comparison, for $30 billion we could buy every household in Seattle and Vancouver a new Tesla with autonomous driving features or buy Delta Airlines at its market cap of $29 billion.  Of course, these are outlandish suggestions that serve only to illustrate the fact that traditional transpiration projects are much more expensive than observers typically realize.

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Building additional autonomous vehicle lanes would be far cheaper and less time-consuming. These take advantage of the infrastructure that is already built around HOV and expands on it. It is also an incremental, flexible project, whose benefits will be felt much earlier and would provide point to point solutions from where you live or work to the specific location of where you want to go.

Another worthy but limited proposal for speeding movement between Seattle and Vancouver is to institute regularly scheduled seaplane service from South Lake Union in Seattle to Coal Harbour in Vancouver.  Currently, there are no direct regular seaplane flights from Seattle to Vancouver, BC, although Kenmore Air operates dozens of flights a day from docks at South Lake Union and Harbour Air likewise handles many flights from docks at Coal Harbor which is adjacent to the downtown business district.

Charter flights are allowed between these destinations, but they are prohibitively expensive for even most business people. But a daily scheduled service easily could be instituted comparable to the cost of the international seaplane flight from Seattle to Victoria – that is, $160+ one way, which is regularly used every day by business people and tourists. Facilitating a seaplane service between Lake Union and Coal Harbour would be a convenient, quick and scenic option from Seattle to Vancouver. Its utility, however, is severely limited in scale. Kenmore Air and Harbour Air, the largest American and Canadian seaplane services in the Pacific Northwest respectively, have fleets of 24 and 43 planes respectively. More importantly, most seaplanes only fit 6 to 7 passengers at a time. Even assuming eight daily flights this would only mean about 50 passengers per day.  Nonetheless, these flights would provide an attractive alternative to conventional travel, particularly for business people and tourists.

 

Section 5: Conclusion recommending joint US/Canada I-5 Planning Committee and advocating for forward thinking regarding autonomous and ride-sharing infrastructure projects

 As leaders in the global information economy, the Cascadia region needs to explore innovative ways to incorporate new technologies into our transportation planning process that can significantly reduce the cost of transportation and improve connectivity within the region.

Most technology industry experts believe that the widespread adoption of autonomous vehicles is a “when, not if” question. Indeed, we are already seeing public pilots of autonomous vehicles ferrying passengers to their destinations both domestically in Pittsburgh and internationally in . As transportation planners examine different options to connect the Cascadia corridor that may take 30 years or more to build, it is critical to consider the impact of these autonomous vehicle technologies in that planning process.

Autonomous vehicles will drastically change the way people get from Point A to Point B, and major transportation and technology companies are investing heavily in an autonomous future. Recently, GM paid more than $1 billion for the autonomous vehicle startup Cruise (more than 2% of its $50 billion market cap), and Uber acquired a self-driving truck company for $680 million. Our local, state, and federal governments need to understand this technology and invest accordingly as well.  As the region moves forward in exploring different ways to connect the Cascadia region, autonomous vehicle technology needs to be a major consideration in any transportation plan.

We recommend that lawmakers enact legislation that allow autonomous vehicles to operate in the state of Washington and the province of British Columbia with clear guidelines. We also recommend establishing a joint US-Canadian commission composed of private and public sector leaders who could engage the University of Washington’s Mobility Innovation Center and a comparable group from the University of British Columbia to make recommendations on the best ways to incorporate autonomous vehicles in transportation planning and specifically to implement a plan for I-5.  (9.19.16)

 

References

 

Anderson et al., Autonomous Vehicle Technology: A Guide for Legislators, RAND Corporation, 2016.

Foxx, Anthony, Remarks on Automated Vehicles at the Detroit Auto Show, Department of Transportation, January 14, 2016.

Insurance Institute for Highway Safety, “New Estimates of Benefits of Crash Avoidance Features on Passenger Vehicles,” Status Report, Vol. 45, No. 5, May 20, 2010.

President’s Council of Advisers on Science and Technology, Technology and the Future of Cities, Office of Science and Technology Policy, February 2016.

Shoup, Donald C., The High Cost of Free Parking, Planner’s Press, 2005.

POSTED IN: Madrona News

Why Madrona Invested in Accolade

World-class teams, compelling problems to solve and sustainably differentiated solutions. These are ingredients for a promising venture investment. At Madrona, we usually focus on early-stage companies that are differentiated through information technology and based in the Pacific Northwest. We believe, now more than ever, our strategy will help build a next-generation of leading companies in the region.

So, why did Madrona participate for the first time in the Series E round of Accolade? Accolade is an on-demand healthcare concierge for employers, health plans, health systems and the people they serve. Founded nine years ago in Pennsylvania, the company attracted Raj Singh as CEO last year. Raj has already brought together a strong group of existing executives with some former Concur executives, including CTO Mike Hilton, to build the world-class Accolade leadership team based primarily in Seattle.

We believe, now more than ever, our strategy will help build a next-generation of leading companies in the region.

Raj Singh

Rajeev Singh, CEO Accolade

Raj and Mike co-founded Concur in 1993 before Madrona even existed and played leadership roles throughout, culminating in an $8.3 billion sale to SAP in 2014. Madrona has maintained a close working relationship with many members of the Concur executive team. Raj is on the board of Madrona-backed companies Apptio and Amperity. We have seen first-hand the value Raj and Mike provide entrepreneurs in operationally scaling companies. They are experts at applying innovative technologies to improve customer experiences in a cost-effective manner.

. . .  the Accolade team has the opportunity to be a very large player in the evolving healthcare landscape.

Healthcare is a massive market and Accolade has many opportunities to apply mobile and data-driven capabilities to their existing concierge solutions and scale their personalized service to more healthcare consumers. As the healthcare industry continues to evolve to an outcome-per-patient model with increased use of medical records and data, patients need guidance and advice more than ever. With technology enabled services, employers and their families can make better health care choices benefitting their own lives. Accolade has a distinct model for remaining people-centered while leveraging modern technologies and techniques.

We believe the Accolade team has the opportunity to be a very large player in the evolving healthcare landscape. By doing so, they can build another leading technology company here in Seattle. And, we have found that Madrona can add the most value when teams are based in region. The combination of a proven, Seattle-based team and a compelling business model convinced us that investing at a later stage in Accolade was the right fit.

 

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Evolving the Application Platform from Software to Dataware

Every decade, a set of major forces work together to change the way we think about “applications.” Until now, those changes were principally evolutions of software programming, networked communications and user interactions.

In the mid-1990s, Bill Gates’ famous “The Internet Tidal Wave” letter highlighted the rise of the internet, browser-based applications and portable computing.

By 2006, smart, touch devices, Software-as-a-Service (SaaS) and the earliest days of cloud computing were emerging. Today, data and machine learning/artificial intelligence are combining with software and cloud infrastructure to become a new platform.

Microsoft CEO Satya Nadella recently described this new platform as “a third ‘run time’ — the next platform…one that doesn’t just manage information but also learns from information and interacts with the physical world.”

I think of this as an evolution from software to dataware as applications transform from predictable programs to data-trained systems that continuously learn and make predictions that become more effective over time. Three forces — application intelligence, microservices/serverless architectures and natural user interfaces — will dominate how we interact with and benefit from intelligent applications over the next decade.

In the mid-1990s, the rise of internet applications offered countless new services to consumers, including search, news and e-commerce. Businesses and individuals had a new way to broadcast or market themselves to others via websites. Application servers from BEA, IBM, Sun and others provided the foundation for internet-based applications, and browsers connected users with apps and content. As consumer hardware shifted from desktop PCs to portable laptops, and infrastructure became increasingly networked, the fundamental architectures of applications were re-thought.

By 2006, a new wave of core forces shaped the definition of applications. Software was moving from client-server to Software-as-a-Service. Companies like Salesforce.com and NetSuite led the way, with others like Concur transforming into SaaS leaders. In addition, hardware started to become software services in the form of Infrastructure-as-a-Service with the launch of Amazon Web Services S3 (Simple Storage Service) and then EC2 (Elastic Cloud Compute Service).

Smart, mobile devices began to emerge, and applications for these devices quickly followed. Apple entered the market with the iPhone in 2007, and a year later introduced the App Store. In addition, Google launched the Android ecosystem that year. Applications were purpose-built to run on these smart devices, and legacy applications were re-purposed to work in a mobile context.

As devices, including iPads, Kindles, Surfaces and others proliferated, application user interfaces became increasingly complex. Soon developers were creating applications that responsively adjusted to the type of device and use case they were supporting. Another major change of this past decade was the transition from typing and clicking, which had dominated the PC and Blackberry era, to touch as a dominant interface for humans and applications.

Software is programmed and predictable, while the new dataware is trained and predictive.

Matt McIlwain

In 2016, we are on the cusp of a totally new era in how applications are built, managed and accessed by users. The most important aspect of this evolution is how applications are being redefined from “software programs” to “dataware learners.”

For decades, software has been ­programmed and designed to run in predictable ways. Over the next decade, dataware will be created through training a computer system with data that enables the system to continuously learn and make predictions based on new data/metadata, engineered features and algorithm-powered data models.

In short, software is programmed and predictable, while the new dataware is trained and predictive. We benefit from dataware all the time today in modern search, consumer services like Netflix and Spotify and fraud protection for our credit cards. But soon, every application will be an intelligent application.

Three major forces underlie the shift from software to dataware which necessitates a new “platform” for application development and operations and these forces are interrelated.

Application intelligence

Intelligent applications are the end product of this evolution. They leverage data, algorithms and ongoing learning to anticipate and improve interactions with the people and machines they interact with.

They combine three layers: innovative data and metadata stores, data intelligence systems (enabled by machine learning/AI) and the predictive intelligence that is expressed at an “application” layer. In addition, these layers are connected by a continual feedback loop that collects data at the points of interaction between machines and/or humans to continually improve the quality of the intelligent applications.

Microservices and serverless functions

Monolithic applications, even SaaS applications, are being deconstructed into components that are elastic building blocks for “macro-services.” Microservice building blocks can be simple or multi-dimensional, and they are expressed through Application Programming Interfaces (APIs). These APIs often communicate machine-to-machine, such as Twilio for communication or Microsoft’s Active Directory Service for identity. They also enable traditional applications to more easily “talk” or interact with new applications.

And, in the form of “bots,” they perform specific functions, like calling a car service or ordering a pizza via an underlying communication platform. A closely related and profound infrastructure trend is the emergence of event-driven, “serverless” application architectures. Serverless functions such as Amazon’s Lambda service or Google Functions leverage cloud infrastructure and containerized systems such as Docker.

At one level, these “serverless functions” are a form of microservice. But, they are separate, as they rely on data-driven events to trigger a “state-less” function to perform a specific task. These functions can even call intelligent applications or bots as part of a functional flow. These tasks can be connected and scaled to form real-time, intelligent applications and be delivered in a personalized way to end-users. Microservices, in their varying forms, will dominate how applications are built and “served” over the next decade.

Natural user interface

If touch was the last major evolution in interfaces, voice, vision and virtual interaction using a mix of our natural senses will be the major interfaces of the next decade. Voice is finally exploding with platforms like Alexa, Cortana and Siri. Amazon Alexa already has more than 1,000 voice-activated skills on its platform. And, as virtual and augmented reality continue to progress, voice and visual interfaces (looking at an object to direct an action) will dominate how people interact with applications.

Microsoft HoloLens and Samsung Gear are early examples of devices using visual interfaces. Even touch will evolve in both the physical sense through “chatbots” and the virtual sense, as we use hand controllers like those that come with a Valve/HTC Vive to interact with both our physical and virtual worlds. And especially in virtual environments, using a voice-activated service like Alexa to open and edit a document will feel natural.

What are the high-level implications of the evolution to intelligent applications powered by a dataware platform?

SaaS is not enough. The past 10 years in commercial software have been dominated by a shift to cloud-based, always-on SaaS applications. But, these applications are built in a monolithic (not microservices) manner and are generally programmed, versus trained. New commercial applications will emerge that will incorporate the intelligent applications framework, and usually be built on a microservices platform. Even those now “legacy” SaaS applications will try to modernize by building in data intelligence and microservices components.

Data access and usage rights are required. Intelligent applications are powered by data, metadata and intelligent data models (“learners”). Without access to the data and the right to use it to train models, dataware will not be possible. The best sources of data will be proprietary and differentiated. Companies that curate such data sources and build frequently used, intelligent applications will create a virtuous cycle and a sustainable competitive advantage. There will also be a lot of work and opportunity ahead in creating systems to ingest, clean, normalize and create intelligent data learners leveraging machine learning techniques.

New form factors will emerge. Natural user interfaces leveraging speech and vision are just beginning to influence new form factors like Amazon Echo, Microsoft HoloLens and Valve/HTC Vive. These multi-sense and machine-learning-powered form factors will continue to evolve over the next several years. Interestingly, the three mentioned above emerged from a mix of Seattle-based companies with roots in software, e-commerce and gaming!

The three major trends outlined here will help turn software applications into dataware learners over the next decade, and will shape the future of how man and machine interact. Intelligent applications will be data-driven, highly componentized, accessed via almost all of our senses and delivered in real time.

These applications and the devices used to interact with them, which may seem improbable to some today, will feel natural and inevitable to all by 2026 — if not sooner. Entrepreneurs and companies looking to build valuable services and software today need to keep these rapidly emerging trends in mind.

I remember debating with our portfolio companies in 2006 and 2007 whether or not to build products as SaaS and mobile-first on a cloud infrastructure. That ship has sailed. Today we encourage them to build applications powered by machine learning, microservices and voice/visual inputs.

This post was originally published by TechCrunch

 

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Algorithmia Lands In-Q-Tel Deal, Adds Deep Learning Capabilities

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Why We Invested in Wrench

I am incredibly excited to be working with Ed and his team at Wrench and today we are announcing an investment in their Series Seed fundraise.  I have known Ed for awhile and we started talking early about his vision and conviction for changing a big market – auto repair.

When I was a kid my dad and many other parents could fix cars.  Changing the oil, replacing air/oil filters, fan belts, spark plugs and other things were commonly done by car owners.  Somewhere over the last 40 years cars got harder to fix or people became more nervous about dealing with their cars.

Len Jordan-Madrona-Venture Capital SeattleThere are more than 120 million cars on the road that are three years or older and the repairs on those vehicles run to $57 billion annually.  I personally have a couple of these in my household and believe the day Ed and I first talked about his plan to deliver car repair to consumers I had spent too many frustrating hours at the repair shop.  Wrench delivers the service to you – where your car is.  A bunch of folks at Madrona and in our network used the service and loved it.  Those were not singular experiences – they have happy customers all over the city.

Ed and his experienced team who worked together at Talentwise and Intelius have come together to tackle an ambitious plan to disrupt a market that hasn’t changed in decades.  Wrench takes a brick and mortar industry and leverages mobile, cloud, advanced analytics and supply chain efficiencies to bring a service to market that resonates with anyone who owns an older car. As an investor the Wrench team has what we look for in early stage funding – a great team, a big idea, and a big market.

I am happy to be along for the ride and support this team!

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The Intelligent App Ecosystem (It’s not just bots!)
Intelligence App thumbnail

Click to view the full image PDF (1.8 MB)

Today we interact with many intelligent applications like the Google and Bing search engines, Spotify and Netflix media services, and the Amazon shopping experience. The machine learning technologies that power these services are becoming mainstream and setting the stage for the Intelligent App Era.

Application intelligence is the process of using machine learning technology to create apps that use historical and real-time data to make predictions and decisions that deliver rich, adaptive, personalized experiences for users.

We believe that every successful, new application built today will be an intelligent application. The armies of chat bots and virtual assistants, the ecommerce sites that show the right recommendations at the right time, and the software that detects anomalous behavior for cybersecurity threats, to name a few, are all built to learn and create continuously improving experiences. In addition, legacy applications are becoming more and more intelligent to compete and keep pace with this new wave of applications.

We believe that every successful, new application built today will be an intelligent application.

S. Somasegar & Daniel Li

Now is an exciting time to be investing in the broader intelligent app ecosystem because several important trends are coming together in application development:

  • The availability of massive computational power and low-cost storage to feed machine learning models
  • The ease with which developers can take advantage of data sources and machine learning techniques,
  • The adoption of microservices as a development paradigm for applications, and
  • The proliferation of platforms on which to develop applications, and in particular platforms based on “natural user interfaces” like messaging and voice

We have spent time thinking about the various ways Intelligent Apps emerge – and how they are built.  This Intelligent App Stack illustrates the various layers of technology that are crucial to the creation of Intelligent Apps.  (Please send us feedback on this world view! @SSomasegar @danielxli )

As investors we like to think about the market dynamics of major industry shifts, and the rise of intelligent apps will certainly create many new opportunities for startups and large technology companies alike. Here are some thoughts on the key implications for companies operating at various layers of the intelligent app stack:

“Finished Services”: Applications will define the end user’s experience with machine learning
At the application layer there will be two primary classes of applications: net-new apps that are enabled by application intelligence and existing apps that are improved by application intelligence.

somasagar-gridNet-new apps will need to solve the tough problem of determining how much end users will pay for “artificial intelligence” and how to ensure they capture a portion of the value delivered to users. More broadly, it will be interesting to see if our thesis that the value proposition of machine learning will primarily be a revenue generator comes true.

Also because of the importance of high-quality, relevant data for machine learning models, we think that use-case specific or industry-specific applications will be the most immediate pockets of opportunity at the Finished Services or application layer. Today, we see the main categories of use-case specific applications as autonomous systems, security and anomaly detection, sales and marketing optimization, and personal assistants. We are also seeing a number of interesting vertically focused intelligent applications especially serving the retail, healthcare, agriculture, financial services, and biotech industries.

The killer apps of the last generation were built by companies like Amazon for ecommerce, Google for search and advertising, Facebook for social, Uber for transportation, and Netflix for entertainment. These companies have a significant head-start in machine learning and user data, but we believe there will be apps that are built from the ground up to be more intelligent that can win in these categories and brand new categories that are enabled by application intelligence.

Interfaces: New interfaces will transform applications into cross-platform “macro-services”
As we think about how new intelligent applications will be developed, one significant approach will be the transformation of an “app” to a service or experience that can be delivered over any number of interfaces. For example, we will see companies like Uber build “services” that can be delivered via an app, via the web, and/or via a voice interface.

It will also be easier for companies to deliver their services across platforms as they design their apps using a microservices paradigm where adding a new platform integration might be as simple as adding a new API layer that connects to all of the existing microservices for authentication, product catalog, inventory, recommendations, and other functions.

The proliferation of new platforms such as Slack, Facebook Messenger, Alexa, and VR stores will also be beneficial for developers because platforms will become more open, add features that make developers lives easier, and compete for attention with offerings such as investment funds.

Finally, at the interface layer, we see the “natural interfaces” of text, speech, and vision unlocking new categories such as conversational commerce and AR/VR. We are incredibly optimistic about the future of these interfaces as these are the ways that humans interact with one another and with the world.

Building Blocks and Learning Services: Intelligent building blocks and learning services will be the brains behind apps
As companies adopt the microservices development paradigm, the ability to plug and play different machine learning models and services to deliver specific functionality becomes more and more interesting. The two categories of companies we see at this layer are the providers of raw machine intelligence and the providers of trained models or “Models as a Service.”

In the first category, companies provide the “primitives” or core building blocks for developers to build intelligent apps, like algorithms and deployment processes. In the second category, we see intermediate services that allow companies to plug and play pre-trained models for tasks like image tagging, natural language processing, or product recommendations.

These two categories of companies provide a large portion of the value behind intelligent apps, but the key question for this layer will be how to ensure these building blocks can capture a portion of the value they are delivering to end users. IBM Watson’s approach to this is to provide developer access to its APIs for free but charge a 30% revenue share when the app is released to customers. Others are charging based on API calls, compute time, or virtual machines.

li-gridThe key differentiators for companies in this layer will be the ability to provide a great user experience for developers and the accuracy and performance of machine leaning algorithms and models. For complicated, but general problems like natural language understanding, it will likely be easier and more performant to use a pre-built model from a provider who specializes in generating the best data, models, and processes. However, for specialized, business-specific problems, startups and enterprises will need to build their own models and data sets.

Data Collection and Prep: The difficult and boring tasks of data collection and preparation will get smarter
Before data is ready to be fed into a machine intelligence workflow or model, it needs to be collected, aggregated, cleaned, and prepped. Sources of data for consumer and enterprise apps include photos and video, websites and text, customer behavior data, IT operations data, IOT sensor data, and data from the web.

After applications are instrumented to collect the right pieces of raw data, the data needs to be transformed into a machine-ready format. For example, companies will need to take unstructured data like text documents and photos and transform it into structured data (think of rows and columns) that is ready for a machine to review.

The important part of this step is realizing that the quality of a model is highly dependent on the quality of its input data. Creating bots or ‘artificial intelligences’ without high quality training data can lead to unintended consequences (see Microsoft’s Tay), and the creation of this training data often relies on semi-manual processes like crowdsourcing or finding historical data sets.

The other area of this space to keep an eye on is the companies that have traditionally served as “dumb” pipes for data sources like clickstream data or application performance logs. Not only will they try to build predictive and adaptive features, they will also see competition from intelligent services that draw insights from the same data sources. This will be an area of innovation for finance, CRM, IT Ops, marketing, HR, and other key business functions that have traditionally collected data without receiving immediate insights. For example, HR software will become better at providing feedback for interviewers and highlighting the best candidates for a position based on historical data from previous hires.

Data Infrastructure: Intelligent apps will be built on the “Big Data” infrastructure
The amount of data in the world is doubling every 18 months, and thanks to this explosion in big data, enterprises have invested heavily in storage and data analysis technologies.

Projects like Hadoop and Spark have been some of the key enablers for the larger application intelligence ecosystem, and they will continue to play a key role in the intelligent app stack. Open source will remain an important feature for choosing an analytics infrastructure because customers want to see what is ‘under the hood’ and avoid vendor lock in when choosing where and how to store their data.

The amount of data in the world is doubling every 18 months, and thanks to this explosion in big data, enterprises have invested heavily in storage and data analysis technologies.

S. Somasegar & Daniel Li

Within the IaaS bucket, each of the major cloud providers will compete to run the workloads that power intelligent apps. Already we are seeing companies open source key areas of IP such as Google’s TensorFlow ML platform, in a bid to attract companies and developers to their platform.  Google, in particular, will be an interesting company to watch as they give users access to their machine learning models, trained on some of the world’s largest data sets, to grow their core IaaS business.

Finally, hardware companies that specialize in storing and managing the massive amount of photos, videos, logs, transactions, and IOT data will be critical to help businesses keep up with the new data generated by intelligent applications.

There will be value captured at all layers of this stack, and there is the opportunity to build significant winner-take-all businesses as the machine learning flywheel takes off. In the world of intelligent applications, data will be king, and the services that can generate the highest quality data will have an unfair advantage from their data flywheel – more data leading to better models, leading to a better user experience, leading to more users, leading to more data.

Ten years from now, all applications will be intelligent, and machine learning will be as important as the cloud has been for the last 10 years. Companies that dive in now and embrace intelligent applications will have a significant competitive advantage in building the most compelling experiences for their users and as a result, the most valuable businesses.

This post was previously published on TechCrunch.com 

 

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On-demand tech support companies HelloTech, Geekatoo merge

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Julie Sandler – Qualities to Consider when Choosing an Investor

Julie was interviewed on some hot topics related to venture, investing, and startups.  One excerpt here is on the topic of what to look for in an investor. She would add “hustle, horsepower, network, experience and savvy!” to her commentary here!

Watch the Video on Inc.com

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Kitt.ai Introduces Voice Control for Devices

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Why We Invested in Integris.io

Today at Madrona we are excited to announce our seed investment in Integris, a data risk intelligence solution that enables companies to discover, classify and control how they’re using their customers’ data.  This control enables companies to protect customer data and more easily comply with the complex set of privacy and consumer data regulations around the globe.

From our first conversations with Kristina, her vision resonated strongly and immediately with us.  The ability to collect, store and analyze huge amounts of data from customers is immeasurably important to companies across every industry, but is being met with a wave of new (and frequently changing) regulations.  The legal, customer-privacy, financial, and reputational risk of failing to meet this set of ever-evolving and complex regulations are dire, and companies experience significant challenges (and spend a lot of time, money, and resources) trying to keep up.  It’s early days for Integris, but the team is building a powerful data risk intelligence solution that uniquely addresses this precise challenge for big companies in a proactive, automated, and continuous way.

We were so compelled by this team, the overall vision, and their progress, that we issued a term sheet before the company had even officially incorporated.  When you encounter a team – and opportunity – like Integris’ and have the chance to back them even before “Day One”, you simply take it.

Following her stint as a highly respected Group PM at Microsoft, Integris’ CEO and co-founder Kristina Bergman became an all-star investor at Ignition, where she has been a principal for the past several years. On a personal note, there obviously aren’t too many women investors at institutional VC funds (much less in Seattle), so for years Kristina and I have always made a point of getting together at a regular cadence to exchange notes and ideas.  I have profound respect for Kristina and for the way she both thinks and leads.  Kristina spent late evenings and weekends over the past year and a half developing Integris, and it was energizing to spend time with her on the concept as it developed.  It’s a delight today to officially partner with her as she and her talented co-founders – Uma Raghavan and Frank Martinez – bring Integris to life.  We discussed with Kristina from the get-go the idea of creating a diverse syndicate for a seed round with complementary sets of experiences and networks; we’re very excited to partner up with Amplify, with Ignition, and such strong angel investors.  It has been exciting to have our regular meetups evolve from two VCs chatting over interesting companies… to entrepreneur and board-member spending our time together on just one.

It’s just the beginning for Integris, but this is a world-class team taking a huge swing at a massive enterprise challenge.  We’re all excited here at Madrona to get behind them.

 

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Echodyne – Radar Systems Promise to Let Drones Cut the Leash

https://www.technologyreview.com/s/601355/compact-radar-system-promises-to-let-small-drones-cut-the-leash/

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Focus on What You Can Control

The following is an excerpt from a memo that Madrona shared with our investors as we plan for the year ahead and provides guidance to venture backed companies about what they should expect from their investors, especially with current market conditions.  

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Alberg and Li on Driverless Cars and Ride Sharing as Part of Seattle’s Transportation Solution

Only a few years ago, driverless cars were a pipe dream. Because of technological challenges and regulatory obstacles, experts believed that driverless cars would not be feasible for decades in the future, if at all. Nevertheless, in a surprisingly short period of time, we’ve observed rapid innovations which are bringing driver assisted and autonomous vehicles to our roads today. In fact, Google just brought their autonomous car test program to Kirkland.

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David & Ben’s ‘Acquired’ Podcast Dissects What Went Right – New Episode Available

David Rosenthal of Madrona and Ben Gilbert of Pioneer Square Labs team up every two weeks or so to eat some food and talk about acquisitions. These can so easily go wrong. This podcast is about the ones that mainly went right – the “The 1+1=3 deals” as the authors say on their site.  Why did these go right? What were the elements of success? And they are only 30 minutes!  Perfect. The newest episode looks at YouTube (and includes a link to the original internal investment memo from funders, Sequoia) 

Get the podcast from iTunes or from Acquired.  

We sat down and asked a couple questions about the podcast and the response so far. Read on for fun insight and a good restaurant recommendation. 

How did you get the idea for Acquired? 
The idea was Ben’s— he had wanted to do a podcast for a while, and thought a theme around analyzing tech acquisitions would be a good mix of unique, focused and atomized-enough content that we could keep it going with fresh and interesting material. So far we’ve been very happy with how it’s working, and we have a few fun ideas in the works riffing on the theme— acquisitions that DIDN’T happen, “real-time” analysis when a big one gets announced, more special guests, etc. 

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Julie Sandler Joins the 2016 Class of Presidential Leadership Scholars

Madrona Venture Group announced today that Julie Sandler has been selected to be a member of the Presidential Leadership Scholars class of 2016. 

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