In the span of five years, UniKey has grown from a tiny startup to a pioneer in the smart lock industry. Its founder and president, Phil Dumas, has gone from a budding Florida entrepreneur who appeared on “Shark Tank” to the head of a company that’s raised $14.3 million.
UniKey now powers a leading smart lock on the market with partner Kwikset, the largest residential lock manufacturer in the U.S. and one of the largest in the world.
“We basically set out to replace your entire keychain with your phone,” says Dumas. “As long as you have your phone on you or in your pocket or purse, all you have to do is walk up and touch the door and it magically unlocks.” In other words, no fumbling for your keys — or your phone to open an app.
UniKey is one of many successful startups around the world to grow and raise funding with the help of Microsoft programs and tools. Microsoft BizSpark gives thousands of dollars in free Azure cloud services to startups, and Microsoft Ventures operates accelerators in seven cities worldwide that give startups guidance and mentoring to pitch investors.
Microsoft helps startups grow in all stages of funding, whether they’re bootstrapping, crowdfunding, talking to angels or going through their first Series A. With free access to Azure, startups have a secure, reliable, open-source-friendly platform to deploy apps, store data and create virtual machines as they develop their solutions.
And go-to-market support and networking with Microsoft customers help startups grow around the world and navigate a funding landscape that’s changed over the years. Gone are the days when startups went directly from friends and family to venture capitalists. These days, startups have access to many more funding types, with accelerators, crowdfunding platforms, angel investors and angel groups.
“There’s no better external validation than seeing our startups raise significant funding,” says Scott Coleman, general manager of Microsoft Ventures. “It tells us we are finding and mentoring great startups and that our support is having a positive impact. Their success is our success.”
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Engineering management may be the most unnatural act of all
The best management decision I ever made took five seconds. “I can build a better database,” were the words accompanied by a blank but confident stare from the most talented developer I’d ever hired. “Okay,” I nodded, “see what you can do.”
It wasn’t our top priority at the time, but five years later, the Druid database he conceived stores 300 billion daily events for digital media firms around the globe.
When it comes to software engineering, that management is best which manages least — to borrow Thoreau’s quip about government. While it’s rarely as easy as nodding at a brilliant developer and getting out of the way, the best systems are, like good software, minimalist and lightweight.
Through my experiences as a CTO and through talking to other technical founders, leaders and heads of engineering over the years, I’ve developed the “LITE” philosophy of engineering management. It focuses on a well-leveraged developer, whose innovation drives the development of products that users love and trust, at the right cost efficiency. These are its four pillars: leverage, innovation, trust and efficiency.
Leverage: Protecting your most precious resource
The foremost priority of good engineering management is protecting the quality of your engineers’ work time and ensuring a distraction-free office environment.
On the time side, mind the distinction between managers’ and makers’ time. Group meetings during certain times of the day or week; clustering and cancelling meetings frees up the contiguous blocks of time, which enables engineers to achieve the flow state essential to creative endeavors. Engineers often don’t realize they are the owners of their own time, and telling them they are under no obligation to attend every meeting empowers them.
On the office environment side, invest in noise-cancelling headphones, egg-shaped pod chairs, stand up desks or any other tools that help your engineers concentrate. Offering meals, snacks and varieties of caffeinated experiences are also part of that equation — software may be eating the world, but the engineers who write that software need to eat food.
The development environment matters too, namely the tools and systems that developers rely on to write, debug, test and ship code into production. Anything that induces drag on the critical path from developer laptop to production system should be treated as an obstacle and cleared. Like many optimization problems, there is an inner loop where small improvements in the development chain can yield large savings in time.
Engineers are a unique bunch: persuadable by logic yet driven by ego.
Finally, right-sizing teams is essential to their working well together. Stu Feldman, the inventor of make, offers a piece of collective wisdom that emerged from his early days at Bell Labs: Groups bigger than 10 people tend to suffer communication breakdowns.
Innovation: Cooking with chaos, risk and chemistry
If enabling leverage on developers’ skills is a practical means of engineering management, fostering innovation is its highest end. Innovation is a combustive mix of ideas and unmet needs that sparks invention, and ultimately births breakthrough products.
While much digital ink has been spilled on the topic of how to unleash innovation in organizations, here are three ingredients of innovation that I’ve observed are essential based on my experiences.
Chaos: Intel’s Andy Grove described his philosophy as “let chaos reign, then rein in the chaos.” Google celebrates anarchy as part of its edge, and in Facebook’s early days, the “spirit of subversive hackery guided everything.” The lesson: Structure can be stifling to a merry band of rebels conspiring on the next new thing. Whether through allowing engineers 20 percent of their time to work on unorthodox projects, hosting week-long office hackathons or giving a helpful nudge (or turning a blind eye) to an internal skunkworks initiative, innovation breeds best in a bit of chaos.
Risk: This is a truism that deserves unpacking. Unmitigated risk-taking is, by itself, not an intelligent strategy for engineering innovation, any more than an explorer sailing aimlessly out to sea is a strategy for discovery. But development teams must take calculated risks where the expected value is positive. Moonshots with high cost and a low chance of success can be worth it if they have big pay-offs. Perhaps no one exemplifies this engineering strategy more successfully, with billion-dollar, bankruptcy-defying bets on electric automobiles and reusable space rockets, than Elon Musk.
Chemistry: The more genetically distant two parents are, the more successful their offspring. This same kind of “hybrid vigor” applies to the world of innovation. Cryptography combined with one of accounting’s oldest ideas, the general ledger, brought us bitcoin. Psychology, mathematics and computer science have all contributed strains to the modern machine learning behind self-driving cars and speech recognition. Teams with diverse academic and professional backgrounds who dabble at the intersections of disciplines are more likely to innovate.
Trust: Software systems with human accountability
I’ve long believed the true driver of success in technology is trust: software that performs as expected. Google’s search engine gained loyal users because it was fast and always up. WhatsApp rose on the strength of reliable messaging. Whatever it is that users trust your software to do, measure it, and manage toward improving it.
Trust matters not only for external users, it matters for internal engineers. Production systems that are on fire will ultimately consume engineers’ hours. At best, this de-leverages their time and makes them less productive; at worst, it will burn them out and they will quit.
Exposing engineers directly to the stability of their own services enforces ownership.
The best lever of software trust is human accountability. When a site goes down or load times veer out of bounds, someone must ultimately own and solve it. Under the covers of most web-scale applications are dozens of specialized services — such as user authentication, data processing and archiving — but these services have human owners that should be held accountable if a service violates its contract (e.g. “I’ll authenticate users within 500 milliseconds”).
Engineers should not be insulated from the operation of the services. When something breaks badly enough, developers are best equipped to firefight and resolve the issue. Exposing engineers directly to the stability of their own services enforces ownership: They are motivated and capable of writing code that avoids the 3 a.m. red alert. A useful rule of thumb for DevOps investment is as follows: Every hour of firefighting earns at least one hour of development effort to properly solve that problem.
Efficiency: The essence of high performance
Like trust, efficiency is often viewed warily by engineers. Unlike the call to innovate, the call to “reduce our server footprint!” is rarely met with happy emojis. And yet, efficiency by any other name would be just as sweet: Engineers celebrate faster compression algorithms and scoff at slow apps.
Performance and efficiency are often in tension. Yet unlike performance, which often has a useful upper bound in products, the gains from efficiency have only a lower bound of zero. (No one needs a car that goes more than 200 mph and everyone would love a car that requires no gas or electricity.) Thus, engineering teams that drive toward these zero lower bounds open new business models, often with zero in their prices: free search, email and photo sharing on the web were made possible by radically efficient engineering infrastructure.
Efficiency is also a core value because it ties back to our first pillar: developer leverage. The most precious resource that ought to be most efficiently managed is not hardware cycles, but human cycles. This is why Google’s universal measure of cost is not dollars, but developer hours.
Management is an unnatural act, as Ben Horowitz has written, and engineering management may be the most unnatural of all, because engineers are a unique bunch: persuadable by logic yet driven by ego. But getting engineering management right matters: Software engineering talent is the most precious resource on the planet earth, and enabling engineers to do their best work is often the difference between a startup’s success and its failure.
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Decades of computer vision research, one ‘Swiss Army knife’
When Anne Taylor walks into a room, she wants to know the same things that any person would.
Where is there an empty seat? Who is walking up to me, and is that person smiling or frowning? What does that sign say?
For Taylor, who is blind, there aren’t always easy ways to get this information. Perhaps another person can direct her to her seat, describe her surroundings or make an introduction.
There are apps and tools available to help visually impaired people, she said, but they often only serve one limited function and they aren’t always easy to use. It’s also possible to ask other people for help, but most people prefer to navigate the world as independently as possible.
That’s why, when Taylor arrived at Microsoft about a year ago, she immediately got interested in working with a group of researchers and engineers on a project that she affectionately calls a potential “Swiss Army knife” of tools for visually impaired people.
“I said, ‘Let’s do something that really matters to the blind community,’” said Taylor, a senior project manager who works on ways to make Microsoft products more accessible. “Let’s find a solution for a scenario that really matters.”
That project is Seeing AI, a research project that uses computer vision and natural language processing to describe a person’s surroundings, read text, answer questions and even identify emotions on people’s faces. Seeing AI, which can be used as a cell phone app or via smart glasses from Pivothead, made its public debut at the company’s Build conference this week. It does not currently have a release date.
Taylor said Seeing AI provides another layer of information for people who also are using mobility aids such as white canes and guide dogs.
“This app will help level the playing field,” Taylor said.
At the same conference, Microsoft also unveiled CaptionBot, a demonstration site that can take any image and provide a detailed description of it.
Very deep neural networks, natural language processing and more
Seeing AI and CaptionBot represent the latest advances in this type of technology, but they are built on decades of cutting-edge research in fields including computer vision, image recognition, natural language processing and machine learning.
In recent years, a spate of breakthroughs has allowed computer vision researchers to do things they might not have thought possible even a few years before.
“Some people would describe it as a miracle,” said Xiaodong He, a senior Microsoft researcher who is leading the image captioning effort that is part of Microsoft Cognitive Services. “The intelligence we can say we have developed today is so much better than six years ago.”
The field is moving so fast that it’s substantially better than even six months ago, he said. For example, Kenneth Tran, a senior research engineer on his team who is leading the development effort, recently figured out a way to make the image captioning system more than 20 times faster, allowing people who use tools like Seeing AI to get the information they need much more quickly.
A major a-ha moment came a few years ago, when researchers hit on the idea of using deep neural networks, which roughly mimic the biological processes of the human brain, for machine learning.
Machine learning is the general term for a process in which systems get better at doing something as they are given more training data about that task. For example, if a computer scientist wants to build an app that helps bicyclists recognize when cars are coming up behind them, it would feed the computer tons of pictures of cars, so the app learned to recognize the difference between a car and, say, a sign or a tree.
Computer scientists had used neural networks before, but not in this way, and the new approach resulted in big leaps in computer vision accuracy.
Several months ago, Microsoft researchers Jian Sun and Kaiming He made another big leap when they unveiled a new system that uses very deep neural networks – called residual neural networks – to correctly identify photos. The new approach to recognizing images resulted in huge improvements in accuracy. The researchers shocked the academic community and won two major contests, the ImageNet and Microsoft Common Objects in Context challenges.
Tools to recognize and accurately describe images
That approach is now being used by Microsoft researchers who are working on ways to not just recognize images but also write captions about them. This research, which combines image recognition with natural language processing, can help people who are visually impaired get an accurate description of an image. It also has applications for people who need information about an image but can’t look at it, such as when they are driving.
The image captioning work also has received accolades for its accuracy as compared to other research projects, and it is the basis for the capabilities in Seeing AI and Caption Bot. Now, the researchers are working on expanding the training set so it can give users a deeper sense of the world around them.
Margaret Mitchell, a Microsoft researcher who specializes in natural language processing and has been one of the industry’s leading researchers on image captioning, said she and her colleagues also are looking at ways a computer can describe an image in a more human way.
For example, while a computer might accurately describe a scene as “a group of people that are sitting next to each other,” a person may say that it’s “a group of people having a good time.” The challenge is to help the technology understand what a person would think was most important, and worth saying, about the picture.
“There’s a separation between what’s in an image and what we say about the image,” said Mitchell, who also is one of the leads on the Seeing AI project.
Other Microsoft researchers are developing ways that the latest image recognition tools can provide more thorough explanations of pictures. For example, instead of just describing an image as “a man and a woman sitting next to each other,” it would be more helpful for the technology to say, “Barack Obama and Hillary Clinton are posing for a picture.”
That’s where Lei Zhang comes in.
When you search the Internet for an image today, chances are high that the search engine is relying on text associated with that image to return a picture of Kim Kardashian or Taylor Swift.
Zhang, a senior researcher at Microsoft, is working with researchers including Yandong Guo on a system that uses machine learning to identify celebrities, politicians and public figures based on the elements of the image rather than the text associated with it.
Zhang’s research will be included in the latest vision tools that are part of Microsoft Cognitive Services. That’s a set of tools that is based on Microsoft’s cutting-edge machine learning research, and which developers can use to build apps and services that do things like recognize faces, identify emotions and distinguish various voices. Those tools also have provided the technical basis for Microsoft showcase apps and demonstration websites such as how-old.net, which guesses a person’s age, and Fetch, which can identify a dog’s breed.
Microsoft Cognitive Services is an example of what is becoming a more common phenomenon – the lightning-fast transfer of the latest research advances into products that people can actually use. The engineers who work on Microsoft Cognitive Services say their job is a bit like solving a puzzle, and the pieces are the latest research.
“All these pieces come together and we need to figure out, how do we present those to an end user?” said Chris Buehler, a software engineering manager who works on Microsoft Cognitive Services.
From research project to helpful product
Seeing AI, the research project that could eventually help visually impaired people, is another example of how fast research can become a really helpful tool. It was conceived at last year’s //oneweek Hackathon, an event in which Microsoft employees from across the company work together to try to make a crazy idea become a reality.
The group that built Seeing AI included researchers and engineers from all over the world who were attracted to the project because of the technological challenges and, in many cases, also because they had a personal reason for wanting to help visually impaired people operate more independently.
“We basically had this super team of different people from different backgrounds, working to come up with what was needed,” said Anirudh Koul, who has been a lead on the Seeing AI project since its inception and became interested in it because his grandfather is losing his ability to see.
For Taylor, who joined Microsoft to represent the needs of blind people, it was a great experience that also resulted in a potential product that could make a real difference in people’s lives.
“We were able to come up with this one Swiss Army knife that is so valuable,” she said.
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