Making Your Models Work For the Long Haul

Too often, engineers are brainwashed into thinking they can create an impeccable artificial intelligence (AI) model — a blank slate they release into the wild for independent learning. They think: “If I create flawless math on top of the right infrastructure, I’ll have the perfect model.” Train the algorithm, let it run free, and that’s the end of the story, right?

Unfortunately, no. Just like human intelligence, artificial intelligence requires continuous learning to advance its expertise.

Instead, great ML / AI innovators need to plan ahead to include the right people, in the right loops, to train, test, validate and continuously improve their models' application. The best results come from a carefully tuned merger of computer and human cognition. Here's how we think about the data flows at Mighty AI:

Our Training Data as a Service™ platform enables us to guarantee accurate human insights, across domains, at scale and on demand.

Our Training Data as a Serviceplatform enables us to guarantee accurate human insights, across domains, at scale and on demand.

Train, test, validate, repeat

Training a commercially applied AI is not a one-and-done exercise. It requires regular validation to understand whether the AI is working as it should. Otherwise, you’re practically begging for bias to worm its way in. Look no further than the troubling example of an AI designed to predict criminal recidivism that turned out to be biased against black people. And who can forget the now-infamous fiasco that was Microsoft’s Tay, a well-intentioned chatbot experiment that quickly soured? These and countless other examples underscore the need for continuous human validation of AI to keep it on its intended trajectory.

In addition to mitigating against bias, human validation helps AI keep up with changing knowledge. Take language, for example. The meanings of words constantly evolve. As the father of a teenager, I can personally attest to the fact that by the time a new slang term goes mainstream (“lit!”), a trendier alternative has already replaced it (“savage!”). If the only education we provide chatbots is the initial data sets we train them on, how will they keep up with the changing ways people talk to them? Like human intelligence, the only way artificial intelligence can adapt to accommodate a growing body of knowledge is if we continually educate it.

The AI value chain

As humans retrain them, AIs get smarter. And once an AI has achieved its initial ambition, it can continue learning and growing.

Imagine you’re a retailer that sells clothes and shoes online, and now you’ve created a recommendation engine. In its infancy, the AI is a form of visual search. When a customer searches your website for women’s brown boots, the shopper gets back results of brown boots from your catalog. Once your AI has mastered visual search, its next ambition might be association. Instead of simply returning results for brown boots, it begins serving up images of models wearing outfits that pair well with the boots. Once it’s conquered association, it moves to even more personalized intelligence. The AI knows you’re a software engineer who lives in Seattle and is shopping in March, so it begins personalizing recommendations based on a dress code that is decidedly casual and a climate that is frequently wet.

This progression from search to association to personalization is the AI value chain in action. Behind the scenes, humans are in the loop, validating the AI’s performance and retraining it with new data sets. The AI’s advancement up the value chain is only possible with the aid of human intelligence.

The right human in the right loops

So you’re sold on integrating humans into your AI training loop. Now what? It’s time to identify the right humans with the specialized knowledge your business needs. Take our retail example. If your target customer is a millennial American woman, at the end of the day, her opinions — what she perceives as fashionable, what she wants to wear — are what matters. You want individuals like her annotating your data, helping to make your recommendation engine as relevant as possible for your customers. The same is true for “expert” AIs, which need to integrate the latest human knowledge in fields such as accounting, education, law, and medicine.

But it doesn’t end there. You’ve got the right humans, and now you need to think about the right loops. Remember, the initial training data set is just the first loop. The validation loop, which is where you determine if your AI worked as intended, is also a critical juncture for incorporating that specialized knowledge your customers bring to the table. The validation loop is as much about improving the AI as it is your human intelligence engine. It’s about getting smarter about who should perform annotation tasks, how those individuals perform, and whether the results are accurate.

Just like no one says you’re done learning once you’ve graduated from college, an AI isn’t finished once it’s trained. Training is merely the first step.

The good folks at VentureBeat originally published this article: http://venturebeat.com/2017/03/12/sorry-but-your-ai-needs-to-go-back-to-school/

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Matthew Bencke

Matt Bencke is an entrepreneur, leader and change agent who drives new business and product strategies based on deep analysis, inspired leadership and focused execution. He has strong successes across technology, strategy, business development, design, e-commerce, marketing and manufacturing. His passion is attracting great talent, fostering a meaningful team culture, and taking performance to new levels. During his tenures at Microsoft, Getty Images and Boeing he has created, advised, led and grown businesses ranging from several millions to billions in size.

Grab Your Capes!

Spare5 is Now Mighty AI, Relaunches with $14M in New Funding and New Agreements with Intel and Accenture

New agreements increase market reach and access across multiple industries to tools, data and services to build artificial intelligence models

SEATTLE, Wash. — January 10, 2017 — Spare5 Inc. announced today it relaunched the company as Mighty AI and raised a Series A1 round of $14M. In addition, Mighty AI signed agreements with Intel and Accenture (NYSE: ACN). Intel will be able to promote and sell Mighty AI’s training data services to its global customers, and Accenture will recommend and integrate Mighty AI’s training data services for its global clients.

Intel Capital led the round with participation from new investors GV (formerly Google Ventures) and Accenture Ventures and additional investment from Foundry Group, Madrona Venture Group and New Enterprise Associates (NEA).

Relaunching as Mighty AI reflects the company’s core business of delivering the human insights artificial intelligence (AI) engines need to see, hear, talk and “think” like people. Its Training Data as a Service™ (TDaaS™) platform helps businesses obtain the datasets they need to train and scale their computer vision and natural language models.

“In the future, we will count on AI to make every aspect of our lives better,” said Mighty AI Founder and CEO Matt Bencke. “AI will manage our information, schedules and energy use. It will make our factories, roads and homes safer and more efficient. It will provide us each with a personal shopper and investment advisor. But, for AI models to do all this and more, they need to understand what and how we think. Mighty AI is the critical link between computer and human cognition, putting the right humans in the right learning loops.”

Mighty AI will use the new funding to extend both its TDaaS platform and community of Fives—a network of tens of thousands of individuals who conduct microtasks in their spare time, such as identifying and labeling objects in images or doing sentiment analysis of text. With the funding, Mighty AI will also grow its team by adding new data scientists, engineers and product, sales and marketing professionals.

“As our CEO, Brian Krzanich, has said, AI is not only the next big wave in computing; it’s the next major turning point in human history,” said Doug Fisher, Intel Corporation Senior Vice President and General Manager of the Software and Services Group. “Intel is investing in AI, with the goal of driving breakthrough performance while making AI solutions more accessible and easier to deploy for all. We see a significant opportunity across applications such as automated driving, robotics, healthcare and the Internet of Things. To produce high quality data for training models to deploy, it is essential to obtain clean, accurate human annotations at scale. With today’s announcement, Intel is pleased to team with Mighty AI to power the world’s AI engines, operating on Intel® architecture and guided by Mighty AI’s training data platform.”

“As a leader in applying AI to both our own business and our clients’ businesses, we know that AI will drive the next wave of business and societal transformation,” said Paul Daugherty, Accenture’s Chief Technology & Innovation Officer. “Data is the fuel required to power AI solutions, but many clients lack the right tools and solutions to achieve the business value they are looking for from their AI initiatives. By teaming with Mighty AI, we see a great opportunity to break this bottleneck, accelerate the integration of training data, and drive better business outcomes more rapidly.”

Mighty AI’s agreements with Intel and Accenture gives customers better access to tools and services across the AI stack, also known as the four components of any AI model:

  1. The infrastructure, such as the chips Intel builds;

  2. The large-scale mathematical algorithms that process tremendous amounts of calculations in parallel to “think;”

  3. The training data that turns these blank slates into simulations of human knowledge; and

  4. The application of the resulting AI models in interfaces that make it easy for people to use.

With the new agreement, customers of Accenture and Mighty AI will get a more streamlined experience as they execute across the full AI stack to apply their models. The formation of the relationship with Mighty AI was led by Accenture Ventures, which focuses on teaming with and investing in companies that create innovative enterprise technologies.

“We couldn’t be more excited to team with Intel and Accenture, both of which are second to none in their respective industries,” said Bencke. “Our mission at Mighty AI is to tap the world’s potential brainpower so that our customers’ AI engines have heroic impact. Words matter, and today we declare that we operate the world’s best platform to make our customers’ AI models mighty, together with our industry-leading partners.”

 

About Mighty AI

Founded in 2014, Mighty AI delivers the human insights artificial intelligence (AI) engines need to see, hear, talk and “think” like humans. Our Training Data as a Service™ platform helps companies get the accurate, high-quality datasets they need to train and scale their computer vision and natural language models. Visit www.mty.ai to learn more, and follow us at @mighty_ai.

 

About Intel Capital

Intel Capital, Intel's strategic investment organization, backs innovative startups targeting computing and smart devices, cloud, datacenter, security, the Internet of Things, wearable and robotic technologies and semiconductor manufacturing. Since 1991, Intel Capital has invested US$11.8 billion in 1,478 companies worldwide, and 617 portfolio companies have gone public or been acquired. Through its business development programs, Intel Capital curates thousands of introductions each year between its portfolio executives and Intel's customers and partners in the Global 2000. For more information on what makes Intel Capital one of the world’s most powerful venture capital firms, visit www.intelcapital.com or follow @Intelcapital.

 

###

Contact:

Mighty AI

Angela Cherry, Director of Communications

angela@mty.ai

Comment

Matthew Bencke

Matt Bencke is an entrepreneur, leader and change agent who drives new business and product strategies based on deep analysis, inspired leadership and focused execution. He has strong successes across technology, strategy, business development, design, e-commerce, marketing and manufacturing. His passion is attracting great talent, fostering a meaningful team culture, and taking performance to new levels. During his tenures at Microsoft, Getty Images and Boeing he has created, advised, led and grown businesses ranging from several millions to billions in size.

Dear President-elect Trump: Please don’t ignore artificial intelligence

Every White House leadership change causes speculation about what pre-existing initiatives the incoming administration will embrace or eliminate. I encourage President-elect Trump and his appointees to start their term ready to ensure that artificial intelligence (AI) gives our economy the competitive edge it needs.

The Obama administration recently released its recommended approach for how the U.S. should promote AI research and development. It published balanced suggestions to guide public investment, encourage private-public collaboration, and account for national security, public safety, diversity, and ethics. It was a good start. But it’s broad and not yet deep.

I’d like to offer a few specific suggestions for the next administration.

 

Make America first again

Our past successes are no guarantee of America’s future innovation leadership. It’s easy to take for granted that we led the world into space, “smart weapons,” the internet, and the cloud. It’s not as dramatic as putting a man on the moon, but make no mistake: AI will dominate the next wave of economic growth and national security. U.S. economic growth has been slow and unevenly distributed, so it’s imperative that our federal government play a leadership role.

  1. Invest 10X. Total federal investment in unclassified AI R&D in 2016 will be an estimated $1.2 billion. That is less than IBM will invest in AI this year, and less than 1 percent of total federal R&D. Trump wants to stimulate the economy. No dollars will create larger economic multipliers than those invested in AI.
  2. Centralize and drive accountability. How do you increase your investment tenfold without it being frittered away among dozens of bureaucracies? Centralize AI under one team of experts accountable to U.S. taxpayers. I nominate the Defense Advanced Research Projects Agency (DARPA), which brought us the internet, led the way with autonomous vehicles, and undoubtedly is driving classified AI investments.
  3. Compete globally. The U.S. needs to own and act on the best new intellectual property we can muster, but it would be naive to think we will create it all. DARPA should expand its successful “Grand Challenge” model to welcome the world’s best researchers to compete across fields that will be transformed in the next decade by AI.

 

Harness entrepreneurs to serve our country

The world’s largest technology companies are all-in on AI, investing billions. They have the heft to ensure they have a seat at the right tables in Washington — but they’re also the incumbents. Most of the disruptive AI innovations will come from startups.

  1. Engage innovators. Defense Secretary Ash Carter created the Defense Digital Service as a “vestibule” to safely incorporate the best innovations from the private sector. The next administration should expand this unit so the Pentagon and Department of Homeland Security can incorporate the best and brightest, more quickly and easily. Cyberwarfare, autonomous drones, and systems for screening immigrants and visitors all badly need AI to keep our country safe. In addition, the Pentagon should bolster the Defense Innovation Unit Experimental (DIUX) efforts to harness the best of the startup world, including through venture-style investments.

 

Disruption is inevitable, but we can spread the gains

AI is inevitable. It will disrupt many jobs, and that’s a big problem. But, like ATMs created more banking jobs, so can AI — if we get out ahead of it.

  1. Start job retraining now. Many jobs in warehouses, factories, and transportation will be displaced. It will take hard work, ambitious investments, and private-public partnerships to make sure the people affected end up with better jobs, here at home. Trump has talked about making massive infrastructure investments. Under FDR, that meant bridges. Today that means TVA-scale retraining programs.
  2. Education for all. Take a cue from organizations like Code.org and spread the benefits of computer education broadly.
  3. Make health care smart. More than one in every six American dollars goes to health care. However, very few would argue our system works. Consider the Department of Veterans Affairs. The vast majority of their records are not digitized. AI can penetrate this unending data fog to help providers deliver the right care, more quickly and efficiently.

 

Build ethical machines

We are far from creating sentient machines, but ethical questions are already pressing. Take autonomous vehicles, which must decide in a split second: Do you hit the kid or the curb?

  1. Teach ethics. We’re teaching computers to see, listen, talk, and think like us. But who is “us”? To see the dangerous social consequences of getting AI wrong, look no further than the bias against black Americans Pro Publicauncovered in software that predicts criminal recidivism. We should encourage all schools to incorporate ethics into computer science, engineering, and math courses by publishing optional curricula. The administration should require that recipients of federal AI funding undergo bias awareness training.

 

The only thing we have to fear … is still fear

We need to debunk the notion that it’s computers versus people.The combination of human and computational intelligence can serve as a powerful engine for progress.

  1. Make humans super. As Accenture CTO Paul Daugherty recently quipped, “Our goal with AI is not to make super humans, it’s to make humans super.” Trump and his advisers have the opportunity to inspire our nation to apply AI to cure cancer, keep our country safe, and make our workers the world’s most productive. AI is a race, and make no mistake: China is in it to win.

The AI revolution is well under way. Personally, I am bullish about its implications — even as I look plainly at its unknowns, challenges, and threats. I hope our President-elect is as well, and that his administration moves proactively so that our United States leads that revolution, for the good of all Americans.

(Thanks to the good folk at VentureBeat, who published this article on December 1, 2016: http://venturebeat.com/2016/12/01/dear-president-elect-trump-please-dont-ignore-artificial-intelligence/)

Comment

Matthew Bencke

Matt Bencke is an entrepreneur, leader and change agent who drives new business and product strategies based on deep analysis, inspired leadership and focused execution. He has strong successes across technology, strategy, business development, design, e-commerce, marketing and manufacturing. His passion is attracting great talent, fostering a meaningful team culture, and taking performance to new levels. During his tenures at Microsoft, Getty Images and Boeing he has created, advised, led and grown businesses ranging from several millions to billions in size.

Puppies are Cute. Biases in Training Data for AIs? Not So Much.

Biased training data makes this puppy sad. Do you want this puppy to be sad?

Biased training data makes this puppy sad. Do you want this puppy to be sad?

Most artificial intelligence models are built and trained by humans, and therefore have the potential to learn, perpetuate and massively scale the human trainers’ biases. This is the word of warning put forth in two illuminating articles published earlier this year by Jack Clark at Bloomberg and Kate Crawford at The New York Times.

Tl;dr: The AI field lacks diversity — even more spectacularly than most of our software industry. When an AI practitioner builds a data set on which to train his or her algorithm, it is likely that the data set will only represent one worldview: the practitioner’s. The resulting AI model demonstrates a non-diverse “intelligence” at best, and a biased or even offensive one at worst.

The articles focus on two related areas in which diversity and demographics matter when it comes to building AI: the data scientist, and the data scientist’s choices for training data. Again, the theory is that though it’s subconscious, the practitioner’s selection of training data — say, images of peoples’ eyes or tweets in English — reflect the types of objects, experiences, etc. with which the practitioner is most familiar (perhaps images of a particular demographics’ eyes, or tweets written in British English).

There’s a third area in which demographics and diversity matter, though. It’s just as important, and it’s often overlooked — it’s the annotators.

Many people = many (varying) viewpoints

Data used for training AI and machine learning models must be labeled — or annotated — before it can be fed into the algorithm. For instance, computer vision models need annotations describing the categories to which images belong, the objects within them, the context in which the objects appear and so on.

Natural language models need annotations that teach the modelsthe sentiment of a tweet, for example, or that a string of words is a question about the status of an online purchase. Before a computer can know or “see” these things itself, it must be shown many confident positive and negative examples (aka ground truth or gold standard data). And you can only get that certainty from the right human annotators.

So what happens when you don’t consider carefully who is annotating the data? What happens when you don’t account for the differing preferences, tendencies and biases among varying humans? We ran a fun experiment to find out.

Gender makes a significant difference

Actually, we didn’t set out to run an experiment. We just wanted to create something fun that we thought our awesome tasking community would enjoy. The idea? Give people the chance to rate puppies’ cuteness in their spare time. While we design all of our tasks to be fun and engaging, they still require smarts and skills, and we figured it would be cool of us to throw in some just-for-smiles tasks. An adorable little brain break, if you will.

And so we set up a “Rate the Puppies” task, served users puppy pics and asked them to rate each pooch’s cuteness on a scale of 1 to 5 stars. Everyone loved it. Including us. Duh! We love dogs! (Also cats! We love cats, too. And cat people. For the record.) Butwhen we analyzed the data, one thing immediately jumped out: On average, women gave higher cuteness ratings — a statistically significant 0.16 stars higher.

There was a clear gender gap — a very consistent pattern of women rating the puppies as cuter than the men did. The gap between women’s and men’s ratings was more narrow for the “less-cute” (ouch!) dogs, and wider for the cuter ones. Fascinating.

I won’t even try to unpack the societal implications of these findings, but the lesson here is this: If you’re training an artificial intelligence model — especially one that you want to be able to perform subjective tasks — there are three areas in which you must evaluate and consider demographics and diversity:

  • Yourself

  • Your data

  • Your annotators

his was a simple example: binary gender differences explaining one subjective numeric measure of an image. Yet it was unexpected and significant. As our industry deploys incredibly complex models that are pushing to the limit chip sets, algorithms and scientists, we risk reinforcing subtle biases, powerfully and at a previously unimaginable scale. Even more pernicious, many AIs reinforce their own learning, so we need to carefully consider “supervised” (aka human) re-training over time.

Artificial intelligence promises to change all of our lives — and it already subtly guides the way we shop, date, navigate, invest and more. But to make sure that it does so for the better, all of us practitioners need to go out of our way to be inclusive. We need to remain keenly aware of what makes us all, well… human. Especially the subtle, hidden stuff.

 

Thanks to TechCrunch for originally publishing this https://techcrunch.com/2016/09/11/a-cautionary-tale-about-humans-creating-biased-ai-models/

Comment

Matthew Bencke

Matt Bencke is an entrepreneur, leader and change agent who drives new business and product strategies based on deep analysis, inspired leadership and focused execution. He has strong successes across technology, strategy, business development, design, e-commerce, marketing and manufacturing. His passion is attracting great talent, fostering a meaningful team culture, and taking performance to new levels. During his tenures at Microsoft, Getty Images and Boeing he has created, advised, led and grown businesses ranging from several millions to billions in size.

The Reality of AI Today

Artificial intelligence is changing at a breathtaking pace the way we interact with our devices, friends and data. It promises to revolutionize how we work, shop, socialize, date, bank, heal, navigate… how we live.

Yet here I am in the engine room of the revolution, getting increasingly frustrated with the prevalent AI conversations and memes. We’re painting a partial, bipolar, even misleading picture. “We” sometimes includes me, and it includes journalists, pundits, analysts and even practitioners.

What do I mean? Specifically, we highlight the highs (AlphaGo beats world champion!) and lambast the lows (Chatbot Tay turns racist and hateful!), while brushing off smaller (but meaningful) advances. We keep giving air time to robot-takeover fears, while failing to evangelize and thus guide the real value of AI today.

As humans, we’re drawn to the dramatic, but as an industry, we have a responsibility to get everyone on the same, grounded-in-reality page. So forgive me for sharpening my proverbial axe. Here are three specific AI topics that need a reboot.

Pervasive if mundane

We are all rightly in awe of the splashy, sexy and sometimes scary AI concepts we read about in the headlines. The end of appsthe end of screensautonomous vehiclesautonomous weapon systems… Though not difficult to imagine, many of these scenarios are several years out. But less sensational AI models are already here, now, improving our everyday experiences. They’re great. And we should talk about them more. And thereby avoid Gartner's "trough of disillusionment."

A broad array of companies are quietly innovating on top of AI technologies — and integrating the resulting innovations into services today. Their models are showing us the right products (e.g. ExpediaGetty Images), serving smarter ads (e.g. GumGum,Pinterest), organizing our content (e.g. GoProMicrosoft), guiding investments (e.g.Sentient), talking to us in the words we understand (e.g. DoCoMo, IBM Watson) and more. Each of these is one of our customers, and each of them is filled with smart people working hard to improve their products using AI. Many of them work on agile software teams. Over time, their incremental progress will revolutionize technology. But don’t expect to wake up tomorrow as Bruce Willis in “Surrogates.”

Not just for the big platforms anymore

In this AI Spring, we’re seeing big news almost daily, much of it brought to us by the tech behemoths we all know and love. Google, Amazon, Microsoft, IBM, Facebook, Apple and others are constantly making waves with new AI projects and developments. And these companies are paying big money for AI startups (recent examples include Turi and Nervana). It’s war.

But AIs are not just for the big guys. Why? Three reasons. First, the affordability of massive computing power. Indeed, the commoditization of compute power is a large part of why the big cloud platforms want you developing models on top of Azure, AWS, Watson and GCP, and why Intel and NVIDIA want you doing it on their silicon. Second, there are more ML- and AI-trained engineers than ever before, taking advantage of decades of science advancing the underlying algorithms.

Third, largely behind the scenes, product teams from startups to retailers and systems integrators are applying others’ AI platforms using their own proprietary training data. The most sophisticated algorithms are only able to see, listen, talk and “think” like humans if the right humans are training and re-training them as our fickle human opinions evolve. Nobody understands shoes like Shoes.com, surfing videos like GoPro, top-shelf stock imagery like Getty — and their customers.

Sometimes the ability to leverage cognitive computing is less about the underlying algorithms and more about how they are tuned and productized. Mark my words: It’s not just who has the models, it’s about who applies the right data.

A means, not an end

Lastly, can we please stop evaluating AIs based on whether they are saving us from cancer or threatening humanity? These questions bait valuable conversations, but miss the bigger, more important “middle.” I love telling my mom that we train AIs. But AIs are not the goal. When we all go to that tech-loving part of our brain, we forget that AIs are the means to improved experiences.

We all accept that a picture is “worth a thousand words.” We also know that most of our communication is non-verbal (just contrast your last date, or in-person interview, with your last telecon call). So how much are 100 million images worth? How about terabytes of CT scans of your loved one’s brain tumor? And what about 24 hours of video, at 30 fps? Well, of course, that depends on what’s in the data — and, more importantly, what we can do with it.

AIs hold the key to unlocking the value buried in the massive, exponentially growing sets of unstructured content with which we each interact daily.

As such, I humbly propose that we measure the value of an AI based on its ability to contribute positive utility to people. We all know that at the end of the day, it’s the people who matter in our lives. Just the same, we need to remember that AIs should be measured by their ability to understand, interact with and improve people’s lives. This utilitarian framework offers hundreds of years of insights from philosophers, economists and anthropologists.

The new nukes

AIs are pervasive, and are creating the power to spread good and bad. Time and time again, people have managed to guide innovations for the good of humanity. In some ways, AIs remind me of nuclear technologies. Handled poorly, they could destroy the world. But they haven’t. In fact, we have avoided another world war thanks in part to mutual assured destruction, and atomic know-how has quietly revolutionized healthcare, energy, robotics, physics and aerospace.

Yes, the extremes are scary, but they are only part of the story. If we are to harness AIs for universal good, we need to be more realistic about their daily implications and the net benefits thereof. Less “duck and cover” and more comprehensive conversation.

 

Thanks to TechCrunch for publishing this byline initially on September 8, 2016

Comment

Matthew Bencke

Matt Bencke is an entrepreneur, leader and change agent who drives new business and product strategies based on deep analysis, inspired leadership and focused execution. He has strong successes across technology, strategy, business development, design, e-commerce, marketing and manufacturing. His passion is attracting great talent, fostering a meaningful team culture, and taking performance to new levels. During his tenures at Microsoft, Getty Images and Boeing he has created, advised, led and grown businesses ranging from several millions to billions in size.

Best Place We've Ever Worked

Last Thursday night, our Spare5 team celebrated our inclusion in Seattle Business Magazine's 100 Best Companies to Work For in Washington list. To make the cut, companies needed favorable scores across survey categories, as well as high employee participation in the survey itself. Responses were anonymous and assessed benefits, communication, corporate culture, executive leadership, hiring/retention, performance standards, responsibility/decision making, rewards/recognition, training/education, and workplace environment.B

Spare5 has won other awards, but this one is special to me personally. We have been intentional about building a great culture. I want Spare5 to be the best place each of our remarkable employees has ever worked. While our product and industry make for a rewarding day-to-day, working alongside colleagues who are sharp, accomplished, ambitious, and just good people ups the happiness factor considerably. At least, I can say as much for myself; I’m delighted the survey indicates everyone else is feeling the good vibes, too.

The award has prompted me to once again reflect on our seven leadership principles:

  • Ambition. We are hungry to build something awesome and enduring. We are proud to work here.

  • Balance. We take care of ourselves and each other. Every individual is responsible for earning each other’s trust, and for trusting others to do the same.

  • Accountability. We each step up when we see something that needs done, and we do what we say we will – without prompt or reminder.

  • Debate and Commit. We have strong opinions, loosely held. We debate, then move forward with unity.

  • How We Roll. We are ruthlessly transparent with ourselves based on data and instinct, so that we can bet smart, move fast and learn continuously.

  • Openness. We welcome ideas from everywhere.

  • Appreciation. We show appreciation and celebrate success.

Defining these principles was one of the first things we did in creating Spare5, and it’s wonderful to confirm that they still guide us today.


The other day our soccer-crazed daughter was down on herself for feeling unenthusiastic about practice. I consoled her that nobody loves their work every day. Leading Spare5 is the best job I’ve ever had, but the nature of anything hard is that there are ups and downs. Given that reality, I’m particularly grateful for the culture our team embodies, and look forward to many more special moments.

Comment

Matthew Bencke

Matt Bencke is an entrepreneur, leader and change agent who drives new business and product strategies based on deep analysis, inspired leadership and focused execution. He has strong successes across technology, strategy, business development, design, e-commerce, marketing and manufacturing. His passion is attracting great talent, fostering a meaningful team culture, and taking performance to new levels. During his tenures at Microsoft, Getty Images and Boeing he has created, advised, led and grown businesses ranging from several millions to billions in size.

3–2–1: Launching a New Data Platform!

image credit: Confetti by ADoseofShipBoy via CC BY 2.0

image credit: Confetti by ADoseofShipBoy via CC BY 2.0

Today is a special day for Spare5. We are announcing the launch of our Intelligent Crowdsourcing Platform. It took 18 months for us to earn this right. During that time, our team has worked hard to conceive, hire, design, build, test, improve, deliver, and scale.

Lots of thanks are in order; I’ll keep this to four. First and foremost, to our Spare5 team. We’ve got a special bunch. I’ve been fortunate to work on some amazing teams, and yet I’ve never been on one where the ratio of mad skills : ego is so off-the-charts. Second, to our early adopters. Terrific people at serious companies have placed a bet on Spare5, seeking to be at the cutting edge. So thanks, too, to the good folks at our customers, including Avvo, Expedia, Getty Images, GoPro, IBM, iSpot.tv, Pinterest, Sentient Technologies, and others. Rest assured we never stop working for you.

Third, to our investors at The Foundry Group, Madrona Venture Group, and New Enterprise Associates, and to our predecessors at Madrona’s Venture Labs. And, not least, to our awesome and growing community of users, whom we lovingly call our “Fives.” Your spare time and knowledge are solving large, hard data problems for companies that need to train their models, clean their metadata, and improve their search, browse and organic traffic.

Anybody who’s ever worked in technology knows this feeling. 99% of your days, you crank hard, and 1% of the days, you pop the cork and unveil something new that you’ll tell your grandchildren about. Occasionally, it’s literally a dramatic moment. Back in the day, I was lucky enough to work on, and then attend, the launch of the first module for the International Space Station. Today, Spare5 is launching our platform. There are no literal flames, because, to be fair, we’re operating and continuously improving Intelligent Crowdsourcing 24x7. But it sure feels like we’re standing on the launch pad for something special.

Kazakhstan, November 1998 Product Launch: The International Space Station’s First Module (Yep, I was that nerdy. I wore a belt pouch for a camera with no modem.)

Kazakhstan, November 1998 Product Launch: The International Space Station’s First Module (Yep, I was that nerdy. I wore a belt pouch for a camera with no modem.)

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Matthew Bencke

Matt Bencke is an entrepreneur, leader and change agent who drives new business and product strategies based on deep analysis, inspired leadership and focused execution. He has strong successes across technology, strategy, business development, design, e-commerce, marketing and manufacturing. His passion is attracting great talent, fostering a meaningful team culture, and taking performance to new levels. During his tenures at Microsoft, Getty Images and Boeing he has created, advised, led and grown businesses ranging from several millions to billions in size.

Why Women are the New Tech Unicorns

We all know there’s a shortage of women in tech. The reasons why have been widely debated, but seem to come down to these four factors: gender stereotypes, inadequate talent pool, in-group favoritism, and work culture issues. According to a recent survey by the Guardian, 73 percent of respondents believe the tech industry is sexist. As the CEO of a tech company — and the proud father of two daughters — I keep these factors in mind, and they motivate me to bring more women to our team.

Twenty-eight percent of both our staff, and our coders, are female. On the one hand, I’m proud of those numbers. While there are signs that the tide is turning, that number is a lowly 17 percent at Google, and only 15 percent at Facebook—and less than seven percent of tech positions in Europe are filled by women. On the other hand, I want that number, and the resulting value add we’ve experienced through our diversity efforts, to be higher. Ideally it would be 50 percent.

In building an ambitious team from scratch, I seek diversity, built on a foundation of shared values. Diversity takes many forms — including work style, gender, race, and experience — and brings benefits across the board. Diversity ensures a powerful marketplace of ideas, and a variety of tools to implement them.

Why we actively seek women, and why it’s not easy

Here’s the other reality: We need women to succeed; it’s in our business model. Almost 60 percent of Spare5’s active users are female, and women are the decision makers at a number of our business customers, and their consumers. I can’t define precisely why sometimes it’s easier for women to understand other women, other than to say, I know we’d be doomed if we were all males trying to do so. In addition, we have an open office and open culture. Diversity makes it more fun, and brings out the best in all of us.

Gender is only one color in the diversity spectrum, but as researchers at Google’s Project Aristotle recently learned, the single best determinant of high-performing teams is whether employees feel safe – that is, can they be themselves, be heard, and find colleagues with empathy. In my opinion and experience, all of the above requires a gender balance.

Simply having the goal of recruiting more women hasn’t solved the diversity problem. The talent pool gap is real. Organizations such as Code.org, Ada Developers Academy, the National Center for Women & Information Technology, to name a few, are working to close the gap, but it’s taking its sweet time.

In fact, by some measures, we are actually losing ground. And the issue isn’t limited to the Seattle area (Spare5’s headquarters). On a national level, fewer than 20 percent of the computer science bachelor’s degrees are conferred to women (versus over 35 percent in the early 1980s).

Through Spare5’s efforts to diversify the workplace, here are a few key takeaways we’ve learned:

Culture. Culture is everything. It plays an especially important role in employee job satisfaction and according to feedback, particularly for women. My female employees describe Spare5’s culture in particular as “safe,” “respectful,” and “approachable.” One shared with me, “My stress level goes down when I come into work, so I feel like I can do my best work here.” While another employee described a chaotic environment at her prior place of work, and how relieved she feels to be here, “Without all that noise so I can just work.”

Several of them also noted they’d never really explicitly thought about being a woman at Spare5, because they’ve never seen anything that resembles the “brogrammers,” or “men’s club.” We don’t have drinks only for the boys, tell sexist jokes, or, at the bottom line, treat anybody differently based on their gender.

Make the right hires. Several of our female employees observed that creating an environment that is friendly for women is largely about hiring the right people. In particular, we do not tolerate brash, narcissistic, or temperamental behavior. We praise and promote humility, action, and being opinionated and vocal, but amicable.

Fire the wrong hires. We have parted ways with people who don’t live our values. Some of my female colleagues volunteered that this made a huge statement. We all make bad hires. It sucks, but the old cliché holds true: “Hire slow, fire fast.” You define your team’s culture as much by who you are NOT, as much as by who you aspire to be.

Job descriptions. I’d never heard this before, but when I asked for advice about how to recruit more women, my female employees had a field day mocking various job descriptions they’ve read, and declined to apply for two main reasons: First, they use highly-masculine terms like “rock star” or “killer,” and second, they list requirements that they don’t meet. I’ve been guilty of both. For instance, I once posted a job description with a picture of lions. One of our round table members asserted, “I don’t want to be a rock star.” That person is a great programmer and team member. She just doesn’t imagine herself belting out hard rock ballads while submitting code.

Our female employees pointed out that many job descriptions have long lists of requirements, and they tend to take each one seriously. One of our extremely talented technical women confided that she almost didn’t apply for Spare5 because she did not meet one criterion. Early in her first interview, she asked our CTO how important that was, and was fully ready to opt out. Turns out, it was merely a throwaway “perk” more than a requirement. Several of our female employees agreed with this, and came to the conclusion that men, overall, must feel totally fine “winging it” in interviews, whereas women tend to be particularly sensitive about “over-reaching.”

Referrals. We all know that recruiting is largely about reputation and word of mouth. My female employees pointed out that this is particularly true when checking out whether a team is a good fit for them. We have been sponsoring Ada because it’s a good thing, and because we selfishly wanted a good intern. But it turns out that this was also a way of hanging a shingle that says, “We promote having women in tech, and in our company.”

Maybe others will read this, jump in, and have more ideas about the dos and don’ts. If I got something wrong, let me know.

(Thanks to Shaping Influence for originally publishing this on April 5, 2016 at http://www.shapinginfluence.com/topics/shapinginfluence/articles/419821-why-women-the-new-tech-unicorns.htm)

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Matthew Bencke

Matt Bencke is an entrepreneur, leader and change agent who drives new business and product strategies based on deep analysis, inspired leadership and focused execution. He has strong successes across technology, strategy, business development, design, e-commerce, marketing and manufacturing. His passion is attracting great talent, fostering a meaningful team culture, and taking performance to new levels. During his tenures at Microsoft, Getty Images and Boeing he has created, advised, led and grown businesses ranging from several millions to billions in size.

Roll over Rover, Man's New Best Friend is AI

Fifa, one of our beloved Spare5 mascots

Fifa, one of our beloved Spare5 mascots

Roll over dogs, there’s a new human companion in town and it’s smart, omnipresent and perhaps best of all, hair-free. Thanks to massive improvements in artificial intelligence (“AI”), technology is taking data’s utility to a whole new level, with a lot less drama than Elon Musk fears. While Hollywood, journalists and scientists wonder whether AI will turn on us, the less ballyhooed reality is more subtle, pervasive and industry-changing.

Lots of Smoke

Far be it from me to disagree with Stephen Hawking or Bill Gates about the long-term risks of an independent “super intelligence.” But here and now, some of the world’s leading technologists are working hard for much humbler, more pragmatic and benevolent applications. Over the past year, IBM doubled down on Watson and its multi-billion dollar efforts to apply cognitive computing to medicine, financial services, education and sports. Microsoft open-sourced its deep learning platform to accomplish feats like recognizing photos and understanding human speech; Google open-sourced TensorFlow, its latest machine learning platform; Facebook began experimenting with a ubiquitous personal assistant; Elon Musk, Peter Thiel, Reid Hoffman, Jessica Livingston and others are investing $1 billion+ to fund OpenAI; Accenture is doubling down on AI; and the broader ecosystem is growing so fast it’s hard to map (though credible private tallies estimate that VCs have invested over $2.6 billion in 780 startups in this space over the past couple years).

Lots of Fire, Too

So what? Well, the reality is that AI is already enabling experiences we all take for granted. Apple Siri, Microsoft Cortana, Google Now, Amazon Echo and just about every new car have voice-based interaction. None of them are perfect, but they’re changing the way we interact with increasingly personalized machine-powered experiences, every day. Less prevalent, but perhaps even more powerful, AI and its close cousin, machine learning, are fighting fraud, and changing the way we invest, buy a house, find a hotel, drive a car, and meet a mate. Smartphones and tablets changed the way we live, work and play. The next major revolution in how we interact with technology is well under way, and heads up: it isn’t “mobile,” drones, or virtual reality (at least not yet). It’s the AI under the hood of all of these technologies, and more.

Can’t Live Without It

I love dogs. I mean, come on, don’t you always hang a question mark over the head of someone who doesn’t? But let’s face it, you never leave home without your smartphone, which is your personal portal into massive amounts of AI-powered experiences. You often leave home without your keys, wallet, and yes, your furry friend.

AI + Big Data = Whoa

Yes, really. AI is man’s new best friend because the next meta-wave of technology is all about ingesting, understanding, cleaning, parsing and applying massive amounts of unstructured data. It’s inconceivable for people to accomplish these tasks at scale without AI.

Yep, We Need Each Other

Nor can machines accomplish these massive, data-driven tasks including data enrichment, cleansing, and collection, without us. AI will cause disruption, but it will not cause large-scale job loss. Why? Three reasons.

First, humans still have massive advantages in our ability to pattern recognize, synthesize subjective factors (including emotions), and anticipate other human responses. Machine learning algorithms will continue to perform more rote tasks, better, and move up the “value chain” to more complex tasks. But the New York Times accurately concludes that lawyers’ jobs are safe, and a large-scale study by McKinsey estimates that less than 5 percent of jobs can be completely automated based on existing technologies within the next three to five years.

Second, machines need humans to train, test and retrain them. The limits of AI are no longer computing power, or bandwidth, or raw data. The biggest limiting factor in AI’s applicability is the availability of high-quality data that accurately reflect how specific types of people think, act and feel. At our intelligent crowdsourcing company, Spare5, we are powering a diverse set of customers’ needs for domain-specific, reliable training data through the use of machine learning algorithms powered by human insights, and so we are seeing the revolution from the engine room.

Third, big companies are still figuring out how to integrate new AI-powered capabilities into their existing workflows. For example, AI is going to revolutionize how we shop. In the near future, my connected devices will anticipate my purchases, by understanding not just a fraction of my history, but also by considering my existing wardrobe, style, season, weather, geography, personality, life stage, hobbies, preferences and broader fashion trends. That’s a lot, but it is not science fiction. Ironically, though, retailers are going to need to break out of their A/B testing comfort zone to radically incorporate these capabilities. Those that do, will win – big. Those that do not, well…ouch.

In a Harvard Business Review interview, Erik Brynjolfsson and Andrew McAfee, called upon entrepreneurs to “race with machines,” pointing out the massive opportunity we have to create new, better jobs and lives by harnessing the computing power we are creating:

"This is an opportunity for entrepreneurs to think of ways of using humans in new applications, combining them with technology. We call that racing with machines as opposed to racing against them."

Sorry, Rover

There’s been a lot of hype about machines in the media. We’ve heard the good and the bad, making it all the more important to take a step back and examine the actual relationship between humans and machines. Thus far it hasn’t been one of competition—man versus machine. It’s been one of companionship—man with machine. Separate, man’s evolution is limited, and machines are stunted. Together, humans can teach machines to learn and interact with humans, ultimately aiding them in accomplishing their greatest goals and realizing their most innovative visions. Machine, by all accounts, is our new best friend.

(Thanks to TechZone360, who originally published this article http://www.techzone360.com/topics/techzone/articles/2016/02/11/417430-artificial-intelligence-mans-new-best-friend.htm)

 

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Matthew Bencke

Matt Bencke is an entrepreneur, leader and change agent who drives new business and product strategies based on deep analysis, inspired leadership and focused execution. He has strong successes across technology, strategy, business development, design, e-commerce, marketing and manufacturing. His passion is attracting great talent, fostering a meaningful team culture, and taking performance to new levels. During his tenures at Microsoft, Getty Images and Boeing he has created, advised, led and grown businesses ranging from several millions to billions in size.

Big Data - or Big Problem?

iStock_000029495294_Large.jpg

"Without data it's very hard to be intelligent." - Duncan Anderson, CTO of IBM Watson Europe

We are living in the land grab era of "Big Data." Whether you're running an e-commerce business, online directory, healthcare provider, or financial institution, you know that your ability to ingest, process, synthesize, and act based on an ever-growing supply of metadata may very well determine your success or failure. The problem is, unlike land, data is effectively infinite. The average enterprise is seeing the data relevant to them grow geometrically. Ironically, a disproportionate amount of the value theoretically stored in these data are opaque to large-scale computing systems - they're in photos, video, and audio, and they're often highly personal, social, and context-specific. 

So who are the "white hats" in this overwhelming struggle? Cognitive computing, artificial / augmented intelligence, and machine learning are riding in to the rescue. The good news is that AI technologies, approaches, and services are improving significantly. The bad news is that the resulting models' ability to reason, emulate, and predict human responses in ways that actually help your business is limited by - you guessed it - the quality of the human-derived training data. 

In order to train, test, and tune any AI, companies need human insights that are specific to their domain, and of good quality. For example, a computer doesn't know on its own that an insurance claim is valid (or even what an insurance claim is). "Quality" is a dangerously ambiguous term. In this field, quality is really a ratio of confidence in ground truth to the cost (a function of dollars, effort, and time). A data scientist needs to know how many people with specific traits agree, thereby establishing "truth." The more complex, subjective and unstructured the puzzle, the more difficult truth is to determine. In theory, given infinite money, resources, and time, we can ask dozens of qualified medical accountants whether they agree that a given medical claim is valid and reimbursable at 95% for a given patient. But in practice, most engineers need to solve millions, or even billions, of such questions daily - economically and reliably. 

Here's the thing: More than a billion people are regularly providing exactly that kind of domain-specific training data for Google and Facebook every day. We all teach them, with our searches, posts, comments, tags, emails, and reactions. This gives Google and Facebook an almost insurmountable advantage in a winner-take-all flywheel that is further accelerated by their embarrassment of engineering riches. In short, if you're not Google or Facebook, you're screwed. (Okay, there are other major players who are similarly advantaged. Amazon has reams of data about our buying, viewing, and even listening habits. And let's not count out Microsoft, nor forget WeChat or Baidu. Still, the amount of quality data that Google and Facebook are receiving daily is simply unparalleled.) 

As we look to 2016, it's clear that companies need to remain competitive with their data. Those that do, will win. Those that do not find a solution, will be buried. There is a shortage of domain-specific insights of quality and scale. This is where big data management comes into the picture. But contrary to popular belief, machine learning is not the silver bullet. The limiting factor for your success in understanding your data is the domain-specific training data you need to create useful machine-based models. 

So how can companies other than Google and Facebook make sense of their data in 2016? 

Break it down

It's easy to find yourself overwhelmed with big data initiatives, not knowing where to start or what to look for. A wise approach is to break it all into smaller, more manageable chunks. At Spare5, we break complex problems down into digestible tasks in order to provide quality insights at scale. You need to crawl before you can run, so start small to understand how to analyze results and implement new strategies. An economy is only as efficient as its currency is small and fluid. Spare5 is revolutionizing the creation of high-quality, domain-specific training data by reducing the currency of human insight to spare moments provided by targeted members of our curated community. Speaking of community... 

Utilize a community

You're not going to build a community to rival that of Google and Facebook overnight. However, a targeted community willing to provide insights can help solve complex business challenges. "Targeted community" is the key here, though - a "crowd" isn't so helpful. There are a number of community-based resources out there to consider, so spend time understanding what benefits each provides and what makes the most sense for your business. 

Train your machines. Well.

Machine learning is only as smart as the quality of training provided by humans. There needs to be a marriage of machine and humans to provide the best results. Machines are (at best) limited by the quality of human insights that are training them. Seek sources of high-quality, domain-specific training data - it's critical to effective, truly beneficial machine learning technologies. 

Don't let your big data become a big problem in 2016. Get in front of your data tidal wave, and you'll be reaping the benefits by this time next year.

(Thanks to vmblog, who originally published this entry with a bit more, um, color, back on December 18)

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Matthew Bencke

Matt Bencke is an entrepreneur, leader and change agent who drives new business and product strategies based on deep analysis, inspired leadership and focused execution. He has strong successes across technology, strategy, business development, design, e-commerce, marketing and manufacturing. His passion is attracting great talent, fostering a meaningful team culture, and taking performance to new levels. During his tenures at Microsoft, Getty Images and Boeing he has created, advised, led and grown businesses ranging from several millions to billions in size.

The On-Demand, Sharing And Gig Economies Never Existed, So Let's Stop Pretending They Did

(This article was originally published on TechCrunch with a Shutterstock image. Here I republish it with one from Getty Images, and a bit of extra thinking prompted by some readers' comments.)

Photo by aon168/iStock / Getty Images

Words

Words are impactful. They may not break your bones, as the saying goes, but they do guide the way we all think, debate, decide and act.

When it comes to the words we use to define our industry — the one commonly referred to as the “on-demand,” “sharing” or “gig” economy — we must choose more carefully.

Sure, we all try to keep up with the blistering pace of technology. But we should be even quicker to think ahead in terms of the words, concepts and frameworks that ultimately describe and define what we do.

At a recent (impressive) industry conference, the conversation came to revolve around which “on-demand” company would fail next. That is the wrong question to ask. Rather than cast backward-looking stones, leaders in this space should reflect on how far we have come, and start defining the future we are working so hard to build.

What we should be talking about is smart marketplaces, which is a more accurate, realistic and helpful category. But first, here’s why we must trash the old terminology.

Sharing

The “sharing economy” term was naive at best, destructive at worst. Very few people share stuff for free. It’s not that people don’t care, as Fast Company reporter Sarah Kessler wrote in her recent article, “The Sharing Economy Is Dead, And We Killed It.” It’s just too much of a hassle. We’re all too busy. If you ask someone, in theory, if they would loan their neighbor their power drill, of course, most people would say yes.

But then you get down to the details of it: What time can you meet them? Is it charged? When will you get it back? What if they break it or misplace a bit? Suddenly, people decline. It’s not because they’re stingy or uncompassionate, but rather because they have a running list of a thousand other, more pressing things.

“Sharing” was a ridiculous, idealistic promise that was doomed from the beginning. Uber should stop calling their drivers “partners,” because the only people they might be fooling, at this point, are themselves. Major news publications know that the only “sharing economy” is so-called. Let’s make it never-called.

On-Demand

On-demand isn’t new and disruptive, as it has commonly been billed. There has always been the option to call a taxi, hotel or pizza shop to get a ride, room or dinner delivery whenever one so “demanded.” Yes, companies like Uber, Airbnb and Postmates have made it easier, faster and more ubiquitous to get what we want, when we want, through the use of mobile apps. And they’re great.

But in reality, it is not the “on-demand” aspect that is new or, even for that matter, the most important. “Sharing” is deceptive, and “on-demand” is a red herring.

Gig

Even the “gig economy” tells only half of the story. It’s better than “on-demand,” because at least it describes a qualitatively new phenomenon. People can now earn meaningful amounts of money by applying their skills, resources and insights in their spare time.

The problem with this term is that it implies people are stringing together multiple freelancing “gigs” in lieu of a full-time job. Some people are attempting this, but many others are applying their resources when they feel like it, and are free to stop as soon as they don’t.

The bias promoted by the term “gig” is particularly destructive because it confuses politicians — including current presidential hopefuls — leading to a misplaced, confused concern for contributors whom they fear are being swindled out of a “real” job.

It would be more realistic to recognize that smaller bits of useful economic activity accomplished through the gig economy is inevitable, net positive and self-policing. In other words, companies that do not properly value their contributors will inevitably go out of business (Homejoy is an early example, but surely not the last, as venture capital markets tighten and the invisible hand of competition does its thing).

Smart Marketplaces

Words matter. There is a shakeout coming in the private tech space. Companies who confuse themselves, their employees, customers and investors by claiming to be in the “on-demand,” “sharing” or “gig” economy are sub-optimizing their decision making, at best. It would be better if journalists, analysts and, most importantly, entrepreneurs start talking about the “smart marketplace” economy.

A "smart marketplace" (1) matches supply and demand efficiently - which entails providing fair value to both sides; (2) adds value in the process; (3) reduces friction or "middle steps"; and (4) avoids disintermediation. If we look at companies like Spare5, Lyft, Postmates, Uber, AirBnB, Rover and so many others through this lens, then we benefit from four objective criteria by which we can evaluate their performance. We also benefit from a huge collection of economic literature and best practices.

The prevalence of big data, smartphones and cloud computing creates an unprecedented opportunity for companies to match supply and demand in entirely new ways. A smart marketplace has the ability to add value, balance supply and demand and avoid the middleman. What’s great about Uber, Lyft, Postmates, the WeChat payments ecosystem, Rover and Spare5 is that they match excess supply that would otherwise go unused.

They also make their services safer and provide an all-around better experience. Not perfect; not shared; yes, on-demand (yawn) and only sometimes as a “gig.”

The sharing economy isn’t dead. It never existed. It’s all about smart marketplaces and ultimately the best will win, and the ill-defined will fold.

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Matthew Bencke

Matt Bencke is an entrepreneur, leader and change agent who drives new business and product strategies based on deep analysis, inspired leadership and focused execution. He has strong successes across technology, strategy, business development, design, e-commerce, marketing and manufacturing. His passion is attracting great talent, fostering a meaningful team culture, and taking performance to new levels. During his tenures at Microsoft, Getty Images and Boeing he has created, advised, led and grown businesses ranging from several millions to billions in size.

"There Are Still Some Things That Make Us All the Same"

Today we announced that Spare5 raised a $10 million Series A financing round led by The Foundry Group, together with Madrona Venture Group and New Enterprise Associates.

Several journalists were thoughtful in the way they covered our progress, including Geekwire, the The Wall Street Journal and our very own The Seattle Times.

As I mentioned to my family and friends in a Facebook post this morning, given my druthers, I'd stay out of the press. That said, it's a fun moment to pause, look back over path we've traveled thus far, and hike forward.

As any founder or leader knows, it's all about the people. At Spare5, we've just begun. But the reason we have earned this re-investment from our awesome venture capitalists (Jason and Brad, Greg and Jon) is because of our team's hard work. So, here's to the founding folk who have got us this far.

...and the rest of our remarkable product, ops and finance teams who are turning the crank every day. Without them, nothing happens around here.

(Oh, and if my pop reference is too subtle, it may be time for a remedial Blues Brothers viewing - this post's title is from Belushi's intro to their moving rendition of "Everybody Needs Somebody to Love," complete with shout out to the good folk of Illinois' law enforcement community.)

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Matthew Bencke

Matt Bencke is an entrepreneur, leader and change agent who drives new business and product strategies based on deep analysis, inspired leadership and focused execution. He has strong successes across technology, strategy, business development, design, e-commerce, marketing and manufacturing. His passion is attracting great talent, fostering a meaningful team culture, and taking performance to new levels. During his tenures at Microsoft, Getty Images and Boeing he has created, advised, led and grown businesses ranging from several millions to billions in size.

The ‘Do It When I Want Economy’ Is Here To Stay

The public discourse about the “on-demand” economy gets a D+. Wherever I turn, whether it’s the EconomistNew York TimesTechCrunchRe/codeForbes,Washington Post or Business Insider, journalists, politicians and tech influencers all too often flock around a frustrating mix of myths, memes and conflation.

OK, that’s a bold claim. So you be the judge. Here’s what I see as the five mainmeh memes around the on-demand economy, and my version of more complete truths.

Myth One: It’s The “On-Demand” Economy

That’s only half the story. “On-demand” is from the perspective of those who can get their driver, groceries and dog sitter with a few app taps. We should be talking at least as much about the “Do It When I Want” (DIWIW) economy. Give it a shot: "dee-wee". :)

You could get a taxi before Uber, and a hotel before Airbnb. The level of convenience is new, but “on-demand” is not. What is new is that services such as Uber, Lyft, Rover, Airbnb and Spare5 enable people to make money doingwhat they choose, when they choose. When they don’t want to work, they can turn off their app.

Pundits and serious economists alike have concluded that the workers in the DIWIW economy would rather have good, well-paying, regular jobs. This is true of some, but not of all.

What is nearly always left out of stories around the DIWIW economy is the fact that it opens new avenues of connection to people you otherwise wouldn’t have met, and provides value to everyday people looking to change their career, take a step back or just make some extra money.

Myth Two: Flexibility Versus Security

There are a lot of holes in the United States’ social safety net. This is a serious, pressing and intractable problem. But the DIWIW economy is not causing these holes.

The reality is that technology inevitably brings disruption, and improvements, to society. Luddites and lemmings freaked out about the introduction of mass-market automobiles, assembly lines and personal computers.

We are still reckoning with the recent reality that more than 160 million Americans now check their smartphones an average of 150 times a day, and spend more time in their apps (4.7 hours daily) than watching television.

Is this really a bad thing? It’s complex, net positive and overall a continuation of what Durkheim foresaw more than 100 years ago as increased specialization creates more cohesive, productive societies.

Rather than hold our breath for unions, 401ks and universal healthcare, we need more social entrepreneurs like those at Even. And yes, we need more research to inform public policy, investment in education and other programs to benefit citizens looking for a living wage.

In terms of the DIWIW economy, academics should look objectively at the entire population of participants. Who is substituting for a traditional job, supplementing their income, transitioning between life phases and/or experimenting — why, when and how? Academics are starting to dive into this area and uncover results that don’t fit the popular assumptions about “on-demand workers.”

Myth Three: Bad Actors (Amongst DIWIW Company Founders) Are The Norm

This messy progress is not an excuse for those of us working hard to innovate. Homejoy is the latest casualty in the v1 wave of DIWIW companies. “How do we support and do right by those people while remaining a two-way platform?,” CEO Adora Cheung lamented to Re/code recently.

Uber is an easy whipping post. Sure, its leaders could benefit from more tact. When you grow that quickly and get that many people involved, there will be problems.

All entrepreneurs struggle, and most “fail,” but by chasing facile targets, journalists risk missing the big picture. Airplane crashes are spectacular, but commercial airliners are still the safest form of travel.

Or, as a friend is fond of saying, the plural of anecdote is not data. We need more real analysis, like what Jonathan Hall and Alan Krueger of Princeton arepublishing about the diversity of the new economy.

Those of us building DIWIW/on-demand businesses have a responsibility to respect our contributors (who supply value) and our paying customers (who demand that value). The marketplace will continue to weed out those who fail to do so.

Myth Four: We Will All Become Anonymous Autobots

That brings me to the fourth and most annoying meme. But I see where it comes from. Amazon’s Mechanical Turk is the poster child of sourcing from the “crowd.” Amazon treats its “workers” as providers of commoditized human labor as an API. It’s dystopia as a platform, and its contributors resent it even as they plug in.

Amazon’s welcome instructions tell people not to share any personal information while “performing HITs.” It’s hard not to conjure up the image of the Matrix’s robots plugging us all into a giant battery farm.

What should be happening is valuing these individuals for the skills they contribute via the DIWIW economy. As you read reviews of Airbnb stays, you find that the host or hostess is as important as the accommodations. And who doesn’t have a story of a fascinating conversation with an Uber or Lyft driver?

Yes the DIWIW/on-demand economy is about getting things done — and its by-product is connections with real people you would likely have never met otherwise.

Myth Five: It’s Man Versus Machine, And We (The People) Will Inevitably Lose

In reality, it’s not human insights versus machine learning, free will versus Terminators. MIT Professor Erik Brynjolfsson nails it when he points out that one of the great challenges for innovators is to marry the best of minds and machines.

Other respected tech observers, including Farhad Manjoo, see machine learning advances as just a step away from automating all work. But software does not disable our cerebral cortex. In fact, quite the opposite. Thanks to innovative technologies and marketplaces, people are better able to contribute what they choose, more conveniently and efficiently.

The best of the DIWIW companies will reward people accordingly, and give us all a chance to establish reputations, improve ourselves and yes, earn money. It’s definitely not perfect, it’s hard to get there and the destination is better capitalism, not utopia.

Still, we have the opportunity to improve billions of peoples’ lives, and reinforce the very free will that empowers us to do it when we want (or not).

Yes, we are in the on-demand economy. But by focusing on half the story, we’re missing the big picture. We are also in the DIWIW economy, as people now contribute and leverage assets that used to lie fallow — when they want. That’s net good, sometimes bad and often complex.

Making the best of this transformation requires liberating ourselves from backward-looking paradigms. The U.S. has one of the most dynamic labor markets in the world. Will that be the case when millennials come of age?

Empowering people to contribute fallow assets when they want is a strategic advantage for the U.S. economy

Empowering people to contribute fallow assets when they want is a strategic advantage for the U.S. economy

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Matthew Bencke

Matt Bencke is an entrepreneur, leader and change agent who drives new business and product strategies based on deep analysis, inspired leadership and focused execution. He has strong successes across technology, strategy, business development, design, e-commerce, marketing and manufacturing. His passion is attracting great talent, fostering a meaningful team culture, and taking performance to new levels. During his tenures at Microsoft, Getty Images and Boeing he has created, advised, led and grown businesses ranging from several millions to billions in size.

Spare5: Six Months Into our Journey

It's hard to believe that six months have passed since we started Spare5.

On a whim I recently compared building our company to taking a great, long hike. Maybe I've become just another typical Northwesterner, pining for the next summit. That said, the more steps we take together, the more this metaphor resonates. Everybody who has ever gone backpacking, hiking or on a walk knows that the journey is at least as important as the destination. Building a great company, or at least one that aspires to become great, is a step-by-step journey. In the case of Spare5, we have been fortunate enough to start our quest well provisioned by a massive idea (that businesses can benefit from peoples' passions, hobbies and insights during their spare time) and solid funding. (Thank you Madrona / Greg Gottesman, New Enterprise Associates / Jon Sakoda and Foundry / Jason Mendelson; and our awesome angels & advisors, including Aaron Easterly, Oren EtzioniJoe HeitzebergYusuf Mehdi, Bob Nelsen, Hadi PartoviCharles Songhurst, and Dan Weld!)

Even in these, our early days, we realize that we need to work hard to chart our own course. Our values are our compass. Good work, when we see progress, is a reward unto itself. Some parts of our trail are a grind. That's life. When we hit a beautiful viewpoint, we ring our gong, celebrate, and shoulder on.

One of the best parts of our early days has been finding our first customers, and making them super happy. It's one thing to present our shtick. Which I love doing. (That's a good thing, since I do it a lot.)

So here's our (shameless) pitch: Do you help run an online business? If so, you already know how vital it is to understand what specific people think about your products, site and experiences. It's important for your organic traffic, paid media performance, social marketing, customer acquisition and retention, conversion, cross-sell and retention. In short, understanding what people think of your business is the critical first step to winning.

There are many tools out there to help. But they leave a gap. Spare5 is gaining momentum as an innovative way to source human insights with the quality, scale and value that will change your business.

We are:

  • Cultivating a community of talented people with a broad range of skills. We call them our "Fives."
  • Assigning your tasks only to the Fives who have the right traits and proven reputations
  • Designing tasks that make it fun and easy for Fives to give you the answers you need
  • Operating proprietary algorithms to optimize quality
  • Providing you insights with confidence scores
  • Pushing tasks to get you your data fast, with scale

Here's the best part: when our customers ring the gong.

“Thanks very much for all of your analysis, and making it easy to figure out where to draw the line."

Spare5 is a pleasure to work with. I turn to them whenever I need industry-leading accuracy on a large, human-sourced project.”

Get in on the action! Below are some sample tasks - see the latest at www.spare5.com/product  

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Matthew Bencke

Matt Bencke is an entrepreneur, leader and change agent who drives new business and product strategies based on deep analysis, inspired leadership and focused execution. He has strong successes across technology, strategy, business development, design, e-commerce, marketing and manufacturing. His passion is attracting great talent, fostering a meaningful team culture, and taking performance to new levels. During his tenures at Microsoft, Getty Images and Boeing he has created, advised, led and grown businesses ranging from several millions to billions in size.

The Economist Gets It: The On-Demand Economy is a Big Deal (and Responsibility)

It's dangerous to blog on a dinner of Ritz crackers and peanut butter, but I couldn't resist a couple reactions to The Economist’s long article about the on-demand economy.

As The Economist points out, the smartphone, demographic and cultural underpinnings of the on-demand economy are profound and irreversible. It's still early days, but companies like Uber, AxiomMedicastand many more seek to bring power to the people, as consumers AND producers. OK, add Spare5 as an aspirant to that list. The industrial revolution reduced transaction costs by bringing together thousands of people under one corporate roof (vertical- and Dilbert-ized). Today's information revolution is enabling people to deliver value from wherever they are, with an evolving set of network-based infrastructure that provides good experiences, infrastructure and quality:

"This boom marks a striking new stage in a deeper transformation. Using the now ubiquitous platform of the smartphone to deliver labour and services in a variety of new ways will challenge many of the fundamental assumptions of 20th-century capitalism, from the nature of the firm to the structure of careers."

Collectively we have the opportunity to make our economy more productive and our lives more fulfilled. The hard reality, though is that creation sometimes requires disruption. Creation always requires care, learning and adjustment. On-demand providers, like any marketplace, are imperfect. Occasionally these inefficiencies result in mistakes and mishaps. So those of us laboring to create revolutionary new on-demand services bear a particular responsibility. We hope to make proud the entrepreneurs who laid the transcontinental railroads, and who built the first airplanes. 

There's no doubt in my mind that our children will enjoy the benefits of targeted products more or less whenever, and wherever they want them. So it's up to us to make sure that our companies match the supply of services with demand, along with the best possible infrastructure. "Us" includes entrepreneurs building new marketplaces and services; and, as The Economist points out, politicians and voters who make sure that everyone wins from this inevitable innovation while providing the right incentives for innovation. Personally, I'm all in.

Durkheim would be blown away, but not surprised.

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Matthew Bencke

Matt Bencke is an entrepreneur, leader and change agent who drives new business and product strategies based on deep analysis, inspired leadership and focused execution. He has strong successes across technology, strategy, business development, design, e-commerce, marketing and manufacturing. His passion is attracting great talent, fostering a meaningful team culture, and taking performance to new levels. During his tenures at Microsoft, Getty Images and Boeing he has created, advised, led and grown businesses ranging from several millions to billions in size.

Winter biathletes as role models

It may sound a bit nutty, but winter biathletes are my role models.  

Why?  These remarkable athletes cross-country ski with a 7.7-pound .22-caliber rifle. Their heart rates get above 200 beats per minute.  Then they stop and, using incredible control, quickly lower their heart rate enough to squeeze off several rounds at a tiny target 50 meters away.  Rinse and repeat, five attempts in each of four rounds.

Here's the kicker: every time you miss, you need to ski a penalty loop of 150m.  I was lucky enough to attend the Sochi 2014 Olympics.  This Loop of Shame must be the toughest mental space in all of athletics.  Every meter, every second counts.  If you don't slow down just enough to nail that target, this unforgiving sport dishes up even more punishment - under the eyes of thousands of spectators.

The Loop of Shame

The Loop of Shame

Doesn't that sound a lot like our lives?  Sometimes my failures are awfully visible.  Just recently I pushed a new product idea with excessive enthusiasm.  That cost time.  Later, when my team pushed back, I had to agree and take my "lap of shame" with gratitude.  And every day is frantic.  Just yesterday when I finally got back to my desk, my mind was eager to tick off the long list of work I'd left undone during the previous stream of meetings.  Within five minutes, three of my colleagues asked for my attention.  Usually I manage to put off my task list, because the truth is those emails can wait better than somebody standing next to me.  Whenever one of my daughters asks for help with homework, the answer needs to be yes, it's a good time.  Take a breath, slow my heartbeat, and focus.  Or risk another lap of shame.

Biathletes give every stride 100%, then give 100% on the present moment. Multi-tasking (aka "multi-slacking") produces an illusion of productivity sometimes.  The great leaders I've admired most blew me away by their ability to laser in on me, when the moment demanded it.  

Are you present?

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Matthew Bencke

Matt Bencke is an entrepreneur, leader and change agent who drives new business and product strategies based on deep analysis, inspired leadership and focused execution. He has strong successes across technology, strategy, business development, design, e-commerce, marketing and manufacturing. His passion is attracting great talent, fostering a meaningful team culture, and taking performance to new levels. During his tenures at Microsoft, Getty Images and Boeing he has created, advised, led and grown businesses ranging from several millions to billions in size.

Durkheim predicted the sharing economy >100 years ago

I hear this blogging thing could really catch on.  Like so many of us, my reading time is divided among my favorite magazines and newspapers, more apps than I can count, and yes, others' blogs.  It's been a long time since I've published anything.  So it seems a good time to give back a bit.

In my first blog, I thought I'd pay homage to my UC Berkeley Political Science Professors (especially Ken Jowitt) and the great Emile Durkheim.  Durkheim is broadly credited with being the father of sociology.  He's pretty much a complete intellectual stud.

So here's a new idea: Durkheim also deserves credit for predicting many elements of our evolving "sharing economy."  In his epic 1893 doctoral dissertation, "The Division of Labor in Society," Durkheim explained how "primitive" societies evolve to modern, capitalist societies based on systems of specialized skills, merits and rewards.  He predicted that we would form coherent societies based on an organic solidarity, or, in simple terms, the fact that we all need each other.  Sure, I could paint my own house.  But I'd be slow and do a crappy job.  I am glad to pay the experts.  They need me too.  The more we each contribute based on our particular skills, the better off society is as a whole.  And so on.

Recently there's been this huge "glocal" movement whereby many of us realize that we're all better off finding these specialists nearby.  That way we build community - and reduce our environmental impact.  Durkheim would have recognized this movement as furthering organic solidarity.

More recently, we're getting even more efficient via the "sharing economy."  CraigsList, eBay, Uber, AirBnB and Rover are innovations in terms of society as well as technology.  They all make us more efficient.  Aggregated across millions of people, these marketplaces make it easier for us to take better advantage of each other's skills.  More sharing means a more competitive economy, more earning potential, and more social cohesion.

It's a pretty exciting time to be alive.

Well, there it is.  A blog entry.

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Matthew Bencke

Matt Bencke is an entrepreneur, leader and change agent who drives new business and product strategies based on deep analysis, inspired leadership and focused execution. He has strong successes across technology, strategy, business development, design, e-commerce, marketing and manufacturing. His passion is attracting great talent, fostering a meaningful team culture, and taking performance to new levels. During his tenures at Microsoft, Getty Images and Boeing he has created, advised, led and grown businesses ranging from several millions to billions in size.