RonAmok!

When I was a young engineer in the mid 1980’s, I was fascinated with the concept of neural networks–computing machines that attempted to solve problems similarly to the way that people solved them. You see, the human brain differs from the machine that we’ve come to know as the “computer.” Computers are built upon a very powerful processor that can execute millions of instructions per second. The human brain, on the other hand, consists of billions of simple processors (neurons) that are highly interconnected with one another. Computers process things serially (one after another), while humans compute things in parallel (all at once). Each method has its respective strengths and weaknesses.

Computers are better than humans at repetitive tasks, which makes them the right choice when problems need to be brute-forced through quickly. And although people can perform repetitive tasks, their forte is assessing complex situations. The differences in each process are profound. For example, if we asked a computer and a person to calculate pi to the one-millionth digit, a computer would do so in the blink of an eye, while a person would require a month of Sundays. Yet, if we asked that same computer to sort zoo photographs by species, a child would finish the task before the computer could determine the difference between an animal and a zookeeper. Computers may be able to calculate the live path of an asteroid traveling through a gravitational field with ease, but when it comes to discerning a monkey from a mongoose, they are not smarter than a 5th grader.

For some reason, billions of simple, highly-interconnected neurons are much better at pattern recognition than powerful individual microprocessors.

For many years, scientists have been trying to emulate the brain’s method for solving problems through the creation of Artificial Neural Networks (ANNs). The problem with neural networks is that they need to be “taught” through a trial and error process, much like humans are taught a new language, mathematics, or music. And although we’ve found some limited applications for ANNs, we still haven’t found a cost-effective use for them to solve many real-world problems.

But, perhaps we’ve been looking at the problem the wrong way. What if, instead of building application-specific computers based on the type of problem to solve, we created a hybrid system that used the best attributes of human and machine computation?

Hybrid Human-Machine Computing

Professor Luis von Ahn’s mission is to “…build systems that combine humans and computers to solve large-scale problems that neither can solve alone.” If you don’t recognize von Ahn’s name, you probably recognize his work–he helped developed CAPTCHA, the web-based challenge system that allows websites to determine the difference between machines and humans. CAPTCHA, which stands for Completely Automated Public Turing test to tell Computers and Humans Apart, is built on the fundamental principle that human brains are better at pattern recognition than computers. Since people have better abilities to identify highly distorted letters than computers, CAPTCHA provides website owners and their customers a level of security with respect to web-based transactions. According to von Ahn, the CAPTCHA verification process happens about 200 million times per day [1]–a number that got him thinking differently about another problem that he was working on–the accurate digitization of books.

Companies such as Google and Amazon have large scale projects to digitize books. The process involves scanning physical books and converting the captured images to text through optical character recognition (OCR) software. And while OCR technology is effective at translating clear and perfectly aligned images, it makes many mistakes while translating less-than-perfect ones.

But that’s when professor von Ahn got an idea. If he could find a way to tap into the collective intelligence manifested within 200 million transactions per day, would it be possible to extract additional value from each transaction, such as helping computers with difficult OCR problems? He did so by retooling CAPTCHA to offer two words instead of one. One would be used to pass the security test while the other would help a stumped computer.

“Whenever you type the distorted characters,” Professor von Ahn explains reCAPTCHA, “not only are you authenticating yourself as a human, but in addition, you’re helping us to digitize books…With this method, we are digitizing approximately 100 million words a day, which is the equivalent of two-million books per year.”[2]

Let’s put 100 million words per day into a business perspective:

• 100 million transactions at 10 seconds per translation represents about 277,778 person-hours per day.
• 277,778 person-hours at 8-hours per workday forms an equivalent “project team” of about 34,722 people, which is slightly larger than Google’s workforce.
• If project team members were paid at the federal minimum wage of $7.25 per hour, its daily payroll would cost $251,736.

But there’s more to these numbers. Even if it were economically feasible to hire 34,000 full-time employees who did nothing but translate obscure images into text, that team could never approach the efficiency of 100 million people performing the task once per day. Normal human limitations, such as fatigue or boredom, would surely slow them down.

Companies Must Think Differently

Not too long ago, the ability to access 10 seconds of 100 million people’s time to perform a menial task would have been cost-prohibitive. Today, it’s free. CAPTCHA and reCAPTCHA are examples of how hybrid networks drive down transaction costs associated with distributed labor, and companies must think about how these technologies will affect their businesses.

Many companies talk about the power of networked computers. They use fancy terms such as cloud computing, the Internet of Things, and machine-to-machine (M2M) communications. And while only a few of them are thinking past obvious uses, only a handful are pushing the boundaries of possibility, such as professor von Ahn, who is now wondering how to “…get 100 million people to help us translate the whole web into every major language, for free?”[4]

Professor von Ahn’s newest project, Duolingo, offers the following value proposition to the 1.2 billion people who want to learn another language:

If you’ll help us translate web pages, we’ll help you learn a new language for free.

Duolingo is presently in beta testing, but is already showing positive results. “The translations that we get from people are as accurate as those from professional language translators,”[5] von Ahn said, offering good news for society, yet not-so-good news for professional translators.

Hybrid computer human networks offer lessons for any CEO. They may identify untapped resources that hold the keys to unlocking the same problems that the company has been stuck on for years.

It’s time to think past the obvious. It’s time to think hybrid.

There are two things that we know about the great game of business:

• Companies are pretty good at predicting costs
• and they’re not very good at predicting demand.

If they were, companies like Ford, Coca-Cola and Columbia Pictures never would have released the Edsel, New Coke, or Ishtar.

The problem is that until recently, the financial success of a project was determined posteriori (after the fact), by how much revenue was generated. But wouldn’t it be better if we could actually predict the project’s financial success a priori (before the fact?) Recent advances in “social” technologies have resulted in a new sort of crystal ball that can not only predict marketplace viability accurately, but can also establish the holy grail of marketing: value-based pricing.

A New Way

Imagine for a moment that you have an idea for a great new product. You know how much that product will cost to develop and manufacture, but don’t yet have a good feeling on how well the marketplace will respond to it. Sound familiar? So how do you determine whether or not to continue with the project? Product testing? Focus groups? Surveys? And even after performing all of these expensive pseudo-scientific actions, doesn’t the decision still come down to a gut-feel based on “intangibles?”

But, what if, instead of surveys or gut-feelings, we had a way to know exactly how many people would prepay for your product? And what if, those same customers would also determine an average selling price (ASP) for the product too?

Fantasy? Nope. Just another business innovation that is resulting from our experimentation with social technologies.

Musician Julia Nunes explains.

“Normally I’d record the album and incur a fun amount of debt, and then, I would try to make that money back by selling the album,” she says in her Kickstarter project video where she’s requesting $15,000 to fund her project. “But now we have this awesome platform called Kickstarter, and I can basically pre-sell the album, and offer up a bunch of stuff that I’d never sell on my regular website, with the added bonus of you guys knowing exactly where your money is going…directly into the studio.”

Had Julia decided to follow the traditional business decision cycle, she’d have to consider investing $15,000 of her own (or borrowed) money to record, manufacture, market, and distribute her album. If she assumed an ASP for each album at $9.99 on iTunes, she’d need to sell well over 1,500 of them (to also cover sales costs) just to break even. If that number passed her gut-check, she’d probably green-light the project.

However Kickstarter opened a new way for Julia to assess the financial viability of her project. Having already calculated the $15,000 that would make the project worth her while, she didn’t have to wait to determine the financial success of her project posteriori, instead the marketplace determined that for her a priori, as 1,685 people prepaid $77,888 for her to create her album.

Business Ramifications

Many people look at Kickstarter as a cute way for the little guy to make it. And they’re missing the point. Something much bigger is happening here as networked technologies are squeezing inefficiencies out of traditional business decision-making processes. The concept of network funding has applicability in any business, not just musicians, as indicated by the 5,258 people who gave $364,000 to Peter Dering to manufacture his Camera Clip System, or the 12,521 folks who gave $1.464M to Casey Hopkins to manufacture his Elevation Dock: The Best Dock for the iPhone.

The business ramifications of network-based funding run even deeper. Consider the fact that companies are never quite certain that they’ve optimized the price-to-demand ratio to generate the most revenue. Companies spend many hours trying to determine pricing, constantly “guesstimating” the consequences of setting prices high to establish a perceived value, or “diving the boat” to drive revenue through volume. Either way, the uncertainty frequently leaves companies with a nagging suspicion that they’ve left money on the table.

Yet, Julia Nunes established a value-based selling price for her album a priori to recording its first note. The ASP for her album was $46.22 [($77,888/1,685), with a median of $30 (determined by the breakdown of backers provided by Kickstarter]. Without a service like Kickstarter, had Julia gone to a record publisher and said, “I can sell exactly 1,685 albums at an ASP of $46.22,” she would have been laughed out of the building, because even the best A&R person in the world can’t predict sales with that level of accuracy. However, Julia’s prediction wasn’t based on the squishiness of a gut-feel; it was based on fact.

So, what does this mean for your business?

The more we use our social networks, the more we learn about their value. As we learn, new uses will emerge, such as helping businesses assess risk in a way that was impossible to do just a few years ago. Services like Kickstarter offer producers the ability to see exactly what consumers are willing to commit to their credit cards before getting too deep into the product development cycle.

So, I have a question. Is your company actively considering the power of networks in their business, or has it already dismissed them as minor tools to be placed into the hands of corporate communicators?

Note: If you’d like to read the full story behind Julia Nune’s successful use of social media technologies, please feel free to get your free copy of The Rule of Thumbs.

While driving home last fall, I noticed that a company called Microsemi had moved into the area. I was pretty excited because Microsemi is a world-wide semiconductor manufacturer whose product-line falls into the sweet-spot of my technical knowledge as an analog/mixed-signal circuit designer.

I went online to learn that the building that I saw represented their new corporate headquarters. I learned that they were growing, having acquired twelve additional semiconductor companies over the past three years–who each brought some pretty cool technologies with them. After a few more mouse-clicks, I also noticed that the company had a very small social media presence. Thinking that this might be Kismet (like how many analog/mixed signal companies with cool new technologies yet very little social media presence can I expect to move into the area?), I decided to write a report, on spec, complete with specific recommendations on how the company might use its online properties to better communicate with its customers–electrical engineers.

I wrote the report and snail-mailed a hardcopy to a Microsemi contact with whom I’d been introduced through a mutual colleague. Unfortunately, it wasn’t meant to be. No complaints. Such are the risks of spec research.

However, as I shared the report with a few friends, it occurred to me that independent of the subject-company’s name on the report, the document contained relevant information for any high-tech, B2B company who is considering the use of online publishing platforms. So, rather than having this report remain lost forever on my hard-drive, I decided to share it with my readers.

If you work for a B2B company in a high-tech industry, you should read this report: Microsemi Corporation: Online Properties Analysis and Recommendations. Perhaps it’ll help provide a new perspective by which to evaluate your own online properties.