Interview Article in America Economia magazine in Latin America

Link to Article (In Spanish Only)

Solving the world economic crisis

It seems like a tall order, solving the world economic problems, and, given that so many have tried unsuccessfully, I thought that it wouldn’t hurt for me to take a crack at it. I’ve actually gotten a few phone calls from people who asked me: “Bob, you work at Babson, a business school, right? How do you think we should solve this problem?” First, my degree is in engineering and most of what I’ve learned through starting and running several businesses has been in the field of psychology, not business. But, maybe they’re the same.

My first observation is that the stock market and the economy both have the deceiving attribute that they are described with numbers which would lead one to believe erroneously that they have something to do with mathematics, statistics, and formulas. My own impression is that nothing could be further from the truth. Both are really driven by mass psychology much more than by number theory. So the roots to the solutions may lie not in interest rates but rather in the hearts and minds of people.

It seems fairly obvious that if more of the population was employed, then productivity would be higher, and with more productivity, each additional dollar earned would cycle through the economy several times (I believe that I read that each dollar cycles an average seven times) and the GDP, and tax revenues would also climb wiping out the national debt and making everybody happy. It all starts with jobs.

An increase in jobs and productivity would also drive the real estate market as people looked to improve their living conditions with their new found salaries.

But the problem is that employers are reluctant to speculate on growth and add resources without clear evidence that their customers are ready to buy and so goes the vicious cycle. If “you” don’t invest, then I’m not going to either. And if neither of us invest in hiring, and building, the jobs and the spending associated with those newly employed people just doesn’t materialize.

So what happens is that downturns in the economy can be precipitous as many shed jobs quickly in response to fear, but the growth back occurs gradually as confidence builds in their ability to sell what they’re going to make.

I see this in very much the same way traffic at a stop light works. All of the cars come to a full stop and there are two ways that they can move as a group when the light turns green. In the first way, the common way, the first car moves, then second sees the first move, and begins to move. The third sees the second, and so on. The cars propagate through the light, but slowly, and some, at the end of the line don’t make it.

It would be much more efficient, and many more would go through, if everyone applied gas to their car simultaneously, upon seeing the light turn green, with confidence that the car in front of them was also accelerating at the same pace.

So, we’re stuck at the light waiting for the consumer or business before us, our customer, to flinch first.

There are four approaches to solving this problem.

First, do nothing and eventually, maybe in 10 or 20 years, the economy will slowly grow back. Each year, the consumers buy a bit more, the manufacturers build a bit more and we all get moving. Keep in mind that 60 to 70 percent of our economy is driven by consumer spending.

Second, stimulate the economy by having the government buy a whole bunch of stuff, like roads, airports, and infrastructure. After all, we need that stuff, the stuff we have is getting old, and we’ll all enjoy the better roads, schools, trains and airports, so why not. Remember that every dollar that the government spends is a tax dollar, but it does cycle through the economy and generates more tax revenues through the growth of employment.

The TARP funds were an attempt at this and it would appear in comparison to the UK, where last I looked there were riots, the concept was successfully responsible for growth. But, the problem lies not with the concept, but rather in the fact that we are a bitterly divided country on political grounds and if one side says blue, then the other side feels compelled to say red. And with our government divided, it leaves us at a standstill. One would have hoped that there was a possibility that our elected leaders could form rational decisions, but having met a few of them, the concept of rational and elected official seems somewhat oxymoronic.

Third, we could start, or participate in, a war. That would give us all something to focus on that we could unite in, namely the mobilization of our resources for the protection of our country. Everybody gets to work, the government borrows the money from the citizens (through bonds) to pay for the labor and tax revenues go up, and productivity is maximized. Note that this isn’t all that different from the second choice only that the elected officials agree to borrow the money to boost productivity in this case because they’re doing something good overall for the country.

There are only two problems with this. First, you have to kill a whole bunch of people in a war, and that’s fairly unpleasant. And second, the productivity is directed at the creation of assets that are of no real use to the society. Unlike the second case, you don’t have a new airport, you just have more bombs.

In both the second and third cases, there is another problem, the accumulation of additional debt by the government. I’d like to discuss this in two ways.

First, governments, unlike businesses don’t seem to publish their complete balance sheet. They do disclose their current and long term debt but they don’t seem to disclose their assets in the same way. As in the case of Greece and other struggling companies, the sale of assets can be used to pay off debts. The US has enormous assets including the Post Office, roads, bridges, parks, mineral rights, waterways, as well as several operating “businesses.” Back during the Vietnam War Lyndon Johnson sold off Fannie Mae and Freddie Mac to private investors as a way to finance the war. It was a good idea to reduce the debt, a bad use of the money.

Not carrying a record of the assets on our national books leads us to misunderstand our debt. If, for example, we assumed 10 trillion dollars of additional debt for the purpose of building a national solar energy generation facility and distribution system that completely eliminated our need for imported energy, that investment would be paid for in 10 years. But, getting our government to make those long term investments is virtually impossible if we don’t actually understand or record the asset and the impact that it has on our collective spending. Keep in mind that our government could sell us the electricity and the income would retire the debt. We do leave this to business in the US, which isn’t a bad idea, but in some cases, it’s just too large a problem.

So, one way to deal with all this debt is to sell off some of our collective assets. When selling assets, one typically considers whether the interest that you’re paying on the debt is greater or less than the income that the asset would otherwise provide. Or, as an example, one could either sell mineral rights to a tract of land, or choose to lease or license the mineral rights for an annual fee. While interest rates are low, it’s likely that the fees that can be generated from these assets is actually the better deal. This implies that the entire concept of getting energized about the national debt is silly since at any time we want, we could sell assets to cover the debt, but with the interest rates so low, it makes more sense to hold on to the asset and collect the revenue that they offer. If interest rates climb, then the sale of assets becomes more compelling.

Finally, here’s the fourth method for raising productivity. To use the car analogy, we all need to step on the accelerator simultaneously. But, nobody wants to take the risk. This is exactly where the collective force and wisdom of government can help. If the government would effectively “guarantee” the investment in increased productivity, then it would all just likely work out OK. This means that the government, instead of paying unemployment insurance and letting someone sit and watch Jeopardy should guarantee the wages of new hires if, and only if, revenue and profits don’t grow in accordance with the additional investment.

Companies are shielded from the additional risk, unemployment goes down, productivity goes up and the tax base along with it. The risk to this plan is that positions are created in companies that have no impact on their actual productivity. For example, the CEO gets two additional “assistants” to shine his shoes and bring coffee as opposed to adding two more workers on the assembly line. Thus, companies need to be able to demonstrate the growth of their productivity, which is measured through revenue or an increase in their assets through the additional hiring. Of course, there are ways to cook the books and defeat the intent of the program, but that goes without saying with any government program and the critical question is whether the end justifies the means.

The Fifth Generation of Digital Imaging

Introduction I began my career in electrical engineering in 1969 when I graduated from NYU’s school of engineering and science. At that time, the best that the school could afford were a collection of vacuum tube laboratory systems, DC motors and a single large IBM mainframe computer. Students could practice programming through the creation of punch card decks with one programming instruction per card and therefore, a program was typically represented by a stack of cards often several feet high. It’s fun to recall that special features were added so that if you accidentally dropped your program on the floor, and shuffled the cards, you could reorder the deck though the use of a sorting machine that was the size of several washing machines.

Clearly, technology has come a long way. In one of his recent books called “The Singularity” Ray Kurzweil, the popular futurist and entrepreneur speculates about the ever increasing speed with which technology seems to move. His point is that technology feeds upon itself, the latest computer is used to design the next computer, and so on, making the rate of change exponential as opposed to a linear progression of advancement. We are taught how the progressive doubling of some quantity can quickly build into an enormous number through the simple example of attempting to repeatedly fold a piece of paper, doubling its thickness with each fold. After a mere 17 folds (if one could do it) the paper would be taller than the average house, and after 50 folds would reach to the sun!

For the duration of my career, I have been involved in the digital imaging industry and the effect of this exponential growth can be illustrated over the last forty years by observing how this one field of technology has evolved.

In the early 1970s, digital imaging computers were referred to as signal processors or digital signal processors, and eventually shortened to the DSP. The only applications that one could find back then were related to the military because of the prohibitive cost of these computers. One of the first projects that I worked on was an “over the horizon” radar system that would watch for enemy intercontinental ballistic missiles (ICBMs). The radar data would be processed by a room full, over 300 cubic feet, of computer hardware that would create images that could be viewed and evaluated by an operator. This multi-computer system would accomplish something in the order of around 6 million arithmetic multiplies per second.

In 1965, Gordon E. Moore, cofounder of Intel, predicted quite accurately that digital electronic circuits would double their density approximately every two years. So it was fairly soon into the 1970s that these same mathematical algorithms used for radar image processing were finding their way into the first commercial market application, medical imaging, specifically in the form of CAT scanners. The specific mathematical equations that were being used to process radar images were similar to those needed to reconstruct x-ray images as done within a CAT scanner but the price of this equipment and its size were dropping rapidly as predicted by Moore. Early CAT scanners could be purchased for 1 to 2 million dollars.

In the 1980s it became affordable to bring the same mathematical processes to yet a new market, graphic arts imaging, in the form of computers that would help an artist or designer lay out a magazine or newspaper page on a computer screen in place of the manual techniques which would require a razor blade and film. These systems had again dropped in price again, breaking into a new application.

It is interesting to note that the basic science of image processing was not really changing that much. For example, Johannes Kepler, a German astronomer and mathematician had defined the equations that would be used in a CAT scanner back in the early 1600s. What was changing was that with each progressive cycle of redesign of the basic hardware, its speed, size and cost were all rapidly improving and frequently surging through market opportunities that became practical when cost performance objectives could be achieved.

From graphic arts imaging, basic digital imaging techniques were next found in consumer imaging, most famously in the form of digital cameras that today can be found in virtually every cell phone of which there are billions already in use.

From a room full of computer equipment costing millions of dollars, today the digital camera portion of a cell phone can cost in the range of tens of dollars, and yet exceed the performance of their early forbearers by a factor of 30 times in speed. A common cell phone might perform mathematical multiplies at a rate of 1 GHz or 1 billion per second. This is an improvement of cost and performance of roughly 1000 times overall in 40 years.

From a generational perspective, we might consider the DSP as having gone through four generations from military, to medical, to graphic arts, and finally consumer applications. And now we stand that the edge of the fifth generation which I call surrogacy.

Surrogacy

The concept of the imaging surrogacy market is that cameras in combination with small fast computers are capable of watching and making autonomous decisions based upon what is being seen. Those decisions can be communicated to other systems, or can, for example, in a simple case, activate a machine. As in prior markets, the application of imaging to the surrogacy market is not a question of “if it will occur” but rather, more simply, “when it will occur,” and current technological advancements have brought us to that moment now.

It was estimated by the automotive industry that over the next 10 years, one would find tens if not hundreds of digital cameras in every automobile. But most would not be used in the way that one might expect. It’s true that you would find low cost digital cameras used in place or as an adjunct to mirrors to assist the driver in seeing otherwise hidden vantage points. Back-up cameras are common in many vehicles. But this is a provincial use of digital imaging and the real volume of applications are represented by imaging surrogacy systems.

It has been common to think of low cost imaging as simply a way to bring images to a human operator at a remote location. For example, a security camera in a store delivers its images to a console in the security office where a guard can scan and watch for thieves.

However, the concept of surrogacy implies that the camera along with its DSP not only watches and potentially records what is seen, but more importantly it can make fundamental decisions, autonomously, without the need for human intervention.

So, for example, the pressure switch that might be located in the passenger seat that would disable a passenger airbag from being deployed when a child is sitting there would be replaced by an inexpensive digital camera and DSP that would “see” the passenger, measure his/her size and position and make an airbag deployment decision based upon the processed result.

In automotive applications, it is anticipated that cameras will be used to observe and make decisions on: • Operator fatigue by watching the head and eyes of the operator, • Lane drifting by watching the dashed or solid lines, • Distance to an obstruction by watching (often in stereo) objects in front and behind the car • Dimming the headlamps by watching for an approaching vehicle • Deploying the airbag (as described) • Obtaining the speed limit by reading signs • Observing danger by reading signs • Steering by observing the road • Measuring the speed by observing the road • Watching for rain and dirt

As well, insurance companies are deploying automotive cameras that record the last few minutes or seconds before an accident which can be used to assign blame or improve designs.

Today Volvo is testing a car which contains cameras that automatically avoid collisions and accidents by seeing and processing the objects and more importantly pedestrians in the path of the car.

As a completely different example that is already in common use, fully autonomous systems can already be found in Las Vegas gambling casinos watching for the faces of known card counters (or cheats from the perspective of the casinos) so that they can be escorted from the property.

As the cost of these systems continues to drop, it is only more likely that they permeate our environment as sensors that are used to make basic decisions like: should the lights and heat (or A/C) be on based upon whether there is any person in the room, or should the brakes be applied based upon whether there is an obstruction in the path of a car.

One can also envision imaging systems that are hybrid in nature and include human operators for whom the responsibility of decisions is aided by the processor through either the reduction of information to only relevant images, or through the enhancement or highlighting of suspected regions of interest. A common example today is the highlighting of mammography images to aid a radiologist in finding micro-calcifications, an early sign of cancer. Other examples include: • Security cameras that detect motion automatically and pass video clips of interest to an operator. • Security cameras that can discriminate between a dog that has been intentionally left on the property from an intruder who should not be there.

One must consider the application of surrogacy cameras from the perspective of: “if I could put a tireless human at this position and ask them to watch for a simple (or in some cases, not so simple) event at virtually no cost and with no environmental or size limitations, would I choose to do so?” Such cameras can be mounted on the wings of airplanes looking for “out of the ordinary” changes, or on cruise ships looking for an intoxicated passenger who accidentally falls overboard. These cameras have the advantages of never becoming tired, or bored. They can see in a variety of spectra, for example IR or UV, and can watch and observe endlessly.

Soon, cameras along with their autonomous computers will be less than a few dollars each which will cause an explosion of these types of applications.

Finding Surrogacy Solutions

Finding surrogacy applications begins with forgetting commonly held beliefs about the use of cameras in industrial or consumer environments.

The key is to identify events, which if observed, have a direct cost or profit impact on one’s business. Often, a safety or theft issue can drive the need for continuous observation, processing and a resulting decision that in some cases, calls for the attention of a human and in other cases makes a decision relating to the operation of the system.

Keep in mind that these cameras can be tiny, especially if used in higher quantities or for applications where high value is obtained. In these cases, one can integrate the camera and processor into a single tiny package, often no larger than a pencil eraser head. Systems that are this small are often easier to hide or protect environmentally. Today, there are complete wireless cameras that can be swallowed in order to inspect the patient’s GI tract.

Camera systems can be completely encased in plastic thereby eliminating many environmental considerations. Camera systems can provide mono or stereo vision and, like humans, can therefore make accurate distance assessments based upon a stereo pair. Camera systems can observe changes over time periods that are too slow or fast for human observation.

For me, it’s fun to ponder some future applications. Here are some of my favorites. All of these are possible within the context of currently available hardware and software. • A lapel camera and earphone that reminds me of the name of an approaching customer at a trade show. Imagine this used in retail environments to remind the sales agent of the identity and history of a shopper that enters the store. • A door camera that unlocks my door when I or my family approach. • A light switch and thermostat camera that saves money when no one is in the room. • Cameras used in retail environments that watch for theft by observing that objects are moved past the register without properly being scanned. Or taken by a customer without being paid for. • Cameras on complicated machines like airplanes and boats that call attention to malfunctions so as to create a faster appreciation for the situational emergency. • Cameras at train stations that stop trains if tracks are obstructed by a fallen passenger. • Cameras that watch for fires or leaks or floods. • Cameras that protect hands and fingers from machinery. • Cameras that scan skin for cancerous lesions.

It’s endless.

Business Evolution

During the Jurassic period, some 150 to 200 million years ago, large dinosaurs ruled the land, and like all other life forms on this planet, they share a common cell structure that includes a genome which contains the entirety of each organism’s hereditary information. This tiny data storage bank therefore defines the organization of biomass into complex organisms and frequently into social structures involving multiple organisms. Sometimes it even defines symbiotic relationships between dissimilar creatures. In each case, through the process known as evolution, the size, shape and function of each organism and their social structure adapts, albeit slowly, to environmental pressures. Stephen Hawking, the renowned physicist, remarks in his essay on “Life in the Universe – 1996” that evolution accumulates data at a rate of about 1 bit per year, consistent with the current estimate of a dinosaur’s DNA as being several billion bits long and early life, consisting of simple cells, as being about 3.8 billion years old. It is the very stability of patterns in the environment, for example, the sun rising each day in the east that gives organisms their ability to adapt. Over millions of years, plants evolved the ability to point to the sun for more energy.

The process of extinction can be seen as occurring when an organism is unable to adapt to a rapidly changing environment which includes the pressures from competitive organisms. For example, it is believed that the rapid disappearance of large dinosaurs (and their currently believed evolution to birds) was driven by a cataclysm, likely a meteor strike, which in a short matter of time radically changed their environment. Many species simply did not have the time to evolve into new forms that could survive. Their evolutionary traits that gave them dominance up until that time were no longer an advantage. For dinosaurs, it’s likely that meteor strike and the “nuclear winter” that followed vastly reduced the available plant material and the subsequent collapse of the entire dinosaur community started with the herbivores, and then eventually, ended with the starvation of the T-Rex who fed upon them.

Humans also adapt to our environment for survival. But, unlike our neighboring creatures, as Hawking observes, through language we can pass hereditary information to our offspring far more efficiently. He estimates that we add some 100 billion bits of information each year just through the books we write. And while, most of this information doesn’t actually change our individual cell organization, it does change the organizational social structures that we create including governments, businesses and religions. Our social structures are defined through language in many ways, including: laws, management structures, religious tomes, and even marketing programs that influence and control the behavior of individual members of the group. And we observe that between groups there is both symbiosis and competition.

But, keep in mind that the “goal” is the same, namely that the stored data is used to promote the evolutionary success of the organism, whether it’s the individual organism that we’re referring to, or the organization of many individuals into a group. We have seen in recorded human history the emergence and demise of organizational structures, like fiefdoms and feudal estates that have become extinct and today are replaced by governments and corporations.

Wal-Mart, which is only 60 years old, is currently the largest public corporation in the world, measuring in at $258 Billion in revenue while some150 million years ago, the world’s largest dinosaur, the Bruhathkayosaurus, weighed in at around 220 tons. The mega-corporation is relatively recent in human history. Can it go the way of the Bruhathkayosaurus?

We live in interesting times. As observed by Ray Kurzweil in his book “The Singularity,” technology feeds upon itself and is therefore increasing its rate of change to levels that are unimaginable. There was a 25 year gap between the invention of the telephone in 1876 and Marconi’s discovery of wireless communication in 1899. A shoe manufacturer back in the early 1900s would have had more than fifty years to adapt its business model to the use of the telephone before being threatened with extinction. But look around you today. New technological inventions are appearing daily. Many, like social networking, are still not understood in their potential for changing business models. It is not a huge leap of science fiction to believe that eventually, for example, the Internet will be integrated effectively into every business model, whether it’s a Wal-Mart or a local garage. Yet, we have trouble however anticipating whether we’re thinking about adaptation times of tens of years or just a few. But there is no doubt that whatever it is, it’s getting shorter.

Observing evolutionary changes and extinctions has never been possible for humans. Our lifetimes are simply too short. But that is changing. The rapid changes in our environment that are driven by technology are occurring within time scales that are going to modify organizational models and force the extinction of structures that are unable to adapt. It’s likely that many large institutions are simply, like the dinosaur, unable to change quickly enough to survive.

It’s quite possible that the industrial revolution of 300 or so years, along with contemporary concepts of governance which are roughly the same age and even religious institutions that are measured in thousands of years are all teetering on the edge of their own existence simply because in an age of the democratization of information and universality of communication, their structures are no longer optimum or even relevant. We may be nearing the end of the era of the mega-corporation and its extinction may occur right before our eyes.