Technology Transfer for Improving Competition

High Industrial Engineering College of Madrid. Photo Credit: Public Domain

Technology Transfer is the delivery of the knowledge required to manufacture a product, to apply a procedure or to provide a service. It is a common way to expand a business all over the world using the commercial network of other companies where your company has not got physical presence without a huge investment in your own commercial network. For the recipient company, it is a way to enter a market without the risk of investing in R & D.

This kind of agreement can be done due to the following fact. Industrial property rights are a monopoly of the inventor. They cannot be copied without his authorization and, usually, an economic compensation.

The concept of technology transfer does not include only industrial property rights. It is wider. The knowledge involved in production of goods and services is not always protected under patents. The complexity of many activities is many times a protection way enough, because the time required to make reverse engineering of a product can be long, and the first manufacturer can acquire competitive advantages and establish entry barriers in the market.  This kind of knowledge that is not protected by a legal patent can be transferred under a legal agreement.

In a startup company, the knowledge of a product can be in the mind of a person or a few people, usually the entrepreneurs. Large companies with large R & D departments usually define strict procedures of documentation for any R & D activity. Large companies can provide the information required for a technology transfer easily, however, small companies that are not based in a product under a legal patent are not prepared to make the technology transfer. That is a reason why many startups are bought when they reach success and they need to grow. In other words, to buy the knowledge of a small company with a single product is, in fact, to buy the whole company, entrepreneurs included. The new company will translate the knowledge into the production procedures that will be properly documented and standardized, and the knowledge will not be more in a few people.

The value of a startup is related to the complexity of the knowledge involved in the production process that makes a process of reverse engineering unfeasible from an economic viewpoint. The payment of a high price for a small company avoids that it can get the financial resources required to grow alone and puts the market again under the control of the large one.

The technology transfer through agreements between different companies without mergers or acquisitions are more common between companies of similar size, because the merger of two companies with different procedures and culture it is a complex process itself, and complexity implies high cost as we can see before. Sharing knowledge about a certain process avoids the costs linked to a merger and it provides an exchange of information about the companies that could be useful for a future merger.

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Artificial Intelligence in Organizational Systems

Robot in an Assembly Line. Photo Credit: Public Domain

There are many definitions of artificial intelligence in the same way as there are many definitions of intelligence. Intelligence is usually defined as capability to understand or capability to solve problems. Artificial intelligence would be the same capability built artificially for a computer.

The second definition is incomplete because any computer is mostly used to solve problems. Many times they do it much better than humans, but we do not think that they are intelligent if they are doing a pre programmed mechanical task. Intelligence is more linked to understanding than to solutions. In common life, we usually think that an engineer is more intelligent than an operator because the engineer has more knowledge and he understands the problem although the operator executes the task and provide the solution without understanding it.

There is no current computer system with the ability to understand a problem in the same way a human can do. However modern computer systems have the ability of adaptation to different circumstances related to the task to be executed. The process made by the computer to define a task to be executed is what defines the computer system as intelligent.

Intelligence is related to adaptability opposite to mechanism related to repeatability. A classic computer algorithm provides the same result for the same inputs. This is far from intelligence, and in the same way, any human system designed to provide repeatability is far from intelligence too.

Repeatability is a desired output in most industrial systems. In fact, industry is a result of the search for repeatability to provide a set of products of similar quality. The use of people in an assembly line is not an advantage instead of automatic machines to get repeatability. That is the reason why modern industries are more automatized.

From an industrial viewpoint, there is not a need to make a distinction between human and artificial systems, but between intelligent and mechanistic ones. Any industrial facility is a system composed by human beings and machines doing different tasks. Some of them are totally predefined and other ones require a new analysis and the adaptation of the processes. What makes a system intelligent is its capability to adapt the tasks to the problem instead of the degree of intelligence of its agents.

Artificial intelligence is not something fashionable. It is the logical evolution of any industry to the changing modern social environment. The environment of any organization changes much faster than a few decades ago. Organizations need to be more intelligent and less mechanistic. The age of quality standards has gone. Modern organizations live in the age of adaptability. Computer systems are not required to implement standardized procedures in an automatic way substituting human operators only. They are required to overcome the capability of decision makers to adapt the organizations the needs of the environment in a much faster way and in a much less complex one.

The limit of human intelligence to drive organizations is well known. The time of big data analysis and artificial intelligence support for decisions makers has come to make organizations more adaptable in modern changing environments. Artificial Intelligence exists to support intelligent organizations farther than human intelligence has got.

In any organization, AI will coexist with classic machines, with human operators working by procedures and with human decision makers. The design of the full system providing the required repeatability and adaptability will provide different organizations, with different degree of organizational intelligence. Although this cannot be measured, a satisfactory design can be evaluated through the measurement of complexity. A good resilience is a sign of proper adaptability and required repeatability for certain tasks.

Organizational intelligence is not an aim, it is a mean. The degree of organizational intelligence will depend on the industry: Nobody contracts an architect to hammer a nail. It should be very expensive and inefficient. And nobody contract a bricklayer to design a large tower. It should be very cheap but ineffective.

Computer Vision and Big Data

A simple algorithm to extract edges from the image Lena commonly used by computer vision scientists. Photo Credit: Public Domain

Large corporations are very interested in the new paradigm of Big Data. But what does it mean exactly? Basically, it is the acquisition and processing of large amount of data about markets in order to make reasoned decisions in a company. Big data can be used in order to improve any area of the company, from great strategic decisions to personalized offers for individual clients.

With this information we can see that it is a great opportunity for companies to improve their benefits. But, how must the use of big data be implemented?

The problem of big data is not the data. Of course, gathering data in not an easy task. A company must implement some source of acquisition in order to get the data. People were not prone to share their own information until social networks arrived. But, nowadays, we can get information about markets or clients from different sources.

The real problem to solve is how to process that great amount of data in order to get useful information for decision-making. The extraction of useful information from a large amount of raw data is a problem common with the technological area of robotics and computer vision. Mobile robots moving in a not structured environment must gather and process a large amount of data from vision sensors and so on, in order to determine a path or strategy in order to reach a destination point. A single image from a current common color camera is a signal 1024x768x3 bytes of information. This implies the processing of 2,359,296 bytes of information. A stereo vision system would use two cameras. If we work with video sequences at 25 images per second, we will need to process 117,964,800 bytes of information per second.

When we are working with big data, we need to diminish the size of data and to extract a more manageable subset of data especially useful for our interests. In computer vision, many times we work with edges. The mathematical meaning of edge is the derivative of the spatial sequence of data. In simpler words, we are interested in abrupt changes on the data that define different zones. This scheme can be used for any kind of big data problem. For instance, if we were analyzing geopolitical data in order to make decisions about the movement of our troops, we would be more interested in the changes of concentration of enemy troops around the borders instead of the internal situation of troops in order to prevent an invasion.

A single worker would last a lot of time in order to analyze the 2 Mbytes of information to extract the edge of an image; however, a computer can do it instantly. To program an algorithm to do it is not very difficult; however, it is not a task for someone who hated derivatives at the high school. Big data analysis is a matter of mathematical knowledge, not only a matter of writing code.

The knowledge required to analyze data fits well the knowledge of computer vision scientists among many other ones.

Fundamental Science and Economic Innovation

Elliptical Galaxy NGC 1316. Photo Credit: NASA and ESA. Public Domain
Elliptical Galaxy NGC 1316. Photo Credit: NASA and ESA. Public Domain

Is to look at the sky the best way to solve the problems of the current world? My answer would be: It depends on how we are looking at the sky. It is very different to look at the sky praying to the gods asking for more rain than to look at the sky gathering measurable data in order to understand better how nature is working and to take advantage of it to improve human lives.

Science provides us with more power to improve our lives. Waiting for the rain is the way as pre-scientific societies were ruled. Looking at the stars with a telescope was the way as societies were advancing through centuries towards the current welfare of their citizens.

Bertrand Russell, the philosopher and mathematician, noticed that the essential novelty of scientific technique is based on the use of natural forces through ways not evident for people without the proper education, but found from a deliberate search. Deliberate search is the basement of modern technologies that let us to increase the productive capability of the economy to the current levels of economic development.

We cannot expect that most people understand the benefits of fundamental science, because the benefits of science are not evident for people without the proper education (as Russell said), and most people are not physicists or engineers. In a democratic society is a responsibility of the rulers to raise awareness about this fact, and the best way to do it should be acting as an example of respect for scientists and their knowledge, instead of antagonizing the work of scientific elites with common people.

Economic innovations cannot be seen as competitors of fundamental science, because this is very far from reality. The most important advances in human societies are the result of great advances in science and in our understanding of the universe. The discovery of America was the result of accepting a not flat model of world and current global economy is a result of a new model of the universe.

Modern satellite communications depends on the acceptance of Einstein’s relativity theory and this theory proceed from paradoxes detected looking at the stars. The abandon of the previous Newtonian universe model has produced a great change in the capability of the world to commerce and economic production, although many people cannot understand easily this fact. In Newtonian gravity a static universe should be infinite, however a static infinite universe would be an unstable solution and over-dense regions of the space should collapse, however, looking at the sky scientists did not find evidence of it. On the other hand, Hubble found that spectral lines of distant galaxies are redshifted. This fact joined to Einstein’s assumption of a constant light speed for any observer in a homogenous and isotropic universe is an indicator that the universe is expanding. Einstein’s model fits the observations and it solves the Newtonian paradox.

This is not evident for the not educated minds although many not educated minds can be using social networks, internet, and satellite communications expressing their opinions about anything for people in the other side of the world.

There is a strong link between fundamental science and economic innovations that cannot be hidden only in order that people without the proper education can manage a large part of the innovation budgets.

When political decisions taken from political motivations far from scientific reasons define the budgets dedicated to promote practical innovation and fundamental science, the future welfare of citizens can be put at risk instead of assured.

Is innovation something old-fashioned?

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Overflying Madrid City with Flightgear Flight Simulator. Photo Credit: Public Domain

Innovation is one these words that in a certain period of time all people have in mind. In the previous decade there was not a company without the word innovation in its values. Every company wanted to provide new products as a way to increase the sales. A company without an innovation department was a company considered a failure. This decade, however, has been defined by a different word: global crisis. Crises imply a need to reduce risks and innovation is an activity inherently risky. This fact has produced a change of paradigm. Many authors have alerted about the complexity increase produced by innovation, and the typical rationalization of resources required in a crisis has produced a reduction of the innovation budgets in many companies.

Thinking in the complexity changes driven by innovation is a good practice; however, thinking in some kind of evilness around it is a great error. Innovation can be a strategic tool as good as in the 90’s to change a company, and crises usually demand changes in any organization.

What is required now is not a new vision about innovation but modern methods to manage it. In this decade we have seen that innovation does not produce a better positioning of the company automatically. We can only assure that it will provide a change, it is positive or negative it must be considered from many viewpoints.

We usually think that the change produced by any innovation will be positive because we are thinking in technological terms. If innovation produces a technological advance, it will be a competitive advantage. Although this is true, many other things must be taken into account.

If we have an electronic device fed by a battery with a certain voltage and current supply and we change the battery with another one with same voltage and more current supply capability, we will have an advantage but the device will be working like in the former case. It will continue providing the same current at a higher cost, because the current depends on the voltage of the battery and the electric load at the device.

In the same way, innovation only will be used if the market is prepared to take advantage of it. Innovation management is a managing function that tries to adjust the offer to the demand for both the offer and the demand side. Innovation management is searching for new technology offers and trying to create new demand for any new technology. Our decade should have been the decade of innovation management because crises must not be faced through the elimination of activities but through better management. In some cases, better management will imply the elimination of activities and in other ones will not.

The new paradigm of innovation will require new ways to analyze the effect of innovation on the robustness of the business, and more control about how innovations fit markets. The recent crisis has shown that the role of the innovation managers must be nearer the strategic directorate and the CEO than before.

Innovation management never has been a matter of technological development only but a matter of thinking about what we must develop, how we must develop it, where and for whom.

Additionally we must think now that there is another aspect that should be considered: how innovation is affecting the structure of our business changing the relationships among suppliers and clients, because a change in the value chain of the business is changing the global risk of the company.

If we look at the current innovation trends we can see that most of them proceed from technologies that arose in the previous decades but could not be used in the markets due to a lack of demand and development capability. These are two examples:

  • Internet of the things: Internet protocols TCP/IP were developed at the 70’s, however, it was necessary to have larger communications networks and smart devices in order to think about a real development of it.
  • Virtual Reality: Computer Vision was born in the 60’s, however, it has been necessary to improve the microprocessors to get real time.
  • Deep learning, big data and Artificial Intelligence: AI exists in computer science from the 50’s. Neural networks are from that decade, however, for its development large computers and a large source of accessible data has been required.

The offer side limits have been overcome, but, now it is the turn of working on the demand side. Is the world really prepared for IoT and VR? Probably, it is, but this development will need a great effort to become true. The case of Big Data and AI probably has a larger demand yet.

Those innovations can change not only a few businesses but even the entire world, in the same way as internet did it. Those kinds of changes will impulse changes in all businesses that will have to be properly analyzed and managed.