AI Based Economy

Reproduction of Maria of the film Metropolis at the Science Museum. Photo Credit: Jeremy Tarling

One of the main challenges of our time is the development of new applications with smart machines. We are expecting a revolution in many industries due to the use of artificial intelligence in business processes. In fact, some experts are advancing that many businesses would change hugely due to the incorporation of this new technology.

This process of change has begun yet. Many large companies have introduced the use of bots for many tasks, from customer support to decision making related to financial investments.

Technology changes are common in any civilization; however, in this case, there is something different. Most technology changes have been related to the support of brute force, reducing the physical effort of humans in production activities, and most recent changes have been related to the simplifications of bureaucratic processes through IT technology, however, AI is substituting humans in tasks that require decision making. AI is driven to managing activities at higher or lower degree.

The aim of moving workers from physical activities to managing ones is well seen by most people, however, from managing activities workers will be moved to… what?

The answer to this question is easy. In an economy dominated by AI, human workers are required for creative tasks and leisure related tasks. Design, innovation, research and development would be incremented. Marketing and fashion would drive the consumption. People will move from manage the production of goods and services to manage the change itself. Changes in technologies and in the promoted goods would be commoner due to both technology changes and fashion changes.

We can expect that the world will change in order to provide a different kind of society where people would be dedicated to humanities, politics and science. This one should be a better society were people would be dedicated to study, training, research, commerce, interpersonal relationships, sports and leisure.

The complexity of technology would provide a competitive advantage for large organizations that it would be tried to be preserved. In this scenario, as the control of the production would be under AI software, the control of economy could be under people controlling that AI software. Many markets could be under the control of AI developers, however, many others that depends more on people preferences and fashion would be under people controlling the mass media as usual.

More people with higher education would drive to more democratic societies, demanding better governance in their states.

This scenario, that seems to be a paradise, has got its own risks. First of all, it would be necessary to manage that process, in order to provide a smooth transition between current economy and future one, avoiding social conflicts that are common in process of change. It will be very important for the rulers to know the limits of these technologies and the probable effects on the society. On the other hand, there will be other risks related to the triumph of technology. For instance, humans can feel comfortable in such kind of society stopping their impulse to improvement. In that case, the society would stop its evolution. The feeling of power that mankind has got over the societies it has created could disappear, because the demand of a single person would not fight against an automated machine in the same way that it cannot fight against bureaucracy. In full automated society but bad designed from an economic and social viewpoint there could not be room for many human impulses and people cannot exist opposite to their own nature.

Many people are advancing that AI will suppose a revolution but it is important to notice that history has shown us that not all the revolutions will produce a better society. It depends on how revolutionary leaders act later.


Cybersecurity: A Little Introduction

Logo of WarGames Film produced by Universal Artists

Internet was built in order to provide full access from anywhere. When DARPA funded the development of internet, it was trying to assure that the destruction of a centralized computing center would never avoid a fast response to a nuclear attack from the enemy. Those engineers were searching for a network configuration that could provide fast reconfiguration. TCP/IP protocols were born in this way. The TELNET standard was created in order to have a direct link to remote computer as if the operator was in front of the mainframe. However, nowadays, the port 23 is usually blocked by most firewalls.

TELNET protocol is not commonly used due to its lack of encryption. Different protocols were developed later as SSH. More complex and secure protocols can be used instead. On the other hand, text terminals are a reminiscence of the past, now people use graphic interfaces instead. Sometimes it is interesting to review the history in order to understand which one is the true aim of many things before criticizing them.

Modern people consider computer security as a defense tool; however, remote access was conceived as a defense tool. From a military viewpoint, connectivity, instead of firewalling, is a defense tool in a nuclear world. A firewall is similar to a company of soldiers defending a nuclear silo. It is not defending directly our lives but protecting a deterrence weapon.

This discussion is very interesting because in the civil world we are very worried due to data hacking; however, we do not care about the effects of a lack of connectivity. Modern societies have usually got a lot of people in the streets demanding more security; however, security is provided by connectivity and the access to information. For instance, a piece of fake news is not a problem by itself. The problem of fake news is that corrupted or manipulated information is equivalent to unavailable information. Democracy resides on proper information for voters. Majorities can never be owners of the truth if they have not a proper access to the full real information. In a competitive world deception is used many times to increase the competitiveness, and information analysis instead of direct assumptions is required to assure that managers are making the best decisions. In a business, management, data analysis and decision making techniques are required in order to drive a company. The complexity of problems and the competitive environment produce that better decisions are made by experts instead of a majority of employees.

The higher the number of people accessing to computer system the higher the probability that information can be hacked and got illegally or corrupted. Limiting the access to information we are increasing the security of the system, but we are limiting its functionality. Encrypted access to computer systems introduces an additional advantage: Encrypted communications are less hackable and they can provide a way of authentication of the identity of the user better than a simple password. The use of encryption systems lets to increase the number of users without a huge increase of the probability of hacking. Connectivity can be preserved through encryption systems because it makes more difficult the hackers’ activity, but the cost of the computer system and its maintenance is higher and it requires specialized personnel with higher qualifications.

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.

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.