Finding challenges and the possibility of their realization with technologies
Technologies permeate almost all human activity, which puts pressure on some differentiation. On the one hand, it is possible to see quite a few activities that try to live without technology or do things in an “older" (historicizing) way. Undoubtedly, part of digital competencies is also to search for and find areas in which it is not adequate to use and work with technology. It is appropriate to use other tools or learn more on the direct human experience for various reasons. This distinction is about the effectiveness of solving multiple problems and in the social context, where it is still possible to meet many people who have a reserved or negative relationship with technology.
The second level of difference is the ability to think about what activities of human labor could be replaced by technology. Tony Buzan has repeatedly written in his books that the 21st century will be a century of the brain - it will be necessary to look for areas in which creative and critical human thinking can compete with artificial intelligence or think critically about which areas would be practical and functional to use it.
These are not primarily banal situations, such as replacing sending a letter by e-mail or using a word processor to write text instead of a typewriter. As a topic focused on problem-solving, it is clearly emphasized that what one should solve with technology is a much more complex area of problems.
An example can be data journalism - the classic journalistic work with sources was supplemented by data analysis and visualization. Thus, in the first step, the journalist defines the topic, obtains relevant data for it, analyzes it using an adequate tool, and visualizes it. He then writes a story about the results processed in this way. We emphasize that ordinary journalistic activity is different - data serve either as a simple starting point, are set in a simple context, or serve as a basis for an article when qualitative. In this respect, data journalism defines an entirely new approach to work, changing the competencies necessary to be a journalist.
The example above shows how even a relatively simple profession of journalists can undergo dynamic development if it can identify the challenges posed by modern technology. And it is precisely this aspect that is part of the competence to look for technology-related problems. It is about developing the ability to observe the world and think about the possibilities of technology to solve the various issues facing man, even if what we said above may not be the only answer to problems. However, their sound knowledge and ability to think about them are undoubtedly practical work in the information society.
Information Society carries what Robert Reich called the symbolic analysts, i.e. persons who are engaged in that world are looking for structure, which can then be analyzed and manipulated. They are information specialists in the broadest sense - from stockbrokers to teachers to doctors. They all have to work with information, and in their practice, there are countless problems for which it is possible (and appropriate) to use technological solutions.
There is another phenomenon that cannot be forgotten - the information society is fundamentally transforming the labor market so that entirely new positions are being created. Some old ones are either completely disappearing or are changing significantly. The ability to effectively use digital technologies in various areas of social life thus represents one of the works or economic (but in the future also social) competencies. The challenge of defining one's profession or profession is not only the “pioneers" of new disciplines in the information society but a large part of the general population.
We have already given today's companies various nicknames - information, knowledge, learning, platform, consumer, etc. Sometimes it is possible to come across the statement that we live in a post-factual period, i.e. in a world where it is not the actual information that matters but more impressions or rapidly disseminated announcements via social networks. The truth seems to have become something that no one cares too much about. Jan Sokol and Jaroslav Peregrin's same time point (p. 159) that the truth is necessary for any human interaction - language is based on truth formulas, computers handle logical expressions, and communication stands on a specific truth base. If we don't believe what the other person is saying, we can't communicate with them. In his definition of man, Friedrich Nietzsche says that he is an animal that can promise. If there is no discourse of truth in society, then there is no promise, and society becomes inhuman, animalistic.
We would like to step out of this somewhat negativistic position and point out that the nickname that we have not given today is data. Indeed, the present is sometimes referred to as the time. In the context of the above, it is crucial that if the truth is not a generally shared value, the data will also make no sense. At the same time, it is one of the vital trade commodities.
The phrase data period contains several exciting aspects. First of all, the production of data is growing enormously. Science today can create larger data files than it can process in a reasonable amount of time. Examples are data from CERN, which usually waits two years for processing, or astronomical data, which we cannot fully process (there are too many possible research topics). We will probably never be able to do so. With improving devices, data production is growing extremely fast resulting in an information explosion.
This fact then raises three possible models of solutions. The first to apply to CERN is open cooperation. Various workplaces in the world can participate in the grid network and participate in their analysis and processing. This distribution of computing power is an exciting phenomenon used, for example, by the well-known project Seti @ Home, which searched for extraterrestrial civilizations so that ordinary users' computers analyzed individual parts of the signal. Thus, a complex problem can be broken down into several parts, which can then be solved by individual users' computers or smaller servers.
The second option, which is widely used in astronomical data, is openness. Anyone who wants can go to the Simbad database and use the available data for their scientific work. Although valuable data is in itself, those that are obtained from public funds should be public. Thanks to this, a person who is not paid by the university or from too poor a field for his institution to actively participate in space research can also work on good data.
A specific form of this openness is civic science, in which citizens do not primarily process data but acquire it. For example, they can map the forest's biodiversity in which they spend their holidays or monitor and record dialects of larks. Researchers are taking advantage of the fact that people can create scientific data that they would otherwise obtain only at a very costly or long time while increasing a particular scientific engagement with the general public.
The third model can be described as protectionism, where an institution or individual retains data for their use. This approach is dominant in today's society, there is a lack of a more developed culture of sharing, a defined data market, or the ability to think appropriately.
We want to dwell on the data market, which is currently one of the topics - large companies own exciting data about their users, which they can further process and use. This prevents the possibility of competition, which would need such datasets to develop their tools and services. On the other hand, the fact that user data needs to be importantly protected must be considered. Therefore, these two principles will probably - together with the activity of a possible regulator - play a role in defining a slowly and gradually developing data market.
Data time has another attribute - data must be analyzed and searched for what is wanted and needed. The data analysis is therefore based on the idea framework of problem-solving. It can be expected that data analytical knowledge, the ability to work with statistical data processing tools, or strengthen the teaching of statistics will undoubtedly be part of the forthcoming curricular reforms if they want to respond to the existence of the information society in some way.
Artificial intelligence is a kind of buzzword that frames almost everything currently happening in the field of information technology. Every new service, every new tool must contain elements of artificial intelligence. Finding a satisfactory definition of what artificial intelligence is is not easy - it is most often said that artificial intelligence has a system that exhibits the features of intelligent behavior, with the human mind being considered a standard. In the following text, we will try to simplify the situation by considering artificial intelligence as a system that can learn and does not proceed only by copying some given deterministic sequence of steps.
Artificial intelligence makes it relatively easy to solve a variable class of problems. There is a difference between sorting some items, finding specific patterns and structures in them, or looking for a connection between variables, etc. Artificial intelligence is not primarily associated with the robotic body, as the film or literary image might suggest. Still, it is software that has a very versatile use - today. It is used, for example, in Google searches to identify suspicious payments by credit card, to control traffic lights in urban transport, or chat applications.
It is a technology that fundamentally redefines many services, tools, and the professions that people perform. On the one hand, it allows you to save time by using routine tasks, such as in a Facebook chat, to answer questions about the availability of goods or sales time of a particular store. It also allows you to process large amounts of data and search for some models or structures. Possibly identify elements that meet specific parameters.
At present, automatic image recognition (pp. 48-50) is a big issue, for example, whether a machine translation (if we look at the menu in a foreign language. We can see it translated into Czech, without knowing in what language it was written), but also for license plate recognition for cars and many other tasks.
At the same time, it should be emphasized that working with systems with artificial intelligence is quite common, so it can not be avoided in any case. Even relatively simple standardized environments that, with the tools behind artificial intelligence, allow people who cannot program or do not have advanced algorithmic thinking to work. Today, almost everyone can create a chatbot. It is not the programming or scripting that is difficult, but the modeling of the dialogue and identifying a meaningful application.
These are probably the two significant challenges that artificial intelligence brings to a wide range of users. Look for areas and ways to lead to its practical and helpful use, which is quite demanding and creative work, which today has great economic potential. The second area is thinking about the interaction interface, whether it is a language and style of dialogue or an animated avatar.
We believe that anyone who wants to understand similar systems better and have the ability to think better about what could be used for them should have experience with creating and interacting with digital content.
