Understanding Information

Modern-day computer power is a great servant for today’s information hungry society. The increasing pervasiveness of such powerful machinery greatly influences fundamental information processes such as, for instance, the acquisition of information, its storage, manipulation, retrieval, dissemination, or its usage. Information society depends on these fundamental information processes in various ways. This chapter investigates the diverse and dynamic relationship between information society and the fundamental information processes just mentioned from a modern technology perspective.

Galaxies, stars, and planets have captivated and inspired human minds for centuries. A relatively young discovery in our universe is so-called extrasolar planets. First discovered in 1995, these planets travel in great distances, far outside of our solar system. A major challenge in extrasolar planet research is that these planets are extremely difficult to detect. Indeed, in many situations, this challenge demands great ingenuity when it comes to data analysis and information processing. The motivation in this chapter is to describe the general environment in which these challenges take place. In the course of this exploration, the reader is going to travel deep into our universe where silent messengers such as the COROT or Kepler space satellites communicate with us silently, reliably, and continuously in order to increase our understanding about the formation of planets, our solar system, and our universe at large.

This chapter outlines the ‘engineering’ and ‘scientific’ approaches to the study of information in classical physics, and the fairly minor changes that came with the arrival of quantum theory in the early twentieth century. The main advances came from the mid-1990s when quantum information theory developed enormously, with important work, theoretical and experimental, carried out in quantum computation, quantum cryptography and quantum teleportation. The concept of information as the fundamental building-block of the Universe also became important, and also the idea that the Universe was a quantum computer.

New approaches for data archiving are required due to a constant increase in digital information production and lack of a capacitive, low maintenance storage medium. High-density information encoding and longevity are the two important advantages which have recently made DNA an attractive target for information storage. However, creating new copies of the same encoded information by producing new, artificial DNA sequences is not financially viable. Moreover, a naked DNA molecule can be greatly affected by environmental influences, thus resulting in DNA mutations and changes in the stored information. Our approach demonstrates the great potential of plants and seeds in circumventing these drawbacks. It shows that artificially encoded data can be stored and multiplied within plants.

The central and peripheral nervous systems manage information processing associated with cognition and behavior and information transmission from/to sensory and effector organs, respectively. Memory, which is the process by which information is encoded, stored, and retrieved, has been extensively studied from the molecular to the whole-organ level. Memory function is managed by multiple parallel neural systems across multiple spatio-temporal scales. Recently, neuroscience data measured by various methods start to be collected to organize big data. New analyses using these data can provide a deeper understanding of information processing in the nervous system. Computational theory is expected to play a dominant role in integrating memory data with knowledge collected from other scientific domains.

The most famous academic contributions of Alan Turing are on Turing machines in 1936 and on the Turing test in 1950. While the motivations of these two works are apparently quite different, this chapter tracks Turing’s chronological development between these two contributions and points out how conceptual continuity can be found in Turing’s thought on machines.

This chapter describes in more detail our work on so-called ‘finite information spaces’. Loosely speaking, finite information spaces include any kind of information agent existing in any type of information environment. From this point of view, human beings, robots, or books represent finite information agents, while a library or the Internet may represent finite information environments. The chapter describes, analyzes, and interprets finite information spaces and related concepts. The chapter also provides an interesting measure called ‘autonomy index’. This index provides an opportunity to attribute a degree of autonomy to any finite information agent. We believe that our findings could be relevant in a wide range of areas.

The Internet has established itself as the universal data and information infrastructure. It is used not only for providing and retrieving data in a diverse range of application domains involving human and machine actors, but also as a distributed processing platform. We investigate Internet data and information with a number of technological questions in mind: In which format should data be represented? Where and how should it be stored? How can data be managed? How can the data relevant to a specific need be found in the vast space of the Internet? How can it be accessed by human and machine users, and how can it be processed into information? We cover the Web including the processing of textual, user-generated and Linked Data, as well as the Internet as a processing platform with data exchange between services and for handling Big Data.

The computational and data handling challenges in big data are immense yet a market is steadily growing traditionally supported by technologies such as Hadoop for management and processing of huge and unstructured datasets. With this ever increasing deluge of data we now need the algorithms, tools and computing infrastructure to handle the extremely computationally intense data analytics, looking for patterns and information pertinent to creating a market edge for a range of applications. Cloud computing has provided opportunities for scalable high-performance solutions without the initial outlay of developing and creating the core infrastructure. One vendor in particular, Amazon Web Services, has been leading this field. However, other solutions exist to take on the computational load of big data analytics. This chapter provides an overview of the extent of applications in which big data analytics is used. Then an overview is given of some of the high-performance computing options that are available, ranging from multiple Central Processing Unit (CPU) setups, Graphical Processing Units (GPUs), Field Programmable Gate Arrays (FPGAs) and cloud solutions. The chapter concludes by looking at some of the state of the art solutions for deep learning platforms in which custom hardware such as FPGAs and Application Specific Integrated Circuits (ASICs) are used within a cloud platform for key computational bottlenecks.

This chapter investigates the complex phenomenon of ‘information overload’ that, despite controversies about its existence, is a major problem, the symptoms of which have to be alleviated. Its sources and nature in academia, business environments and in everyday life information seeking, its particular features in the data-intensive world are described, not forgetting about the role of information technology. The possible ways of mitigating information overload are specified, underlining the imperative of being critical against information. Potential approaches and tools, described in this chapter include utilizing appropriate information architecture, applying information literacy, data literacy and other literacies, as well as making use of personal information management.

This entry looks at information-based causal theories of mental content. Causal theories appeal to causal conditions that exist between a representation (such as a thought or belief) and the part of the world represented (the content of the thought or belief). According to informational theories, mental states acquire their content by standing in appropriate informational and causal relations to objects (and properties) in the world. Very crudely, thoughts of dogs are about dogs (and mean dog) because information about dogs causes the thoughts that our minds use to keep track of dogs. This article explains some of the leading informational and causal theories of mental content—their twists, turns, refinements, and some of their leading criticisms (This article is a very focused history starting with Grice, Stampe, Dretske, and Fodor. There are many other important thinkers not discussed only for lack of space, not import. However, this is the history that started for me with my first graduate course at Wisconsin with Dennis Stampe just prior to his landmark paper. I later worked closely with Fred Dretske during the writing of his 1981 book and even later met and studied briefly with Jerry Fodor. I am grateful to Dretske, Fodor, and Stampe for their help over the years. I also thank Alfons Schuster for his patience, and an unidentified referee for quite useful advice.).