Interactive semantics

Much research pursues machine intelligence through better representation of semantics. What is semantics? People in different areas view semantics from different facets although it accompanies interaction through civilization. Some researchers believe that humans have some innate structure in mind for processing semantics. Then, what the structure is like? Some argue that humans evolve a structure for processing semantics through constant learning. Then, how the process is like? Humans have invented various symbol systems to represent semantics. Can semantics be accurately represented? Turing machines are good at processing symbols according to algorithms designed by humans, but they are limited in ability to process semantics and to do active interaction. Super computers and high-speed networks do not help solve this issue as they do not have any semantic worldview and cannot reflect themselves. Can future cyber-society have some semantic images that enable machines and individuals (humans and agents) to reflect themselves and interact with each other with knowing social situation through time? This paper concerns these issues in the context of studying an interactive semantics for the future cyber-society. It firstly distinguishes social semantics from natural semantics, and then explores the interactive semantics in the category of social semantics. Interactive semantics consists of an interactive system and its semantic image, which co-evolve and influence each other. The semantic worldview and interactive semantic base are proposed as the semantic basis of interaction. The process of building and explaining semantic image can be based on an evolving structure incorporating adaptive multi-dimensional classification space and self-organized semantic link network. A semantic lens is proposed to enhance the potential of the structure and help individuals build and retrieve semantic images from different facets, abstraction levels and scales through time

Edukacinė perspektyva: dirbtinis intelektas, gilusis mokymasis ir kūrybiškumas

Can artificial intelligence (AI) teach and learn more creatively than humans? The article analyses deep learning theory, which follows a deterministic model of learning, since every intellectual procedure of an artificial agent is supported by concrete neural connections in an artificial neural network. Meanwhile, human creative reasoning follows a non-deterministic model. The article analyses Bayes’ theorem, in which a reasoning system makes judgments about the probability of future events based on events that have happened to it. Meillassoux’s open probability and M. A. Boden’s three types of creativity are discussed. A comparison is made between the a priori algorithm of the Turing machine and a playing child, who invents new a posteriori algorithms while playing. The Heideggerian perspective on the co-creativity of humans and thinking machines is analyzed. The authors conclude that humans have an open horizon for teaching and learning, and that makes them superior with respect to creativity in an educational perspective.Straipsnyje analizuojama giliojo mokymosi teorija, kuri laikosi deterministinio mokymosi modelio, nes kiekviena dirbtinio agento intelektinė procedūra yra palaikoma konkrečių dirbtinio neuronų tinklo neuroninių jungčių. Jų yra labai daug, todėl imamas apibendrintas vidutinis vaizdas. O žmogaus kūrybinis mąstymas vadovaujasi nedeterministiniu modeliu. Straipsnyje analizuojama Bayeso teorema, pagal kurią mąstanti sistema, remdamasi jai nutikusiais įvykiais, daro išvadas apie būsimų įvykių tikimybę. Analizuojama Meillassoux atviroji tikimybė ir M. A. Boden trys kūrybiškumo tipai. Lyginamas apriorinis Turingo mašinos algoritmas ir žaidžiantis vaikas, kuris žaisdamas išranda naujus aposteriorinius algoritmus. Analizuojama heidegeriška abipusio kūrybingumo tarp žmogaus ir techninių mąstančių mašinų perspektyva. Daroma išvada, kad dirbtinis intelektas mokosi pagal užprogramuotą algoritmą, o žmogus turi atvirą mokymo ir mokymosi horizontą

Past Visions of Artificial Futures: One Hundred and Fifty Years under the Spectre of Evolving Machines

The influence of Artificial Intelligence (AI) and Artificial Life (ALife) technologies upon society, and their potential to fundamentally shape the future evolution of humankind, are topics very much at the forefront of current scientific, governmental and public debate. While these might seem like very modern concerns, they have a long history that is often disregarded in contemporary discourse. Insofar as current debates do acknowledge the history of these ideas, they rarely look back further than the origin of the modern digital computer age in the 1940s-50s. In this paper we explore the earlier history of these concepts. We focus in particular on the idea of self-reproducing and evolving machines, and potential implications for our own species. We show that discussion of these topics arose in the 1860s, within a decade of the publication of Darwin's The Origin of Species, and attracted increasing interest from scientists, novelists and the general public in the early 1900s. After introducing the relevant work from this period, we categorise the various visions presented by these authors of the future implications of evolving machines for humanity. We suggest that current debates on the co-evolution of society and technology can be enriched by a proper appreciation of the long history of the ideas involved.Comment: To appear in Proceedings of the Artificial Life Conference 2018 (ALIFE 2018), MIT Pres

Society-in-the-Loop: Programming the Algorithmic Social Contract

Recent rapid advances in Artificial Intelligence (AI) and Machine Learning have raised many questions about the regulatory and governance mechanisms for autonomous machines. Many commentators, scholars, and policy-makers now call for ensuring that algorithms governing our lives are transparent, fair, and accountable. Here, I propose a conceptual framework for the regulation of AI and algorithmic systems. I argue that we need tools to program, debug and maintain an algorithmic social contract, a pact between various human stakeholders, mediated by machines. To achieve this, we can adapt the concept of human-in-the-loop (HITL) from the fields of modeling and simulation, and interactive machine learning. In particular, I propose an agenda I call society-in-the-loop (SITL), which combines the HITL control paradigm with mechanisms for negotiating the values of various stakeholders affected by AI systems, and monitoring compliance with the agreement. In short, `SITL = HITL + Social Contract.'Comment: (in press), Ethics of Information Technology, 201

Human Computation and Convergence

Humans are the most effective integrators and producers of information, directly and through the use of information-processing inventions. As these inventions become increasingly sophisticated, the substantive role of humans in processing information will tend toward capabilities that derive from our most complex cognitive processes, e.g., abstraction, creativity, and applied world knowledge. Through the advancement of human computation - methods that leverage the respective strengths of humans and machines in distributed information-processing systems - formerly discrete processes will combine synergistically into increasingly integrated and complex information processing systems. These new, collective systems will exhibit an unprecedented degree of predictive accuracy in modeling physical and techno-social processes, and may ultimately coalesce into a single unified predictive organism, with the capacity to address societies most wicked problems and achieve planetary homeostasis.Comment: Pre-publication draft of chapter. 24 pages, 3 figures; added references to page 1 and 3, and corrected typ