A New Constructivist AI: From Manual Methods to Self-Constructive Systems

The development of artificial intelligence (AI) systems has to date been largely one of manual labor. This constructionist approach to AI has resulted in systems with limited-domain application and severe performance brittleness. No AI architecture to date incorporates, in a single system, the many features that make natural intelligence general-purpose, including system-wide attention, analogy-making, system-wide learning, and various other complex transversal functions. Going beyond current AI systems will require significantly more complex system architecture than attempted to date. The heavy reliance on direct human specification and intervention in constructionist AI brings severe theoretical and practical limitations to any system built that way. One way to address the challenge of artificial general intelligence (AGI) is replacing a top-down architectural design approach with methods that allow the system to manage its own growth. This calls for a fundamental shift from hand-crafting to self-organizing architectures and self-generated code – what we call a constructivist AI approach, in reference to the self-constructive principles on which it must be based. Methodologies employed for constructivist AI will be very different from today’s software development methods; instead of relying on direct design of mental functions and their implementation in a cog- nitive architecture, they must address the principles – the “seeds” – from which a cognitive architecture can automatically grow. In this paper I describe the argument in detail and examine some of the implications of this impending paradigm shift

Biological and Chemical Information Technologies

Biological and chemical information technologies (bio/chem IT) have the potential to reshape the scientific and technological landscape. In this paper we briefly review the main challenges and opportunities in the field, before presenting several case studies based on ongoing FP7 research projects

XANES study of rhenium oxide compounds at the L1 and L3 absorption edges

8 pagesInternational audienceWe report on the study of a set of rhenium oxide reference compounds (NH4ReO4, NaReO4, ReO3, ReO2, and Re2O7) using x-ray-absorption near-edge structure. The parallel use of Re L1 and L3 absorption edges permits a concomitant understanding of both the oxidation state and the local symmetry for these compounds. Experiments are compared with ab initio simulations. A good agreement is reached in most cases. The choice of the cluster size and the calculation method (full potential or not), which are mandatory ingredients allowing a satisfactory reproduction of the recorded spectra, is discussed in detail. In the meantime, these parameters give important pieces of information on the studied materials

Production structure and economic fluctuations

We aim at contributing to the debate on the mechanisms and properties of economic fluctuations. We consider a crucial aspect among many thought to influence this ubiquitous and extremely relevant phenomenon: the interaction structure that characterises the organisation of production, that is, the production relation among sectors of a system. We build — and simulate — a very simple model representing an input–output system where sectors/firms adapt production and desired levels of stocks. Their output serves both an exogenous final demand and the intermediate demand solicited by the other sectors of the system. Series of simulation runs allow to derive relevant and non–obvious conclusions concerning the levels and, more importantly, the volatility of economic activity, as an outcome of the same, inherent, economic structure. We claim that the results that we obtain through the highly abstract representation we use, provide useful intuitions on the working of economic cycles, to be later integrated by further studies. As a by–product of our analysis, we also suggest that the methodology we adopt can provide valuable insights by allowing a detailed analysis of the time path generated in the artificial systems, and there- fore assessing with precisions the same mechanisms that affect real–world systems. The natural following step, left for further research, is to investigate how those mechanisms are empirically generated

Mental Structures

An ongoing philosophical discussion concerns how various types of mental states fall within broad representational genera—for example, whether perceptual states are “iconic” or “sentential,” “analog” or “digital,” and so on. Here, I examine the grounds for making much more specific claims about how mental states are structured from constituent parts. For example, the state I am in when I perceive the shape of a mountain ridge may have as constituent parts my representations of the shapes of each peak and saddle of the ridge. More specific structural claims of this sort are a guide to how mental states fall within broader representational kinds. Moreover, these claims have significant implications of their own about semantic, functional, and epistemic features of our mental lives. But what are the conditions on a mental state's having one type of constituent structure rather than another? Drawing on explanatory strategies in vision science, I argue that, other things being equal, the constituent structure of a mental state determines what I call its distributional properties—namely, how mental states of that type can, cannot, or must co‐occur with other mental states in a given system. Distributional properties depend critically on and are informative about the underlying structures of mental states, they abstract in important ways from aspects of how mental states are processed, and they can yield significant insights into the variegation of psychological capacities

Intelligent Agents as a Modeling Paradigm

Intelligent software agents have been used in many applications because they provide useful integrated features that are not available in “traditional” types of software (e.g., abilities to sense the environment, reason, and interact with other agents). Although the usefulness of agents is in having such capabilities, methods and tools for developing them have focused on practical physical representation rather than accurate conceptualizations of these functions. However, intelligent agents should closely mimic aspects of the environment in which they operate. In the physical sciences, a conceptual model of a problem can lead to better theories and explanations about the area. Therefore, we ask, can an intelligent agent conceptual framework, properly defined, be used to model complex interactions in various social science disciplines? The constructs used in the implementation of intelligent agents may not be appropriate at the conceptual level, as they refer to software concepts rather than to application domain concepts. We propose to use a combina- tion of the systems approach and Bunge’s ontology as adapted to information systems, to guide us in defining intelligent agent concepts. The systems approach will be used to define the components of the intelligent agents and ontology will be used to understand the configurations and interrelationships between the components. We will then provide a graphical representation of these concepts for modeling purposes. As a proof of concept for the proposed conceptual model, we applied it to a marketing problem and imple- mented it in an agent-based programming environment. Using the conceptual model, the user was able to quickly visualize the complex interactions of the agents. The use of the conceptual representation even sparked an investigation of previously neglected causal factors which led to a better understanding of the problem. Therefore, our intelligent agent framework can graphically model phenomena in the social sciences. This work also provides a theoretically driven concept of intelligent agent components and a definition of the inter- relationships between these concepts. Further research avenues are also discussed

Detection and Classification of {GNSS} Jammers Using Convolutional Neural Networks

Global Navigation Satellite Systems (GNSSs) have been established as one of the most significant infrastructures in today's world and play an important role in many critical applications. It is known that the power of the GNSS signals at the receivers' antenna is extremely weak and the transmitted signals are vulnerable to interference, which can cause degraded positioning and timing accuracy or even a complete lack of position availability. Thus, it is essential for GNSS applications to detect interference and further recognize the types of it for the mitigation in GNSS receivers to guarantee reliable solutions. In this paper, the focus is on the automatic detection and classi-fication of chirp signals, known as one of the most common and disruptive interfering signals. The classifier is a Convolutional Neural Networks (CNN) based on multi-layer neural networks that operate on the representation of the signals in transformed domains, Wigner- Ville and Short Time Fourier transforms. The representation of signals is fed to a CNN algorithm to classify the different shapes of chirp signals. The proposed method is performed in two case-study scenarios: the monitoring and classification by a terrestrial interference monitor and from a Low-Earth-Orbit (LEO) satellite. The experimental results demonstrate that the CNN model has a classification accuracy of 93 % and can be a suitable approach to classify different shapes of chirp signals