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

A Survey of Brain Inspired Technologies for Engineering

Cognitive engineering is a multi-disciplinary field and hence it is difficult to find a review article consolidating the leading developments in the field. The in-credible pace at which technology is advancing pushes the boundaries of what is achievable in cognitive engineering. There are also differing approaches to cognitive engineering brought about from the multi-disciplinary nature of the field and the vastness of possible applications. Thus research communities require more frequent reviews to keep up to date with the latest trends. In this paper we shall dis-cuss some of the approaches to cognitive engineering holistically to clarify the reasoning behind the different approaches and to highlight their strengths and weaknesses. We shall then show how developments from seemingly disjointed views could be integrated to achieve the same goal of creating cognitive machines. By reviewing the major contributions in the different fields and showing the potential for a combined approach, this work intends to assist the research community in devising more unified methods and techniques for developing cognitive machines

Tunable n-path notch filters for blocker suppression: modeling and verification

N-path switched-RC circuits can realize filters with very high linearity and compression point while they are tunable by a clock frequency. In this paper, both differential and single-ended N-path notch filters are modeled and analyzed. Closed-form equations provide design equations for the main filtering characteristics and nonidealities such as: harmonic mixing, switch resistance, mismatch and phase imbalance, clock rise and fall times, noise, and insertion loss. Both an eight-path single-ended and differential notch filter are implemented in 65-nm CMOS technology. The notch center frequency, which is determined by the switching frequency, is tunable from 0.1 to 1.2 GHz. In a 50- environment, the N-path filters provide power matching in the passband with an insertion loss of 1.4–2.8 dB. The rejection at the notch frequency is 21–24 dB,P1 db> + 2 dBm, and IIP3 > + 17 dBm

A Differential 4-Path Highly Linear Widely Tunable On-Chip Band-Pass Filter

A passive switched capacitor RF band-pass filter with clock controlled center frequency is realized in 65nm CMOS. An off-chip transformer which acts as a balun, improves filter-Q and realizes impedance matching. The differential architecture reduces clock-leakage and suppresses selectivity around even harmonics of the clock. The filter has a constant -3dB bandwidth of 35MHz and can be tuned from 100MHz up to 1GHz. IIP3 is better than 19dBm, P1dB=2dBm and NF<;5.5dB at Pdiss=2mW to 16mW.\u

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

Knowledge-based System to Support Architectural Design. Intelligent objects, project net-constraints, collaborative work

The architectural design business is marked by a progressive increase in operators all cooperating towards the realization of building structures and complex infrastructures (Jenckes, 1997). This type of design implies the simulta-neous activity of specialists in different fields, often working a considerable dis-tance apart, on increasingly distributed design studies. Collaborative Architectural Design comprises a vast field of studies that em-braces also these sectors and problems. To mention but a few: communication among operators in the building and design sector; design process system logic architecture; conceptual structure of the building organism; building component representation; conflict identification and management; sharing of knowledge; and also, user interface; global evaluation of solutions adopted; IT definition of objects; inter-object communication (in the IT sense). The point of view of the research is that of the designers of the architectural arte-fact (Simon, 1996); its focus consists of the relations among the various design operators and among the latter and the information exchanged: the Building Objects. Its primary research goal is thus the conceptual structure of the building organ-ism for the purpose of managing conflicts and developing possible methods of resolving them

Curriculum architecture - a literature review

The analysis of almost 400 abstracts, articles, books from academic sources, policy documents and the educational press has been undertaken to attempt to illuminate the concept of Curriculum Architecture. The phrase itself is not current in the Scottish educational discourse. This review has attempted to look at the international research literature, available over the past ten years or so, on the sub-themes identified in the SEED specification