Cognitive studies and artificial intelligence have developed distinct models for various inferential mechanisms (categorization, induction, abduction, causal inference, contrast, merge, ...). Yet, both natural and artificial views on cognition lack apparently a unifying framework. This paper formulates a speculative answer attempting to respond to this gap. To postulate on higher-level activation processes from a material perspective, we consider inferential mechanisms informed by symbolic AI modelling techniques, through the simplistic lenses of electronic circuits based on logic gates. We observe that a logic gate view entails a different treatment of implication and negation compared to standard logic and logic programming. Then, by combinatorial exploration, we identify four main forms of dependencies that can be realized by these inferential circuits. Looking at how these forms are generally used in the context of logic programs, we identify eight common inferential patterns, exposing traditionally distinct inferential mechanisms in an unifying framework. Finally, following a probabilistic interpretation of logic programs, we unveil inner functional dependencies. The paper concludes elaborating in what sense, even if our arguments are mostly informed by symbolic means and digital systems infrastructures, our observations may pinpoint to more generally applicable structures.

Various studies have shown the advantages of using Machine Learning (ML) techniques for analog and digital IC design automation and optimization. Data scarcity is still an issue for electronic designs, while training highly accurate ML models. This work proposes generating and evaluating artificial data using generative adversarial networks (GANs) for circuit data to aid and improve the accuracy of ML models trained with a small training data set. The training data is obtained by various simulations in the Cadence Virtuoso, HSPICE, and Microcap design environment with TSMC 180nm and 22nm CMOS technology nodes. The artificial data is generated and tested for an appropriate set of analog and digital circuits. The experimental results show that the proposed artificial data generation significantly improves ML models and reduces the percentage error by more than 50\% of the original percentage error, which were previously trained with insufficient data. Furthermore, this research aims to contribute to the extensive application of AI/ML in the field of VLSI design and technology by relieving the training data availability-related challenges.

Automatic Programming is one of the most important areas of computer science research today. Hardware speed and capability have increased exponentially, but the software is years behind. The demand for software has also increased significantly, but it is still written in old fashion: by using humans. There are multiple problems when the work is done by humans: cost, time, quality. It is costly to pay humans, it is hard to keep them satisfied for a long time, it takes a lot of time to teach and train them and the quality of their output is in most cases low (in software, mostly due to bugs). The real advances in human civilization appeared during the industrial revolutions. Before the first revolution, most people worked in agriculture. Today, very few percent of people work in this field. A similar revolution must appear in the computer programming field. Otherwise, we will have so many people working in this field as we had in the past working in agriculture. How do people know how to write computer programs? Very simple: by learning. Can we do the same for software? Can we put the software to learn how to write software? It seems that is possible (to some degree) and the term is called Machine Learning. It was first coined in 1959 by the first person who made a computer perform a serious learning task, namely, Arthur Samuel. However, things are not so easy as in humans (well, truth to be said - for some humans it is impossible to learn how to write software). So far we do not have software that can learn perfectly to write software. We have some particular cases where some programs do better than humans, but the examples are sporadic at best. Learning from experience is difficult for computer programs. Instead of trying to simulate how humans teach humans how to write computer programs, we can simulate nature.

Multi Expression Programming (MEP) is a Genetic Programming variant that uses linear chromosomes for solution encoding. A unique feature of MEP is its ability of encoding multiple solutions of a problem in a single chromosome. In this paper we use Multi Expression Programming for evolving digital circuits for a well-known NP-Complete problem: the knapsack (subset sum) problem. Numerical experiments show that Multi Expression Programming performs well on the considered test problems.

Machine-learning (ML) solutions are proliferating across a wide variety of industries, but the overwhelming majority of the commercial implementations still rely on digital logic for their solution. With the exception of in-memory computing, analog solutions mostly have been restricted to universities and attempts at neuromorphic computing. However, that's starting to change. "Everyone's looking at the fact that deep neural networks are so energy-intensive when you implement them in digital, because you've got all these multiply-and-accumulates, and they're so deep, that they can suck up enormous amounts of power," said Elias Fallon, software engineering group director for the Custom IC & PCB Group at Cadence. Some suggest we're reaching a limit with digital. "Digital architectural approaches have hit the wall to solve the deep neural network MAC (multiply-accumulate) operations," said Sumit Vishwakarma, product manager at Siemens EDA. "As the size of the DNN increases, weight access operations result in huge energy consumption." The current analog approaches aren't attempting to define an entirely new ML paradigm. "The last 50 years have all been focused on digital processing, and for good reason," said Thomas Doyle, CEO and co-founder of Aspinity.

In the early 1960s, the languages Fortran (John Backus, IBM) for scientific, and Cobol (Jean Sammet, IBM, and DoD) for commercial applications dominated. Programs were written on paper, then punched on cards, and one waited a day for the results. Programming languages were recognized as essential aids and accelerators of the programming process. In 1960, an international committee published the language Algol 60.1 It was the first time a language was defined by concisely formulated constructs and by a precise, formal syntax. Two years later, it was recognized that a few corrections and improvements were needed. Mainly, however, the range of applications should be widened, because Algol 60 was intended for scientific calculations (numerical mathematics) only.

The scientific study of consciousness is currently undergoing a critical transition in the form of a rapidly evolving scientific debate regarding whether or not currently proposed theories can be assessed for their scientific validity. At the forefront of this debate is Integrated Information Theory (IIT), widely regarded as the preeminent theory of consciousness because of its quantification of consciousness in terms a scalar mathematical measure called $\Phi$ that is, in principle, measurable. Epistemological issues in the form of the "unfolding argument" have provided a refutation of IIT by demonstrating how it permits functionally identical systems to have differences in their predicted consciousness. The implication is that IIT and any other proposed theory based on a system's causal structure may already be falsified even in the absence of experimental refutation. However, so far the arguments surrounding the issue of falsification of theories of consciousness are too abstract to readily determine the scope of their validity. Here, we make these abstract arguments concrete by providing a simple example of functionally equivalent machines realizable with table-top electronics that take the form of isomorphic digital circuits with and without feedback. This allows us to explicitly demonstrate the different levels of abstraction at which a theory of consciousness can be assessed. Within this computational hierarchy, we show how IIT is simultaneously falsified at the finite-state automaton (FSA) level and unfalsifiable at the combinatorial state automaton (CSA) level. We use this example to illustrate a more general set of criteria for theories of consciousness: to avoid being unfalsifiable or already falsified scientific theories of consciousness must be invariant with respect to changes that leave the inference procedure fixed at a given level in a computational hierarchy.

Neuromorphic computing has received less fanfare of late than quantum computing whose mystery has captured public attention and which seems to have generated more efforts (academic, government, and commercial) but whose payoff also seems more distant. Intel's introduction this week of Pohoiki Beach – an 8-million-neuron, neuromorphic system using 64 Loihi research chips – brings some (needed) attention back to neuromorphic technology. The newest system will be available to Intel's roughly 60 neuromorphic ecosystem partners and represents a significant scaling up of its development platform with more to come; Intel reportedly plans to introduce a 768-chip, 100-million-neuron system (Pohoiki Springs) near the end of 2019. "Researchers can now efficiently scale up novel neural-inspired algorithms – such as sparse coding, simultaneous localization and mapping (SLAM), and path planning – that can learn and adapt based on data inputs. Pohoiki Beach represents a major milestone in Intel's neuromorphic research, laying the foundation for Intel Labs to scale the architecture to 100 million neurons later this year," according to the official announcement.

Before it started scampering after the machine learning chip market in 2016, but after it was founded at the University of Michigan in 2012, Mythic was trying to build embedded chips that would let surveillance drones run software modeled after the human brain. Part of the funding for the company, then known as Isocline, came from the Department of Defense. But after relaunching two years ago, Mythic refocused on embedded devices like autonomous cars and security cameras. Now the company is only a few months from sampling chips based on an aggressively ambitious architecture, which uses analog computing inside flash memory cells to accelerate machine learning tasks like facial recognition. Helping it over the finish line is $40 million raised last month from new and existing investors, including SoftBank Ventures, Draper Fisher Jurvetson and Lux Capital.

Before it started scampering after the machine learning chip market in 2016, but after it was founded at the University of Michigan in 2012, Mythic was trying to build embedded chips that would let surveillance drones run software modeled after the human brain. Part of the funding for the company, then known as Isocline, came from the Department of Defense. But after relaunching two years ago, Mythic refocused on embedded devices like autonomous cars and security cameras. Now the company is only a few months from sampling chips based on an aggressively ambitious architecture, which uses analog computing inside flash memory cells to accelerate machine learning tasks like facial recognition. Helping it over the finish line is $40 million raised last month from new and existing investors, including SoftBank Ventures, Draper Fisher Jurvetson and Lux Capital.