Cognitive Science and AI typically subscribe to computationalism--the mind is a form of computation in the brain (or the overall nervous system including the brain). In the 1940s, explaining cognition as the brain computing was new, and started catching on in what would become computer science and AI…and eventually to some degree neuroscience. But many were modeling the brain using what you could call analog math.1Piccinini, And there were actual analog computers, many of which were used by the U.S. military starting in World War 2. Nowadays, most people use digital computers for research and AI work…and pretty much everything. But what happened to the non-digital theories, and why aren't there analog computers any more to experiment on those?

Analog Computers are a class of devices in which physical quantities like electrical voltage, mechanical motions, or fluid pressure are represented so that they are analogous to the corresponding amount in the problem to be solved. Here is a simple example of an analog computer. If we turn the black and white wheels by certain amounts, the gray wheel shows the sum of the two rotations. One of the earliest analog computers was The Antikythera Mechanism, constructed around 100-200 B.C. It involved a series of interlocking bronze gears in such a way that the motion of certain dials was analogous to the motion of the sun and the moon.

The future of A.I. is … analog? At least, that's the assertion of Mythic, an A.I. chip company that, in its own words, is taking "a leap forward in performance in power" by going back in time. Before ENIAC, the world's first room-sized programmable, electronic, general-purpose digital computer, buzzed to life in 1945, arguably all computers were analog -- and had been for as long as computers have been around. Analog computers are a bit like stereo amps, using variable range as a way of representing desired values. In an analog computer, numbers are represented by way of currents or voltages, instead of the zeroes and ones that are used in a digital computer.

The future of A.I. is ... analog? Before ENIAC, the world's first room-sized programmable, electronic, general-purpose digital computer, buzzed to life in 1945, arguably all computers were analog -- and had been for as long as computers have been around. Analog computers are a bit like stereo amps, using variable range as a way of representing desired values. In an analog computer, numbers are represented by way of currents or voltages, instead of the zeroes and ones that are used in a digital computer. While ENIAC represented the beginning of the end for analog computers, in fact, analog machines stuck around in some form until the 1950s or 1960s when digital transistors won out.

This book chapter describes a novel approach to training machine learning systems by means of a hybrid computer setup i.e. a digital computer tightly coupled with an analog computer. As an example a reinforcement learning system is trained to balance an inverted pendulum which is simulated on an analog computer, thus demonstrating a solution to the major challenge of adequately simulating the environment for reinforcement learning.

Long disparaged by the Baby Boomer generation as either a childish distraction or a leading cause for the downfall of civilization, video games have weathered that criticism and grown into the dominant storytelling medium of the modern world — not to mention a $136 billion industry. In his latest book, Becoming a Video Game Designer, journalist Daniel Noah Halpern examines the career of gaming titan Tom Cadwell from his roots at MIT, where he became one of the world’s top Starcraft II players, to his meteoric rise as head of design at Riot Games. Through exhaustive interviews with Cadwell and other leading industry figures, Halpern provides a unique and valuable snapshot for aspiring designers into the business of gaming.

Dag Spicer is expecting a special package soon, but it's not a Black Friday impulse buy. The fist-sized motor, greened by corrosion, is from a historic room-sized computer intended to ape the human brain. It may also point toward artificial intelligence's future. Spicer is senior curator at the Computer History Museum in Mountain View, California. The motor in the mail is from the Mark 1 Perceptron, built by Cornell researcher Frank Rosenblatt in 1958.

Despite the advances made in artificial intelligence, software agents, and robotics, there is little we see today that we can truly call a fully autonomous system. We conjecture that the main inhibitor for advancing autonomy is lack of trust. Trusted autonomy is the scientific and engineering field to establish the foundations and ground work for developing trusted autonomous systems (robotics and software agents) that can be used in our daily life, and can be integrated with humans seamlessly, naturally and efficiently. In this paper, we review this literature to reveal opportunities for researchers and practitioners to work on topics that can create a leap forward in advancing the field of trusted autonomy. We focus the paper on the `trust' component as the uniting technology between humans and machines. Our inquiry into this topic revolves around three sub-topics: (1) reviewing and positioning the trust modelling literature for the purpose of trusted autonomy; (2) reviewing a critical subset of sensor technologies that allow a machine to sense human states; and (3) distilling some critical questions for advancing the field of trusted autonomy. The inquiry is augmented with conceptual models that we propose along the way by recompiling and reshaping the literature into forms that enables trusted autonomous systems to become a reality. The paper offers a vision for a Trusted Cyborg Swarm, an extension of our previous Cognitive Cyber Symbiosis concept, whereby humans and machines meld together in a harmonious, seamless, and coordinated manner.

A class of analog computers built from large numbers of microscopic probabilistic machines is discussed. It is postulated that such computers are implemented in biological systems as ensembles of protein molecules. The formalism is based on an abstract computational model referred to as Protein Molecule Machine (PMM). A PMM is a continuous-time first-order Markov system with real input and output vectors, a finite set of discrete states, and the input-dependent conditional probability densities of state transitions. The output of a PMM is a function of its input and state. The components of input vector, called generalized potentials, can be interpreted as membrane potential, and concentrations of neurotransmitters. The components of output vector, called generalized currents, can represent ion currents, and the flows of second messengers. An Ensemble of PMMs (EPMM) is a set of independent identical PMMs with the same input vector, and the output vector equal to the sum of output vectors of individual PMMs. The paper suggests that biological neurons have much more sophisticated computational resources than the presently popular models of artificial neurons.