Distributed Representations Enable Robust Multi-Timescale Computation in Neuromorphic Hardware

Programming recurrent spiking neural networks (RSNNs) to robustly perform multi-timescale computation remains a difficult challenge. To address this, we show how the distributed approach offered by vector symbolic architectures (VSAs), which uses high-dimensional random vectors as the smallest units of representation, can be leveraged to embed robust multi-timescale dynamics into attractor-based RSNNs. We embed finite state machines into the RSNN dynamics by superimposing a symmetric autoassociative weight matrix and asymmetric transition terms. The transition terms are formed by the VSA binding of an input and heteroassociative outer-products between states. Our approach is validated through simulations with highly non-ideal weights; an experimental closed-loop memristive hardware setup; and on Loihi 2, where it scales seamlessly to large state machines. This work demonstrates the effectiveness of VSA representations for embedding robust computation with recurrent dynamics into neuromorphic hardware, without requiring parameter fine-tuning or significant platform-specific optimisation. This advances VSAs as a high-level representation-invariant abstract language for cognitive algorithms in neuromorphic hardware.

Vector Symbolic Finite State Machines in Attractor Neural Networks

Hopfield attractor networks are robust distributed models of human memory. We propose construction rules such that an attractor network may implement an arbitrary finite state machine (FSM), where states and stimuli are represented by high-dimensional random bipolar vectors, and all state transitions are enacted by the attractor network's dynamics. Numerical simulations show the capacity of the model, in terms of the maximum size of implementable FSM, to be linear in the size of the attractor network. We show that the model is robust to imprecise and noisy weights, and so a prime candidate for implementation with high-density but unreliable devices. By endowing attractor networks with the ability to emulate arbitrary FSMs, we propose a plausible path by which FSMs may exist as a distributed computational primitive in biological neural networks.

Logical Limitations to Machine Ethics with Consequences to Lethal Autonomous Weapons

Lethal Autonomous Weapons promise to revolutionize warfare -- and raise a multitude of ethical and legal questions. It has thus been suggested to program values and principles of conduct (such as the Geneva Conventions) into the machines' control, thereby rendering them both physically and morally superior to human combatants. We employ mathematical logic and theoretical computer science to explore fundamental limitations to the moral behaviour of intelligent machines in a series of "Gedankenexperiments": Refining and sharpening variants of the Trolley Problem leads us to construct an (admittedly artificial but) fully deterministic situation where a robot is presented with two choices: one morally clearly preferable over the other -- yet, based on the undecidability of the Halting problem, it provably cannot decide algorithmically which one. Our considerations have surprising implications to the question of responsibility and liability for an autonomous system's actions and lead to specific technical recommendations.

Variations of the Turing Test in the Age of Internet and Virtual Reality

Inspired by Hofstadter's Coffee-House Conversation (1982) and by the science fiction short story SAM by Schattschneider (1988), we propose and discuss criteria for non-mechanical intelligence. Firstly, we emphasize the practical need for such tests in view of massively multiuser online role-playing games (MMORPGs) and virtual reality systems like Second Life. Secondly, we demonstrate Second Life as a useful framework for implementing (some iterations of) that test.