This article reviews recent progress in the development of the computing framework Vector Symbolic Architectures (also known as Hyperdimensional Computing). This framework is well suited for implementation in stochastic, nanoscale hardware and it naturally expresses the types of cognitive operations required for Artificial Intelligence (AI). We demonstrate in this article that the ring-like algebraic structure of Vector Symbolic Architectures offers simple but powerful operations on high-dimensional vectors that can support all data structures and manipulations relevant in modern computing. In addition, we illustrate the distinguishing feature of Vector Symbolic Architectures, "computing in superposition," which sets it apart from conventional computing. This latter property opens the door to efficient solutions to the difficult combinatorial search problems inherent in AI applications. Vector Symbolic Architectures are Turing complete, as we show, and we see them acting as a framework for computing with distributed representations in myriad AI settings. This paper serves as a reference for computer architects by illustrating techniques and philosophy of VSAs for distributed computing and relevance to emerging computing hardware, such as neuromorphic computing.

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Google will jettison an auction system that forces other providers to bid for the right to be featured as a default search engine option on Android. Following a $5 billion fine and antitrust enforcement action in 2018, people in Europe have been able to choose which core apps and services they use on Android by default, instead of having to use Google products at first. Users in the region see an Android choice screen while setting up a device or after performing a factory reset. They can select their default search engine from a number of options. However, the three providers that are presented alongside Google Search have been determined by a sealed bidding process.

Scientists have demonstrated how a live cockroach equipped with a computerised'backpack' could be steered remotely for search and rescue missions. The backpack, created by a team at Nanyang Technological University in Singapore, is a small computer chip fitted with an infrared camera, carbon dioxide sensor and a temperature/humidity sensor, among other functions. In lab trials, the team fitted the backpack to a Madagascar hissing cockroach and successfully used it to find humans in a simulated disaster scene. The cockroach fitted with the backpack also had electrodes implanted in its cerci – the protruding appendages on its left and right side. Electrical currents were delivered to the two cerci via the electrodes to induce turning, allowing the scientists to control the direction it moved in.

Facility management is the part of the business that has always been under pressure to'do more for less' and to deliver the magic 10% cost savings that the core business demands of it. As businesses start the slow road to recovery and begin to emerge from the pandemic and enforced lockdowns, facility management and its associated costs will again be under the microscope. Traditionally, these cost savings have come from market testing, outsourcing, re-tendering, re-scoping, head count reduction and other areas of efficiencies that have by now, challenged the simultaneous demand for improved service quality and performance. Whist technology has played an important part of facility management for some time now, through a hunger for data to measure performance and through BIM and SMART or intelligent buildings, enabling informed decisions to be made, there is now a new opportunity for the use of technology in facility management and this is arguably the biggest opportunity yet. Facility management is involved across every organisation, and markets across both the private and public sectors and in commercial and non-commercial entities.

An AI arms race is already underway. That's the reality we have to deal with," Maas told DW, speaking in a new DW documentary, "Future Wars -- and How to Prevent Them." "This is a race that cuts across the military and the civilian fields," said Amandeep Singh Gill, former chair of the United Nations group of governmental experts on lethal autonomous weapons. "This is a multi-trillion dollar question." This is apparent in a recent report from the United States' National Security Commission on Artificial Intelligence. It speaks of a "new warfighting paradigm" pitting "algorithms against algorithms," and urges massive investments "to continuously out-innovate potential adversaries." And you can see it in China's latest five-year plan, which places AI at the center of a relentless ramp-up in research and development, while the People's Liberation Army girds for a future of what it calls "intelligentized warfare." As Russian President Vladimir Putin put it as early as 2017, "whoever ...

Motivated by packet routing in computer networks, online queuing systems are composed of queues receiving packets at different rates. Repeatedly, they send packets to servers, each of them treating only at most one packet at a time. In the centralized case, the number of accumulated packets remains bounded (i.e., the system is \textit{stable}) as long as the ratio between service rates and arrival rates is larger than $1$. In the decentralized case, individual no-regret strategies ensures stability when this ratio is larger than $2$. Yet, myopically minimizing regret disregards the long term effects due to the carryover of packets to further rounds. On the other hand, minimizing long term costs leads to stable Nash equilibria as soon as the ratio exceeds $\frac{e}{e-1}$. Stability with decentralized learning strategies with a ratio below $2$ was a major remaining question. We first argue that for ratios up to $2$, cooperation is required for stability of learning strategies, as selfish minimization of policy regret, a \textit{patient} notion of regret, might indeed still be unstable in this case. We therefore consider cooperative queues and propose the first learning decentralized algorithm guaranteeing stability of the system as long as the ratio of rates is larger than $1$, thus reaching performances comparable to centralized strategies.

We present a novel offline-online method to mitigate the computational burden of the characterization of conditional beliefs in statistical learning. In the offline phase, the proposed method learns the joint law of the belief random variables and the observational random variables in the tensor-train (TT) format. In the online phase, it utilizes the resulting order-preserving conditional transport map to issue real-time characterization of the conditional beliefs given new observed information. Compared with the state-of-the-art normalizing flows techniques, the proposed method relies on function approximation and is equipped with thorough performance analysis. This also allows us to further extend the capability of transport maps in challenging problems with high-dimensional observations and high-dimensional belief variables. On the one hand, we present novel heuristics to reorder and/or reparametrize the variables to enhance the approximation power of TT. On the other, we integrate the TT-based transport maps and the parameter reordering/reparametrization into layered compositions to further improve the performance of the resulting transport maps. We demonstrate the efficiency of the proposed method on various statistical learning tasks in ordinary differential equations (ODEs) and partial differential equations (PDEs).

We present the Multiple Criteria Decision Analysis Methods Selection Software (MCDA-MSS). This decision support system helps analysts answering a recurring question in decision science: Which is the most suitable Multiple Criteria Decision Analysis method (or a subset of MCDA methods) that should be used for a given Decision-Making Problem (DMP)?. The MCDA-MSS includes guidance to lead decision-making processes and choose among an extensive collection (over 200) of MCDA methods. These are assessed according to an original comprehensive set of problem characteristics. The accounted features concern problem formulation, preference elicitation and types of preference information, desired features of a preference model, and construction of the decision recommendation. The applicability of the MCDA-MSS has been tested on several case studies. The MCDA-MSS includes the capabilities of (i) covering from very simple to very complex DMPs, (ii) offering recommendations for DMPs that do not match any method from the collection, (iii) helping analysts prioritize efforts for reducing gaps in the description of the DMPs, and (iv) unveiling methodological mistakes that occur in the selection of the methods. A community-wide initiative involving experts in MCDA methodology, analysts using these methods, and decision-makers receiving decision recommendations will contribute to expansion of the MCDA-MSS.