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An intelligent agent operating in the real-world must balance achieving its goal with maintaining the safety and comfort of not only itself, but also other participants within the surrounding scene. This requires jointly reasoning about the behavior of other actors while deciding its own actions as these two processes are inherently intertwined - a vehicle will yield to us if we decide to proceed first at the intersection but will proceed first if we decide to yield. However, this is not captured in most self-driving pipelines, where planning follows prediction. In this paper we propose a novel data-driven, reactive planning objective which allows a self-driving vehicle to jointly reason about its own plans as well as how other actors will react to them. We formulate the problem as an energy-based deep structured model that is learned from observational data and encodes both the planning and prediction problems. Through simulations based on both real-world driving and synthetically generated dense traffic, we demonstrate that our reactive model outperforms a non-reactive variant in successfully completing highly complex maneuvers (lane merges/turns in traffic) faster, without trading off collision rate., Comment: ICRA 2021

Although the first commercial LED packages using GE RadiantRed™ technology, specifically the K 2 SiF 6 :Mn4+ (KSF) phosphor, came more than a year after the commercial introduction of quantum dot LCD displays, packages using this red-line emitting LED phosphor (λ max = 631nm) have penetrated the market at more than five times the rate of quantum dot displays. Over 20 billion LEDs containing GE RadiantRed™ technology have been sold, making this phosphor the red emitting luminescent material of choice for wide color gamut displays. Continual improvements in absorption, quantum efficiency and stability under high humidity and high light flux will be presented that have resulted in commercialization of this material as GE TriGain™ phosphors. Additionally, GE has developed new types of TriGain™ phosphor that include reducing the photoluminescent decay time by 30% and decreasing the particle size by a factor of 10 to meet the needs for next generation, wide color gamut displays. Lastly, progress on narrow band green phosphors will be discussed that when combined with RadiantRED™ technology provide an on-chip, RoHS compliant package that reaches up to 95% rec2020.

This paper details an extension to an architecture for robust bidirectional hetero-associative recall. Our proposed Protein Processor Associative Memory (PPAM) is fundamentally different from the traditional processing methods which use arithmetic operations and consequently Arithmetic and Logic Units (ALUs). In this paper, we improve on our initial work addressing concerns surrounding hardware implementation. We present the improved computational architecture, coupled with a corresponding hardware architecture for implementation. Results of applying the hardware implementation on a small dataset are included, along with reports from synthesis tools about hardware utilisation.

The evolution of Artificial Intelligence has passed through many phases over the years, going from rigorous mathematical grounding to more intuitive bio-inspired approaches. Despite the abundance of AI algorithms and machine learning techniques, the state of the art still fails to capture the rich analytical properties of biological beings or their robustness. Most parallel hardware architectures tend to combine Von Neumann style processors to make a multi-processor environment and computation is based on Arithmetic and Logic Units (ALU). This paper introduces an alternate architecture that is inspired from the biological world, and is fundamentally different from traditional processing which uses arithmetic operations. The architecture proposed here is targeted towards robust artificial intelligence applications.