Building on the founders' pioneering research in deep imitation learning, deep reinforcement learning and meta-learning, Embodied Intelligence is developing AI software (aka robot brains) that can be loaded onto any existing robots. While traditional programming of robots requires writing code, a time-consuming endeavor even for robotics experts, Embodied Intelligence software will empower anyone to program a robot by simply donning a VR headset and guiding a robot through a task. These human demonstrations train deep neural nets, which are further tuned through the use of reinforcement learning, resulting in robots that can be easily taught a wide range of skills in areas where existing solutions break down. Complicated tasks like the manipulation of deformable objects such as wires, fabrics, linens, apparel, fluid-bags, and food; picking parts and order items out of cluttered, unstructured bins; completing assemblies where hard automation struggles due to variability in parts, configurations, and individualization of orders, are all candidates to benefit from Embodied Intelligence's work.
From CNNs to GPUs, there's a whole spectrum of technologies and tools you can use to bring AI and machine learning into your business. But if you fail to manage your projects correctly, you just won't get the benefits you'd hoped for. That's why at MCubed our speakers don't just dive into the most important concepts and technologies, they show you how to implement them in production and skirt some of the major traps. So as well as covering core concepts and tools such as TensorFLow and Keras, our speakers will be discussing how to make your AI development more efficient, and how you can develop and deploy your machine learning models faster with DevOps. We'll also examine how to avoid vendor lock-in.
We introduce the Binary Matrix Guessing Problem and provide two algorithms to solve this problem. The first algorithm we introduce is Elementwise Probing Algorithm (EPA) which is very fast under a score which utilizes Frobenius Distance. The second algorithm is Additive Reinforcement Learning Algorithm which combines ideas from perceptron algorithm and reinforcement learning algorithm. This algorithm is significantly slower compared to first one, but less restrictive and generalizes better. We compare computational performance of both algorithms and provide numerical results. reason for withdrawal: Paper will be rewritten with experiments replicated on verified and validated hardware and software.
Reinforcement learning is one of the most general problems in artificial intelligence. It has been used to model problems in automated experiment design, control, economics, game playing, scheduling and telecommunications. The aim of the reinforcement learning competition is to encourage the development of very general learning agents for arbitrary reinforcement learning problems and to provide a test-bed for the unbiased evaluation of algorithms.