The battle to change the computer industry so that machines can better compute artificial intelligence tasks, especially deep learning, continues to birth new and interesting potential future stars. On Monday, Ceremorphic of San Jose, California, formally debuted chip efforts that have been kept in a stealth mode for two years, discussing a chip the company claims will revolutionize the efficiency of A.I. computing in terms of power consumption. "It's counterintuitive today, but higher performance is lower power, said Venkat Mattela, founder and CEO of the company, in an interview with ZDNet via Zoom. Mattela believes that numerous patents on low-power operation will enable his company's chip to produce the same accuracy on signature tasks of machine learning with much less computing effort. "What I'm trying to do is not just building a semiconductor chip but also the math and the algorithms to reduce the workload," he said.
The control problem—which of its potential actions should an AI system perform at each point in the problem-solving process?—is fundamental to all cognitive processes. This paper proposes eight behavioral goals for intelligent control and a ‘blackboard control architecture’ to achieve them. It enables AI systems to operate upon their own knowledge and behavior and to adapt to unanticipated problem-solving situations. The paper shows how opm, a blackboard control system for multiple-task planning, exploits these capabilities. It also shows how the architecture would replicate the control behavior of hearsay-ii and hasp. The paper contrasts the blackboard control architecture with three alternatives and shows how it continues an evolutionary progression of control architectures.
Developing architectures for NN's is still in the early stages of development. The multi-network designs so far have been limited to narrowly defined concepts within a single domain. The design of a complex system such as a ship will first require extensive training in current designs and second, the ability to envision inter-processings between system components. The cutting edge in NN architectures is in massive NN's such as those done by Google for recognizing hand written digits and the "Borg Cube" NN architecture developed by Affectiva for recognizing image components in pictures . In Google's approach, a number of layers, each of a number of NN's were run through with the image data.
The neural architecture search (NAS) algorithm with reinforcement learning can be a powerful and novel framework for the automatic discovering process of neural architectures. However, its application is restricted by noncontinuous and high-dimensional search spaces, which result in difficulty in optimization. To resolve these problems, we proposed NAS in embedding space (NASES), which is a novel framework. Unlike other NAS with reinforcement learning approaches that search over a discrete and high-dimensional architecture space, this approach enables reinforcement learning to search in an embedding space by using architecture encoders and decoders. The current experiment demonstrated that the performance of the final architecture network using the NASES procedure is comparable with that of other popular NAS approaches for the image classification task on CIFAR-10. The beneficial-performance and effectiveness of NASES was impressive even when only the architecture-embedding searching and pre-training controller were applied without other NAS tricks such as parameter sharing. Specifically, considerable reduction in searches was achieved by reducing the average number of searching to 100 architectures to achieve a final architecture for the NASES procedure. Introduction Deep neural networks have enabled advances in image recognition, sequential pattern recognition, recommendation systems, and various tasks in the past decades.