Artificial intelligence (AI), machine learning (ML), artificial neural networks (ANN) and deep learning (DL) are usually used interchangeably, but they do not quite refer to the same things. Artificial intelligence applies to computing systems designed to perform tasks usually reserved for human intelligence using logic, if-then rules, and decision trees. AI recognizes patterns from vast amounts of quality data providing insights, predicting outcomes, and making complex decisions. Machine learning is a subset of AI that utilizes advanced statistical techniques to enable computing systems to improve at tasks with experience over time. Chatbots like Amazon's Alexa and Apple's Siri improve every year thanks to constant use by consumers coupled with the machine learning that takes place in the background.
Artificial Intelligence (AI) has the opportunity to revolutionize the way the United States Department of Defense (DoD) and Intelligence Community (IC) address the challenges of evolving threats, data deluge, and rapid courses of action. Developing an end-to-end artificial intelligence system involves parallel development of different pieces that must work together in order to provide capabilities that can be used by decision makers, warfighters and analysts. These pieces include data collection, data conditioning, algorithms, computing, robust artificial intelligence, and human-machine teaming. While much of the popular press today surrounds advances in algorithms and computing, most modern AI systems leverage advances across numerous different fields. Further, while certain components may not be as visible to end-users as others, our experience has shown that each of these interrelated components play a major role in the success or failure of an AI system. This article is meant to highlight many of these technologies that are involved in an end-to-end AI system. The goal of this article is to provide readers with an overview of terminology, technical details and recent highlights from academia, industry and government. Where possible, we indicate relevant resources that can be used for further reading and understanding.
Artificial intelligence is opening the best opportunities for semiconductor companies in decades. How can they capture this value? Software has been the star of high tech over the past few decades, and it's easy to understand why. With PCs and mobile phones, the game-changing innovations that defined this era, the architecture and software layers of the technology stack enabled several important advances. In this environment, semiconductor companies were in a difficult position. Although their innovations in chip design and fabrication enabled next-generation devices, they received only a small share of the value coming from the technology stack--about 20 to 30 percent with PCs and 10 to 20 percent with mobile. But the story for semiconductor companies could be different with the growth of artificial intelligence (AI)--typically defined as the ability of a machine to perform cognitive functions associated with human minds, such as perceiving, reasoning, and learning.
Von Neumann Architecture Neuromorphic Architecture Neuromorphic architectures address challenges like high power consumption, low speed, and other efficiency-related bottlenecks prevalent in the traditional von Neumann architecture Architecture Bottleneck CPU Memory Neuromorphic architectures integrate processing and storage, getting rid of the bus bottleneck connecting the CPU and memory Encoding Scheme and Signals Unlike the von Neumann architecture with sudden highs and lows in the form of binary encoding, neuromorphic chips offer a continuous analog transition in the form of spiking signals Devices and Components CPU, memory, logic gates, etc. Artificial neurons and synapses Neuromorphic devices and components are more complex than logic gates Versus Versus Versus 10. NEUROMORPHIC CHIPSETS 10 SAMPLE REPORT Neuromorphic Chipsets vs. GPUs Parameters Neuromorphic Chips GPU Chips Basic Operation Based on the emulation of the biological nature of neurons onto a chip Use parallel processing to perform mathematical operations Parallelism Inherent parallelism enabled by neurons and synapses Require the development of architectures for parallel processing to handle multiple tasks simultaneously Data Processing High High Power Low Power-intensive Accuracy Low High Industry Adoption Still in the experimental stage More accessible Software New tools and methodologies need to be developed for programming neuromorphic hardware Easier to program than neuromorphic silicons Memory Integrated memory and neural processing Use of an external memory Limitations • Not suitable for precise calculations and programming- related challenges • Creation of neuromorphic devices is difficult due to the complexity of interconnections • Thread limited • Suboptimal for massively parallel structures Neuromorphic chipsets are at an early stage of development, and would take approximately 20 years to be at the same level as GPUs. The asynchronous operation of neuromorphic chips makes them more efficient than other processing units.
Machine learning is enabling computers to tackle tasks that have, until now, only been carried out by people. The next wave of IT innovation will be powered by artificial intelligence and machine learning. We look at the ways companies can take advantage of it and how to get started. From driving cars to translating speech, machine learning is driving an explosion in the capabilities of artificial intelligence -- helping software make sense of the messy and unpredictable real world. But what exactly is machine learning and what is making the current boom in machine learning possible? At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data.