Making Sense out of Machine Learning and Deep Learning
Today, artificial intelligence (AI) is mainly used as a generic term for all forms of compute-based intelligence. It can loosely apply to any system that imitates human learning and decision-making processes in responding to input, analyzing data, recognizing patterns, or developing strategies. The phrases machine learning (ML) and deep learning (DL) better describe the reality of present-day intelligent computing systems and the problems they can solve for developers and end users.
AMD and Machine Learning
Intelligent applications that respond with human-like reflexes require an enormous amount of computer processing power. AMD’s main contributions to ML and DL systems come from delivering high-performance compute (both CPUs and GPUs) with an open ecosystem for software development. ML and DL applications rely on computer hardware that can support the highest processing capabilities (speed, capacity, and organization) to simultaneously manage complex data sets from multiple input streams.
For example, in an autonomous driving scenario, the DL algorithm might be required to recognize an upcoming traffic light changing from green to yellow, nearby pedestrian movement, and water on the pavement from a rainstorm, among a variety of other real-time variables, as well as basic vehicle operations. A trained human driver may take these coordinating reactions for granted. However, to simulate the human brain’s capabilities, the autonomous driving algorithm needs efficient and accelerated processing to make its complex decisions with sufficient speed and high accuracy for the safety of passengers and others around them.
The performance of AMD hardware and associated software also offer great benefits to the process of developing and testing for ML and DL systems. Today, a computing platform built with the latest AMD technologies (AMD EPYC™ CPUs and Radeon Instinct™ GPUs) can develop and test a new intelligent application in days or weeks, a process that used to take years.
The Power and Freedom to Go Further
Machine learning patents grew at a rate of 34% between 2013 and 2017.3 While much work is currently being done in this area, the industry is still in the formative stages of helping machines learn more efficiently from different types of data. Intelligent systems featuring ML and DL offer enormous potential for computing that mimics human recall, pattern matching, and data association with speed and accuracy. A path and a platform have been established, and new breakthroughs are not far behind.
- Josh Lovejoy “The UX of AI,” Google Design, January 25, 2018, accessed April 23, 2018.
- Susan Scutti, “Automated dermatologist' detects skin cancer with expert accuracy,” CNN, January 26, 2017.
- Louis Columbus, “Roundup Of Machine Learning Forecasts And Market Estimates, 2018,” Forbes, February 18, 2018.