A look at machine learning via the prism of AMD Instinct accelerators
Systems can use it to find patterns, adapt to new data, and make wise judgements or predictions
The phrase “machine learning” is not brand-new; it was first used by Arthur Samuel to describe a computer employing artificial intelligence to play the game of checkers in the late 1950s
The enormous amounts of data needed for AI models to learn and advance can be processed by today’s sophisticated computers
Deep learning and the emergence of neural networks more specifically, the expansion in size of neural networks have improved the performance of machine learning workloads
Machine learning algorithms need information to learn from patterns observed in data in order to get insights from the data
The University of Turku trained a 13B parameter Finnish large language model (LLM) using the LUMI system, the biggest supercomputer in Europe
Deep learning, reinforcement learning, and quantum computing developments have the potential to solve ever more complicated issues