Uncovering the Basics of AI, Machine Learning and Deep Learning: A Guide for Businesses
Craig Hume - MD @ Utopia
Artificial Intelligence (AI) is one of the most popular buzzwords in the IT industry. Deep learning and AI are the latest trends in this field, but many businesses still need to learn what these terms mean. This guide introduces these technologies and how businesses can benefit from them.
What is AI, Machine Learning and Deep Learning?
AI is an umbrella term used to describe the creation of systems that mimic human intelligence. Machine learning and deep learning are two subsets of AI, each with its distinct goal and approach. Machine learning aims to create a simulation of human learning, allowing applications to adapt to uncertain or unexpected conditions. It uses various techniques, including statistical analysis and predictive analytics, to achieve its goal.
On the other hand, deep learning uses computing units called neurons, arranged into ordered sections known as layers, to process data. This technique is based on a neural network and mimics how the human brain learns. Unlike machine learning algorithms, deep learning's performance continues to improve as the size and volume of its data set increase.
Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two popular deep learning techniques. CNNs use multiple layers to solve problems one step at a time, while RNNs have a built-in feedback loop that allows them to remember input and improve their accuracy in predicting the next event. RNNs are ideal for recognising patterns in data sequences, such as text, genomes, handwriting, or spoken words.
Powering Deep Learning with GPU-Acceleration
Deep learning requires parallel computing, which is only suitable for some processors. The Central Processing Unit (CPU) is designed to perform tasks quickly and simultaneously but quickly reaches a limit for concurrent tasks. Graphics Processing Units (GPUs), designed for rendering high-resolution images and video, are perfect for parallel workloads, including machine learning and scientific computation.
GPU-accelerated computing involves adding GPUs to a system to offload intensive parallel workloads from the CPU and improve performance. Designed with thousands of cores running simultaneously, GPUs enable massive parallelism, making them ideal for deep learning.
In conclusion, deep learning and AI are exciting technologies that have the potential to revolutionise the way businesses operate. Understanding the basics of these technologies, including their principles, terminology, and the benefits of GPU acceleration is the first step in harnessing their power to drive business growth.
Are you looking to harness the power of Nvidia’s GPUs in your workflow? Perhaps you are involved in AI, Machine Learning or Deep Learning and are looking for a System Builder who can help? Whatever your needs, get in touch, and the team at Utopia, along with our partners like Nvidia and Supermicro, will ensure you get the perfect solution.