NVIDIA invented the graphics processing unit (GPU) in 1999. To some, it seems counterintuitive that a chip originally designed to play 3D games has become the engine of today’s AI revolution. But in fact the problem of computer graphics has features in common with many other applications, from computational fluid dynamics and medical imaging to computer vision and natural language processing. At a high level, the unifying factor is that these problems can be parallelised. Our chip might be called a ‘graphics’ processor, but in fact it’s an incredibly versatile parallel processing engine which is playing a pivotal role in democratising AI.Tell us about infinite compute power and how you see this impacting the future of deep learning.
Deep learning is a new software model and as such it needs a new computing platform to run it — an architecture that can efficiently execute programmer-coded commands as well as the massively parallel training of deep neural networks. Several years ago, NVIDIA anticipated deep learning’s potential and invested heavily in ensuring that our compute platform includes features specifically designed for this application. At the time, a lot of people thought we were crazy! But we see this decision to pivot towards computing’s future, rather than focusing only on its present, as crucial.
By collaborating closely with AI developers, we are continuing to improve our GPU designs, system architecture, compilers and algorithms. We’ve been successful in speeding up training deep neural networks by 50x in just three years. Faster training and iteration ultimately means faster innovation and faster time to a solution or market. Recently we’ve responded to demand for a ‘plug and play’ deep learning solution by introducing the NVIDIA DGX-1. It’s the world’s first purpose-built server for deep learning, with fully integrated hardware and software that can be deployed quickly and easily.
Another important factor in the rapid adoption of GPUs for deep learning is the NVIDIA SDK. It’s a suite of powerful tools and libraries that give data scientists and researchers the building blocks for training and deploying deep neural nets. Based on our experience in developing CUDA, our parallel computing platform, as well as feedback from the developer community, we knew a strong deep learning SDK would be vital in helping data scientists and developers make the most of the vast opportunities in deep learning.
The SDK includes DIGITS, NVIDIA’s Deep Learning GPU Training System. This lets data scientists and researchers quickly design the best deep neural network based on their data using real-time network behaviour visualisation. It also includes cuDNN, the NVIDIA CUDA Deep Neural Network. Its optimised routines allow developers to focus on designing and training neural network models rather than low-level performance tuning. It includes other libraries and tools as well — cuBLAS, cuSPARSE, NCCL and the CUDA toolkit — all optimised for machine learning workloads.What do you feel are the most valuable applications of deep learning?
The impact of deep learning in healthcare and life sciences will also touch us all. Today genomics is applying GPU-based deep learning to understand how genetic variations can lead to disease. In the future, companies like French startup DreamQuark will help doctors and insurance professionals combine the vast amounts of data available via medical records with deep learning to create better prevention, diagnosis and care systems.
Deep learning will underpin so many advances that it’s difficult to pick just a few. From real-time speech translation to autonomous robots and machines that can read human emotions through facial analysis, deep learning and artificial intelligence are already having a transformative impact on every industry and research field.Which industries do you think will be disrupted by deep learning in the future, and how?
Startups and established companies are now ramping up an AI arms race to create new products and services or improve their operations. In just two years, the number of companies NVIDIA collaborates with on deep learning has jumped nearly 35x to over 3,400. Big data is an important factor in this trend. Industries such as healthcare, life sciences, energy, financial services, automotive, manufacturing, and entertainment gather massive amounts of information every day. The volumes of data are too massive for manual processing so they have remained an untapped resource, a ‘black box,’ until deep learning offered a means to automate the process of extracting meaning from them. Many problems that were previously assumed to be unsolvable are now within our reach, thanks to the combination of big data and deep learning.What are you looking forward to most at the Machine Intelligence Summit?
I’m particularly interested in discovering start-ups which are deploying deep learning and artificial intelligence. Over the course of NVIDIA’s history, some of the most creative and important applications of our technology have been pioneered by start-ups. In recognition of this, we’ve just launched the NVIDIA Inception Program, which provides supports new companies working in the field of deep learning.
In addition, we’ll be hosting our Emerging Companies Summit in Europe for the first time later this year. Part of the GPU Technology Conference Europe, ECS gives start-ups using GPU technology a platform to connect with potential investors, customers and employees. It’s taking place in Amsterdam on September 28th 2016 and I hope some of the entrepreneurs I meet at the Machine Intelligence Summit will be inspired to attend.
The next Machine Intelligence Summit will take place in New York on 2-3 November 2016. Discounted passes are now available - for more information and to register, please visit the website here. Previous events have sold out so please book early to avoid disappointment.We are holding summits focused on AI, Deep Learning and Machine Intelligence in London, Amsterdam, Boston, San Francisco, New York, Hong Kong and Singapore. See the full events list here.