AI is set to be the most significant development in humanity since the invention of fire. There are many justified reservations around what it will do to the nature of employment as so forth, but they are really besides the point now - it’s happening and we need to learn to control and utilize it for the good of the world.
At the heart of AI is deep learning. Deep learning uses a ‘deep’ neural network that’s loosely modeled on the human brain. It is made up of digital neural nets that mimic the way the human brain learns. When beginning a new task, a certain set of neurons will fire. You then observe the results of the task, and in subsequent trials your brain uses feedback to adjust the neurons that get activated. It is made up of a half dozen or so layers, which allow the neural network to identify certain features. So, when applied to images, the intermediate layers pinpoint features like edges and corners, which enables it to recognize what the image is.
Tech companies like Google and Facebook have already invested heavily in the technology as a core part of automating their services. It is already revolutionizing the way computers recognize images, transcribe speech into text, and various other tasks. The power of the technology itself and its potential for leveraging other technologies to increase their power mean that there is a tremendous opportunity for investment with deep learning, and the amount of data available for it to feed on provides a number of opportunities to solve challenges across numerous industries.
The race is now on for enterprises to harness deep learning, and enterprises are investing heavily in startups innovating in the sector. Among these is inventor of the GPU, NVIDIA, which recently unveiled a comprehensive global program seeking to support growth of these startups that are driving new breakthroughs in artificial intelligence and data science.
Kimberly Powell, senior director of Industry Business Development at NVIDIA, explained that, ‘Startups worldwide are taking advantage of deep learning for its superhuman speed and accuracy in applications like radiology, fraud detection and self-driving cars. We’re committed to helping the world’s most innovative companies break new ground with AI and revolutionize every industry.’
The NVIDIA Inception Program provides early access to the latest GPU hardware, NVIDIA’s deep learning experts and engineering teams, technical training, as well as investment in order help them develop products and services with a first-mover advantage. One of its early collaborations is with NYU, whose researchers are set to work alongside NVIDIA scientists and engineers to develop autonomous driving technology, for which NVIDIA has already created the Drive PX2 chips. The team will seek to grow the the current NVIDIA learning system to encompass all aspects of autonomous driving, eliminating the need for hand-programmed rules and procedures like finding lane markings to avoid the creation of a near infinite number of ‘if, then, else’ statements, which is impractical to code when trying to account for the randomness that occurs on the road.
Deep learning is still some way off truly enabling AI as we might imagine it. It uses thousands of neurons, and millions of connections, while the human brain that it is attempting to recreate has billions of neurons and trillions of connections. It is, however, still extremely useful for doing everything that enterprises require of it, and can solve many challenges around the development of technology that will provide huge benefits to society and enterprises.