Artificial Intelligence is now revered as a second greatest invention by mankind after the invention of fire. The advent of AI has not been without its detractors. A good number of people hold reservations since they fear AI will be a bane to their jobs. These people should appreciate that AI is being used, is here to stay, and they should learn to use and control the technology for the overall good.
Deep learning is the heart of AI. It refers to the use of a profound neural network modeled on the human brain. These digital neural networks are designed to mimic the learning processes of the human brain.
The learning process takes place within half a dozen layers of neurons beginning with the firing of a specific set of neurons. By taking note of the results of a learning task, the brain will use feedback in subsequent trials to adjust the activated neurons. The neural network will effectively identify certain unique features. With reference to images, the intermediate layers of the neural network will identify features such as the edges and other distinctive features that enable us to recognize the image.
We have seen heavy investment in the AI technology by large technology companies like Google and Facebook. These tech companies are using the technology to automate their offerings as well as their workings. As a result, they have revolutionized the way computers are used to achieve different tasks that include image recognition and transcription of speech to text.
The opportunities for deep learning and further investment in AI are immense based on the potential of the technology to leverage on other technologies effectively increasing their power. Opportunities also exist in the amount of data required to feed into AI to help solve challenges across the board in different industry sectors.
Presently, the race is on as the large enterprises seek to establish themselves in deep learning by acquiring leading startups in the sector. Some of the leading enterprises in the deep learning space such as NVIDIA the inventor of GPU are championing worldwide efforts to support startups. NVIDIA have a global program to provide support to startups driving innovation in AI and data science.
Globally, startups are harnessing deep learning for its features such as accuracy and superhuman speed in applications such as fraud detection, self-driving cars, and radiology, according to Kimberly Powell, the senior director of industry business development at NVIDIA. Their endeavor is to support the innovative startups that are blazing the trail in AI and revolutionizing the way we live and work.
The Inception Program designed by NVIDIA is aimed at providing the startups with access to the latest GPU hardware alongside technical training and access to engineering teams and deep learning experts. NVIDIA is also providing investment for the startups to give them first mover advantage in the development and deployment of products and services.
A good example of the alliances created the NVIDIA program is that with NYU in the development of autonomous driving technology. The alliance has brought together NYU researchers and NVIDIA engineers who have crafted the Drive PX2 chips.
The team is working together to expand the existent NVIDIA learning system to include all aspects of autonomous driving. Under development and evaluation are the need to eliminate hand programmed rules and procedures such as finding lane markings. It will help to avoid the generation of an almost infinite number of rules such as if, then, else statements from the random events that may occur on the road. The random occurrence of events on the road is nearly impractical to code.
Deep learning is yet to reach the point of truly enabling AI as it has been envisioned. The challenge lies in the fact that deep learning is just utilizing thousands of neurons and millions of connections. Compare this to the human brain on which deep learning modeling, the brain has billions of neurons and trillions of connections.
Despite these shortcomings, deep learning is quite useful since it is achieving much of what enterprises require of it. It is helping to address numerous challenges in the development of technology useful to the society and business world.