Looking Toward the Future of Deep Learning

Deep learning represents one of the newest and boldest frontiers being explored in the development of artificial intelligence. A subcategory under the broader umbrella of machine learning, this specific technique distinguishes itself by the strength of its architecture and the complexity of the neural networks involved, which allow it to learn behaviors and perform them in automated fashion with greater intuition and much less work on the programming end by human developers.

The breadth of function and utility for existing deep learning technologies is already considerable. But its potential uses are even more varied and quite remarkable, which could come to fruition in the not-too-distant future if AI developers and engineers have the ingenuity, dedication and support necessary to improve upon this technology. AMETEK ECP plays a vital role in that necessary support by providing precision microelectronic components necessary for the manufacturing and peak performance of the servers, mainframes and devices powering deep learning. Let's take a closer look at both where the technology is now and where it appears to be going.

Foundational concepts of deep learning

According to NVIDIA, the key differentiator between deep learning and the other types of machine learning AI is the technology's capacity for examining data in the form of images, sounds and text, rather than as code or another type of signal. Deep learning platforms can thus find patterns that they memorize and use to develop the steps for its automated processes. This allows for the AI in question to develop complex innate behaviors that are beyond the capacity of robotics and most other AI technologies.

It is the closest AI has come thus far to reaching the pinnacle of artificial general intelligence: Unlike AI, AGI would, in theory, be able to pass the Turing test, posses the capability to make its own decisions and perhaps even develop emotions. At the very least it could entirely duplicate the essential processes of the human brain's neurocortex.

RF Connector, Future of deep learning

Major tech players' deep learning developments

For some time, Google held lead position in its level of success with deep learning development. While the search and computing giant was not alone in making major strides with the technology, MIT Technology Review pointed out one of Google's notable advantages aside from its size and resources. Ray Kurzweil, considered one of the biggest authorities on AI and known for predicting the rise of the internet and many other advances, has been with the company as a director of engineering since 2012.

Google's subsidiary DeepMind has also earned a great deal of attention, though not all of it is positive. The U.K.-based deep learning firm received considerable criticism for exposing patient data gleaned from Britain's National Health Service without the knowledge of those detailed in the information, according to Forbes. As a result, in 2017, U.K. judicial authorities ruled that DeepMind's use of such data was illegal. (Health care likely will be a major area of application for deep learning technologies in the near future, but incidents like this - not to mention longstanding laws like the Health Information Portability Assurance Act in the U.S. - guarantee that deep learning and other AI platforms relying on confidential data will be highly regulated.) Forbes also noted Google's desire to have greater direct control over DeepMind, a condition to which the firm agreed Nov. 14, 2018.

Amazon has also made recent strides in the deep learning arms race. Reporting from the Amazon Web Services re:Invent conference Nov. 29, TechCrunch stated that the e-commerce multinational's web tech affiliate planned to release a tool called Amazon Elastic Inference, designed to reduce CPU power usage and the resulting costs that stem from keeping deep learning platforms running for long periods of time.

Intriguing possibilities for the future

The industries and areas to which deep learning could contribute are broadly varied. On the one hand, it will serve numerous behind-the-scenes tech roles, such as reducing the lags and bandwidth bottlenecking that result from the internet's plurality of video content, according to MIT Technology review. Additionally, solutions for increasing business efficiency in countless ways are certain to be released in the future. On the other hand, vital health breakthroughs may be possible, as seen in the development of a platform for the early detection of epilepsy in children at George State University. The value of deep learning is that it can simultaneously serve altruistic needs while enabling business competition.

 



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