After rapid growth over the past few years, artificial intelligence has become one of the biggest focuses of enterprises.
Well, what has made it so hot? With AI, we can design systems that learn and adapt to all the new data we collect. Just a few years ago, AI seemed to be impossible. But now, it’s quickly becoming necessary and expected.
Because of this, major technology corporations, sprawling startups, and research institutes have been working diligently to develop AI infrastructure that augments human intelligence for advantages over their competitors.
The massivespurt of AI advancement is largely due to three cohesive factors: powerful graphics processing units (GPUs) combined with the emergence of big data for complex computations, and the development of Deep learning. Deep learning is an AI computation model that has been around for decades.
It’s always important to enlighten ourselves about the future of AI despite only scratching the surface of what these three factors can accomplish when combined. In the future, old variables will be enhanced into new variables to accomplish much more.
So let’s explore the accelerators of AI innovation. Stay tuned for deep reasoning, the emergence of small data, further development of unsupervised learning, more efficient deep learning models, and new AI hardware.
Just like how deep learning is crucial for AI innovation today, it will also be in the future. We’re realizing the power of deep learning, whether it be for natural language processing (NLP), computer vision, or speech recognition. This won’t end soon.
However, deep learning has struggled with allowing smart machines the ability to reason, which can drive success in many AI applications.
Deep learning is great for perception and classification problems rather than real reasoning problems. It has only been successful when using a lot of labelled data. Now we should put equal focus on solving reasoning problems as much as we do for those of perception and classification.
There are many uses of reasoning such as simple planning, basic common sense, dealing with varied circumstances, and making complex decisions in a specific profession or industry.
We need to build algorithms that understand the basic, natural idea that anything can change.
With a few examples in narrow applications such as autonomous vehicles and other technologies: Most professionals agree that we have a long way ahead of us in terms of teaching systems to deeply reason and making them efficient enough for scaling reasoning capabilities through a general approach rather than narrow.
Currently, we can map a natural language sentence to a logical form in some areas as a result of spending much effort in labelling text. Through the use of formalized reasoning mechanisms, we can work with these extracted formulas. The real challenge is to reduce the work needed to generalize these formulas to fit a variety of applications.
Some AI experts believe that we can use deep learning to solve the reasoning challenge within the next decade.
At the moment, we have the tools to develop a game-changing solution for reasoning. One of the most important tools is large amounts of data, as it is strongly suggested that machine learning strategies use data as a resource required for automated reasoning. We must scale reasoning computations to a wide array of applications, similar to the surge that neural networks experienced as a result of GPUs.
Though deep learning will be used for a while, it is subject to change in the coming waves of AI innovations. Technologists emphasize that to apply deep learning models that scale across more complex and diverse tasks, we must become more efficient at training deep learning models. We can achieve this level of efficiency using “small data” and more unsupervised learning.
Lots of data is needed to teach a task to neural networks of deep learning models.
