Future of Machine Learning

Machine Learning is the newest buzzword in the universe of transformative technology. While the world is being reshaped by the subtle touch of machine learning, we are leveraged from the tediousness of performing complex tasks like text translation and image recognition. But that is not the end of it. Many new machine learning technologies, architectures, and algorithms are headed to our way.

But can machine learning be the end of human efficiency? Certainly the future may look a bit worrisome and bleak but that doesn’t mean that machine learning will totally replace the need for human engineering. The Hollywood science fiction has certainly made a deep imagery about a dystopia where the entire human race will bow down before our robot overlords. But what we need to understand is that machine learning will not automatically grant the power of rationalization and abstraction into the machine. Machine will just adapt to new challenges with some stochastic randomness when a set of inputs and potential actions are given.

Nevertheless, concerns will be raised on regulating the danger that will unleash itself when machine learning evolves beyond a point where it starts reproducing. But that, indeed, is a long way ahead. So what is coming our way in the near future?

·      Neural Networks:

Neural Networks are the most impressive machine learning technology in development at present. It is a perfect example of fusion between biological system and information processing system. Though they are still in infancy, the future will see a leap neural processing.

·      Natural Language Processing:

Though machine learning based natural language processing have yet to travel a long journey, algorithms built to recognize the linguistic concepts and context have started to implement successfully.

·      Collaborative learning:

Different computational entities will collaborate to produce better learning results than they would have achieved on their own. This could be robots or it could be the nodes of an IoT sensor network, or what some would call edge analytics.

·      Platform Wars:

The platform war between major IT in machine learning development will experience a tremendous growth in smart apps, digital assistants and mainstream use of AI.

·      Machine learning propelled Innovation:

When software companies eliminated desktop support in favor of cloud-based services, business firms were forced to adapt to life in the cloud. Similarly, departments and firms will be forced to adopt machine learning to remain competitive since machines can deliver real-time insights, enhance decision making and catapult efficiency.

·      Reinforcement learning:

Awesome things can be achieved with reinforcement learning. So far, industry machine learning is mostly concerned with supervised learning, gaining insights from data. The adoption of intelligent agents will revolutionize many industries in the future.

Machine learning is one of the hottest topics right now with massive prospects. With deep learning in action, many repetitive, mindless tasks will be taken over by machines. It will make doctors better doctors, lawyers better lawyers and engineers better engineers. It may even help cars drive themselves. So, if that is not exciting, what else can be?

Learn more at Future of Technology Summit 2017 confirmed speakers include

·      Steve Wozniak, cofounder of Apple Inc.

·      Randi Zuckerberg, author and founder of Zuckerberg Media

·      Ray Kurzweil, cofounder and chancellor Singularity University and director of engineering Google

·      Chris C. Kemp, former CTO NASA, cofounder Nebula and OpenStack

·      Martin Eberhard, cofounder and former CEO of Tesla Motors

·      Prof. Mary Cummings, one of the first US Navy's first female fighter pilots and Associate Professor Duke University

·      Dr. Boris Konrad, neuroscientist and world record holder for memory