Machine Learning: What Does The Future Look Like?

Machine learning (ML) is the process which enables a computer to perform something that it has not been explicitly told to do. Hence, ML assumes the central role in making sentient machines a reality. With the launch of Sophia, an AI robot developed by Hanson robotics, we wonder how close we are to be outclassed by these smart fellows.

If you are speculating about the future of machine learning in the next ten years, you are at the right place! Let’s get going.

Status Quo

ML has made it less complex for prospective systems by bringing in a way that enables them to enrich their knowledge base from large data sets, keeping programming errors at bay and avoiding logic issues. With the of use of BigData framework in the mainstream applications, smart algorithms can now crunch this colossal repository of both static and dynamic data and continuously learn and improve its efficiency.

This year, ML experts moved away from abstractions and theorizing, focussing on business applications of AI powered by machine learning and the concept of Deep Learning. In the practical arena, ML has been extensively applied in preventive healthcare, medicine, banking, finance, marketing, and media.

Considering the unscathed continuance of previous five years, ML isn’t slowing down anytime soon.

The ML Hype

Among significant breakthroughs in ML, Google recently open sourced its machine learning project Tensorflow, which is already a very active project being used for drug discovery to generating music. Microsoft open sourced CNTK, Baidu announced the release of PaddlePaddle, and Amazon announced to back MXNet in their new AWS ML platform. Facebook, on the other hand, is mainly supporting the development of not one, but two Deep Learning frameworks: Torch and Caffe. Google is also supporting
the highly successful Keras.

This hype is centered on the idea that algorithms and machine learning are going to take center stage in the tech world, for a long time. Demands supply gaps in machine learning have become steeper and platform wars have become fiercer.

Upgrade

In the coming few years, AI applications will become more commonplace than ever, and people will be more accepting towards machines among them. Therefore all service providers will need to seriously upgrade both their hardware (storage, backup, computation power, etc.) and software (servers, networks, ad-hoc networks, etc.) capabilities.

Just like the parallel processing capacity provided by GNUs have made the current AI possible and viable, the computation power would need serious amp up to accommodate what’s coming. All sections of the technological workforce will come under immense pressure to enhance and invent.

We have seen a boom in the use of machine learning in mobile applications, image recognition systems, pattern recognition applications, filtering tools, robotics, etc. Scientists are currently trying to develop a working machine that follows the exact processing that human brain does. If we mapped every node and neural network of our brain and fed data to it, the system should be able to process data like a human brain.

This concept is called cognitive computing. Cognitive computing systems will hence use pattern recognition, natural language processing, data mining to teach itself the thought process of a human being. With their end goal being a sentient AI machine, these systems should garner a lot of attention in the coming years.

Cognitive Learning vs. Deep Learning: Where does the future lie?

Deep learning is a process employed to help the system learn from unstructured or unlabeled data, all the while being unsupervised. Whereas, cognitive computing will use well-structured and segmented data to train and test the model sentient machine, deep learning employs data mining and data processing techniques to scale according to the data, model the data better and make it useful to other machines.

It also uses neural networks but in combination with the enormous IoT data repositories, the scale, and type of processing differentiates it from cognitive learning. Its major application will be in the systems at the back end, systems that will contribute more towards marketing, branding, creating a database for other machines to learn from.

With IoT, deep learning systems will create a data mine that will be the spine of most intelligent systems. While cognitive computing systems will work in collaboration with deep learning trained systems and IoT to perform the mainstream tasks in fields like Healthcare, medicine, scientific research and hypothesis testing, self-driving cars (automation), lip reading from video input and ultimately the sentient computing machine.

These two fields, ML and AI, will grab much of the focus. Now, a sentient machine might be far-fetched but the importance of machine learning in healthcare, cloud systems and marketing cannot be overestimated.

Stronger efforts to automate all routine parts of healthcare like testing for contaminants (viruses, bacteria, other foreign particles) in samples, detecting cancerous growths, examining x-rays and scans for the exact issues (which might escape the attention of the doctor or practitioner) will be made.

Even as of now, some hospitals in developed countries like the USA, the UK, European nations are adopting AI options. More institutions and universities will invest in this field, and the demand will up-shoot manifold.

Wrap up

In the coming ten years, AI applications will become more commonplace than ever, and therefore all service providers will need to seriously upgrade both their hardware (storage, backup, computation power, etc.) and software (servers, networks, ad-hoc networks, etc.) capabilities.

Just like the parallel processing capacity provided by GNUs have made the current AI possible and viable, the computation power would need
a serious boost to accommodate what’s coming. All sections of the technological workforce will come under immense pressure to upgrade and invent.