With the advent of the latest computing technologies, machine learning (ML) today is vastly different from its past avatars. Earlier, it was consistently recognizing patterns and learning to perform specific tasks without needing to be programmed.
In more recent times, machine learning is witnessing major disruption by relying on vast amounts of data to automate tasks and predict business outcomes accurately. Due to their exposure to newer sets of data, these models are now able to adapt independently.
Learnings acquired from earlier computations successfully present reliable and recurrent results and decisions. In a broader sense, machine learning is essentially about learning to do better in the future based on what was experienced in the past.
While machine learning is definitely not a new science, it is constantly evolving into a force capable of revolutionizing the future.
In modern times, there are really only a few problem areas where machine learning cannot be applied; it is practically everywhere, in business applications, sports, science and technology. Below are some of the many areas where machine learning is already being used as a force to reckon with:
- Customer segmentation
- Customer targeting
- Face detection
- Object recognition
- Speech recognition
- Writing recognition
- Machine translation
- Computational Biology
- Drug Discovery/Design
- Fraud Detection
- Email spam classification
- Xbox game player matching
- Amazon shopping recommendations
- Netflix movie recommendations
- YouTube video recommendations
….and many more!
In an increasingly automated world, dependency on manual processes are being drastically reduced, giving way to data science and machine learning. And this is simply the beginning.
According to Norwegian Software R&D Engineer & Data Scientist — Håkon Hapnes Strand: Listed below are some realistic future trends we are likely to witness, that are associated with machine learning:
Our understanding of neural networks will improve greatly.
Neural networks are arguably the most impressive learning algorithms we have at our disposal at present. Yet, we don’t really understand how or why they work. I believe that will change.
Natural Language Processing will begin to make sense
So far, ML-based NLP is in such a sorry state that it can barely beat rule-based engines. Well, that’s an exaggeration, but I would say that this subfield of ML is still in its infancy. The main problem is that words have different meanings in different contexts. Algorithms that recognize those contexts and understand linguistic concepts on a higher level have not yet been successfully implemented, but there’s no reason why they can’t be.
Collaborative learning will emerge
No, I’m not talking about collaborative filtering. I’m talking about different computational entities collaborating 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.
Reinforcement learning will gain widespread industry adoption
You can achieve awesome things with reinforcement learning, and I’m not just talking about learning to play Super Mario. So far, industry ML is mostly concerned with supervised learning, gaining insights from data. The adoption of intelligent agents will revolutionize many industries in the future.
Machine learning pipelines will have increased levels of automation.
I’m sure that there are many data scientists and machine learning engineers out there that have implemented very efficient pipelines to automate and abstract away low level implementation. I have done that in a lot of my own work. However, the current tools are still sort of low level. And the ones that have not taken away all control of what’s actually happening. As engineers, we need tools that are high-level, yet allow fine-grained algorithm control when needed.
Machine learning will be embedded everywhere.
So far, machine learning is usually reserved for research, Adhoc analysis or top-level systems. In the future, tons of little devices and software components will be embedded with some sort of artificial intelligence. Again, refer to the IoT nodes and edge analytics.
[Source: Quora] Over the coming years, the demand for ML powered intelligence is seen as rising substantially in all types of end-use industries, specifically in the BSFI, retail, telecom, and manufacturing, in order to enhance the decision-making capability of machines used in these industries. This is truly ‘Back To The Future’. At Gamooga, we offer an omni-channel customer engagement platform backed by a powerful predictive intelligence engine. Get in touch with us to know more about our software and how it can help you!