So many sophisticated Machine Learning libraries are available. They have already implemented all the popular algorithms. Daily, we experience how companies like Google, Facebook learn and predict about their users with uncanny accuracy.
“Is Machine Learning approaching its end? What’s new in the field?”
Machine learning is perhaps the hottest thing in Silicon Valley right now. Especially deep learning. We have Google’s TenserFlow, which teaches you everything you need to know to work in Silicon Valley’s top companies.
The reason why it is so hot is because it can take over many repetitive, mindless tasks. It’ll make doctors better doctors, and lawyers better lawyers. And it makes cars drive themselves and all this is incredibly exciting.
Is Machine Learning approaching its end?
Far from it. What we are seeing now is just the humble beginning. The world is quietly being reshaped by machine learning. We no longer need to teach computers how to perform complex tasks like image recognition or text translation: instead, we build systems that let them learn how to do it themselves.
“It’s not magic,” says Greg Corrado, a senior research scientist at Google. “It’s just a tool. But it’s a really important tool.”
What is the future of Machine Learning ?
In a simple sense, we can say that the future will be machine learning.
The current applications of machine learning will progress greatly to pave the path for future great applications. The trends in machine learning can be said to improve to great extent as –
1.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. This will change in the future.
2.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. 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.
3.Collaborative learning will emerge
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.
4.Reinforcement learning will gain widespread industry adoption
So far, industry ML is mostly concerned with supervised learning, gaining insights from data and slowly adapting to reinforcement… learning. The adoption of intelligent agents will revolutionize many industries in the future.
5.Machine learning pipelines will have increased levels of automation
The current tools are still sort of low level. And the ones that are not have 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.
6.Machine learning will be embedded everywhere
Machine learning is usually reserved for research, ad hoc analyses or top-level systems. In the future, tons of little devices and software components will be embedded with some sort of artificial intelligence.
What comes in future applications of Machine Learning ?
1.Deeper personalization
In the future, users will receive more precise recommendations and ads will become both more effective and less annoying.
2.Neural networks running on our mobile devices
Mobile device may have the ability to conduct machine learning tasks locally, opening up a wide range of opportunities for object recognition, speech, face detection, and other innovations for mobile platforms.
3.Mobile experience automation
There are a lot of apps that automate the work of different connected apps (like IFTTT) or the device’s OS the whole (like Tasker). However, such apps may be clumsy or difficult to use. What if a device could be automated by machine learning algorithms? And what if this automation could be extended to the Internet of Things? Google already patented a similar idea back in 2012, so it’s possible we’ll see an implementation of this sort of technology sooner or later.
4.Real-time speech translation
In late 2014, Skype launched Skype Translator. It’s been improving the service since then, and currently provides real-time audio translation among seven languages. If this technology continues to develop, it could significantly improve the quality of international communication or even eradicate language barriers.
5.Health and fitness
Fitness tracking wearables and apps are pretty popular right now. People gladly use wearables and connected apps to track their sport activities and everyday life. Machine learning has the potential to take this a step further, however, by providing more detailed feedback and tips about a user’s activity and condition, making fitness trackers more effective.
6.Prolonging a mobile device’s battery life
This may sound a lot less epic than other possibilities of machine learning, but preserving battery life is one of the most frustrating concerns for mobile app users. Along with the automation of system resource allocation for apps, machine learning could also reduce the amount of unnecessary battery consumption by apps.