A constant form of silent evolution is machine learning. We thought computers were the big all-that that would allow us to work more efficiently; soon, machine learning was introduced to the picture, changing the discourse of our lives forever. The reshaping of the world started with teaching computers to do things for us, and now it has reached the stage where even that simple step is eliminated. It is no longer imperative for us to teach computers how to execute complex tasks like text translation or image recognition: instead, we built systems that let them do it themselves. It’s as close to magic as the muggle community will ever reach!
The exceptionally powerful form of machine learning being used today goes by the name “deep learning”. On vast quantities of data, it builds complex mathematical structures called a neural network. Constructed to be analogous to how human brain functions, it was in 1930 that neural networks themselves were first introduced. Though, it was only in the past decade or so that computers have become efficient enough to use that ability.
What exactly is Machine Learning?
So, in general terms, machine learning is a result of the application of Artificial Learning. Let’s take the example of you shopping online — have you ever been in a situation where the app or website started recommending products that might in some way be associated or similar to the purchase you made? If yes, then you have seen machine learning in action. Even the “bought together” combination of products is another byproduct of machine learning.
This is how companies target their audience, and divide people into various categories to serve them better, make their shopping experience tailored to their browsing behavior.
Machine learning is merely based on predictions made based on experience. It enables machines to make data-driven decisions, which is more efficient than explicitly programming to carry out certain tasks. These algorithms are designed in a fashion that gives exposure to new data that can help organisations learn and improve their strategies.
What is the future of Machine Learning?
- Improved cognitive services
With the help of machine learning services like SDKs and APIs, developers are able to include and hone the intelligent capabilities into their applications. This will empower machines to apply the various things they come across, and accordingly carry out an array of duties like vision recognition, speech detection, and understanding of speech and dialect. Alexa is already talking to us, and our phones are already listening to our conversations— how else do you think the machine “wakes up” to run a google search on 9/11 conspiracies for you? Those improved cognitive skills are something we could not have ever imagined happening a decade ago, yet, here we are. Being able to engage humans efficiently is under constant alteration to serve and understand the human species better.
We already spend so much time in front of screens that our mobiles have become an extension of us- and through cognitive learning, it has literally become the case. Your machine learns all about you, and then accordingly alters your results. No two people’s Google search results are the same: why? Cognitive learning.
- The Rise of Quantum Computing
“Quantum computing”— sounds like something straight out of a science fiction movie, no? But it has become a genuine phenomenon. Satya Nadella, the chief executive of Microsoft Corp., calls i7t one of the three technologies that will reshape our world. Quantum algorithms have the potential to transform and innovate the field of machine learning. It could process data at a much faster pace and accelerate the ability to draw insights and synthesize information.
Heavy-duty computation will finally be done in a jiffy, saving so much of time and resources. The increased performance of machines will open so many doorways that will elevate and take evolution to the next level. Something as basic as two numbers- 0 and 1 changed the way of the world, imagine what could be achieved if we ventured into a whole new realm of computers and physics?
Join the AI & ML course online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career.
- Rise of Robots
With machine learning on the rise, it is only natural that the medium gets a face on it— robots! The sophistication of machine learning is not a ‘small wonder’ if you know what I mean.
Multi-agent learning, robot vision, self-supervised learning all will be accomplished through robotisation. Drones have already become a normality, and have now even replaced human delivery men. With the rapid speed technology is moving forward, even the sky is not the limit. Our childhood fantasies of living in an era of the Jetsons will soon become reality. The smallest of tasks will be automated, and human beings will no longer have to be self-reliant because you will have a bot following you like a shadow at all times.
Career Opportunities in the field?
Now that you are aware of the reach of machine learning and how it can single-handedly change the course of the world, how can you become a part of it?
Here are some job options that you can potentially think of opting –
- Machine Learning Engineer – They are sophisticated programmers who develop the systems and machines that learn and apply knowledge without having any specific lead or direction.
- Deep Learning Engineer – Similar to computer scientists, they specialise in using deep learning platforms to develop tasks related to artificial intelligence. Their main goal is to be able to mimic and emulate brain functions.
- Data Scientist – Someone who extracts meaning from data and analyses and interprets it. It requires both methods, statistics, and tools.
- Computer Vision Engineer – They are software developers who create vision algorithms for recognising patterns in images.
Machine learning already is and will change the course of the world in the coming decade. Let’s eagerly prep and wait for what the future awaits. Let’s hope that machines do not get the bright idea of taking over the world, because not all of us are Arnold Schwarzenegger. Fingers crossed!
If you’re interested to learn more about machine learning, check out IIIT-B & upGrad’s Executive PG Programme in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.
What are the qualifications required to become a computer vision engineer?
In order to become a computer vision engineer, a bachelor’s, master’s or PhD degree in computer vision or science is mandatory. One can also get a computer vision engineer job by completing engineering with a specialization in computer science. Apart from educational qualifications, you need to have a fair knowledge of different programming languages like Python, C, C++, etc. Also, you need to know about matrix multiplication, linear algebra, linear transformation, etc. Above all, you should have a solid interest in the field of computer vision to do well in your job.
Which one should I learn first: machine learning or AI?
Machine learning and artificial intelligence are interconnected. Machine learning is just a subcategory of artificial intelligence. However, if you are focused on getting a stable job, you should focus on machine learning as it holds a higher scope than AI. If you are interested in learning about AI and machine learning in general, then focus on learning the one you are most interested in. Thus, to answer the question, you should learn whatever aligns with your future needs.
What are the cons of using quantum computing?
Heating problems and efficiency issues arise in quantum CPUs. Thus, the technology required to implement quantum computers effectively is not available presently. When using quantum computing, secure communication or any type of online transaction could be hacked, with the data being misused or resold.