Machine learning is gaining popularity as a career choice among the young students of computer science and other quantitative fields. And of course, the study of machine learning does open up a few ways into the AI industry which is already quite big in spite of being at a nascent stage at best. 2019 has been a great year for machine learning and artificial intelligence; both in terms of innovations and popularity. It is time to gauge what the new year holds for us as far as these disruptive technologies are concerned.
Machine learning adoption is at an all time high
The concept of machine learning is pretty old. We can trace it back to the bloody days of the second world war where the legendary Alan Turing applied machine learning to break an impenetrable German code and ended up winning the war for England. The discipline has long come out of the academic hiatus and entered the dynamic arena of business.
53% of the world’s data and analytics decision makers have confirmed that they are in the process of adopting or upgrading some form of machine learning process. 29% of the managerial developers have already used machine learning. This tells us a very convincing story of the rise of machine learning.
Before we move towards further declarations about machine learning and artificial intelligence, it will be well suited to discuss what makes machine learning such a hot cake in today’s market. What is it that is making so many students and professionals take a turn towards machine learning.
Machine learning means enabling machines to learn from information without any human intervention. Machine learning algorithms are exposed to a humongous amount of data and it is taught to recognize certain patterns in those. As more data is provided to the program it recognizes patterns in the data all by itself. The reason behind the surge in the popularity of machine learning is the availability and accessibility of an unfathomable amount of data. Machine learning requires data and good data. The data collection, storage and computational features have reached the stage of advancement needed for machine learning.
The field of Natural language processing burgeons
We have become used to using voice commands to make our phones do certain things for us. It does not fascinate most of us any more that a machine can actually understand our speech, even recognize, to an extent, our accents. The road leading to this had not been easy. NLP was in the process of development for years and it kept hitting roadblocks. In 2018 it really took off.
Google open sourced the state of the art NLP system BERT in 2019. It was a transformer based model. This was out performed by Google’s Transformer – XL. Later Open AI’s GP2 became very famous for generating human like texts.
Developers started pre-training NLP systems with language models so that they could learn the slight nuances of the language with the help of comparatively small amounts of data.
The focus is now on end to end speech recognition and multilingual models. 2020 will show how far NLP is to reach in these areas. We are likely to see an increased focus on efficiency and sample quality.
Reaching the peak in computer vision
Thanks to the developments in deep learning and neural networks machines can now detect and recognize objects around them and that too with more speed and accuracy than human beings. BMW has recently launched an autonomous car which has the ability to detect objects that may go unnoticed by the human driver and warn him or her even when the car is not driving it self.
Computer vision too had been hitting roadblocks for years. 2020 can be a watershed year for this technology. The object detection algorithms have really taken off.
Deep fake, that is highly realistic fake images and videos made with the help of deep learning, is going to be a matter of concern in 2020. The world is a data sensitive place. One video can start a riot in some countries. That is why detecting deep fakes is going to be a challenge that has to be taken.
Towards more unbiased machine learning
Machine learning depends on data. It delivers tasks based on the patterns recognized from previously entered data. This data is likely to be the vehicle of all sorts of biasness, racism and ethical failures. Hence there is no stopping the machine from becoming the same. This is something that has to be checked in 2020 and developers are on it.
Machine learning training opens up an exciting world of opportunities for you. Get yourself trained and then go train some machines.