Both Artificial Intelligence (AI) and Machine Learning (ML) are the two important tech buzzwords at the moment. Both the terminologies are frequently used in the context of Big Data, analytics, and the broader waves of disruptive technologies which are sweeping through the landscape.
Essentially, AI is the wider concept of machines being able to carry out tasks in a way that could be considered “smart”. On the other hand, Machine Learning is a current application of AI. The technology is based on the idea that that we should really just be able to give machines access to data, and let them learn for themselves.
How Artificial Intelligence has grown over the years?
In 1956, a handful of computer scientists rallied around the term at the Dartmouth Conferences, which led to the inception of the field of AI. Since then, the technology has been part of human imagination, besides simmering in research labs.
As technology, and, importantly, our understanding of how our minds work, has progressed, our concept of what constitutes AI has changed. Rather than increasingly complex calculations, work in the field of AI concentrated on mimicking human decision making processes and carrying out tasks in ever more human ways.
AI devices, which are designed to act very intelligently are often categorized into one of two fundamental groups, applied or general. Applied AI systems can intelligently trade stocks and shares, or even stretch as far as maneuvering an autonomous vehicle. These type of AI systems are generally widespread.
Generalized AIs can be described in essence as systems or devices which can in theory handle any task. These systems are usually less common, but this is the landscape where most of significant and exciting advancements are happening today. Most importantly, this is the AI class that has led to the development of Machine Learning. Because of the current state-of-the-art features, generalized AI is often referred to as subset of AI.
Since 2015, businesses and organizations around the world have noticed an Artificial Intelligence explosion. Wide availability of GPUs can be considered as a primary reason behind this transformation. Parallel processing power not only increases with GPUS, but it also becomes faster and cheaper. Besides, infinite storage and the flood of multiple data types– images, text, transactions, mapping data, and more, are the other reason behind this AI explosion. Interestingly, AI has alternately been heralded as the key to our civilization’s brightest future.
How did Machine Learning come into existence?
Machine Learning is considered as the primary driver behind AI development. Two important breakthroughs in the direction is what’s propelling machine learning technology ahead.
The first breakthrough came in 1959, with Arthur Samuel’s realization that it might be possible to teach computers to learn for themselves. This approach takes a twist over the traditional approach, where focus was laid on teaching computers everything they need to know about the world and how to carry out tasks.
The second attribute was a result of the emergence of internet. It brought into account the huge increase in the amount of digital information being generated, stored, and made available for analysis.
These innovations helped engineers realize that it would be a far more efficient approach to code computers to think like human beings instead of teaching computers and machines how to do everything. These machines are plugged to the internet, from where they gain access to all of the information in the world.
Machine Learning can’t be imagined without neural networks
A computer system designed to work by classifying information in the same manner a human brain does is essentially what defines a neural network. Neural networks have been instrumental in teaching machines to understand world the same way we do. They accomplish this objective, while retaining innate advantages, such as speed, accuracy, and lack of bias.
The neural networks incorporate a system of probability. It depends on the data fed to it, utilizing which it is able to make statements, decisions, or predictions with a degree of certainty. The ‘learning’ aspect is taken into consideration with the feedback loop. The networks sense if its decisions are right or wrong, to modify the approach accordingly for future.
Neural networks can be taught to recognize images, and classify them according to elements they contain. They enable machine learning applications to read text, and figure out whether the person who wrote it is making a complaint or offering congratulations. Interestingly, they can also listen to a piece of music, and decide whether it is likely to make someone happy or sad. Besides, they can also find other pieces of music to match the mood.
Basically, there are several opportunities and applications offered by systems based around machine learning and neural networks. Machine Learning will assist machines in understanding the major nuances in human language, besides teaching them how to respond in a way that a particular audience is likely to comprehend.
Today, machine learning has certainly been grabbed as an opportunity by marketers. Much of the progress noticed in the space within the last couple of years can be largely attributed to the fundamental changes in how we envisage AI working. This has also been brought about by developments in the field of machine learning.
However, machine learning must make bigger jumps in terms of performance for AI to keep progressing. Unfortunately, this is rarely possible in the traditional high-performance computing world, where problems are well-defined. Moreover, optimization work has already been happening over years. In other words, Machine learning algorithms still have room for improvement. Many large technology companies are working tirelessly to make it more intelligent.
To sum it up, Artificial Intelligence and Machine Learning certainly has a lot to offer for the world we live in. With its promise of automating mundane tasks as well as offering creative insight, industries in every sector from banking to healthcare and manufacturing will reap the benefits.
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With a background in Engineering, Amit has assumed the mantle of content analyst at Analytics India Magazine. An audiophile most of the times, with a soul consumed by wanderlust, he strives ahead in the disruptive technology space. In other life, he would invest his time into comics, football, and movies.