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today’s date, Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are a few terms that are being used by Technology evangelists interchangeably. These terms have their own distinct place and meaning when it comes to Analytics-driven projects. AI is actually a big umbrella that houses ML and DL inside it. AI is primarily used in places where it is expected that Machines will be able to mimic Human-like behavior like the ability to think, the ability to solve a problem and learn from the environment. Because of continuous evolution in the field of AI, machines have become better at some tasks when compared to humans. As the data around us continues to grow at breakneck speed, there will be a situation very soon that such the human mind will no longer be capable of manage such amount of data. In all such scenarios, AI-driven machines will be the only hopes for us.
ML is a set of all those techniques where the machine is required to learn all by itself and is able to recognize all the patterns in the data which can help machines solve a problem. These techniques are getting better with every single day, passing. They are helping us solve several problems in the areas of forecasting, regression, classification and in areas of Natural Language Processing. ML algorithms now have better accuracy and can perform a lot of tasks with minimal guidance. The Jurassic era algorithms like Logistic regression performs classification with a lot of data transformation and is also regarded as a weak classifier but with the advent of modern-day algorithms like Gradient Boost and Adaptive Boosting, pattern identification has become much powerful and better. However, ML algorithms are very much still a part under that big umbrella of AI.
Deep Learning has extended the envelope of AI to a great extent and these are again very much a kind of Machine Learning algorithms only. The major difference being that with DL, all the algorithms are now centered around Perception (single neuron) structure. With the enhancement in the processing capabilities, we can do so many new things such as Image Classification, Voice recognition, Language translation, Caption generation from an Image, Music synthesis and many more such activities which were earlier considered beyond the ambit of Machines. So DL has opened up a new avenue for Data Scientists wherein several Perceptrons are tied up with each other through layered connections and are able to pick up patterns from large datasets. These patterns are then used to learn the unique styles of Music composition, learn unique brush patterns of an artist and even being deployed by Google’s search engine to effectively and correctly process the search queries.
The bottom line of the whole discussion is that irrespective of the technology we wish to deploy, ML, AI, and DL are all about the patterns in the data automatically and then reacting to the new inputs on the basis of the patterns being learned by the machine. The field of AI is undergoing a phase where the evolution from mediocre methods has really speeded up towards far better algorithms. There are changes that are rapidly changing the landscape of AI-driven innovation and in the next few years, we hope to see changes that won’t be short of being called Miracle, by today’s generation.
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