AI & Big Data — Making Sense of Information Overload
Big Data has an indispensable role in the accelerated progress of AI & need for clean big data only seems to increase with time.
IBM suggests that Big Data can be characterized by any or all of three “V” words to investigate situations, events, and so on: volume, variety, and velocity. Michael Cox and David Ellsworth were among the first to use the term big data literally, referring to using larger volumes of scientific data for visualization (the term large data also has been used).
Artificial Intelligence (AI) is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. From robotics to hospitality to medicine, AI is slowly making inroads into almost every industry.
But how is AI progressing so rapidly? How are scientists & engineers rapidly making such progress in this field of computing which once was limited to the fantasy of Hollywood story writers? The answer lies in one word: Data.
The advent of the internet & its penetration over the past couple of decades has enabled for a large volume of information being generated, stored & available for analysis. Engineers slowly realised that rather than teaching computers how to do everything, writing code enabling computers to mimic human behaviour, pattern recognition, data sifting & then providing them access to the data for analysis would be much more efficient. This has led to the progress of “machine learning”, arguably the most successful approach to AI.
Machine Learning models are designed to make statements, decisions, predictions & deductions (outcomes) with a certain degree of certainty. Add to that a certain “feedback loop” — where the machines are informed if their results were right or wrong & enabling the model to modify its approach to be more accurate. So as a result, the more data that a system processes, the more accurate it will become at predicting outcomes.
Thus, the dependency on data. The more volume of data we are able to generate & supply machine learning models, the smarter AI we are able to develop. The unprecedented volume of data being generated today is being put to use to develop these “smart” models of automation.
This synergy between data & AI models have led to breakthroughs in image recognition techniques, speech & pattern recognition, natural language processing, mapping complex statistics & probabilities (games like chess & go).
Computers can now recognize faces(Facebook), understand your speech with various accents (Google Now, Alexa, Siri) pretty accurately and in probability beat you in a game of chess. It could also converse with you & answer your questions via speech or text (chatbots).
If you notice, there is a huge connect between data and the companies who are pioneering in the AI field. Google has autonomous self-driving cars, virtual assistants and amazingly accurate translators through both visual & text. Apple & Amazon also have amazing AI capabilities. These are also the companies that are leaders in the industry that collect & store the maximum data across the board. This is enabling them to use the same data to build these amazing platforms.
What’s better is that a lot of the work being done in AI is also collaborative. Amazon and Google also have made their big data platforms available publicly for interested users/ companies to build their own models.
Big Data has an indispensable role in the accelerated progress of AI & need for clean big data only seems to increase with time. This is just the beginning of AI taking a huge leap towards mainstream utility and the potential for the future seems endless.
Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house
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