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What the history of AI tells us about the importance of data

October 27, 2020

Artificial Intelligence (AI) and Machine Learning (ML) are hot topics in the HR and talent world right now. New AI solutions are released almost daily and are claiming (sometimes rightly!) to revolutionize many aspects of the business, recruiting, and HR world. AI is not as new as it may seem, it has been developed over decades - but in spurts, not a smooth line.  Learn from history, anyone?

There have been two key surges of AI through the last few decades that together have brought it to a place where it's here to stay.  The first surge, several decades ago, was processing power - enough computing power to make AI work well.  What is driving the second (current)  AI surge? Data. AI only works well with good data and yet businesses still do not focus their time and money on data as they should.

The concept of AI has been evolving for some time. The first part of modern AI is generally considered Turing's concept of the learning machine. From that concept, progress was made through prototypes during the 50s and 60s.

After that, progress in the realm of AI slowed significantly due to cuts in funding and research. This slump period ended when Kunihiko described "Necognitron" - the first artificial neural network. The new development in that time period was a massive increase in processing power and the ability of machines to handle much more complex calculations and algorithms.

The 80s and 90s saw another surge in AI and another slump period. During this time period, machine learning developed and emerged. Continued research and development into machine learning and the rise of importance in data has brought us to our current state.

Data is what machine learning learns from and now, with the increasing size of datasets, machines have more information to work with and better chances of identifying patterns and learning from them. Good data drives AI development, yet data is still a backburner item for a lot of companies. Handling data falls to the bottom of project priority lists more often than not.

With major focus turning towards data in the business sector, why aren't companies making data its own, highly important project? When you plan your projects how much do you consider the role data will play? Focusing on and handling data is not as daunting as it sounds and it can make a significant impact on your AI projects.

SwoopTalent can help you automate all of your talent data processes and help make your data work for you. Contact SwoopTalent and make a plan!

Photo by Giammarco Boscaro on Unsplash

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