The Birth of Artificial Intelligence

Artificial intelligence (A.I.) is said to have had its humble beginning in 1956 at a summer-long conference called the Dartmouth Summer Research Project on Artificial Intelligence. The purpose of the conference was to investigate which aspects of human learning could be ‘programmed into thinking machines,’ the precursor to computers. The intent was to find ways for these thinking machines to solve problems and learn from their mistakes, just as humans do.

Since then, Artificial intelligence has not so quietly been the engine behind transformative changes in how people think, make buying decisions, and relate to one another in a way while redefining the very definition of community, friendship and social discourse.

A.I. in Today’s World

AI is disrupting the status quo across a broad cross section of industries by demonstrating its superiority and effectiveness as a powerful data mining, pattern recognition and forecasting tool that can tackle challenges that no human or other mathematical-based technology can achieve.

Siri (Apple) and Alexa (Amazon), have already worked their way into our daily lives as A.I. personal assistants. Social media platforms like Facebook have helped warm up the public to this disruptive technology that is reshaping nearly every industry and government worldwide.

Now, with more than 60 years of progress, this transformative technology is poised to make the difference between growth and decline for businesses worldwide as it disrupts and transforms practically every aspect of the human experience touched by technology.

Artificial intelligence, as it’s been understood to date by the public, conjures up fearful images of armies of robots rising up and challenging humankind for supremacy in intelligence as well as in physical prowess. Taken to its logical extreme this doomsday perspective of this incredible predictive technology has been widely promoted over the years in science fiction books and in big screen horror movies. While such fears are not entirely unfounded, they are, at this point in history, quite premature given the current state-of-the-art in artificial intelligence.

Yet, despite these concerns and even fear of where this technology will take us over the next century, artificial intelligence has, not so quietly, been the engine behind transformative changes in how people think, make buying decisions, and relate to one another in a way which has been redefining the very definition of community, friendship and every other aspect of social discourse. These changes to date have been mainly due to the application of A.I. to such areas as facial recognition and speech recognition, within the overall general arena known as pattern recognition.

How does A.I. work?

The broad category of “artificial intelligence” comprises of multiple sub-categories with 2 standing out among the rest; Machine Learning and Deep Learning.

Machine Learning

Machine Learning constructs mathematical algorithms that are able to parse data, learn from that data and make data-related predictions based on what’s been learned to date.

A popular application of A.I. that is a good example of this is Netflix. When it creates viewing suggestions tailored to each subscriber’s preferences it’s using A.I. But it applies machine learning to update those recommendations after learning the subscriber’s interests and habits, what they watch, how long they watch it for, and what they don’t watch.

Deep Learning

Deep learning is a more sophisticated refinement, of machine learning. The key difference is that it can learn on its own, independently, through iterative learning or training process similar to how the human brain, processes information and arrive at intelligent decisions.

Deep learning models must organize its processes into structured layers that form an ‘artificial neural network’ that can learn from the data it was presented with and can make intelligent decisions and informed predictions based on what it has learned.

Artificial Neural Networks

Artificial neural networks (ANNs) are modeled after human brains to best mimic this process. A major reason why deep learning with artificial neural networks has become so popular recently is that they are powered by huge amounts of data which increases their ability to learn from that data and make highly accurate forecasts.

There have been considerable advancements made in computing hardware related to how ANNs crunch its data which has sparked renewed interest in artificial intelligence and its applications in numerous industries such as medicine,

More recently, Internet-connected cloud computing technology emerged, in which neural network software code and big data can be stored and run on powerful physical or virtual servers that make up the ‘Cloud’. This development made the application of deep learning with neural networks more practical and far more cost-effective. Now, large-scale projects involving big data can utilize cloud computing to store its data.

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