Artificial intelligence (AI) has only in recent years become a part of our day-to-day topics. It is easy to think that it is a young concept, a recent discovery. But where did it originate?
AI is a specific area of computer science, that aims to create machines able not only to work and think, but also to act and react as human beings would.
The pioneer of this discipline is Alan Turing, a British mathematician and logician back in 1950, who started advancing the idea that machines could be able to think. Fast forward a few years and Dartmouth College math professor, John McCarthy, stated that “every aspect of learning of any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”
AI has been advancing quickly and presently we use it many times a day — often, without even realizing it. Organizations are always looking for ways to implement it into their current framework and processes. However, there are some companies and startups have been founded on the grounds of AI itself.
AI algorithms analyze quantities of data that until now had represented a big concern for human beings. Big data sets are very hard to examine in a fast and accurate way, while avoiding major mistakes.
With AI being more prolific than ever in this race to get the most out of the technology, is anyone monitoring the ethical side of the process? Is anyone asking “should we” in addition to “can we”?
As companies spend billions researching and developing AI, they are faced with deciding what responsible AI looks like. It’s all still new and exciting, but it also has serious implications for society. For example: How do you control bias in this new environment?
Before companies begin to address the ethics questions, data governance should be a priority. Because of the quick advance of AI technology, neural networks are better than humans at some tasks, particularly in certain image classification systems. This makes it very easy for organizations to utilize these advanced AI capabilities to streamline their operations, boost profits, cut costs, and improve customer service. However, if the underlying data is poorly managed and badly governed, that will be a wasted effort.
As we near the end of the year, there are many people predicting trends for the new year (and next five years) to come. Some of the common technology themes include AI, as well as machine learning, Internet of Things (IoT) and cybersecurity.
Melody K. Smith
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