Scott Koegler

Scott Koegler

Scott Koegler is Executive Editor for PMG360. He is a technology writer and editor with 20+ years experience delivering high value content to readers and publishers. 

Find his portfolio here and his personal bio here

AI Still Needs Training To Understand Us

While artificial intelligence (AI) continues making headlines and breaking barriers that were thought to be impossible a decade ago, humans are now looking at it with both awe and fear. This is understandable because no one can allow their work to be taken away or to be replaced in places where they have for years looked for their daily bread. When people hear that companies are building AI-powered assistants to help in different tasks, they get afraid that AI is close to becoming independent of humans. The questions that they often ask are; Is AI close to matching human intelligence? Are we close to the fictional Skynet predicted in the Terminator? Is AI smart in its own and will it take positions long occupied by humans?

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AI is Getting Closer to Understanding Humans

Artificial intelligence (AI) is one of the leading (if not the leading) topics of conversation these days. The recent advances in AI have made it even more than before and are now closer to reality than you might imagine. Until the last decade, computers were not as sophisticated at producing sentences that make sense. They could not compete with humans in many aspects and humans seem irreplaceable in areas such as language. However, the past few years has changed the narrative completely. The field of natural language processing (NLP) has advanced significantly and machines are now competing humans and are even showing signs of understanding human language.

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Science is Benefiting from AI Too

Artificial intelligence, machine learning, and big data are revolutionizing manufacturing, marketing and supply chain. The same is happening to scientific research where AI is proving to be a critical technology in scientific discovery. For instance, it is proving indispensable in predicting the outcomes of experiments, an example being quantum systems. Fully incorporated with fundamental laws of physics, AI can easily predict scientific simulations. Materials science, just like other experimental science branches such as drug discovery, leverage AI to model sophisticated experiments from data that is collected.

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