Read about artificial intelligence, machine learning and cognitive computing.
Artificial intelligence (AI) embraces machine learning and cognitive computing, but they are not the same.
They are similar as AI researchers are necessary for building smart machines, but experts in machine learning are necessary to make those machines genuinely intelligent.
The term artificial intelligence dates back to the 1940s when Alan Turing developed the codebreaking machine and subsequently the now famous Turing Test, which was based on the concept that a machine was capable of thinking.
The codebreaking machine which helped to end World War II, used machine learning to identify the codes needed. Today, the major players in tech, like Nvidia and Google, are working on developing machine learning and pushing hard for computers to learn to think the same way humans do so that they can progress into what is being hailed as the next technological revolution: machines that can ‘think’ like people, that’s cognitive computing.
In the last decade, machine learning has led to innovations such as self-driving cars, far superior web searches, practical speech recognition and a massively improved understanding of the human genome.
At its most basic, AI and machine learning are exemplified in the handy way in which Google picks up on the fact you made a typo and changes your search parameters to what it thinks you meant instead. A little note pops up saying ‘Did you mean…’ or ‘Showing results for…’, giving you the option to stick to your original (potentially wrong) search or see the results for what Google thinks you really meant. Nine times out of 10, Google is right and you made a mistake. This machine learning algorithm is a small thing, but it saves people a lot of time trawling through search results that aren’t relevant because they failed to notice they made a spelling mistake. Google’s search results are predominantly delivered through Rankbrain which is a form of artificial intelligence.
Here are two examples of artificial intelligence in practice:
Mainly focused on travel, two examples of machine learning include:
Cognitive computing in healthcare automates processes, such as computers collating all possible knowledge surrounding a particular condition. This may include the best practices and treatments advised, what the patient’s history is and how this could impact the condition and what academic evidence is available. This enables doctors to provide evidence-based treatment plans based on the individual and the latest research available.
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