“Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.” So far the definition of SAS, which is named a World's Most Innovative Company in 2021.

The term artificial intelligence goes back to 1956, but AI has become more popular today because of increased data volumes, advanced algorithms, and improvements in computing power and storage as well as a huge growing interest in its business applications. AI specifically refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

In short:

• Artificial intelligence refers to the simulation of human intelligence in machines.

• The goals of artificial intelligence include learning, reasoning, and perception.

• AI is being used across different industries including finance and healthcare.

• Weak AI tends to be simple and single-task oriented, while strong AI carries on tasks that are more complex and human-like.

We are about to reach a point where artificial intelligence is as common as human intelligence. Problems have been solved with no need for a mess of variables to be added. This is accompanied by the potential to make areas of the business to be more productive. We're living at the dawn of a new era.

Artificial intelligence is going to change every industry, but we have to understand its limits.

• The principal limitation of AI is that it learns from the data. There is no other way in which knowledge can be incorporated. That means any inaccuracies in the data will be reflected in the results. And any additional layers of prediction or analysis have to be added separately.

• Today’s AI systems are trained to do a clearly defined task. The system that plays poker cannot play solitaire or chess. The system that detects fraud cannot drive a car or give you legal advice. In fact, an AI system that detects health care fraud cannot accurately detect tax fraud or warranty claims fraud.

• In other words, these systems are very, very specialized. They are focused on a single task and are far from behaving like humans.

• Likewise, self-learning systems are not autonomous systems. The imagined AI technologies that you see in movies and TV are still science fiction. But computers that can probe complex data to learn and perfect specific tasks are becoming quite common.

In short, the potential is infinite and many areas of our life can be improved in the sense of innovation (allowing new paths to be accessed).

Beyond these questions, there is another layer that needs to be addressed. The area of human self-determined action, complex reasoning and the attribution of responsibility. How does gainful work look in future and who knows or controls the outcomes of AI-based decisions?

Picking up on this in the next blog article…

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