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Five common myths and misconceptions about artificial intelligence: Gartner

Five common myths and misconceptions about artificial intelligence: Gartner

There are several misconceptions about the potential of artificial intelligence (AI) among the IT and business leaders. AI is the one the hottest technology today that is making its way into every organization. In such a scenario, the CXOs need to ensure what AI can do for them, what value it can bring to their business, and what its limitations are. This is important because organizations can make the most of AI only when they use it the right way.

The analyst firm Gartner has identified five AI misconceptions that will help organizations to separate reality from myths.

1. AI is equivalent to human intelligence

The current state of AI contains of software solutions that can solve certain problems. There are a number of forms of AI that have proven to be really clever, but it would be wrong to say that AI works in the same way as the human brain does.

“Some forms of machine learning (ML) – a category of AI – may have been inspired by the human brain, but they are not equivalent,” said Alexander Linden, research vice president at Gartner.

“Image recognition technology, for example, is more accurate than most humans, but is of no use when it comes to solving a math problem. The rule with AI today is that it solves one task exceedingly well, but if the conditions of the task change only a bit, it fails.”

2. AI systems can learn on their own

To build an AI-based system, it requires intervention from human. This human intervention may come from human data scientists and engineers who execute their experience in framing a problem, preparing data, determining suitable datasets, removing bias in training data, etc.

3. AI can be bias-free

The AI systems are developed by humans who integrate data, rules and other inputs. Hence, AI is also biased in some way. Gartner says that there are no ways to make AI completely free of bias. What can be done is to reduce the biasing to minimum.

“In addition to technological solutions, such as diverse datasets, it is also crucial to ensure diversity in the teams working with the AI, and have team members review each other’s work. This simple process can significantly reduce selection and confirmation bias,” added Linden.

4. AI can only replace mundane and repetitive tasks

AI-based systems allow organizations to make decisions with accuracy through predictions, classifications, and clustering. IT and business leaders think that these capabilities of AI systems can only automate the repetitive tasks. But AI can also simplify complex tasks.

For example, healthcare organizations are using AI for x-rays. An X-ray application based on AI can be used to detect diseases faster than radiologists.

Also read: 10 technologies that CXOs should incorporate in their roadmaps and strategies in 2019

5. Every organization doesn’t require AI strategy

AI has the potential to transform every organization regardless of the industry. Hence, every organization should build an AI strategy on the basis of business challenges and use cases.

“Even if the current strategy is ‘no AI’, this should be a conscious decision based on research and consideration. And – as every other strategy- it should be periodically revisited and changed according to the organization’s needs. AI might be needed sooner than expected,” Mr. Linden concluded.

Source: https://www.gartner.com/en/newsroom/press-releases/2019-02-14-gartner-debunks-five-artificial-intelligence-misconce

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