Gartner Clarifies Five Common Myths and Misconceptions about Artificial Intelligence (AI)

Some time tech leaders are little confused by the knowledge they have about Artificial Intelligence (AI) capabilities and what AI can do for their organizations. AI technology enabling new opportunities for organization IT and tech leaders should have clarity and free from common myths and misconceptions towards the adaptation of AI technology.

Five common myths and misconceptions about AI

1. AI Works in the Same Way the Human Brain Does

First common AI myths: AI is a computer engineering discipline. In its current state, it consists of software tools aimed at solving problems. While some forms of AI might give the impression of being clever, it would be unrealistic to think that current AI is similar or equivalent to human intelligence.

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

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2. Intelligent Machines Learn on Their Own

2nd Common AI myths: Human intervention is required to develop an AI-based machine or system. The involvement may come from experienced human data scientists who are executing tasks such as framing the problem, preparing the data, determining appropriate datasets, removing potential bias in the training data (see myth No. 3) and – most importantly- continually updating the software to enable the integration of new knowledge and data into the next learning cycle.

3. AI Can Be Free of Bias

3rd common AI myths: Every AI technology is based on data, rules and other kinds of input from human experts. Similar to humans, AI is also intrinsically biased in one way or the other. “Today, there is no way to completely banish bias, however, we have to try to reduce it to a minimum,” Mr. Linden said. “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.”

4. AI Will Only Replace Repetitive Jobs That Don’t Require Advanced Degrees

4th common AI myths: AI enables businesses to make more accurate decisions via predictions, classifications and clustering. These abilities have allowed AI-based solutions to replace mundane tasks, but also augment remaining complex tasks.

An example is the use of imaging AI in healthcare. A chest X-ray application based on AI can detect diseases faster than radiologists. In the financial and insurance industry, roboadvisors are being used for wealth management or fraud detection. Those capabilities don’t eliminate human involvement in those tasks but will rather have humans deal with unusual cases. With the advancement of AI in the workplace, business and IT leaders should adjust job profiles and capacity planning as well as offer retraining options for existing staff.

5. Not Every Business Needs an AI Strategy

Fifth common AI myths: Every organization should consider the potential impact of AI on its strategy and investigate how this technology can be applied to the organization’s business problems. In many ways, avoiding AI exploitation is the same as giving up the next phase of automation, which ultimately could place organizations at a competitive disadvantage.

In the conclusion Mr. Linden concluded said “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,”.