Debunking AI Myths
IT and business leaders are often confused about what Artifical Intelligence (AI) can do for their organizations and are challenged by several AI misconceptions. Gartner, Inc. suggests that IT and business leaders developing AI projects must separate reality from myths to devise their future strategies.
Myth 1: 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. 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.
Myth 2: Intelligent Machines Learn on Their Own
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.
Myth 3: AI Can Be Free of Bias
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. 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.
Myth 4: AI Will Only Replace Repetitive Jobs That Don’t Require Advanced Degrees
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.
Myth 5: Not Every Business Needs an AI Strategy
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.
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.