Artificial intelligence in business is all the rage, getting coverage in all sorts of articles and conferences, but there are serious problems with it. Gartner and Chris Skinner show what is really happening.

Gartner debunks five artificial intelligence misconceptions

IT and business leaders are often confused about what artificial intelligence (AI) can do for their organizations and are challenged by several AI misconceptions. Gartner, Inc. said IT and business leaders developing AI projects must separate reality from myths to devise their future strategies. They identified five common myths and misconceptions about AI in their Press Release:

1. AI Works in the same way the human brain does:
   • 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.
2. Intelligent machines learn on their own:
   • Human intervention is required to develop an AI-based machine or system. 
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. 
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.
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.

(Analysts to Discuss AI Use Cases and Best Practices at Gartner Data & Analytics Summit, March 4-6, 2019 in London, U.K.)

Chris Skinner - we are still a long way off

In his blog today, he stresses the difference between AI and machine learning much more directly, “Machines can learn whatever they’re programmed to learn, but they’re not intelligent. Intelligence takes what we learn and processes it, qualifies it and prioritises and censors it. That’s why we’re still a ways off true AI.”

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CTMfile take: But what are you going to use machine learning for? What not for? What is your strategy on machine learning and artificial intelligence, and the future of work?