The Crux of Machine Learning is Realistic Expectations

Venturebeat has an article, based on IDC research, titled For 1 in 4 companies, half of all AI projects fail.

“Firms blamed the cost of AI solutions, a lack of qualified workers, and biased data as the principal blockers impeding AI adoption internally. Respondents identified skills shortages and unrealistic expectations as the top two reasons for failure, in fact, with a full quarter reporting up to 50% failure rate.”

We believe a key part of this is ‘unrealistic expectations’. Half of all AI projects failing for 1 in 4 companies isn’t unreasonable. AI and machine learning should be viewed as a research rather than a development activity in that it’s often the case that it’s not known if the goal is achievable until you try.

Another unrealistic expectation of machine learning is often to have 100% accuracy. The use of an accuracy % in assessing machine learning models focuses stakeholders minds too much on the perceived need for a very high accuracy. In reality, human-assessed, non-machine learning, processes such as medical diagnosis tend to have much less than 100% accuracy and sometimes have undetermined accuracy but these are reasonably seen as being acceptable.

In summary, there has to be upfront realistic expectations of both the possible outcome and the accuracy of the outcome for projects to correctly determine if AI activities are an unexpected failure.

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