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AI may be making errors for vehicle maintenance

AI is a complex amalgamation of code. As we entrust it with more responsibilities we need to be cautious.

Cars weighing more than 1.6 tonnes would be slapped with swingeing parking charges if the vote passes
Cars weighing more than 1.6 tonnes would be slapped with swingeing parking charges if the vote passes - Copyright AFP/File Dimitar DILKOFF
Cars weighing more than 1.6 tonnes would be slapped with swingeing parking charges if the vote passes - Copyright AFP/File Dimitar DILKOFF

Many vehicle manufacturers have taken advantage of artificial intelligence. How successful is artificial intelligence when applied to the car sector? It may not always be successful. AI in auto maintenance could turn into a nightmare. A recent study reveals that 65 percent of AI-based car tools give incorrect advice, resulting in $2.5 billion spent globally on unnecessary repairs. The International Driver’s Association Team delved into this staggering data and thought you would find it interesting.

The question that arises is “did you ever think AI in auto maintenance could turn into a nightmare?”

Evidence from a Boston-based mechanical workshop offers some insight into this question. Misdiagnosis is the recurring theme observed in this case study. Over a span of two years, the workshop reported an 87 percent increase in repair costs for vehicles maintained by AI-based tools. Most of these expenses surfaced from damages incurred due to the adoption of incorrect advice or procedures suggested by the AI programs.

Dominic Wyatt of the International Drivers Association lends his expertise to this pressing issue. He explains to Digital Journal: “AI is a complex amalgamation of code. As we entrust it with more responsibilities, the risk of consuming erroneous advice also escalates.” Wyatt indicates that the fundamental issue lies not so much with the technology itself but in the programming logic and the decision-making algorithms that guide AI tools.

Wyatt shares three key lessons learned from the AI shortcomings in auto maintenance:

Erroneous Programming

AI may give incorrect maintenance advice due to coding errors. This constitutes the bulk of recorded tool malfunctions. Approximately 75 percent of reported AI tool malfunctions in auto maintenance are attributed to coding errors, highlighting the critical role of accurate programming in ensuring reliability.

Inaccuracy in Machine Learning

As AI learns and grows through data accumulation, any anomaly in the data pattern could trigger inaccuracies. Studies show that a mere 5 percent anomaly in the learning dataset can lead to a 15 percent increase in inaccuracies in AI-based automotive tools, underlining the sensitivity of machine learning processes.

Overall Tool Dependability

Typically, AI tools are as reliable as their programming allows. Ensuring developer competency is thus crucial for the success of these tools. Analysis reveals that tools developed by highly experienced developers show a 90 percent dependability rate, whereas those by less experienced developers have a 50 percent rate, emphasizing the direct correlation between developer competency and tool dependability.

Vehicle owners can challenge certain findings if they are knowledgeable about their car’s technology coding.

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Written By

Dr. Tim Sandle is Digital Journal's Editor-at-Large for science news. Tim specializes in science, technology, environmental, business, and health journalism. He is additionally a practising microbiologist; and an author. He is also interested in history, politics and current affairs.

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