Combining human expertise and machine learning: the grail of Industry 4.0
The industry’s fascination with artificial intelligence and machine learning in particular has raised questions about the imminent ascendancy of technology over man. But as these highly sophisticated tools become more widely available, it becomes clear that the prowess of artificial intelligence has little future unless it is complemented by the finesse and discernment of the human mind.
When AI compensates for human weaknesses and vice versa
The (very numerous) representations of Artificial Intelligence in popular culture and the media have contributed to implanting a mistaken idea in the collective unconscious: that artificial intelligence could overtake the human mind, inevitably rendering us obsolete. In reality, this is not the case. Just like the human brain, AI and Machine Learning, in addition to their undeniable strengths, also have their flaws. Despite computing power and levels of precision that would make the world’s greatest minds green with envy, AI and ML suffer from major flaws:
- a total lack of critical thinking,
- an inability to take risks,
- and a lack of discernment in the face of ambiguity.
For the time being, Machine Learning has proved its worth on sometimes highly complex technical tasks, but it is still no substitute for human intelligence or expertise. This is particularly true when it comes to global vision, inventive problem-solving, cross-disciplinary expertise, discernment and uncertainty.
According to experts on the subject, the future of the industry rests largely on the collaboration between human expertise and artificial intelligence, rather than on AI’s assumption of dominance. If the main ambition of the solutions currently being developed is to compensate for the inherent weaknesses of the human mind, it is up to us to compensate for AI’s weaknesses by complementing its strengths with our expertise.
Thanks to this alliance, it will be possible to develop Industry 4.0 in a sustainable, viable and intelligent way.
Predictive maintenance: an illustration of the complementarity between man and AI/ML
Predictive maintenance, which is winning over more and more industrial players keen to remain competitive, is a perfect example of man-machine complementarity. This maintenance method relies on the use of machine learning algorithms to identify equipment failure signals, before they break down.
Predictive maintenance enables maintenance teams to :
- plan their interventions
- anticipate the routing of spare parts needed to ensure their availability
- maintain the operational continuity of production resources.
While the precision of the data supplied by sensors and the sophistication of the software that interprets them are a technological feat, they are of little interest if they are not enriched by the expertise of the technicians and engineers who use them to optimize their maintenance operations (labeling, interpretation, corrections).
For the time being, Machine Learning, as we know it today and as we can imagine it in the years to come, is more a tool for perfecting human intervention than a technology destined to replace it. Technology will provide the necessary information in time to optimize the intervention on the equipment, but the expert’s enlightened view of this information and his knowledge will ensure the quality and relevance of the intervention.
This man-machine alliance is one of the major pillars of Industry 4.0, and an indispensable asset for companies wishing to remain – or rise – to the forefront.
Expert intervention in DiagFit
With DiagFit, the generation of predictive models is now at your fingertips, without the need to write a single line of code. Thanks to guidance based on a rigorous methodology, Business Experts can explore and annotate their data to extract the information essential to model building. In this way, the technology makes itself available to those who have the knowledge and expertise. It facilitates the implementation of effective predictive maintenance.
