Speaking, as we were last month, about artificial intelligence and its adoption into electronics design and manufacturing, we observed that a current obstacle to implementation is the use by vendors of customer data in order to build their models.

And while vendors insist the data are aggregated and anonymized, said customers, naturally, have been generally circumspect over the perceived cost of the lessons they have learned – often painstakingly – being used to enable competitors, not to mention ultimately paying those same vendors for the courtesy.

To that I will add the musings of Neil Thompson, who is the director of the future tech research project at MIT’s Computer Science and Artificial Intelligence Laboratory.

Thompson argues that AI systems must not just be capable of performing “human” tasks but also must overcome the costs of implementation, including redesigning processes and methodologies. “There are a lot of places where ... humans are a more cost-efficient way,” he says.

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