![]() ![]() Instead, choose a system that is designed to be intuitive and easy to use by anyone, whether an inexperienced engineer on their first day or a 40-year veteran who may not be a digital native. ![]() Theoretically, a low-skilled operator could behave like a material science PhD, if the technology delivers to them the right insights, guidance, and prescriptive and predictive ideas, with very little friction.įor manufacturers looking to invest in technology, it means bypassing the cumbersome technologies that only the company’s experts or those that have undergone weeks of training could use. It also means that if your friction reduces to zero, and you start adding more power with data analytics, artificial intelligence, machine learning and other Industry 4.0 technologies, then you will make the most of your manufacturing operations. It means that reducing the friction, or making it easier for manufacturers to use technology, will increase the output of their workforce that are interacting with computers and software. This reveals the incredible power of manufacturing’s digital transformation. The result of the open chess tournament suggests a new equation: the power of the person, times the power of the machine, divided by 1+f (where “f” is the friction of how easy the machine is to use). Historically, the equation for this type of intelligence amplification was the following: the power of the person, times the power of the machine, equals the power of the system. ![]() Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.” “Their skill at manipulating and ‘coaching’ their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grandmaster opponents and the greater computational power of other participants. Kasparov explained the outcome in his book, Chess Metaphors: Artificial Intelligence and the Human Mind : This duo beat out a grandmaster chess player using an advanced, but complex chess supercomputer. The “surprise” outcome of one of those competitions was that a team of two amateur chess players won the competition using three laptops designed to easily help with decision-making. However, fewer are familiar with the results of a subsequent open chess tournament, where entrants were free to use whatever support they wanted, including any combination of humans and computers. This loss launched earlier predictions about machines taking over the work of humans, creating much concern and fear. Most people know the story of Garry Kasparov, a grandmaster chess champion who lost to IBM’s Big Blue supercomputer. Why am I confident that a similar transition will work for lower-skilled manufacturing workers? Consider a classic experiment, detailed by Ari Gesher from Palantir’s now archived blog. In each of these instances, technology eliminated tedious, time-consuming manual work, even as it augmented the education, skills and experience of the professionals. And few scientists and engineers use the once ubiquitous slide rule to assist with calculations. ![]() Product designers and architects have transitioned from manual to automated drawing tools. Accountants have abandoned handwritten ledgers in favor of electronic spreadsheets. We know this from historical experience-and not just the old go-to story of the first Industrial Revolution. In fact, human-machine symbiosis is not a new concept. ![]()
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