It is this foundation that AI is now disrupting, providing the none-expert with expert like qualities. But this progression is a fallacy. If we let a junior in a consulting firm, for example, use tools to create presentations that are better than what she could produce on her own, are we teaching her anything? Could she repeat the results with a paper and with a pen? How will she gain the needed knowledge, critical thinking, and expertise if creates or assists the work? It’s all very well that engineers can prompt the code they need, but does this make them good engineers?

The trend of heavily relying on AI automation to complete tasks is the face of the future. Its here to stay. But there is a challenge we must acknowledge. We need to bridge two extremes. On one extreme is the irresistible temptation to benefit as much as possible from the automation AI provides. On the other extreme is the need to let our employees battle through their work themselves so they improve their skills and grow to become the experts their industry needs. How can we do one without losing the other?

This article is not a rant aimed at stopping the progress of technology. There is no stopping it; we can only join it. The challenge is how to build experts and expertise in an AI-generated world. How can we benefit from the optimizations AI can provide without forgetting how to build boats, aqueducts, or manufacture paper if we want to learn from the experience of the Portuguese, the Romans, and the Chinese? The challenge is not this or that but this and that. We want to benefit from AI, and we need to build a generation of new experts. But how do we connect these two dots?

In an interview at the Aspen Ideas Festival on Tuesday, Mustafa Suleyman, CEO of Microsoft AI, made it very clear that he admires OpenAI CEO Sam Altman.

CNBC’s Andrew Ross Sorkin asked what the plan will be when Microsoft’s enormous AI future isn’t so closely dependent on OpenAI, using a metaphor of winning a bicycling race. But Suleyman sidestepped.

“I don’t buy the metaphor that there is a finish line. This is another false frame,” he said. “We have to stop framing everything as a ferocious race.”

Memristors with controllable resistive switching (RS) behavior have been considered as promising candidates for synaptic devices in next-generation neuromorphic computing. In this work, two-terminal memristors with controllable digital and analog RS behavior are fabricated based on two-dimensional (2D) WSe₂ nanosheets. Under a relatively high operating voltage of 4 V, the memristor demonstrates stable and reliable non-volatile bipolar digital RS with a high switching ratio of 6.3 × 10⁴. On the other hand, under a relatively low operation voltage, the memristor exhibits analog RS with a series of tunable resistance states. The fabricated memristors can work as an artificial synapse with fundamental synaptic functions, such as long-term potentiation (LTP) and depression (LTD) as well as paired-pulse facilitation (PPF). More importantly, the memristor demonstrates high conductance modulation linearity with the calculated nonlinear parameter for conductance as-0.82 in the LTP process, which is beneficial to improving the accuracy of neuromorphic computing. Furthermore, the neuromorphic computing of file types and image recognition can be emulated based on a constructed three-layer artificial neural network (ANN) with a recognition accuracy that can reach up to 95.9% for small digits. In addition, memristors can be used to emulate the learning-forgetting experience of the human brain. Consequently, the memristor based on 2D WSe₂ nanosheets not only exhibits controllable RS behavior but also simulates synaptic functions and is expected to be a potential candidate for future neuromorphic computing applications.

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