Lifeboat Foundation

Kenyan runners, like many others, are grappling with the impact of expensive, high-performance shoes.

The track at Moi University’s Eldoret Town Campus doesn’t look like a facility designed for champions.

Explore the concept of the singularity— the point where AI could surpass human intelligence—and its potential impact on society.

As new AI models make their way into the mainstream, and business leaders scramble to adapt, one sometimes overlooked aspect of computing is the measure of conservation.

In an age where efficiency matters, teams are trying to provide new frameworks for compute.

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.”

OpenAI has delayed its Voice Mode feature that allows users to have near-real-time conversations with ChatGPT.

Researchers propose a neuron-as-controller model, challenging the simplistic 1960s-era computational model of neurons.

Researchers at the University of Vienna led by Philip Walther just pioneered the field of quantum mechanics and general relativity by measuring “the effect of the rotation of Earth on quantum entangled photons,” as stated in a press release.

In the Vienna experiment, they used an interferometer, which is the most sensitive to rotations. Its unparalleled precision makes it the ultimate tool for measuring rotational speeds, limited only by the boundaries of classical physics.

The team wondered if they could somehow leverage crystalline structures to identify a perfect candidate, sans building thousands of them in a lab.

The researchers were mostly on the lookout for 3D crystals with the right structural and electronic properties, so they could be “exfoliated.” 2D materials like graphene were extracted using this process from 3D.

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