Lifeboat Foundation

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.

GXO has partnered with Apptronik to explore Apollo humanoid robot’s use in warehouses. Apollo is the first commercial humanoid robot that was designed for friendly interaction and mass manufacturability.

A recent study published in the journal Artificial Intelligence in Geosciences introduced an advanced method for automatic microfossil detection and analysis. The research team consisted of members from the machine learning group at the University of Tromso (UiT) The Arctic University of Norway.

They have developed a pipeline for extracting fossil information from microscope slide images. They found that deep learning techniques outperform traditional image processing methods and that self-supervision can be effectively used for feature extraction.

Kinetic Automation utilizes computer vision and machine-learning software for diagnosing and recalibrating advanced vehicle systems.

First-of-its-kind brand video premieres during cannes lions festival.

Toys R Us brand is leaping ahead of the curve by creating the first-ever brand film using OpenAI’s new text-to-video tool, Sora.

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