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Category: robotics/AI – Page 832

Every day, tens of thousands of songs are released. This constant stream of options makes it difficult for streaming services and radio stations to choose which songs to add to playlists. To find the ones that will resonate with a large audience, these services have used human listeners and artificial intelligence. This approach, however, lingering at a 50% accuracy rate, does not reliably predict if songs will become hits.

Now, researchers in the US have used a comprehensive technique applied to brain responses and were able to predict hit songs with 97% accuracy.

“By applying machine learning to neurophysiologic data, we could almost perfectly identify hit songs,” said Paul Zak, a professor at Claremont Graduate University and senior author of the study published in Frontiers in Artificial Intelligence. “That the neural activity of 33 people can predict if millions of others listened to new songs is quite amazing. Nothing close to this accuracy has ever been shown before.”

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Airbnb CEO Brian Chesky isn’t afraid of artificial intelligence displacing jobs. In fact, he thinks it’ll create more of them — particularly in the world of entrepreneurship.

Since ChatGPT started gaining popularity last winter, tech icons from Apple co-founder Steve Wozniak to billionaire entrepreneur Mark Cuban have admitted they’re worried that AI will replace human workers in just about every industry.

But they’re forgetting something, Chesky recently told the “This Week in Startups” podcast: We don’t even know what kinds of jobs it’ll create.

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Rocks and minerals contribute essential raw materials for any civilization, and in a technological society minerals (and the rare elements they contain) are especially sought after. In the past, most discoveries of mineral deposits have resulted from perseverance and luck.

In the last 200 years scientists realized that minerals are not distributed randomly. Many of the over 5,000 different minerals occurring on Earth exist in a so-called paragenesis. A paragenesis is a mineral assemblage formed under specific physico-chemical rules, like a certain chemical composition of the host rock or when the right conditions — like temperature and pressure — are met.

A machine learning model can predict the locations of minerals on Earth — and potentially other planets — by taking advantage of patterns in mineral associations.

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Self-driving efforts today focus on particular niches, such as the urban robotaxi, delivery, trucking or freeeway driving. Other than Tesla, most major players don’t have a focus on the general personal robocar — a car which consumers will buy, which will drive them door to door on city streets and most other roads. Tesla is very far behind other teams, and barely counts in the minds of many in the industry, thought it gets the most press. A few startups pursue the full robocar dream, but thinking has changed.

In spite of that perceived dream, that is not what the industry is building, or what it is going to release for some time. It may be some time before you can buy a car for yourself with this ability, not just because it’s hard, but because it’s not where the money is. This has led some people to think that robocars are still very far away, and also to a common perception that the technology is many years behind what people expected. Indeed, some people expected, or at least hoped for, faster timelines, but others did not.

The public has a different perception, in part because of Tesla, but also because of a document written over a decade ago by NHTSA (the federal road safety agency) and now manged by the Society of Automotive Engineers known as “the levels.” This document filled the need for a taxonomy of self-driving, but it was written by non-developers when the technology was immature. As such it’s largely useless and even counterproductive, but people are so hungry for a taxonomy that it still is often referred to. The leading teams (mostly tech companies not auto OEMs) do not use these level or attempt to adhere to them. They are mostly a way to talk about the dwindling role of the human in the operation of a self-driving car, a bit like a document about the role of the horse in the horseless carriage.

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Generative AI in supply chains will be able to forecast demand, predict when trucks need maintenance and work out optimal shipping routes, according to analysts.“AI may be able to totally (or nearly) remove all human touchpoints in the supply chain including ‘back office’ tasks,” said Morgan Stanley analysts.

But “Generative AI, in my mind is, once in a lifetime kind of disruption that’s going to happen … so there are going to be losses of jobs in the more traditional setting, but I also believe it’s going to create new jobs like every prior technology disruption has,” said Navneet Kapoor, chief technology and information officer at shipping giant Maersk.

Artificial intelligence is likely… More.

Artificial intelligence is likely to shake up the transportation industry — transforming how supply chains are managed and reducing the number of jobs carried out by people, according to analysts and industry insiders.

Continue reading “A.I. could ‘remove all human touchpoints’ in supply chains. Here’s what that means” | >

As AI image generators continue to rock visual industries such as photography and illustration, Nikon is taking a stand against AI and on behalf of humans, cameras, and “natural intelligence.” Little Black Book reports that Nikon Peru recently partnered with the ad agency Circus Grey Peru on a new “Natural Intelligence” ad campaign.

Even though AI can now generate photorealistic images with just a simple text prompt, Nikon wants to remind everyone that the real world is full of incredible scenes that are best captured with a camera rather than imagined with AI.

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