Best AI image generators: DALL-E 2, Stable Diffusion, and more apps
by ADRIAN WILLINGS  | Pocket Lint

Machine learning with its advancement has a big issue called Carbon release There are tremendous computational costs of Machine Learning and AI.

Artificial intelligence algorithms, which power some of technology’s most cutting-edge applications, inclusive of producing logical stretches of text or growing visuals from descriptions, can also additionally want large quantities of computational electricity to train.

This, in turn, necessitates a vast amount of electricity, prompting many to worry that the carbon footprint of the increasingly famous ultra-large AI structures might render them environmentally unsustainable. Machine learning (ML) is great for augmenting human intelligence, however academic and industry researchers were debating the severity of its carbon footprint. We’re still learning about the significance of machine learning’s outcomes and solving ML issues in the environment, however, there are answers to be had to assist groups to choose the maximum green options for managing and distributing workloads.

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Artificial intelligence to hit workplace 'like a freight train', energy boss warns
by SkyNews | SkyNews

Photo by Mapbox on Unsplash

The government must act to prepare for artificial intelligence (AI) to hit the workplace "like a freight train", the boss of one of Britain's leading energy companies has told Sky News.

Greg Jackson, founder of Octopus, says the adoption of AI across industry will ultimately improve the workplace and spawn new roles, but the startling pace of development means millions of jobs could be at risk in the short-term.

Octopus has seen huge benefits from the adoption of generative artificial intelligence in its customer service operations, with 44% of customer emails being answered, at least in part, by AI just seven weeks after it was rolled out.

Human employees still manage and check all the AI's output, and Mr Jackson said it would not cost any jobs at Octopus.

He warned however, that the technology posed a threat to jobs at companies looking to cut costs, and business, regulators and politicians need to prepare for a rapid transition.

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Discovering Use Cases for Generative AI
by Dr Francis Gaffney | Open Access Government

Photo by Google DeepMind on Unsplash

Generative AI is becoming a powerful tool to help address a wide range of business problems in creative ways. However, areas for applying generative AI are broad, and many uses diverge from how we work today.

This struggle to understand AI’s potential is causing most of us (myself included) to get stuck in beginner mode, unable to see how the technology might be applied inside our organizations. What I needed was a better understanding of what generative AI is good at. I hoped this understanding might help me imagine how to put the technology to work in relevant and meaningful ways. (I also wanted to help my fellow participants at InnoLead’s upcoming “Leveraging A.I. in Business” workshop think about how we could use the technology as part of our prototyping work there.)

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🌙 NASA - Best Photo from Last Week
Hubble Peers at Celestial Cloudscape

The densely packed globular cluster NGC 6325 glistens in this image from the NASA/ESA Hubble Space Telescope. This concentrated group of stars lies around 26,000 light-years from Earth in the constellation Ophiuchus.

Globular clusters like NGC 6325 are tightly bound collections of stars with anywhere from tens of thousands to millions of members. They can be found in all types of galaxies and act as natural laboratories for astronomers studying star formation. This is because the constituent stars of globular clusters tend to form at roughly the same time and with similar initial composition, meaning astronomers can use them to fine-tune their theories of how stars evolve.

Astronomers inspected this particular cluster not to understand star formation, but to search for a hidden monster. Though it might look peaceful, astronomers suspect this cluster could contain an intermediate-mass black hole that is subtly affecting the motion of surrounding stars. Previous research found that the distribution of stars in some highly concentrated globular clusters – those with stars packed relatively tightly together – was slightly different from what astronomers expected.

This discrepancy suggests that at least some of these densely packed globular clusters – including perhaps NGC 6325 – could have a black hole lurking at the center. To explore this hypothesis further, astronomers turned to Hubble’s Wide Field Camera 3 to observe a larger sample of densely populated globular clusters, which included this star-studded image of NGC 6325. Additional data from Hubble’s Advanced Camera for Surveys was also incorporated into this image.

Text credit: European Space Agency (ESA)
Image credit: ESA/Hubble & NASA, E. Noyola, R. Cohen

Media Contact:
Claire Andreoli
NASA's Goddard Space Flight Center, Greenbelt, MD

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