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Copper is the mineral most fundamental to the human future because it is essential to electricity generation, distribution, and storage. Copper availability and demand determine the rate of electrification, which is the foundation of current climate policy. Many studies have raised concerns that copper supply cannot meet the copper demands of both the green energy transition and equitable global development, but the seemingly universal presumption persists that the copper needed for the green transition will somehow be available. This need not be the case for even the first step of vehicle electrification.

This paper addresses this issue by projecting copper supply and demand from 2018 to 2050 and placing both in the historical context of copper mine output. Discussion is focused on a single diagram that illustrates the unprecedented nature of the copper mining challenge and ways to reduce copper demand.

Just to meet business-as-usual trends, 115% more copper must be mined in the next 30 years than has been mined historically until now. To electrify the global vehicle fleet requires bringing into production 55% more new mines than would otherwise be needed. On the other hand, hybrid electric vehicle manufacture would require negligible extra copper mining.

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The Gefion AI Supercomputer (GAIS) project, which delivers Denmark’s first artificial intelligence (AI) turbo-charged supercomputer, has positioned Denmark as the most advanced of the Nordic region’s quantum computing investing nations.

It also serves to accelerate the use of AI to drive innovation across Denmark’s business and industrial sectors.

Built on the Nvidia DGX SuperPOD AI supercomputer, GAIS is powered by 1,528 Nvidia H100 Tensor Core graphics processing units (GPUs) and interconnected using Nvidia Quantum-2 InfiniBand networking.

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Satya Nadella, CEO of Microsoft, shares the groundbreaking potential of AI Copilot — a powerful tool that’s transforming how we work. From streamlining everyday tasks to revolutionizing healthcare workflows, AI Copilot is designed to seamlessly integrate with the tools we already use, like Teams, Word, and Excel.

Satya Nadella explains how AI Copilot is helping doctors prepare for high-stakes meetings, automatically generating agendas, summaries, and even PowerPoint presentations. Plus, see how it empowers professionals to gather the latest insights, collaborate with teams, and create smarter workflows with ease.

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Google has started rolling out a new end-to-end encryption (E2EE) model for Gmail enterprise users, making it easier to send encrypted emails to any recipient.

While businesses also have the option to configure the Secure/Multipurpose Internet Mail Extensions (S/MIME) protocol to send digitally signed and encrypted messages, this requires significant resources, including deploying certificates to all users and exchanging them before sending the emails.

Google says that after Gmail’s new E2EE model rolls out, business users will be able to send fully encrypted emails to any user on any email service or platform without having to worry about complex certificate requirements.

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Can we really trust AI to make better decisions than humans? A new study says … not always. Researchers have discovered that OpenAI’s ChatGPT, one of the most advanced and popular AI models, makes the same kinds of decision-making mistakes as humans in some situations—showing biases like overconfidence of hot-hand (gambler’s) fallacy—yet acting inhuman in others (e.g., not suffering from base-rate neglect or sunk cost fallacies).

Published in the Manufacturing & Service Operations Management journal, the study reveals that ChatGPT doesn’t just crunch numbers—it “thinks” in ways eerily similar to humans, including mental shortcuts and blind spots. These remain rather stable across different business situations but may change as AI evolves from one version to the next.

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The Investor Relations website contains information about Eli Lilly and Company’s business for stockholders, potential investors, and financial analysts.

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A new coalition of rights-holders has called on the government to support growth in the creative and tech sectors by protecting copyright ahead of an imminent AI consultation.

The BPI, PRS For Music, PPL, MPA and UK Music are among the group of publishers, authors, artists, music businesses, specialist interest publications, unions and photographers.

Launching today, the Creative Rights In AI Coalition has published three key principles for copyright and generative AI policy and a statement supported by all member organisations. The coalition is calling on government to adopt the principles as a framework for developing AI policy.

Here’s my take: I was in the music industry for many years, so I know how it operates. People pay royalties every time an artists music is used. My friend Ayub Ogada made an ungodly amount of money from only one album that supported him all the way past death. His music still generates rotalties. Much of it was due to the smarts of Rob Bozas who ran royalties for Peter Gabriel’s Real World Records. AI companies also will have to start paying royalties to creatives whose intellectual property they use to train their AI just like royalties are paid in the music industry. Many AI companies may not be as profitable as many may think due to liabilities from use of intellectual property to train the AI, as without the content the AI could not be trained. Many lawsuits will happen in the foreseeable future.

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Assistive artificial intelligence technologies hold significant promise for transforming health care by aiding physicians in diagnosing, managing, and treating patients. However, the current trend of assistive AI implementation could actually worsen challenges related to error prevention and physician burnout, according to a new brief published in JAMA Health Forum.

The brief, written by researchers from the Johns Hopkins Carey Business School, Johns Hopkins Medicine, and the University of Texas at Austin McCombs School of Business, explains that there is an increasing expectation of physicians to rely on AI to minimize medical errors. However, proper laws and regulations are not yet in place to support physicians as they make AI-guided decisions, despite the fierce adoption of these technologies among health care organizations.

The researchers predict that will depend on whom society considers at fault when the fails or makes a mistake, subjecting physicians to an unrealistic expectation of knowing when to override or trust AI. The authors warn that such an expectation could increase the risk of burnout and even errors among physicians.

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