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Wall Street is getting very bullish on copper, despite the metal's recent rallies . The rallies have been fueled by supply risks and rising demand for it amid the energy transition and the artificial intelligence boom. Copper is used in data centers for power cables, electrical connectors, power strips and more, Jefferies noted in an April 10 note. It estimates that global copper demand by data centers will increase from 239 kt (thousand tons) in 2023 to at least 450 kt per annum in 2030. "Our analysis shows that this potential demand growth will exacerbate an underlying copper market deficit, ultimately leading to higher prices," Jefferies analysts wrote. Data centers house vast amounts of computing power needed for AI workloads, and that need is set to grow as many tech companies are rapidly developing infrastructure for artificial intelligence. Large language models require a lot of data center capacity. In a recent note, Morgan Stanley predicted that the price of the metal will reach $10,500 per ton by the fourth quarter of this year representing around 12% upside. "Hopes for GenAI / data centre copper demand growth are adding to investor bullishness on copper, against a backdrop of constrained supply," it wrote. Demand for copper is also widely considered an indicator of economic health. The metal has a wide range of applications throughout construction and industry. It's also a critical component in electric vehicles, used in batteries, wiring, charging points and more. For those looking to buy into the sector, CNBC Pro screened for stocks in theGlobal X Copper Miners ETF. The following stocks have buy ratings from 50% or more of analysts covering them, average price target upside of 10% or more, and are covered by at least five analysts. Canadian firm Solaris Resources stood out for having more than 200% potential upside the highest in the list and a 100% buy rating. Filo Mining also made the cut, getting 25% upside from analysts and a 92% buy rating. In addition to the Global X Copper Miners ETF, those who want to invest in this sector via exchange-traded funds can consider the Sprott Copper Miners ETF and the iShares Copper and Metals Mining ETF.

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Wall Street is bullish on copper, thanks to AI. Analysts love these stocks, giving one 234% upside - CNBC

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Thursday, April 11, 2024

NIH-funded study suggests model could identify large percentage of those at risk.

Researchers have adapted an artificial intelligence (AI) program to identify signs of childbirth-related post-traumatic stress disorder (CB-PTSD) by evaluating short narrative statements of patients who have given birth. The program successfully identified a large proportion of participants likely to have the disorder, and with further refinementssuch as details from medical records and birth experience data from diverse populationsthe model could potentially identify a large percentage of those at risk. The study, which was funded by the National Institutes of Health, appears in Scientific Reports.

Worldwide, CB-PTSD affects about 8 million people who give birth each year, and current practice for diagnosing CB-PTSD requires a physician evaluation, which is time-consuming and costly. An effective screening method has the potential to rapidly and inexpensively identify large numbers of postpartum patients who could benefit from diagnosis and treatment. Untreated CB-PTSD may interfere with breastfeeding, bonding with the infant and the desire for a future pregnancy. It also may worsen maternal depression, which can lead to suicidal thoughts and behaviors.

Investigators administered the CB-PTSD Checklist, which is a questionnaire designed to screen for the disorder, to 1,295 postpartum people. Participants also provided short narratives of approximately 30 words about their childbirth experience. Researchers then trained an AI model to analyze a subset of narratives from patients who also tested high for CB-PTSD symptoms on the questionnaire. Next, the model was used to analyze a different subset of narratives for evidence of CB-PTSD. Overall, the model correctly identified the narratives of participants who were likely to have CB-PTSD because they scored high on the questionnaire.

The authors believe their work could eventually make the diagnosis of childbirth post-traumatic stress disorder more accessible, providing a means to compensate for past socioeconomic, racial, and ethnic disparities.

The study was conducted by Alon Bartal, Ph.D., of Bar Ilan University in Israel, and led by senior author Sharon Dekel, Ph.D., of Massachusetts General Hospital and Harvard Medical School, Boston. Funding was provided by NIHs Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD).

Maurice Davis, D.H.A., M.P.A., M.H.S.A., of the NICHD Pregnancy and Perinatology Branch, is available for comment.

Bartal A, et al. AI and narrative embeddings detect PTSD following childbirth via birth stories. Scientific Reports (2024).

About the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD): NICHD leads research and training to understand human development, improve reproductive health, enhance the lives of children and adolescents, and optimize abilities for all. For more information, visit https://www.nichd.nih.gov.

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit http://www.nih.gov.

NIHTurning Discovery Into Health

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AI model has potential to detect risk of childbirth-related post-traumatic stress disorder - National Institutes of Health (NIH) (.gov)

By Adam Freeman, University Communications and Marketing

The University of South Florida has announced its intention to create a college focused on the rapidly evolving fields of artificial intelligence (AI), cybersecurity and computing, with the goal of positioning the Tampa Bay region and state of Florida as a national leader. USF is the first university in Florida and among the first in the nation to announce plans to create a college dedicated to AI and cybersecurity.

The vision for the college would be to offer undergraduate and graduate programs aligned with USFs strategic plan and the states Programs of Strategic Emphasis to prepare students for high-demand careers, empower faculty to conduct innovative research that leads to new discoveries or technological advancements, grow industry partnerships and promote ethical considerations and trust throughout the digital transformation underway in society. Research shows that there has been a five-fold increase in the demand for AI skills with jobs in the U.S., while more than 40% of organizations experiencing a shortage of cybersecurity professionals say they are unable to find enough qualified talent.

The creation of a new college would leverage USF's existing strengths and partnerships in AI, cybersecurity and computing, as well as its location in the Tampa Bay region, a hub for technology and defense industries. At USF, there are approximately 200 faculty members currently engaged in research in related disciplines, which are seeing significant growth in funding awards. Last year the National Science Foundation reportedly awarded more than $800 million for AI-related research.

"As AI and cybersecurity quickly evolve, the demand for professionals skilled in these areas continues to grow, along with the need for more research to better understand how to utilize powerful new technologies in ways that improve our society, USF President Rhea Law said. Through the expertise of our faculty and our strong partnerships with the business community, the University of South Florida is strategically positioned to be a global leader in these fields.

The formation of a new college is subject to continued consultation with faculty through shared governance processes and approval from the USF Board of Trustees. In recent months, an internal task force has been evaluating USFs faculty strengths and exploring opportunities to enhance multidisciplinary collaboration that will help advance USFs academic and research excellence in AI, cybersecurity and computing. The addition of this new college would bring together expertise currently housed across several different colleges to serve as a complement to USFs 13 existing colleges, which would remain operational and continue to be positioned for success.

Establishing this college would align with USFs strategic initiative to enhance academic and research excellence in key areas of societal need and opportunity, said USF Provost and Executive Vice President for Academic Affairs Prasant Mohapatra. By building on our multidisciplinary strengths, such as health, engineering, arts and humanities, and cybersecurity, we aim to support our strategic goals of advancing student success, promoting continuous professional growth, fostering industry, government and community partnerships, and propelling the university towards a top-25 ranking.

More information is available here.

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USF plans to launch college focused on artificial intelligence, cybersecurity and computing - University of South Florida

Emad Mostaque is stepping down from his role as CEO of Stability AI, the startup that helped bring Stable Diffusion to life. In a press release late on Friday night, Stability AI says Mostaque is leaving the company to pursue decentralized AI. Mostaque will also step down from his position on the board of directors at Stability AI.

The board has appointed two interim co-CEOs to lead Stability AI COO Shan Shan Wong and CTO Christian Laforte while it conducts a search for a permanent CEO. As we search for a permanent CEO, I have full confidence that Shan Shan Wong and Christian Laforte, in their roles as interim co-CEOs, will adeptly steer the company forward in developing and commercializing industry-leading generative AI products, says Jim OShaughnessy, chairman of the board at Stability AI.

That push toward developing commercialized AI products is likely a big part of why Mostaque has stepped down. Not going to beat centralized AI with more centralized AI, said Mostaque in a post on X, following his resignation. It is now time to ensure AI remains open and decentralized, says Mostaque in a separate statement.

There was a lot of drama in the AI startup world this week

Mostaques departure comes just days after Forbes reported that Stability AI was in trouble after other key developers resigned. Three out of the five researchers who originally created the technology behind Stable Diffusion have left the company recently. The leadership changes at Stability AI also come in the same week rival startup Inflection AI experienced what was effectively a Microsoft talent acquisition.

Google DeepMind co-founder and former Inflection AI CEO Mustafa Suleyman joined Microsoft earlier this week as the CEO of a new AI team. Microsoft also hired some key Inflection AI employees, including co-founder Karn Simonyan who is now the chief scientist of Microsoft AI. Most of Inflections staff is joining Microsoft AI, leaving the AI startup to pivot to enterprise offerings.

Stabilitys flagship AI product, Stable Diffusion, is used by many to offer text-to-image generation AI tools. Stability released its newest model, Stable Cascade, just weeks ago as an option for researchers on GitHub. Stability AI also started offering a paid membership for commercial use of its models in December, to help fund its research.

Stability AI has popularized the stable diffusion method of AI, but has faced lawsuits around the data that Stable Diffusion is allegedly trained on. A lawsuit in the UK from Getty Images is heading to trial soon, and it could be a big moment for the legislative framework around generative AI products.

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Stability AI CEO resigns to pursue decentralized AI - The Verge

The next evolution in artificial intelligence (AI) could lie in agents that can communicate directly and teach each other to perform tasks, research shows.

Scientists have modeled an AI network capable of learning and carrying out tasks solely on the basis of written instructions. This AI then described what it learned to a sister AI, which performed the same task despite having no prior training or experience in doing it.

The first AI communicated to its sister using natural language processing (NLP), the scientists said in their paper published March 18 in the journal Nature.

NLP is a subfield of AI that seeks to recreate human language in computers so machines can understand and reproduce written text or speech naturally. These are built on neural networks, which are collections of machine learning algorithms modeled to replicate the arrangement of neurons in the brain.

Once these tasks had been learned, the network was able to describe them to a second network a copy of the first so that it could reproduce them. To our knowledge, this is the first time that two AIs have been able to talk to each other in a purely linguistic way, said lead author of the paper Alexandre Pouget, leader of the Geneva University Neurocenter, in a statement.

The scientists achieved this transfer of knowledge by starting with an NLP model called "S-Bert," which was pre-trained to understand human language. They connected S-Bert to a smaller neural network centered around interpreting sensory inputs and simulating motor actions in response.

Related: AI-powered humanoid robot can serve you food, stack the dishes and have a conversation with you

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This composite AI a "sensorimotor-recurrent neural network (RNN)" was then trained on a set of 50 psychophysical tasks. These centered on responding to a stimulus like reacting to a light through instructions fed via the S-Bert language model.

Through the embedded language model, the RNN understood full written sentences. This let it perform tasks from natural language instructions, getting them 83% correct on average, despite having never seen any training footage or performed the tasks before.

That understanding was then inverted so the RNN could communicate the results of its sensorimotor learning using linguistic instructions to an identical sibling AI, which carried out the tasks in turn also having never performed them before.

The inspiration for this research came from the way humans learn by following verbal or written instructions to perform tasks even if weve never performed such actions before. This cognitive function separates humans from animals; for example, you need to show a dog something before you can train it to respond to verbal instructions.

While AI-powered chatbots can interpret linguistic instructions to generate an image or text, they cant translate written or verbal instructions into physical actions, let alone explain the instructions to another AI.

However, by simulating the areas of the human brain responsible for language perception, interpretation and instructions-based actions, the researchers created an AI with human-like learning and communication skills.

This won't alone lead to the rise of artificial general intelligence (AGI) where an AI agent can reason just as well as a human and perform tasks in multiple areas. But the researchers noted that AI models like the one they created can help our understanding of how human brains work.

Theres also scope for robots with embedded AI to communicate with each other to learn and carry out tasks. If only one robot received initial instructions, it could be really effective in manufacturing and training other automated industries.

The network we have developed is very small, the researchers explained in the statement. Nothing now stands in the way of developing, on this basis, much more complex networks that would be integrated into humanoid robots capable of understanding us but also of understanding each other.

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Scientists create AI models that can talk to each other and pass on skills with limited human input - Livescience.com