bt Bryson Masse - venturebeat -- AI’s carbon footprint is no open-and-shut case, according to scientists from the University of California-Irvine and MIT, who published a paper earlier this year on the open access site arXiv.org that shakes up energy use assumptions of generative AI models, and which set off a debate among leading AI researchers and experts this past week. The paper found that when producing a page of text, an AI system such as ChatGPT emits 130 to 1500 times fewer carbon dioxide equivalents (CO2e) compared to a human.

The paper concludes that the use of AI has the potential to accomplish several significant activities with significantly lower emissions than humans. However, an ongoing dialogue among AI researchers reacting to the paper this week also highlights how accounting for interactions between climate, society, and technology poses immense challenges warranting continual reexamination. From blockchain to AI models, environmental effects need to be measured In an interview with VentureBeat, the authors of the paper, University of California at Irvine professors Bill Tomlinson and Don Patterson, and MIT Sloan School of Management visiting scientist Andrew Torrance, offered some insight into what they were hoping to measure.

Originally published in March, Tomlinson said that the paper was submitted to the research journal Scientific Reports where it is currently under peer review. The study authors analyzed existing data on the environmental impact of AI systems, human activities, and the production of text and images. This information was collected from studies and databases that study how AI and humans affect the environment. For example, they used an informal, online estimate for ChatGPT based on traffic of 10 million queries generating roughly 3.82 metric tons of CO2e per day while also amortizing the training footprint of 552 metric tons of CO2e. As well, for further comparison, they included data from a low impact LLM called BLOOM. On the human side of things, they used both examples of the annual carbon footprints of average persons from the US (15 metric tons) and India (1.9 metric tons) to compare the different per-capita effects of emissions over an estimated amount of time it would take to write a page of text or create an image.

by Carl Franzen -- venturebeat.com -- Do you want to put an implant designed by Elon Musk’s company Neuralink — perhaps best known for killing 1,500 test animals — into your brain? Are you at least 22 years old and do you have quadriplegia (loss of function in four limbs) from a spinal cord injury, or amyotrophic lateral sclerosis (ALS)?

Then you may qualify to participate in the first-ever volunteer human trials of Neuralink’s first brain-computer interface, which has begun recruitment for participants, as the company announced on its website today. “The PRIME Study (short for Precise Robotically Implanted Brain-Computer Interface) – a groundbreaking investigational medical device trial for our fully-implantable, wireless brain-computer interface (BCI) – aims to evaluate the safety of our implant (N1) and surgical robot (R1) and assess the initial functionality of our BCI for enabling people with paralysis to control external devices with their thoughts,” explains the blog post. The company has courted controversy for testing its implant on monkeys that allegedly resulted in their death (Musk has posted on his social network X, formerly Twitter, that the monkeys were terminally ill, anyway), but that apparently isn’t stopping it from moving forward to try the tech on humans, next, after receiving an exemption from the U.S. Food and Drug Administration in May.

What’s involved in the Neuralink implant human trials?

ALBAWABA – by Mayar Alkhatieb — According to a study by the University of California and the University of the Republic of …

The AI Beat by Sharon Goldman ----  A medieval alchemist wearing a gray robe with a long white beard and glasses adjusts a machine made of beakers and vats A New York Times article this morning, titled “How to Tell if Your AI Is Conscious,” says that in a new report, “scientists offer a list of measurable qualities” based on a “brand-new” science of consciousness. The article immediately jumped out at me, as it was published just a few days after I had a long chat with Thomas Krendl Gilbert, a machine ethicist who, among other things, has long studied the intersection of science and politics. Gilbert recently launched a new podcast, called “The Retort,” along with Hugging Face researcher Nathan Lambert, with an inaugural episode that pushes back on the idea of today’s AI as a truly scientific endeavor. Gilbert maintains that much of today’s AI research cannot reasonably be called science at all. Instead, it can be viewed as a new form of alchemy — that is, the medieval forerunner of chemistry, that can also be defined as a “seemingly magical process of transformation.”

Like alchemy, AI is rooted in ‘magical’ metaphors Many critics of deep learning and of large language models, including those who built them, sometimes refer to AI as a form of alchemy, Gilbert told me on a video call. What they mean by that, he explained, is that it’s not scientific, in the sense that it’s not rigorous or experimental. But he added that he actually means something more literal when he says that AI is alchemy. “The people building it actually think that what they’re doing is magical,” he said. “And that’s rooted in a lot of metaphors, ideas that have now filtered into public discourse over the past several months, like AGI and super intelligence.” The prevailing idea, he explained, is that intelligence itself is scalar — depending only on the amount of data thrown at a model and the computational limits of the model itself.

But, he emphasized, like alchemy, much of today’s AI research is not necessarily trying to be what we know as science, either. The practice of alchemy historically had no peer review or public sharing of results, for example. Much of today’s closed AI research does not, either. “It was very secretive, and frankly, that’s how AI works right now,” he said. “It’s largely a matter of assuming magical properties about the amount of intelligence that is implicit in the structure of the internet — and then building computation and structuring it such that you can distill that web of knowledge that we’ve all been building for decades now, and then seeing what comes out.”