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.”

by MIT Technology Review by Cassandra Willyard -- Just last week Microsoft announced that it had partnered with a digital pathology company, Paige, in order to build the world’s largest image-based AI model for identifying cancer. This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here. I just had a birthday, and you know what that means—I’m newly eligible for a screening colonoscopy. (#milestones!). I’ve been thinking about cancer screening a lot recently, because I’ve seen a handful of headlines in the past few months about how AI will revolutionize cancer detection. Just last week Microsoft announced that it had partnered with a digital pathology company, Paige, in order to build the world’s largest image-based AI model for identifying cancer. The training data set for the algorithm contains 4 million images. “This is sort of a groundbreaking, land-on-the-moon kind of moment for cancer care,” Paige CEO Andy Moye told CNBC.

Well, it might be. Last month, results from the first clinical trial of AI-supported breast cancer screening came out. The researchers compared two methods for reading a mammogram: a standard reading by two independent radiologists, and a system that used a single radiologist and an AI to assign patients a numerical cancer risk score from 1 to 10. In the latter group, those who scored a 10—the highest risk—then had their images read by two radiologists. The AI-supported model reduced workload by 44% and detected 20% more cancers. That sounds like a good thing. In theory, catching cancers earlier should make them easier to treat, saving lives. But that’s not always what the data shows. A study published in late August combed the literature for randomized clinical trials that compared mortality (from any cause, not just cancer) in two groups: people who underwent cancer screening and people who did not. For most common types of cancer screening, they found no significant difference. The exception was sigmoidoscopy, a type of colon cancer screening that involves visualizing only the lower portion of the colon.