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Neural network approach makes AI uncertainty checks far more efficient

McGill University researchers have developed a more energy-efficient method of building AI systems that are better at measuring—and indicating—their own uncertainty. This will help users determine when human oversight is needed, when additional data should be collected and when a model is being asked to work beyond the conditions it was trained for, the researchers said.

“Artificial intelligence systems now play a central role in daily life, from medical diagnosis and content moderation to autonomous driving and AI agents that act on our behalf,” said Mame Diarra Touré, lead author and Ph.D. candidate in the Department of Mathematics and Statistics. “As these systems take on more responsibility, they need to become more trustworthy. They should recognize when they are uncertain, rather than giving confident answers in situations where they may be wrong.”

The research was supervised by David A. Stephens, professor in the Department of Mathematics and Statistics. “Singular Bayesian Neural Networks,” by Mame Diarra Touré and Stephens, was presented at the Forty-Third International Conference on Machine Learning (ICML 2026).

No.1 Ibogaine Scientist: “Trump’s Decision On Ibogaine Changes EVERYTHING!”

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Deborah Mash is a neuropharmacologist who holds the first FDA licence ever granted to give ibogaine to a human being, built one of America’s largest brain banks, and discovered noribogaine — the metabolite now entering its first US clinical trial. She has spent over thirty years on a drug that appears to interrupt addiction after a single dose. This April a US president named it specifically. This is the scientist who was right three decades early.

Expect to learn what Trump’s executive order actually changes for ibogaine, why Sasha Shulgin put ibogaine in a box of its own, how noribogaine blocks nicotine, alcohol, cocaine and opioid use in animal models, why ibogaine holds the brain’s neuroplasticity window open for four weeks when ketamine manages days, why some patients get the full therapeutic benefit with no psychedelic experience at all, why the reported ibogaine deaths are almost impossible to interpret, why more than 80% of her patients said they never wanted to repeat it, and much more…

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Production of autonomous AI drone for first responders to be scaled up

The production of the world’s first autonomous AI drone designed for first responders is set to be scaled up. For this, DefendEye — the developer of fully autonomous drone systems — announced the closing of a major investment round led by NovaCapital.

Backed by a premier syndicate of international venture capital and strategic investors, this capital infusion empowers DefendEye to aggressively scale production of its flagship Overwatch Drone, an advanced aerial intelligence system built primarily for first responders.

“This investment and support will allow us to scale and expand operations,” said James Buchheim, CEO of DefendEye.

Industrial Robotics Funding Hit $1.56B, But 5 Deals Took 80% Of It

Pure-play industrial robotics companies raised $1.56 billion over the past 24 months, but the top five deals captured 80.58% of that capital. Strip out rounds above $50 million and disclosed funding drops to $151.75 million, revealing that most industrial robotics companies are competing for a fraction of the money the headline number suggests.

Industrial robotics funding looks abundant until you look at who’s actually getting it. Between August 2024 and July 2026, pure-play industrial robotics companies raised $1.56 billion in disclosed capital across 19 equity deals and 16 unique companies, according to a New Market Pitch funding analysis. The top deal alone represents 31.97% of that total. The top five deals together account for 80.58%.

Remove rounds above $50 million from the dataset and disclosed capital falls from $1.56 billion to $151.75 million, according to the same analysis. That’s the real size of the market most industrial robotics companies are actually competing in. Industrial robotics funding at the top end is now dominated by a handful of platform bets large enough to make headlines on their own, while everyone else is raising within a much smaller, much more competitive band.

AI Companies Are Buying—And Destroying—Antique Books. Here’s Why

In 2006, Vernor Vinge published a novel in which a company digitizes a university library by destroying it. Books stripped from their bindings. Pages blown through the air, photographed in flight, reassembled as searchable data. The paper goes to pulp.

The novel is Rainbows End. Vinge set it in 2025.

He was off by a year.

Every #AI lab now wants text written before machines started writing, and that means old paper. So books get bought by the million, spines get sliced off, pages get fed through high-speed scanners, and the originals get discarded. Rare editions included. A US federal judge has already ruled the practice legal. Buy the book, destroy the book, keep the file.

Authors, archivists, and librarians have started organizing against it.

Vinge wrote it as a warning. The industry read it as a workflow.

Cognition and consciousness arise from analog computations, says new theory

A new theory from neuroscientists at MIT’s Picower Institute proposes that cognition and consciousness may depend not only on neurons and synaptic connections, but also on the traveling electrical waves generated by neural activity.

The traditional “brain as circuitry” analogy captures an important part of neuroscience: synaptic connections store and transmit information. But Earl Miller and colleagues argue that synapses alone may be too slow and inflexible to explain how the brain rapidly assembles and reorganizes neural networks from moment to moment.

Their proposal centers on brain waves as a dynamic control system.

Slower alpha and beta oscillations, associated with internal information such as memories, goals and expectations, may regulate faster gamma activity associated with incoming sensory information. Because these waves can travel across the cortex, they could rapidly determine which populations of neurons participate in processing at a particular place and time.

The researchers describe this as “spatiotemporal computing.” Where electrical waves interact, their amplitudes can add or subtract, potentially allowing the brain to perform a form of analog computation through wave interference rather than relying entirely on sequential, digital-like operations.

The theory also incorporates ephaptic coupling—the possibility that electrical fields generated by populations of neurons can directly influence the firing of nearby neurons, providing another rapid mechanism for coordinating neural activity.

The authors extend the idea to consciousness, proposing that conscious awareness emerges when these wave dynamics organize widespread cortical activity into a coherent, globally integrated state. Supporting evidence includes anesthesia research showing that drugs with very different molecular mechanisms can all produce unconsciousness while disrupting large-scale brain-wave organization.

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