Lifeboat Foundation

Meta is developing a machine learning model that scans these citations and cross-references their content to Wikipedia articles to verify that not only the topics line up, but specific figures cited are accurate.

This isn’t just a matter of picking out numbers and making sure they match; Meta’s AI will need to “understand” the content of cited sources (though “understand” is a misnomer, as complexity theory researcher Melanie Mitchell would tell you, because AI is still in the “narrow” phase, meaning it’s a tool for highly sophisticated pattern recognition, while “understanding” is a word used for human cognition, which is still a very different thing).

Meta’s model will “understand” content not by comparing text strings and making sure they contain the same words, but by comparing mathematical representations of blocks of text, which it arrives at using natural language understanding (NLU) techniques.

A new study in Science overthrew the whole gamebook. Led by Dr. David Baker at the University of Washington, a team tapped into an AI’s “imagination” to dream up a myriad of functional sites from scratch. It’s a machine mind’s “creativity” at its best—a deep learning algorithm that predicts the general area of a protein’s functional site, but then further sculpts the structure.

As a reality check, the team used the new software to generate drugs that battle cancer and design vaccines against common, if sometimes deadly, viruses. In one case, the digital mind came up with a solution that, when tested in isolated cells, was a perfect match for an existing antibody against a common virus. In other words, the algorithm “imagined” a hotspot from a viral protein, making it vulnerable as a target to design new treatments.

The algorithm is deep learning’s first foray into building proteins around their functions, opening a door to treatments that were previously unimaginable. But the software isn’t limited to natural protein hotspots. “The proteins we find in nature are amazing molecules, but designed proteins can do so much more,” said Baker in a press release. The algorithm is “doing things that none of us thought it would be capable of.”

Well, now we have a robot version of this classic Serengeti scene.

The fawn in this case is a robotic dog at the University of California, Berkeley. And it’s likewise a surprisingly quick learner (relative to the rest of robot-kind). The robot is also special because, unlike other flashier robots you might have seen online, it uses artificial intelligence to teach itself how to walk.

Continue reading “This Robot Dog Has an AI Brain and Taught Itself to Walk in Just an Hour” »

While the planet is too large and hot for life, it could help us study more Earth-like exoplanets.

WASP-39b, a distant gas giant “hot Jupiter,” shows signs of water vapor and carbon dioxide, helping Webb scientists hone in on how to find them on smaller planets.

Matthew Cobb’s latest book is a disturbing history of genetic engineering, which asks whether it is worth the money – or the risk.

This analysis of 2-year retrospective cohort studies of individuals diagnosed with COVID-19 showed that the increased incidence of mood and anxiety disorders was transient, with no overall excess of these diagnoses compared with other respiratory infections. In contrast, the increased risk of psychotic disorder, cognitive deficit, dementia, and epilepsy or seizures persisted throughout. The differing trajectories suggest a different pathogenesis for these outcomes. Children have a more benign overall profile of psychiatric risk than do adults and older adults, but their sustained higher risk of some diagnoses is of concern. The fact that neurological and psychiatric outcomes were similar during the delta and omicron waves indicates that the burden on the health-care system might continue even with variants that are less severe in other respects. Our findings are relevant to understanding individual-level and population-level risks of neurological and psychiatric disorders after SARS-CoV-2 infection and can help inform our responses to them.

National institute for health and care research oxford health biomedical research centre, the wolfson foundation, and MQ mental health research.

A proportion of patients experience long-lasting symptoms in the weeks and months after a diagnosis of COVID-19. ^1–3 Of those symptoms, cognitive impairment (also referred to as ‘brain fog’) is particularly worrisome: it is one of the most common, ^{4, 5} can affect those with even relatively mild acute COVID-19 illness ^{1, 5} and results in the inability to work for many affected patients. ³ While emerging research is starting to characterize the clinical presentation of post-COVID cognitive deficits, ⁶ its pathogenesis remains elusive. Identifying therapeutic targets is critical to reducing the burden of this COVID-19 complication.

Endotheliopathy has been hypothesized as one potential mechanism underlying post-COVID cognitive deficits. ⁷ According to recent research, microvascular brain pathology following COVID-19 can be caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease M^pro cleaving nuclear factor-κB essential modulator thus inducing the death of brain endothelial cells. ⁸ The same study showed that pharmacologically inhibiting receptor-interacting protein kinase (RIPK) signaling prevents the M^pro-induced microvascular pathology. ⁸

This research leads to the following hypothesis: exposure to a pharmacological inhibitor of RIPK signaling at the time of COVID-19 infection reduces the risk of post-COVID cognitive deficits. In this study, we tested this hypothesis using a retrospective cohort study based on electronic health records (EHRs) data. While many pharmacological agents inhibit RIPK signaling, ⁹ most are only used in very rare clinical scenarios (e.g. sunitinib for the treatment of advanced renal cell carcinoma or pancreatic neuroendocrine tumors). The exception is phenytoin which is used as an anti-epileptic drug and which, among its other effects, is a RIPK1 inhibitor protecting against necroptosis. ^{10, 11} In this study, we compared the incidence of post-COVID cognitive deficits between patients exposed to phenytoin and matched cohorts of patients exposed to other anti-epileptic drugs at the time of their COVID-19 diagnosis.

As the size of modern technology shrinks down to the nanoscale, weird quantum effects—such as quantum tunneling, superposition, and entanglement—become prominent. This opens the door to a new era of quantum technologies, where quantum effects can be exploited. Many everyday technologies make use of feedback control routinely; an important example is the pacemaker, which must monitor the user’s heartbeat and apply electrical signals to control it, only when needed. But physicists do not yet have an equivalent understanding of feedback control at the quantum level. Now, physicists have developed a “master equation” that will help engineers understand feedback at the quantum scale. Their results are published in the journal Physical Review Letters.

“It is vital to investigate how feedback control can be used in quantum technologies in order to develop efficient and fast methods for controlling quantum systems, so that they can be steered in real time and with high precision,” says co-author Björn Annby-Andersson, a quantum physicist at Lund University, in Sweden.

An example of a crucial feedback-control process in quantum computing is quantum error correction. A quantum computer encodes information on physical qubits, which could be photons of light, or atoms, for instance. But the quantum properties of the qubits are fragile, so it is likely that the encoded information will be lost if the qubits are disturbed by vibrations or fluctuating electromagnetic fields. That means that physicists need to be able to detect and correct such errors, for instance by using feedback control. This error correction can be implemented by measuring the state of the qubits and, if a deviation from what is expected is detected, applying feedback to correct it.

When asked to write the numbers from one to ten in a sequence, how do you order them? Horizontally? Vertically? Left to right? Top to bottom? Would you place them randomly?

It has been often been assumed, and taught in schools in Western countries, that the “correct” ordering of numbers is from left to right (1, 2, 3, 4…) rather than right to left (10, 9, 8, 7…). The ordering of numbers along a horizontal dimension is known as a “mental number line” and describes an important way we represent number and quantity in space.

Studies show humans prefer to position larger numbers to the right and smaller numbers to the left. People are usually faster and more accurate at comparing numbers when larger ones are to the right and smaller ones are to the left, and people with brain damage that disrupts their spatial processing also show similar disruptions in number processing.