Lifeboat Foundation

Radiation therapy is a key component of care for many types of cancers. But some tumors can be more difficult than others to treat. Cancers in the lungs, for example, move with each breath, while tumors that have metastasized to many places in the body can require repeated radiation sessions.

Earlier this week, Stanford Medicine launched a new method of delivering radiation that uses signals from cancer-targeting molecules called tracers to target tumors in real time. It is the first time the new approach, known as biology-guided radiation therapy or SCINTIX^TM, has been used in a clinic.

Continue reading “Stanford Medicine first to try out novel tumor-targeting radiation therapy machine” »

A paper published in GeroScience has described a gene responsible for a key biomarker of cellular senescence.

However, SA-ß-gal’s actual relationship to senescence processes has not been fully explored. In order to explore its potential as a biomarker, these researchers developed an RNA-binding protein that restricts SA-ß-gal expression both in C.elegans worms and in human cell cultures, and their experiments provided some insights into how this compound works.

Woa, 😲, my Wave after Wave of AI controlled fighter aircraft idea. If you like that one you will love my mini UAV idea, i dont know if Ion drive or electric centrifuge weapons are up to it yet though, maybe.

The Times, citing congressional expectations, reported that the costs of the Air Force’s collaborative combat aircraft will be between $3 million and $25 million depending on their status as expendable, attritable, or exquisite. Even the higher-end figure is far less than a manned aircraft with a pilot, both of which are valuable to the force.

Air Force and Department of Defense representatives did not immediately respond to Insider’s request for comment. Kratos Defense, which makes the Valkyrie, would not comment on collaborative combat aircraft, citing the classified nature of the program.

Continue reading “The US Air Force wants $5.8 billion to build 1,000 AI-driven unmanned combat aircraft, possibly more, as part of its next generation air dominance initiative” »

An artificial intelligence algorithm defeated a human F-16 fighter pilot in a virtual dogfight sponsored by DARPA Thursday.

Aditya-L1 will carry out scientific studies of the Sun from a vantage point 1.5 million km above Earth.

Travel around the world with author Dan Buettner to discover five unique communities where people live extraordinarily long and vibrant lives.

The mysterious phenomenon that Einstein once described as “spooky action at a distance” was seen as a wavefunction between two entangled photons.

Quantum physics, the realm of science that describes the Universe at the smallest scales, is known for its counter-intuitive phenomena that seem to defy every law of physics on an everyday scale.

Arguably none of the aspects of quantum physics are as surprising or as troubling as entanglement, the idea that two particles can be connected in such a way that a change to one is instantly reflected in the other, even if the two particles are at opposite sides of the Universe. It’s the word “instantly” that troubled Albert Einstein enough to describe entanglement as “spooky action at a distance”.

X’s new privacy policy, which is due to come into effect on September 29, states that the company “may use the information we collect and publicly available information to help train our machine learning or artificial intelligence models for the purposes outlined in this policy.” This policy is not included in its previous terms, which are still posted online.

Musk responded to a post about this change on X, saying that it would only use publicly available information to train the AI and would not use “DMs or anything private.”

During a live audio session on X – formerly Twitter – in July, Elon Musk said that his AI startup, xAI, would use public data from his social media platform to train its AI models. Insider reached out to X for comment but didn’t immediately hear back. It is not clear how it will use the information from X and which AI models this relates to.

In what can only bode poorly for our species’ survival during the inevitable robot uprisings, an AI system has once again outperformed the people who trained it. This time, researchers at the University of Zurich in partnership with Intel, pitted their “Swift” AI piloting system against a trio of world champion drone racers — none of whom could best its top time.

Swift is the culmination of years of AI and machine learning research by the University of Zurich. In 2021, the team set an earlier iteration of the flight control algorithm that used a series of external cameras to validate its position in space in real-time, against amateur human pilots, all of whom were easily overmatched in every lap of every race during the test. That result was a milestone in its own right as, previously, self-guided drones relied on simplified physics models to continually calculate their optimum trajectory, which severely lowered their top speed.

This week’s result is another milestone, not just because the AI bested people whose job is to fly drones fast, but because it did so without the cumbersome external camera arrays= of its predecessor. The Swift system “reacts in real time to the data collected by an onboard camera, like the one used by human racers,” an UZH Zurich release reads. It uses an integrated inertial measurement unit to track acceleration and speed while an onboard neural network localizes its position in space using data from the front-facing cameras. All of that data is fed into a central control unit — itself a deep neural network — which crunches through the numbers and devises a shortest/fastest path around the track.