The dead pigs moved. I repeat, the dead pigs moved!
Mad Scientists Created Franken-Pigs
Posted in futurism
A model for autonomous underwater robots could help them navigate previously-unexplored waters.
I don’t buy the Jeanne Calment story.
How can we live longer and be healthier? These startups are trying to extend our lives.
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Multivariable calculus, differential equations, linear algebra—topics that many MIT students can ace without breaking a sweat—have consistently stumped machine learning models. The best models have only been able to answer elementary or high school-level math questions, and they don’t always find the correct solutions.
Now, a multidisciplinary team of researchers from MIT and elsewhere, led by Iddo Drori, a lecturer in the MIT Department of Electrical Engineering and Computer Science (EECS), has used a neural network model to solve university-level math problems in a few seconds at a human level.
The model also automatically explains solutions and rapidly generates new problems in university math subjects. When the researchers showed these machine-generated questions to university students, the students were unable to tell whether the questions were generated by an algorithm or a human.
Scientists and engineers are constantly developing new materials with unique properties that can be used for 3D printing, but figuring out how to print with these materials can be a complex, costly conundrum.
Often, an expert operator must use manual trial-and-error—possibly making thousands of prints—to determine ideal parameters that consistently print a new material effectively. These parameters include printing speed and how much material the printer deposits.
MIT researchers have now used artificial intelligence to streamline this procedure. They developed a machine-learning system that uses computer vision to watch the manufacturing process and then correct errors in how it handles the material in real-time.
Within minutes of the final heartbeat, a cascade of biochemical events triggered by a lack of blood flow, oxygen, and nutrients begins to destroy a body’s cells and organs. But a team of Yale scientists has found that massive and permanent cellular failure doesn’t have to happen so quickly.
The researchers stressed that additional studies are necessary to understand the apparently restored motor functions in the animals, and that rigorous ethical review from other scientists and bioethicists is required.
The experimental protocols for the latest study were approved by Yale’s Institutional Animal Care and Use Committee and guided by an external advisory and ethics committee.
The OrganEx technology could eventually have several potential applications, the authors said. For instance, it could extend the life of organs in human patients and expand the availability of donor organs for transplant. It might also be able to help treat organs or tissue damaged by ischemia during heart attacks or strokes.
Unlike existing treatments that focus on rehabilitation or reducing the risk of an additional stroke, the experimental drug targets the “stickiness” that can prevent injured brain tissue from functioning as it should.
Researchers who previously revived some brain cells in dead pigs succeeded in repeating the process in more organs.
Cold Fusion is possible by replacing Hydrogen Electrons with Muons.
- Takes 5GeV to create a Muon, they only live for 2.2 microseconds.
- Muons provide a catalyst for 150 fusion events, before sticking to Helium Atom.
- Net fusion output is 2.7GeV per 5GeV Muon.
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Swiss researchers have done the (theoretically) impossible, creating not one but two silicon-based solar cells with efficiencies greater than 30% — breaking a world record and potentially illuminating the path to a future of cheaper clean energy.
The status quo: Solar cells absorb light and convert it into electricity. They’re the basis of most solar power tech, and about 95% of them are made from silicon because it’s abundant, long-lasting, and relatively cheap.
Most of the silicon solar cells sold today are about 22% efficient, meaning they convert 22% of the solar energy that hits them into electricity. We don’t have too much room for improvement with silicon solar cells, either, as they have a theoretical efficiency limit of about 29%.