Scientists are using ever more sophisticated AI algorithms trained on vast, unlabeled datasets to develop models that can ‘interpret’ biological data to help guide biomolecule design.
“If a tree falls in the forest, and no one hears it, does it make a sound?” I remember seeing an actual argument get started on this subject—a fully naive argument that went nowhere near Berkeleyan subjectivism. Just:
“It makes a sound, just like any other falling tree!” “But how can there be a sound that no one hears?”
The standard rationalist view would be that the first person is speaking as if “sound” means acoustic vibrations in the air; the second person is speaking as if “sound” means an auditory experience in a brain. If you ask “Are there acoustic vibrations?” or “Are there auditory experiences?”, the answer is at once obvious. And so the argument is really about the definition of the word “sound”
Researchers Propose a #Smaller, more #Noise-#Tolerant #Quantum #Circuit for #Cryptography.
MIT researchers new algorithm is as fast as Regev’s, requires fewer qubits, and has a higher tolerance to quantum noise, making it more feasible to implement…
The most recent email you sent was likely encrypted using a tried-and-true method that relies on the idea that even the fastest computer would be unable to efficiently break a gigantic number into factors.
Outperforms dermatologists in detecting melanoma, offering better diagnosis for challenging cases and improving patient care. 🩺🖥️
Heinlein, Maron, Hekler et al. evaluate an AI algorithm for detecting melanoma and compare its performance to that of dermatologist on a prospectively collected, external, heterogeneous dataset. The AI exhibits a significant performance advantage, especially in diagnosing challenging cases.
AI may equal human intelligence without matching the true nature of our experiences.
A future where the thinking capabilities of computers approach our own is quickly coming into view. We feel ever more powerful machine-learning (ML) algorithms breathing down our necks. Rapid progress in coming decades will bring about machines with human-level intelligence capable of speech and reasoning, with a myriad of contributions to economics, politics and, inevitably, warcraft. The birth of true artificial intelligence will profoundly affect humankind’s future, including whether it has one.
Can conscious self-awareness be coded in an algorithm? According to distinguished computer scientist Lenore Blum and Turing Award Laureate Manuel Blum the answer is \.
How can machine learning help individuals with type 1 diabetes (T1D)? This is what a study presented at this year’s Annual Meeting of the European Association for the Study of Diabetes (EASD) hopes to address as a team of researchers have developed a system using machine learning capable of managing blood sugars levels with such proficiency that those using system were able to lead lives far more active than the average T1D patient.
For the study, the researchers developed the AID system, which uses closed-loop technology that delivers insulin based on readings from the machine learning algorithm, resulting in a 50-year-old man, a 40-year-old man, and a 34-year-old woman with T1D being able to run hours-long marathons in Tokyo, Santiago, and Paris, respectively. This study holds the potential to help develop better technology capable of allowing T1D diabetes patients to stay in shape without constantly fearing for their blood sugar levels, which can lead to long-term health problems, including hyperglycemia, nerve damage, or a heart attack.
“Despite better systems for monitoring blood sugars and delivering insulin, maintaining glucose levels in target range during aerobic training and athletic competition is especially difficult,” said Dr. Maria Onetto, who is in the Department of Nutrition at the Pontifical Catholic University of Chile and lead author of the study. “The use of automated insulin delivery technology is increasing, but exercise continues to be a challenge for individuals with T1D, who can still struggle to reach the recommended blood sugar targets.”
A wormhole is a hypothetical structure connecting disparate points in spacetime, and is based on a special solution of the Einstein field equations. [ 1 ]
A can be visualized as a tunnel with two ends at separate points in spacetime (i.e., different locations, different points in time, or both).
Scientists Create Matter from Pure Light, Demonstrating Einstein’s E=mc² Equation in Action.
Physicists at Brookhaven National Laboratory have achieved a groundbreaking experiment, creating matter from light by demonstrating the Breit-Wheeler process. Using the Relativistic Heavy Ion Collider, they accelerated heavy ions to generate nearly real photons, leading to the formation of electron-positron pairs. This experiment showcases Einstein’s E=mc² equation in action, aligning with predictions for transforming energy into matter. While these virtual photons act similarly to real ones, the experiment is a crucial step towards proving the process with real photons when technology advances to create gamma-ray lasers. Don’t forget to comment your thought about this!
A team of roboticists at the German Aerospace Center’s Institute of Robotics and Mechatronics finds that combining traditional internal force-torque sensors with machine-learning algorithms can give robots a new way to sense touch.
In their study published in the journal Science Robotics, the group took an entirely new approach to give robots a sense of touch that does not involve artificial skin.
