Daily biometrics, smart scales and AI companions are quietly rewriting the rules of aging.
Category: robotics/AI – Page 16
Let’s unravel what happens when AI merges with quantum, and starts knowing EVERYTHING ♾️ Go to to get 83% off from our sponsor Private Internet Access with 4 months free!
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Special thanks to our beloved YouTube members this month: Powlin Manuel, Saïd Kadi, Chenxi, Lord, Sudhir Paranjape, Nate Lachae, Alison Rewell, Thomas Lapins, Ahmad Salahudin, Antonio Ferriol Colombram, Anton Nicolas Burger 🚀🚀🚀
Experts featured in this video include Demis Hassabis, Tristan Harris, Aza Raskin, Elon Musk, David Deutsch, Michio Kaku, Brian Greene and Nick Bostrom.
Chapter:
0:00 A dangerous truth?
1:29 AI advancement.
3:46 AI pretending not to know.
7:29 Interactive tutoring.
9:37 That’s it from our sponsor!
10:21 The merging of QC and AI
12:03 IBM 100,000 qubits.
14:34 AI wipes out humanity?
16:05 Google Willow.
17:06 The misuse of AI and QC
18:22 Singularity and Turing test.
22:51 Reverse Turing test.
29:39 Quantum-AI consequences.
32:25 The double slit experiment.
36:15 Quantum multiverse.
41:05 Computing history.
46:49 AGI timeline.
51:45 Philosophical consequence.
#AI #quantumcomputing #singularity
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Nonlinear photonic neuromorphic chips for spiking reinforcement learning
Photonic computing chips have made significant progress in accelerating linear computations, but nonlinear computations are usually implemented in the digital domain, which introduces additional system latency and power consumption, and hinders the implementation of fully functional photonic neural network chips. Here, we propose and fabricate a 16-channel programmable incoherent photonic neuromorphic computing chip by co-designing a simplified Mach–Zehnder interferometer (MZI) mesh and distributed feedback lasers with saturable absorber (DFBs-SA) array using different materials, enabling implementation of both linear and nonlinear spike computations in the optical domain through two separate chips. Furthermore, previous studies mainly focused on supervised learning and simple image classification tasks. Here, we propose a photonic spiking reinforcement learning (RL) architecture for the first, to our knowledge, time, and develop a software–hardware collaborative training-inference framework (in situ photonic training and hardware-aware fine-tuning) to address the challenge of training spiking RL models. We achieve large-scale, energy-efficient (photonic linear computation: 1.39 TOPS/W, photonic nonlinear computation: 987.65 GOPS/W), and low-latency (on-chip 320 ps) deployment of an entire layer of photonic spiking RL. Two RL benchmarks including the discrete CartPole task and the continuous Pendulum task are demonstrated experimentally based on the spiking proximal policy optimization (PPO) algorithm. The hardware–software collaborative computing reward value converges to 200 (−250) for the CartPole (Pendulum) tasks, respectively, comparable to that of a traditional PPO algorithm. This experimental demonstration addresses the challenge of the absence of large-scale on-chip photonic nonlinear spike computation and spiking RL training difficulty, and presents a high-speed and low-latency photonic spiking RL solution with promising application prospects in fields such as robot control, autonomous driving, and embodied intelligence.
Mitochondrial complex-derived ROS induces lysosomal dysfunction and impairs autophagic flux in human cells carrying the APOE4 allele
The APOE4 allele is the strongest genetic risk factor for sporadic Alzheimer’s disease (sAD), yet its cell-autonomous effects remain poorly understood. While young, asymptomatic APOE4 carriers exhibit abnormal brain metabolism, the mechanistic link between mitochondrial dysfunction and lysosomal-autophagic failure remains unclear. In this study, we conducted a comprehensive analysis of primary human fibroblasts from APOE3 controls, APOE4, and sAD donors to assess mitochondrial bioenergetics, oxidative stress, autophagy, and lysosomal function. APOE4 fibroblasts displayed increased mitochondrial content-associated markers (PGC1α, mtDNA) accompanied by reduced respiratory capacity, elevated proton leak, and excessive mitochondrial ROS. In parallel, APOE4 fibroblasts showed impaired autophagic flux and reduced LC3-TOMM20 colocalization, indicating defective mitophagy. Lysosomal proteolytic activity, assessed using DQ-BSA, was significantly reduced and remained unresponsive under to starvation, in contrast to the partial recovery observed in sAD cells. Pharmacological targeting of mitochondrial ROS with site-specific inhibitors revealed that complex III-derived ROS is the predominant driver of redox stress in APOE4 fibroblasts, while complex I contributes primarily in sAD. Notably, selective inhibition of complex III-derived ROS with S3QEL restored lysosomal degradation, autophagic flux, and mitochondrial respiration in APOE4 cells. Together, these findings demonstrate that mitochondrial oxidative stress disrupts the mitochondria-lysosome axis in an APOE4-specific manner, revealing early and mechanistically distinct vulnerabilities that may precede neurodegeneration. Our results challenge the notion that APOE4 merely amplifies AD pathology and instead identity site-specific redox signaling as a promising target for allele-informed interventions.
Keywords: APOE4; Autophagy; Human fibroblasts; Lysosome; Mitochondria; Mitochondrial complex III; S3QEL.
Copyright © 2024. Published by Elsevier B.V.
What Happens When Quantum-AI Knows TOO MUCH?
Let’s unravel what happens when AI merges with quantum, and starts knowing EVERYTHING ♾️ Go to https://piavpn.com/beeyondideas to get 83% off from our sponsor Private Internet Access with 4 months free!
Want to support our production? Feel free to join our membership at https://youtu.be/_Z4W6sWDo_4?si=Q8eRZoNFUv7sAd9y Special thanks to our beloved YouTube members this month: Powlin Manuel, Saïd Kadi, Chenxi, Lord, Sudhir Paranjape, Nate Lachae, Alison Rewell, Thomas Lapins, Ahmad Salahudin, Antonio Ferriol Colombram, Anton Nicolas Burger 🚀🚀🚀 Experts featured in this video include Demis Hassabis, Tristan Harris, Aza Raskin, Elon Musk, David Deutsch, Michio Kaku, Brian Greene and Nick Bostrom. Chapter: 0:00 A dangerous truth? 1:29 AI advancement 3:46 AI pretending not to know 7:29 Interactive tutoring 9:37 That’s it from our sponsor! 10:21 The merging of QC and AI 12:03 IBM 100,000 qubits 14:34 AI wipes out humanity? 16:05 Google Willow 17:06 The misuse of AI and QC 18:22 Singularity and Turing test 22:51 Reverse Turing test 29:39 Quantum-AI consequences 32:25 The double slit experiment 36:15 Quantum multiverse 41:05 Computing history 46:49 AGI timeline 51:45 Philosophical consequence #AI #quantumcomputing #singularity.
Special thanks to our beloved YouTube members this month: Powlin Manuel, Saïd Kadi, Chenxi, Lord, Sudhir Paranjape, Nate Lachae, Alison Rewell, Thomas Lapins, Ahmad Salahudin, Antonio Ferriol Colombram, Anton Nicolas Burger 🚀🚀🚀
Experts featured in this video include Demis Hassabis, Tristan Harris, Aza Raskin, Elon Musk, David Deutsch, Michio Kaku, Brian Greene and Nick Bostrom.
Chapter:
0:00 A dangerous truth?
1:29 AI advancement.
3:46 AI pretending not to know.
7:29 Interactive tutoring.
9:37 That’s it from our sponsor!
10:21 The merging of QC and AI
12:03 IBM 100,000 qubits.
14:34 AI wipes out humanity?
16:05 Google Willow.
17:06 The misuse of AI and QC
18:22 Singularity and Turing test.
22:51 Reverse Turing test.
29:39 Quantum-AI consequences.
32:25 The double slit experiment.
36:15 Quantum multiverse.
41:05 Computing history.
46:49 AGI timeline.
51:45 Philosophical consequence.
#AI #quantumcomputing #singularity
Learning makes brain cells work together, not apart
When you get better at a skill—recognizing a familiar face in a crowd, spotting a typo at a glance, or anticipating the next move in a game—sensory neurons in your brain become more coordinated, sharing information rather than acting more independently. That’s the conclusion of a new study by researchers at the University of Rochester and its Del Monte Institute for Neuroscience, published in Science, which challenges a long-held assumption in neuroscience that learning improves efficiency by minimizing repetition across neural signals.
Led by Shizhao Liu, a graduate student in the labs of Ralf Haefner and Adam Snyder, both faculty members in the Department of Brain and Cognitive Sciences, the study shows that learning instead increases shared activity among neurons. The findings could provide insights into learning disorders and inspire more flexible, human-like artificial intelligence tools.
“The dominant view in neuroscience has been that learning makes the brain more efficient by pushing neurons to act more independently, so information can be read out more cleanly,” Liu says. “Our results support a different idea, that sensory areas of the brain aren’t just passively encoding the world. They’re actively performing inference by combining what’s coming in with what the brain has learned to expect.”
A new University of Rochester study could reshape how scientists think about perception, learning disorders, and artificial intelligence.
Beyond silicon: An indium selenide roadmap for ultra-low-power AI and quantum computing
A research team led by Prof. Seunguk Song from the Department of Energy Science at Sungkyunkwan University (SKKU), in collaboration with the Institute for Basic Science (IBS), the University of Pennsylvania, and the U.S. Air Force Research Laboratory, has developed a comprehensive technical roadmap for two-dimensional (2D) indium selenides (InSe)—a key material for next-generation low-power and quantum computing.
The study, titled “Indium selenides for next-generation electronics and optoelectronics,” was published in Nature Reviews Electrical Engineering. This research provides a deep dive into the physical properties and device applications of 2D quantum semiconductors, which are viewed as a definitive alternative to silicon as it reaches its physical scaling limits.
As current silicon-based semiconductors shrink to the sub-nanometer scale, they face critical hurdles such as surging power consumption, overheating, and leakage current. To address these challenges, Professor Song’s team focused on InSe, an atomically thin material.
Andrew Yang: UBI Before UHI
Solving Job Loss, and the Future of Work ## Andrew Yang advocates for the implementation of Universal Basic Income (UBI) as a necessary solution to address job loss, income inequality, and societal unrest caused by technological advancements and AI-driven changes in the economy ## ## Questions to inspire discussion.
Universal Basic Income Implementation.
🔹 Q: What UBI amount should be set to provide an effective safety net?
A: UBI should be set at twice the poverty level, around $25,000 per person per year, providing enough for survival but not happiness to maintain work incentives while protecting against economic collapse.
🔹 Q: How can UBI be funded without government action initially?
A: Well-resourced tech billionaires could fund UBI directly to local communities to keep the middle class afloat during AI-driven changes, potentially catalyzing further philanthropy and government action.