Musk said on a Twitter Spaces call that he had discussed the possibility of AI seizing power during a recent trip to China.
Research led by Sichuan University and Huazhong University of Science and Technology, China, has revealed genetic mechanisms that could prolong healthy aging. In the paper, titled “Partial inhibition of class III PI3K VPS-34 ameliorates motor aging and prolongs health span,” published in PLOS Biology, the team details the methods they used to narrow down the potential genomic pathways to a single gene that could be critical to extending healthy human longevity.
With a combination of genetic manipulation, behavioral assays, microscopy techniques, and electrophysiology, the researchers investigated the role of VPS-34 in motor aging. These methods allowed the researchers to gain insights into the molecular mechanisms underlying motor aging and the effects of VPS-34 inhibition on motor function, synaptic transmission, and muscle integrity.
According to the authors, increased life expectancy in recent decades has not been accompanied by a corresponding increase in health span. Aging is characterized by the decline of multiple organs and tissues and motor aging, in particular, leads to frailty, loss of motor independence, and other age-related issues. Identifying mechanisms for therapeutics to delay motor aging is crucial for promoting healthy aging.
Prosthetics moved by thoughts. Targeted treatments for aggressive brain cancer. Soldiers with enhanced vision or bionic ears.
These powerful technologies sound like science fiction, but they’re becoming possible thanks to nanoparticles.
And, as with any great power, there comes great responsibility.
Summary: Researchers have uncovered genes essential for learning, memory, aggression, and other complex behaviors originated around 650 million years ago.
The study utilized computational methods to trace the evolutionary history of these genes involved in the production, modulation, and reception of monoamines like serotonin, dopamine, and adrenaline. This discovery suggests that this new method of modulating neuronal circuits could have played a role in the Cambrian Explosion, contributing to the diversification of life.
The finding offers new research avenues to understand the origins of complex behaviors and their relation to diverse processes like reward, addiction, aggression, feeding, and sleep.
As scientists explore the quantum world, the known number of phases of matter continues to grow. The newest addition to the list is a chiral bose-liquid state.
Generative AI techniques like ChatGPT, DALL-e and Codex can generate digital content such as images, text, and the code. Recent progress in large-scale AI models has improved generative AI’s ability to understand intent and generate more realistic content. This text summarizes the history of generative models and components, recent advances in AI-generated content for text, images, and across modalities, as well as remaining challenges.
In recent years, Artificial Intelligence Generated Content (AIGC) has gained much attention beyond the computer science community, where the whole society is interested in the various content generation products built by large tech companies. Technically, AIGC refers to, given human instructions which could help teach and guide the model to complete the task, using Generative AI algorithms to form a content that satisfies the instruction. This generation process usually comprises two steps: extracting intent information from human instructions and generating content according to the extracted intentions.
Generative models have a long history of AI, dating to the 1950s. Early models like Hidden Markov Models and Gaussian Mixture Models generated simple data. Generative models saw major improvements in deep learning. In NLP, traditional sentence generation used N-gram language models, but these struggled with long sentences. Recurrent neural networks and Gated Recurrent Units enabled modeling longer dependencies, handling ~200 tokens. In CV, pre-deep learning image generation used hand-designed features with limited complexity and diversity. Generative Adversarial Networks and Variational Autoencoders enabled impressive image generation. Advances in generative models followed different paths but converged with transformers, introduced for NLP in 2017. Transformers dominate many generative models across domains. In NLP, large language models like BERT and GPT use transformers. In CV, Vision Transformers and Swin Transformers combine transformers and visual components for images.
The Big Data revolution has strained the capabilities of state-of-the-art electronic hardware, challenging engineers to rethink almost every aspect of the microchip. With ever more enormous data sets to store, search and analyze at increasing levels of complexity, these devices must become smaller, faster and more energy efficient to keep up with the pace of data innovation.
Ferroelectric field effect transistors (FE-FETs) are among the most intriguing answers to this challenge. Like traditional silicon-based transistors, FE-FETs are switches, turning on and off at incredible speed to communicate the 1s and 0s computers use to perform their operations.
But FE-FETs have an additional function that conventional transistors do not: their ferroelectric properties allow them to hold on to electrical charge.
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This video explores Super Intelligent AIs and the capabilities they will have. Watch this next video called Super Intelligent AI: 10 Ways It Will Change The World: https://youtu.be/cVjq53TKKUU.
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https://www.youtube.com/watch?v=zFbMJ-2QpG8
This video explores Super Intelligent AI and 10 scientific discoveries it could make. Watch this next video called Super Intelligent AI: 10 Ways It Will Change The World: https://youtu.be/cVjq53TKKUU.
► My Business Ideas Generation Book: https://bit.ly/3NDpPDI
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► Jasper AI: Write 5x Faster With Artificial Intelligence: https://bit.ly/3MIPSYp.
SOURCES:
• https://www.britannica.com/science/tachyon.
• https://plato.stanford.edu/entries/qm-manyworlds/#:~:text=Th…ion%20(MWI, and%20thus%20from%20all%20physics.
Continue reading “Super Intelligent AI: 10 Scientific Discoveries It Will Make” »
Integrated optical semiconductor (hereinafter referred to as optical semiconductor) technology is a next-generation technology for which many researches and investments are being made worldwide because it can make complex optical systems such as LiDAR and quantum sensors and computers into a single small chip.
In existing semiconductor technology, the goal was to achieve units of 5 nanometers or 2 nanometers, but increasing the degree of integration in optical semiconductor devices can be said to be a key technology that determines performance, price, and energy efficiency.
A research team led by Professor Sangsik Kim of the Department of Electrical and Electronic Engineering discovered a new optical coupling mechanism that can increase the degree of integration of optical semiconductor devices by more than 100 times.
