New studies on astronauts and space biology bring humanity one step closer to the final frontier.
The Space Omics and Medical Atlas (SOMA) package of manuscripts, data, protocols, and code represents the largest-ever compendium of data for aerospace medicine and space biology.
An AT&T outage map shows a reported issue covering much of San Antonio on Wednesday evening, July 24. The outage could last into Thursday, AT&T says.
Machine learning (ML) is one of the most important subareas of AI used in building great AI systems.
In ML, deep learning is a narrow area focused solely on neural networks. Through the field of deep learning, systems like ChatGPT and many other AI models can be created. In other words, ChatGPT is just a giant system based on neural networks.
However, there is a big problem with deep learning: computational efficiency. Creating big and effective AI systems with neural networks often requires a lot of energy, which is expensive.
Neural networks biological and artificial.
Neural Networks have found applications across various domains due to their ability to learn from data and improve over time without human intervention. They can solve challenging problems that are hard or impossible to solve using traditional methods. Here are some of the examples of how neural networks and artificial neurons are used in real-world scenarios:
Voice assistants: Voice assistants like Siri and Alexa use neural networks to understand spoken language commands and questions. They use trained models based on artificial neurons processing vast datasets of speech and text data. They can also generate natural-sounding responses and perform various tasks, such as playing music, setting reminders, searching the web, etc.
Self-driving cars: Self-driving cars use neural networks to perceive the environment and make decisions. They use trained models based on artificial neurons processing vast datasets of images, videos, and sensor data. They can also learn from their own experiences and improve their driving skills over time.
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A lot of big banks are banking on quantum computing because they think it’ll give them an edge in trading. Though I have on previous occasions noted my doubt that we’ll see any useful quantum computers within the next ten years, two new papers detailing new methods of scaling quantum computers have shifted my perspective. Let’s have a look.
Paper 1: https://www.nature.com/articles/s4158…
Paper 2: https://arxiv.org/abs/2404.
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Quantum: Check out Our Video on Aduro: https://twobit.link/AduroVideoLearn More About Aduro: https://twobit.link/AduroQuantum Computers are are confusing and…
New research may have found a link between supermassive black holes and dark matter particles which might solve an issue which has irked astrophysicists for decades: the “final parsec problem.”
Last year, an international team of researchers discovered a background “hum” of gravitational waves. They hypothesised that this background signal is emanating from millions of merging pairs of supermassive black hole.
Supermassive black holes are hundreds of thousands to billions of times larger than our Sun.
A team of scientists from Montana State University has provided the first experimental evidence that two new groups of microbes thriving in thermal features in Yellowstone National Park produce methane—a discovery that could one day contribute to the development of methods to mitigate climate change and provide insight into potential life elsewhere in our solar system.
In an era where the internet connects virtually every aspect of our lives, the security of information systems has become paramount. Safeguarding critical databases containing private and commercial information presents a formidable challenge, driving researchers to explore advanced encryption techniques for enhanced protection.
Data encryption, a cornerstone of modern security practices, transforms readable plaintext into encoded ciphertext, ensuring that only authorized recipients can decipher the data using a decryption key or password. Optical techniques have emerged as promising tools for encryption due to their capabilities for parallel, high-speed transmission, and low-power consumption. However, traditional optical encryption systems often suffer from vulnerabilities where plaintext-ciphertext forms remain identical, potentially compromising security.
Reporting in Advanced Photonics Nexus, scientists have unveiled an approach inspired by bio-inspired neuromorphic imaging and speckle correlography. Their innovative technique leverages computational neuromorphic imaging (CNI) to encrypt images into event-stream ciphertexts, marking a significant departure from conventional methods. This method introduces a new paradigm in optical encryption by converting data into event-driven formats, thereby significantly enhancing security and complexity.
