Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: information science – Page 3

The healthcare industry faces a significant shift towards digital health technology, with a growing demand for real-time and continuous health monitoring and disease diagnostics [1, 2, 3]. The rising prevalence of chronic diseases, such as diabetes, heart disease, and cancer, coupled with an aging population, has increased the need for remote and continuous health monitoring [4, 5, 6, 7]. This has led to the emergence of artificial intelligence (AI)-based wearable sensors that can collect, analyze, and transmit real-time health data to healthcare providers so that they can make efficient decisions based on patient data. Therefore, wearable sensors have become increasingly popular due to their ability to provide a non-invasive and convenient means of monitoring patient health. These wearable sensors can track various health parameters, such as heart rate, blood pressure, oxygen saturation, skin temperature, physical activity levels, sleep patterns, and biochemical markers, such as glucose, cortisol, lactates, electrolytes, and pH and environmental parameters [1, 8, 9, 10]. Wearable health technology includes first-generation wearable technologies, such as fitness trackers, smartwatches, and current wearable sensors, and is a powerful tool in addressing healthcare challenges [2].

The data collected by wearable sensors can be analyzed using machine learning (ML) and AI algorithms to provide insights into an individual’s health status, enabling early detection of health issues and the provision of personalized healthcare [6,11]. One of the most significant advantages of AI-based wearable health technology is to promote preventive healthcare. This enables individuals and healthcare providers to proactively address symptomatic conditions before they become more severe [12,13,14,15]. Wearable devices can also encourage healthy behavior by providing incentives, reminders, and feedback to individuals, such as staying active, hydrating, eating healthily, and maintaining a healthy lifestyle by measuring hydration biomarkers and nutrients.

Read more | >

This video takes a look at how future technology could change the Fermi Paradox. Asking if humanity is looking for life in the Universe in the wrong ways, or are we looking for the wrong things. Like trying to find smoke signals, in the age of fiber optics.

While the Drake Equation estimates how many civilizations could exist in the Universe, but what is the likelihood that humanity is even capable of detecting them.

Does there need to be another calculation, say the Detection Probability Equation. Showing what’s the likelihood that humanity is able to detect alien life at a given time, and solving the Fermi Paradox.

And does this create a new paradox. Because if future technology advancements increase the number of possible cosmic civilizations, could it also decrease humanity’s ability in detecting them — leading to the detection paradox.

Other topics covered in this sci-fi documentary video include: space telescopes, dyson spheres, the movie Contact by Carl Sagan, Interstellar movie and the time dilation effects, the great silence, the great filter, and solutions and theories for the Fermi Paradox.

PATREON

Continue reading “How Future Technology Will Change the Fermi Paradox (Sci-Fi Documentary)” | >

Snap a photo of your meal, and artificial intelligence instantly tells you its calorie count, fat content, and nutritional value—no more food diaries or guesswork.

This futuristic scenario is now much closer to reality, thanks to an AI system developed by NYU Tandon School of Engineering researchers that promises a new tool for the millions of people who want to manage their weight, diabetes and other diet-related health conditions.

The technology, detailed in a paper presented at the 6th IEEE International Conference on Mobile Computing and Sustainable Informatics, uses advanced deep-learning algorithms to recognize food items in images and calculate their nutritional content, including calories, protein, carbohydrates and fat.

Read more | >

Reinforcement learning (RL) has become central to advancing Large Language Models (LLMs), empowering them with improved reasoning capabilities necessary for complex tasks. However, the research community faces considerable challenges in reproducing state-of-the-art RL techniques due to incomplete disclosure of key training details by major industry players. This opacity has limited the progress of broader scientific efforts and collaborative research.

Researchers from ByteDance, Tsinghua University, and the University of Hong Kong recently introduced DAPO (Dynamic Sampling Policy Optimization), an open-source large-scale reinforcement learning system designed for enhancing the reasoning abilities of Large Language Models. The DAPO system seeks to bridge the gap in reproducibility by openly sharing all algorithmic details, training procedures, and datasets. Built upon the verl framework, DAPO includes training codes and a thoroughly prepared dataset called DAPO-Math-17K, specifically designed for mathematical reasoning tasks.

DAPO’s technical foundation includes four core innovations aimed at resolving key challenges in reinforcement learning. The first, “Clip-Higher,” addresses the issue of entropy collapse, a situation where models prematurely settle into limited exploration patterns. By carefully managing the clipping ratio in policy updates, this technique encourages greater diversity in model outputs. “Dynamic Sampling” counters inefficiencies in training by dynamically filtering samples based on their usefulness, thus ensuring a more consistent gradient signal. The “Token-level Policy Gradient Loss” offers a refined loss calculation method, emphasizing token-level rather than sample-level adjustments to better accommodate varying lengths of reasoning sequences. Lastly, “Overlong Reward Shaping” introduces a controlled penalty for excessively long responses, gently guiding models toward concise and efficient reasoning.

Read more | >

Researchers at the University of Gothenburg have developed a novel Ising machine that utilizes surface acoustic waves as an effective carrier of dense information flow. This approach enables fast, energy-efficient solutions to complex optimization problems, offering a promising alternative to conventional computing methods based on von-Neumann architecture. The findings are published in the journal Communications Physics.

Traditional computers can stumble when tackling —tasks of scheduling logistic operations, financial portfolio optimization and high frequency trading, optimizing communication channels in complex wireless networks, or predicting how proteins fold among countless structural possibilities.

In these cases, each added node—an additional logistic hub, network user, or molecular bond causes the number of possible configurations to explode exponentially. In contrast to linear or polynomial growth, an exponential increase in the number of possible solutions makes even the most powerful computers and algorithms lack the computational power and memory to evaluate every scenario in search of vanishingly small subsets representing satisfactorily optimal solutions.

Read more | >

The use of artificial intelligence (AI) scares many people as neural networks, modeled after the human brain, are so complex that even experts do not understand them. However, the risk to society of applying opaque algorithms varies depending on the application.

While AI can cause great damage in democratic elections through the manipulation of social media, in astrophysics it at worst leads to an incorrect view of the cosmos, says Dr. Jonas Glombitza from the Erlangen Center for Astroparticle Physics (ECAP) at Friedrich-Alexander Universität Erlangen-Nürnberg (FAU).

The astrophysicist uses AI to accelerate the analysis of data from an observatory that researches cosmic radiation.

Read more | >

A new study probing quantum phenomena in neurons as they transmit messages in the brain could provide fresh insight into how our brains function.

In this project, described in the Computational and Structural Biotechnology Journal, theoretical physicist Partha Ghose from the Tagore Centre for Natural Sciences and Philosophy in India, together with theoretical neuroscientist Dimitris Pinotsis from City St George’s, University of London and the MillerLab of MIT, proved that established equations describing the classical physics of brain responses are mathematically equivalent to equations describing quantum mechanics. Ghose and Pinotsis then derived a Schrödinger-like equation specifically for neurons.

Our brains process information via a vast network containing many millions of neurons, which can each send and receive chemical and electrical signals. Information is transmitted by nerve impulses that pass from one neuron to the next, thanks to a flow of ions across the neuron’s cell membrane. This results in an experimentally detectable change in electrical potential difference across the membrane known as the “action potential” or “spike”

Read more | >

Based on how an AI model transcribes audio into text, the researchers behind the study could map brain activity that takes place during conversation more accurately than traditional models that encode specific features of language structure — such as phonemes (the simple sounds that make up words) and parts of speech (such as nouns, verbs and adjectives).

The model used in the study, called Whisper, instead takes audio files and their text transcripts, which are used as training data to map the audio to the text. It then uses the statistics of that mapping to “learn” to predict text from new audio files that it hasn’t previously heard.

Read more | >

The future of AI is here—and it’s running on human brain cells! In a groundbreaking development, scientists have created the first AI system powered by biological neurons, blurring the line between technology and biology. But what does this mean for the future of artificial intelligence, and how does it work?

This revolutionary AI, known as “Brainoware,” uses lab-grown human brain cells to perform complex tasks like speech recognition and decision-making. By combining the adaptability of biological neurons with the precision of AI algorithms, researchers have unlocked a new frontier in computing. But with this innovation comes ethical questions and concerns about the implications of merging human biology with machines.

In this video, we’ll explore how Brainoware works, its potential applications, and the challenges it faces. Could this be the key to creating truly intelligent machines? Or does it raise red flags about the ethical boundaries of AI research?

What is Brainoware, and how does it work? What are the benefits and risks of AI powered by human brain cells? How will this technology shape the future of AI? This video answers all these questions and more. Don’t miss the full story—watch until the end!

#ai.
#artificialintelligence.
#ainews.

******************

Continue reading “The First AI that Runs on Human Brain Cells is Finally Here!” | >

Compare news coverage. Spot media bias. Avoid algorithms. Try Ground News today and get 40% off your subscription by going to https://ground.news/upandatom.

Special thanks to Chuankun Zhang, Tian Ooi, Jacob S. Higgins, and Jack F. Doyle from Prof. Jun Ye’s lab at JILA/NIST/University of Colorado, as well as Prof. Victor Flambaum from UNSW’s Department of Theoretical Physics, for their valuable assistance and consultation on this video.

Hi! I’m Jade. If you’d like to consider supporting Up and Atom, head over to my Patreon page smile
https://www.patreon.com/upandatom.

Visit the Up and Atom store.
https://store.nebula.app/collections/up-and-atom.

https://www.youtube.com/c/upandatom.

For a one time donation, head over to my PayPal smile https://www.paypal.me/upandatomshows.

Continue reading “It took 20 years, but we finally found it” | >