Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: information science – Page 5

Similar to humans going on journeys of self-discovery, quantum computers are also capable of deepening their understanding of their own foundations.

Researchers from Tohoku University and St. Paul’s School, London, have developed a that allows quantum computers to analyze and protect quantum entanglement—a fundamental underpinning of quantum computing. These findings will advance our understanding of quantum entanglement and quantum technologies.

The study was published in Physical Review Letters on March 4, 2025.

Read more | >

In an unprecedented move, precision medicine provider Human Longevity, Inc. (HLI) has effectively guaranteed its Executive Health Program members that it will prevent them from developing late stage prostate cancer. Such is the company’s belief in its preventive approach, it has announced it is committing $1 million for advanced treatment of any member diagnosed with stage four of the disease or higher while under its care.

Founded in 2013 by genomics pioneer Dr J Craig Venter, San Francisco-based Human Longevity Inc. (HLI) aims to extend human health and performance beyond the traditional focus on treating illness. By continuously analyzing health data from its clients, HLI seeks to identify potential health risks – such as prostate cancer – early, enabling targeted interventions to extend both healthspan and lifespan.

Leveraging data collected from more than 5,000 men over the past decade, HLI claims it has developed what it believes to be the most advanced algorithm for early prostate cancer detection. As preventive medicine continues to demonstrate its capacity to mitigate previously life-threatening conditions, will we see commitments of this nature emerging for more diseases?

Read more | >

Joscha Bach is a cognitive scientist focusing on cognitive architectures, consciousness, models of mental representation, emotion, motivation and sociality.

Patreon: / curtjaimungal.
Crypto: https://tinyurl.com/cryptoTOE
PayPal: https://tinyurl.com/paypalTOE
Twitter: / toewithcurt.
Discord Invite: / discord.
iTunes: https://podcasts.apple.com/ca/podcast… https://pdora.co/33b9lfP Spotify: https://open.spotify.com/show/4gL14b9… Subreddit r/TheoriesOfEverything: / theoriesofeverything Merch: https://tinyurl.com/TOEmerch 0:00:00 Introduction 0:00:17 Bach’s work ethic / daily routine 0:01:35 What is your definition of truth? 0:04:41 Nature’s substratum is a “quantum graph”? 0:06:25 Mathematics as the descriptor of all language 0:13:52 Why is constructivist mathematics “real”? What’s the definition of “real”? 0:17:06 What does it mean to “exist”? Does “pi” exist? 0:20:14 The mystery of something vs. nothing. Existence is the default. 0:21:11 Bach’s model vs. the multiverse 0:26:51 Is the universe deterministic 0:28:23 What determines the initial conditions, as well as the rules? 0:30:55 What is time? Is time fundamental? 0:34:21 What’s the optimal algorithm for finding truth? 0:40:40 Are the fundamental laws of physics ultimately “simple”? 0:50:17 The relationship between art and the artist’s cost function 0:54:02 Ideas are stories, being directed by intuitions 0:58:00 Society has a minimal role in training your intuitions 0:59:24 Why does art benefit from a repressive government? 1:04:01 A market case for civil rights 1:06:40 Fascism vs communism 1:10:50 Bach’s “control / attention / reflective recall” model 1:13:32 What’s more fundamental… Consciousness or attention? 1:16:02 The Chinese Room Experiment 1:25:22 Is understanding predicated on consciousness? 1:26:22 Integrated Information Theory of consciousness (IIT) 1:30:15 Donald Hoffman’s theory of consciousness 1:32:40 Douglas Hofstadter’s “strange loop” theory of consciousness 1:34:10 Holonomic Brain theory of consciousness 1:34:42 Daniel Dennett’s theory of consciousness 1:36:57 Sensorimotor theory of consciousness (embodied cognition) 1:44:39 What is intelligence? 1:45:08 Intelligence vs. consciousness 1:46:36 Where does Free Will come into play, in Bach’s model? 1:48:46 The opposite of free will can lead to, or feel like, addiction 1:51:48 Changing your identity to effectively live forever 1:59:13 Depersonalization disorder as a result of conceiving of your “self” as illusory 2:02:25 Dealing with a fear of loss of control 2:05:00 What about heart and conscience? 2:07:28 How to test / falsify Bach’s model of consciousness 2:13:46 How has Bach’s model changed in the past few years? 2:14:41 Why Bach doesn’t practice Lucid Dreaming anymore 2:15:33 Dreams and GAN’s (a machine learning framework) 2:18:08 If dreams are for helping us learn, why don’t we consciously remember our dreams 2:19:58 Are dreams “real”? Is all of reality a dream? 2:20:39 How do you practically change your experience to be most positive / helpful? 2:23:56 What’s more important than survival? What’s worth dying for? 2:28:27 Bach’s identity 2:29:44 Is there anything objectively wrong with hating humanity? 2:30:31 Practical Platonism 2:33:00 What “God” is 2:36:24 Gods are as real as you, Bach claims 2:37:44 What “prayer” is, and why it works 2:41:06 Our society has lost its future and thus our culture 2:43:24 What does Bach disagree with Jordan Peterson about? 2:47:16 The millennials are the first generation that’s authoritarian since WW2 2:48:31 Bach’s views on the “social justice” movement 2:51:29 Universal Basic Income as an answer to social inequality, or General Artificial Intelligence? 2:57:39 Nested hierarchy of “I“s (the conflicts within ourselves) 2:59:22 In the USA, innovation is “cheating” (for the most part) 3:02:27 Activists are usually operating on false information 3:03:04 Bach’s Marxist roots and lessons to his former self 3:08:45 BONUS BIT: On societies problems.
Pandora: https://pdora.co/33b9lfP
Spotify: https://open.spotify.com/show/4gL14b9…
Subreddit r/TheoriesOfEverything: / theoriesofeverything.
Merch: https://tinyurl.com/TOEmerch.

0:00:00 Introduction.
0:00:17 Bach’s work ethic / daily routine.
0:01:35 What is your definition of truth?
0:04:41 Nature’s substratum is a \.

Read more | >

This is the Fourier Transform. You can thank it for providing the music you stream every day, squeezing down the images you see on the Internet into tiny little JPG files, and even powering your noise-canceling headphones. Here’s how it works.

The equation owes its power to the way that it lets mathematicians quickly understand the frequency content of any kind of signal. It’s quite a feat. But don’t just take my word for it—in 1867, the physicist Lord Kelvin expressed his undying love for this fine piece of mathematics, too. He wrote, “Fourier’s theorem is not only one of the most beautiful results of modern analysis, but it may be said to furnish an indispensable instrument in the treatment of nearly every recondite question in modern physics.” And so it remains.

Math Will Tear Us Apart

Read more | >

A drug-resistant type of bacteria that has adapted to health care settings evolved in the past several years to weaponize an antimicrobial genetic tool, eliminating its cousins and replacing them as the dominant strain. University of Pittsburgh School of Medicine scientists made the discovery when combing through local hospital data—and then confirmed that it was a global phenomenon.

The finding, published in Nature Microbiology, may be the impetus for new approaches in developing therapeutics against some of the world’s deadliest . It also validates a new use for a system developed at Pitt and UPMC that couples genomic sequencing with computer algorithms to rapidly detect infectious disease outbreaks.

“Our lab has a front row seat to the parade of pathogens that move through the ,” said senior author Daria Van Tyne, Ph.D., associate professor of medicine in Pitt’s Division of Infectious Diseases. “And when we took a step back and zoomed out, it quickly became apparent that big changes were afoot with one of the world’s more difficult-to-treat bacteria.”

Read more | >

AlphaTensor–Quantum addresses three main challenges that go beyond the capabilities of AlphaTensor25 when applied to this problem. First, it optimizes the symmetric (rather than the standard) tensor rank; this is achieved by modifying the RL environment and actions to provide symmetric (Waring) decompositions of the tensor, which has the beneficial side effect of reducing the action search space. Second, AlphaTensor–Quantum scales up to large tensor sizes, which is a requirement as the size of the tensor corresponds directly to the number of qubits in the circuit to be optimized; this is achieved by a neural network architecture featuring symmetrization layers. Third, AlphaTensor–Quantum leverages domain knowledge that falls outside of the tensor decomposition framework; this is achieved by incorporating gadgets (constructions that can save T gates by using auxiliary ancilla qubits) through an efficient procedure embedded in the RL environment.

We demonstrate that AlphaTensor–Quantum is a powerful method for finding efficient quantum circuits. On a benchmark of arithmetic primitives, it outperforms all existing methods for T-count optimization, especially when allowed to leverage domain knowledge. For multiplication in finite fields, an operation with application in cryptography34, AlphaTensor–Quantum finds an efficient quantum algorithm with the same complexity as the classical Karatsuba method35. This is the most efficient quantum algorithm for multiplication on finite fields reported so far (naive translations of classical algorithms introduce overhead36,37 due to the reversible nature of quantum computations). We also optimize quantum primitives for other relevant problems, ranging from arithmetic computations used, for example, in Shor’s algorithm38, to Hamiltonian simulation in quantum chemistry, for example, iron–molybdenum cofactor (FeMoco) simulation39,40. AlphaTensor–Quantum recovers the best-known hand-designed solutions, demonstrating that it can effectively optimize circuits of interest in a fully automated way. We envision that this approach can accelerate discoveries in quantum computation as it saves the numerous hours of research invested in the design of optimized circuits.

AlphaTensor–Quantum can effectively exploit the domain knowledge (provided in the form of gadgets with state-of-the-art magic-state factories12), finding constructions with lower T-count. Because of its flexibility, AlphaTensor–Quantum can be readily extended in multiple ways, for example, by considering complexity metrics other than the T-count such as the cost of two-qubit Clifford gates or the qubit topology, by allowing circuit approximations, or by incorporating new domain knowledge. We expect that AlphaTensor–Quantum will become instrumental in automatic circuit optimization with new advancements in quantum computing.

Read more | >

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 | >