Lifeboat Foundation: Safeguarding Humanity
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Category: information science – Page 31

The large language models that have increasingly taken over the tech world are not “cheap” in many ways. The most prominent LLMs, such as GPT-4, took some $100 million to build in the form of legal costs of accessing training data, computational power costs for what could be billions or trillions of parameters, the energy and water needed to fuel computation, and the many coders developing the training algorithms that must run cycle after cycle so the machine will “learn.”

But, if a researcher needs to do a specialized task that a machine could do more efficiently and they don’t have access to a large institution that offers access to generative AI tools, what other options are available? Say, a parent wants to prep their child for a difficult test and needs to show many examples of how to solve complicated math problems.

Building their own LLM is an onerous prospect for costs mentioned above, and making direct use of the big models like GPT-4 and Llama 3.1 might not immediately be suited for the complex in logic and math their task requires.

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New study suggests that black holes may not be the featureless, structureless entities that Einstein’s general theory of relativity predicts them to be.

The frozen star is a recent proposal for a nonsingular solution of Einstein’s equations that describes an ultracompact object which closely resembles a black hole from an external perspective. The frozen star is also meant to be an alternative, classical description of an earlier proposal, the highly quantum polymer model. Here, we show that the thermodynamic properties of frozen stars closely resemble those of black holes: frozen stars radiate thermally, with a temperature and an entropy that are perturbatively close to those of black holes of the same mass. Their entropy is calculated using the Euclidean-action method of Gibbons and Hawking. We then discuss their dynamical formation by estimating the probability for a collapsing shell of “normal’’ matter to transition, quantum mechanically, into a frozen star.

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Coronary artery disease (CAD) is the most common cause of illness-based death throughout the world. According to the World Health Organization, CAD causes 17.9 million deaths per year worldwide, nearly one-third of all illness-based deaths annually.

Coronary angiography is currently the best method of confirming a CAD diagnosis, but it is expensive and invasive, poses risks to patients, and is not suitable for early diagnosis and assessing disease risk.

Seeking a safer, lower-cost and more efficient diagnostic method, a research team from Beijing University of Chinese Medicine’s School of Traditional Chinese Medicine, Beijing University of Chinese Medicine’s School of Life Science, and Hunan University of Chinese Medicine’s School of Traditional Chinese Medicine has used artificial intelligence (AI) to develop a diagnostic algorithm based on tongue imaging. Their work is published in Frontiers in Cardiovascular Medicine.

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The potential pathways through which AI could help us escape a simulated reality are both fascinating and complex. One approach could involve AI discovering and manipulating the underlying algorithms that govern the simulation. By understanding these algorithms, AI could theoretically alter the simulation’s parameters or even create a bridge to the “real” world outside the simulation.

Another approach involves using AI to enhance our cognitive and perceptual abilities, enabling us to detect inconsistencies or anomalies within the simulation. These anomalies, often referred to as “glitches,” could serve as clues pointing to the artificial nature of our reality. For instance, moments of déjà vu or inexplicable phenomena might be more than just quirks of human perception—they could be signs of the simulation’s imperfections.

While the idea of escaping a simulation is intriguing, it also raises profound ethical and existential questions. For one, if we were to confirm that we are indeed living in a simulation, what would that mean for our understanding of free will, identity, and the meaning of life? Moreover, the act of escaping the simulation could have unforeseen consequences. If the simulation is designed to sustain and nurture human life, breaking free from it might expose us to a harsher and more dangerous reality.

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“If a tree falls in the forest, and no one hears it, does it make a sound?” I remember seeing an actual argument get started on this subject—a fully naive argument that went nowhere near Berkeleyan subjectivism. Just:

“It makes a sound, just like any other falling tree!” “But how can there be a sound that no one hears?”

The standard rationalist view would be that the first person is speaking as if “sound” means acoustic vibrations in the air; the second person is speaking as if “sound” means an auditory experience in a brain. If you ask “Are there acoustic vibrations?” or “Are there auditory experiences?”, the answer is at once obvious. And so the argument is really about the definition of the word “sound”

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Researchers Propose a #Smaller, more #Noise-#Tolerant #Quantum #Circuit for #Cryptography.

MIT researchers new algorithm is as fast as Regev’s, requires fewer qubits, and has a higher tolerance to quantum noise, making it more feasible to implement…

The most recent email you sent was likely encrypted using a tried-and-true method that relies on the idea that even the fastest computer would be unable to efficiently break a gigantic number into factors.

Quantum computers, on the other hand, promise to rapidly crack complex cryptographic systems that a classical computer might never be able to unravel. This promise is based on a quantum factoring algorithm proposed in 1994 by Peter Shor, who is now a professor at MIT.

But while researchers have taken great strides in the last 30 years, scientists have yet to build a quantum computer powerful enough to run Shor’s algorithm.

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Outperforms dermatologists in detecting melanoma, offering better diagnosis for challenging cases and improving patient care. 🩺🖥️

Heinlein, Maron, Hekler et al. evaluate an AI algorithm for detecting melanoma and compare its performance to that of dermatologist on a prospectively collected, external, heterogeneous dataset. The AI exhibits a significant performance advantage, especially in diagnosing challenging cases.

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AI may equal human intelligence without matching the true nature of our experiences.

By Christof Koch

A future where the thinking capabilities of computers approach our own is quickly coming into view. We feel ever more powerful machine-learning (ML) algorithms breathing down our necks. Rapid progress in coming decades will bring about machines with human-level intelligence capable of speech and reasoning, with a myriad of contributions to economics, politics and, inevitably, warcraft. The birth of true artificial intelligence will profoundly affect humankind’s future, including whether it has one.

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