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Archive for the ‘information science’ category: Page 23

May 19, 2024

Evolutionary Emergence: From Primordial Atoms to Living Algorithms of Artificial Superintelligence

Posted by in categories: biological, cosmology, information science, particle physics, quantum physics, robotics/AI

To be clear, humans are not the pinnacle of evolution. We are confronted with difficult choices and cannot sustain our current trajectory. No rational person can expect the human population to continue its parabolic growth of the last 200 years, along with an ever-increasing rate of natural resource extraction. This is socio-economically unsustainable. While space colonization might offer temporary relief, it won’t resolve the underlying issues. If we are to preserve our blue planet and ensure the survival and flourishing of our human-machine civilization, humans must merge with synthetic intelligence, transcend our biological limitations, and eventually evolve into superintelligent beings, independent of material substrates—advanced informational beings, or ‘infomorphs.’ In time, we will shed the human condition and upload humanity into a meticulously engineered inner cosmos of our own creation.

Much like the origin of the Universe, the nature of consciousness may appear to be a philosophical enigma that remains perpetually elusive within the current scientific paradigm. However, I emphasize the term “current.” These issues are not beyond the reach of alternative investigative methods, ones that the next scientific paradigm will inevitably incorporate with the arrival of Artificial Superintelligence.

The era of traditional, human-centric theoretical modeling and problem-solving—developing hypotheses, uncovering principles, and validating them through deduction, logic, and repeatable experimentation—may be nearing the end. A confluence of factors—Big Data, algorithms, and computational resources—are steering us towards a new type of discovery, one that transcends the limitations of human-like logic and decision-making— the one driven solely by AI superintelligence, nestled in quantum neo-empiricism and a fluidity of solutions. These novel scientific methodologies may encompass, but are not limited to, computing supercomplex abstractions, creating simulated realities, and manipulating matter-energy and the space-time continuum itself.

May 19, 2024

Tesla trick: Elon Musk’s plan to train AI in China takes shape

Posted by in categories: Elon Musk, information science, robotics/AI

Musk’s plan to use Chinese data to train algorithms aligns with the country’s ambitions to lead in autonomous driving technologies.

May 17, 2024

Artificial Intelligence Will Defeat CAPTCHA — How Will We Prove We’re Human Then?

Posted by in categories: information science, internet, mathematics, robotics/AI

If you use the web for more than just browsing (that’s pretty much everyone), chances are you’ve had your fair share of “CAPTCHA rage,” the frustration stemming from trying to discern a marginally legible string of letters aimed at verifying that you are a human. CAPTCHA, which stands for “Completely Automated Public Turing test to tell Computers and Humans Apart,” was introduced to the Internet a decade ago and has seen widespread adoption in various forms — whether using letters, sounds, math equations, or images — even as complaints about their use continue.

A large-scale Stanford study a few years ago concluded that “CAPTCHAs are often difficult for humans.” It has also been reported that around 1 in 5 visitors will leave a website rather than complete a CAPTCHA.

Continue reading “Artificial Intelligence Will Defeat CAPTCHA — How Will We Prove We’re Human Then?” »

May 17, 2024

Artificial intelligence calculates phase diagrams

Posted by in categories: information science, media & arts, robotics/AI

Researchers at the University of Basel have developed a new method for calculating phase diagrams of physical systems that works similarly to ChatGPT. This artificial intelligence could even automate scientific experiments in the future.

A year and a half ago, ChatGPT was released, and ever since, there has been hardly anything that cannot be created with this new form of artificial intelligence: texts, images, videos, and even music. ChatGPT is based on so-called generative models, which, using a complex algorithm, can create something entirely new from known information.

A research team led by Professor Christoph Bruder at the University of Basel, together with colleagues at the Massachusetts Institute of Technology (MIT) in Boston, have now used a similar method to calculate phase diagrams of physical systems.

May 17, 2024

Accurately monitoring tool wear in precision machining

Posted by in categories: information science, robotics/AI

An advanced new technique combines machine-learning algorithms with measurements of vibrations for monitoring tool wear.

May 16, 2024

Computer Scientists Invent an Efficient New Way to Count

Posted by in categories: computing, information science

By making use of randomness, a team has created a simple algorithm for estimating large numbers of distinct objects in a stream of data.

May 16, 2024

Sarcasm, notoriously difficult to interpret, demystified by multimodal approach

Posted by in categories: information science, robotics/AI

Oscar Wilde once said that sarcasm was the lowest form of wit, but the highest form of intelligence. Perhaps that is due to how difficult it is to use and understand. Sarcasm is notoriously tricky to convey through text—even in person, it can be easily misinterpreted. The subtle changes in tone that convey sarcasm often confuse computer algorithms as well, limiting virtual assistants and content analysis tools.

May 16, 2024

A longevity businessman says he gained 10 pounds of muscle in 1 year with a simple protein equation

Posted by in categories: business, information science, life extension, Peter Diamandis

Longevity businessman Peter Diamandis said he prioritized his body composition over everything else last year.

May 15, 2024

An AI Easily Beat Humans in the Moral Turing Test

Posted by in categories: ethics, information science, robotics/AI

Welcome to the era of ethical algorithms.

May 14, 2024

Optimizing Machine Learning Controllers with Digital Twins

Posted by in categories: information science, internet, mapping, robotics/AI

“Big machine learning models have to consume lots of power to crunch data and come out with the right parameters, whereas our model and training is so extremely simple that you could have systems learning on the fly,” said Robert Kent.


How can machine learning be improved to provide better efficiency in the future? This is what a recent study published in Nature Communications hopes to address as a team of researchers from The Ohio State University investigated the potential for controlling future machine learning products by creating digital twins (copies) that can be used to improve machine learning-based controllers that are currently being used in self-driving cars. However, these controllers require large amounts of computing power and are often challenging to use. This study holds the potential to help researchers better understand how future machine learning algorithms can exhibit better control and efficiency, thus improving their products.

“The problem with most machine learning-based controllers is that they use a lot of energy or power, and they take a long time to evaluate,” said Robert Kent, who is a graduate student in the Department of Physics at The Ohio State University and lead author of the study. “Developing traditional controllers for them has also been difficult because chaotic systems are extremely sensitive to small changes.”

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