Lifeboat Foundation

Could artificial intelligence (AI) systems become conscious? A trio of consciousness scientists says that, at the moment, no one knows — and they are expressing concern about the lack of inquiry into the question.

In comments to the United Nations, three leaders of the Association for Mathematical Consciousness Science (AMCS) call for more funding to support research on consciousness and AI. They say that scientific investigations of the boundaries between conscious and unconscious systems are urgently needed, and they cite ethical, legal and safety issues that make it crucial to understand AI consciousness. For example, if AI develops consciousness, should people be allowed to simply switch it off after use?

Such concerns have been mostly absent from recent discussions about AI safety, such as the high-profile AI Safety Summit in the United Kingdom, says AMCS board member Jonathan Mason, a mathematician based in Oxford, UK and one of the authors of the comments. Nor did US President Joe Biden’s executive order seeking responsible development of AI technology address issues raised by conscious AI systems, Mason notes.

The fusion of biological principles with technological innovation has resulted in significant advancements in artificial intelligence (AI) through the development of Brainoware. Developed by researchers at Indiana University, Bloomington, this innovative system leverages clusters of lab-raised brain cells to achieve elementary speech recognition and solve mathematical problems.

The crux of this technological leap lies in the cultivation of specialized stem cells that mature into neurons—the fundamental units of the brain. While a typical human brain comprises a staggering 86 billion neurons interconnected extensively, the team managed to engineer a minute organoid, merely a nanometer wide. This tiny but powerful structure was connected to a circuit board through an array of electrodes, allowing machine-learning algorithms to decode responses from the brain tissue.

Termed Brainoware, this amalgamation of biological neurons and computational circuits exhibited remarkable capabilities after a brief training period. It was discerned between eight subjects based on their diverse pronunciation of vowels with an accuracy rate of 78%. Impressively, Brainoware outperformed artificial networks in predicting the Henon map, a complex mathematical construct within chaotic dynamics.

A team of researchers around Berlin mathematics professor Michael Joswig is presenting a novel concept for the mathematical modeling of genetic interactions in biological systems. Collaborating with biologists from ETH Zurich and Carnegy Science (U.S.), the team has successfully identified master regulators within the context of an entire genetic network.

The research results provide a coherent theoretical framework for analyzing biological networks and have been published in the Proceedings of the National Academy of Sciences.

It is a longstanding goal of biologists to determine the key genes and species that have a decisive impact on evolution, ecology, and health. Researchers have now succeeded in identifying certain genes as master regulators in biological networks. These key regulators exert greater control within the system and steer essential cellular processes. Previous studies have mainly focused on pairwise interactions within the system, which can be strongly affected by genetic background or biological context.

Scientists have fused human brain tissue to a computer chip, creating a mini cyborg in a petri dish that can perform math equations and recognize speech.

Dubbed Brainoware, the system consists of brain cells artificially grown from human stem cells, which have been fostered to develop into a brain-like tissue. This mini-brain organoid is then hooked up to traditional hardware where it acts as a physical reservoir that can capture and remember the information it receives from the computer inputs.

The researchers wanted to explore the idea of exploiting the efficiency of the human brain’s architecture to supercharge computational hardware. The rise of artificial intelligence (AI) has massively increased the demand for computing power, but it’s somewhat limited by the energy efficiency and performance of the standard silicon chips.

At its Quantum Summit 2023, IBM took the stage with an interesting spirit: one of almost awe at having things go their way. But the quantum of today – the one that’s changing IBM’s roadmap so deeply on the back of breakthroughs upon breakthroughs – was hard enough to consolidate. As IBM sees it, the future of quantum computing will hardly be more permissive, and further improvements to the cutting-edge devices it announced at the event, the 133-qubit Heron Quantum Processing Unit (QPU), which is the company’s first utility-scale quantum processor, and the self-contained Quantum System Two, a quantum-specific supercomputing architecture, are ultimately required.

But each breakthrough that afterward becomes obsolete is another accelerational bump against what we might call quantum’s “plateau of understanding.” We’ve already been through this plateau with semiconductors, so much so that our latest CPUs and GPUs are reaching practical, fundamental design limits where quantum effects start ruining our math. Conquering the plateau means that utility and understanding are now enough for research and development to be somewhat self-sustainable – at least for a Moore’s-law-esque while.

Astronomers developed a set of equations that can precisely describe the reflections of the Universe that appear in the warped light around a black hole.

The proximity of each reflection is dependent on the angle of observation with respect to the black hole, and the rate of the black hole’s spin, according to a mathematical solution worked out by physics student Albert Sneppen of the Niels Bohr Institute in Denmark in July 2021.

This was really cool, absolutely, but it wasn’t just really cool. It also potentially gave us a new tool for probing the gravitational environment around these extreme objects.

Advances in Civil Engineering Using Recycled Concrete Powder, Waste Glass Powder, and Plastic Powder to Improve the Mechanical Properties of Compacted Concrete: Cement Elimination Approach Erfan Najaf and Hassan Abbasi.

International Journal of Rotating Machinery Experimental and Numerical Studies of the Film Cooling Effectiveness Downstream of a Curved Diffusion Film Cooling Hole Fan Yang and Mohammad E. Taslim.

Our brains are remarkably energy efficient.

Using just 20 watts of power, the human brain is capable of processing the equivalent of an exaflop — or a billion-billion mathematical operations per second.

Now, researchers in Australia are building what will be the world’s first supercomputer that can simulate networks at this scale.

A team of computer scientists at Google’s DeepMind project in the U.K., working with a colleague from the University of Wisconsin-Madison and another from Université de Lyon, has developed a computer program that combines a pretrained large language model (LLM) with an automated “evaluator” to produce solutions to problems in the form of computer code.

In their paper published in the journal Nature, the group describes their ideas, how they were implemented and the types of output produced by the new system.

Researchers throughout the scientific community have taken note of the things people are doing with LLMs, such as ChatGPT, and it has occurred to many of them that LLMs might be used to help speed up the process of scientific discovery. But they have also noted that for that to happen, a method is required to prevent confabulations, answers that seem reasonable but are wrong—they need output that is verifiable. To address this problem, the team working in the U.K. used what they call an automated evaluator to assess the answers given by an LLM.