Lifeboat Foundation

People like the veteran computer scientist Ray Kurzweil had anticipated that humanity would reach the technological singularity (where an AI agent is just as smart as a human) for yonks, outlining his thesis in ‘The Singularity is Near’ (2005) – with a projection for 2029.

Disciples like Ben Goertzel have claimed it can come as soon as 2027. Nvidia’s CEO Jensen Huang says it’s “five years away”, joining the likes of OpenAI CEO Sam Altman and others in predicting an aggressive and exponential escalation. Should these predictions be true, they will also introduce a whole cluster bomb of ethical, moral, and existential anxieties that we will have to confront. So as The Matrix turns 25, maybe it wasn’t so far-fetched after all?

Continue reading “As ‘The Matrix’ turns 25, the chilling artificial intelligence (AI) projection at its core isn’t as outlandish as it once seemed” »

Artificial intelligence (AI) systems like ChatGPT have taken the world by storm. There isn’t much in which they’re not involved, from recommending the next binge-worthy TV show to helping navigate through traffic. But can AI systems learn the language of life and help biologists reveal exciting breakthroughs in science?

Neutrons are subatomic particles that have no electric charge, unlike protons and electrons. That means that while the electromagnetic force is responsible for most of the interactions between radiation and materials, neutrons are essentially immune to that force.

To engineer proteins with useful functions, researchers usually begin with a natural protein that has a desirable function, such as emitting fluorescent light, and put it through many rounds of random mutation that eventually generate an optimized version of the protein.

This process has yielded optimized versions of many important proteins, including green fluorescent protein (GFP). However, for other proteins, it has proven difficult to generate an optimized version. MIT researchers have now developed a computational approach that makes it easier to predict mutations that will lead to better proteins, based on a relatively small amount of data.

Using this model, the researchers generated proteins with mutations that were predicted to lead to improved versions of GFP and a protein from adeno-associated virus (AAV), which is used to deliver DNA for gene therapy. They hope it could also be used to develop additional tools for neuroscience research and medical applications.

Watch more videos on complexity and emergence: https://bit.ly/3TxyQBmThe world works at different levels—fundamental physics, physics, chemistry, biology, ps…

Evolution processes of complex networked systems in biology and social sciences, and their underlying mechanisms, still need better understanding. The authors propose a machine learning approach to reconstruct the evolution history of complex networks.

In recent years, artificial intelligence technologies, especially machine learning algorithms, have made great strides. These technologies have enabled unprecedented efficiency in tasks such as image recognition, natural language generation and processing, and object detection, but such outstanding functionality requires substantial computational power as a foundation.

In an era where the quest for sustainable energy sources has become paramount, researchers are tirelessly exploring innovative avenues to enhance fuel production processes. One of the most important tools in converting chemical energy into electrical energy and vice versa is electrocatalysis, which is already used in various green-energy technologies.

Combining results of laboratory studies on the infra-red glow of carbon molecules in simulation software has led a team of researchers to a new discovery about the creation of spherical carbon ‘cages’ called fullerenes.

Given these molecules could have protectively carried complex compounds through the harshness of interstellar space, the findings could have implications for how life arose on Earth, and beyond.

Following the confirmed detection of fullerenes surrounding the dusty surrounds of dying stars called planetary nebulas in recent decades, researchers have pondered the process that led to their creation.