Lifeboat Foundation

In the very early universe, physics were weird. A process known as inflation, during which the universe went from a single infinitesimal point to everything we see today, was one such instance of those weird physics. Now, scientists from the Chinese Academy of Science have sifted through 15 years of pulsar timing data in order to put some constraints on what physics looks like.

The 15 years of data come from the North American Nanohertz Observatory for Gravitational Waves or NANOGrav’s goal is to use an unconventional way to detect gravitational waves —by looking at pulsars. These fast-spinning objects are commonly used as “clocks” in astronomical terms.

Continue reading “The echoes from inflation could still be shaking the cosmos today” »

One of the most interesting releases in the recent OpenAI’s DevDay is the GPTs. Essentially, GPTs are custom versions of ChatGPT that anyone can create for specific purposes. The process of configuring a workable GPT involves no coding but purely through chatting. As a result, since the release, a diverse of GPTs have been created by the community to help users be more productive and create more fun in life.

As a practitioner in the domain of physics-informed neural networks (PINN), I use ChatGPT (GPT-4) a lot to help me understand complex technical concepts, debug issues encountered when implementing the model, and suggest novel research ideas or engineering solutions. Despite being quite useful, I often find ChatGPT struggles to give me tailored answers beyond its general knowledge of PINN. Although I can tweak my prompts to incorporate more contextual information, it is a rather time-consuming practice, and can quickly deplete my patience sometimes.

Now with the possibility of easily customizing ChatGPT, a thought occurred to me: why not develop a customized GPT that acts as a PINN expert 🦸‍♀️, draws knowledge from my curated sources, and strives to answer my queries about PINN in a tailored way?

While physics tells us that information can neither be created nor destroyed (if information could be created or destroyed, then the entire raison d’etre of physics, that is to predict future events or identify the causes of existing situations, would be impossible), it does not demand that the information be accessible. For decades physicists assumed that the information that fell into a black hole is still there, still existing, just locked away from view.

This was fine, until the 1970s when Stephen Hawking discovered the secret complexities of the event horizon. It turns out that these dark beasts were not as simple as we had been led to believe, and that the event horizons of black holes are one of the few places in the entire cosmos where gravity meets quantum mechanics in a manifest way.

Continue reading “The origins of the black hole information paradox” »

The human mind is by far one of the most amazing natural phenomena known to man. It embodies our perception of reality, and is in that respect the ultimate observer. The past century produced monumental discoveries regarding the nature of nerve cells, the anatomical connections between nerve cells, the electrophysiological properties of nerve cells, and the molecular biology of nervous tissue. What remains to be uncovered is that essential something – the fundamental dynamic mechanism by which all these well understood biophysical elements combine to form a mental state. In this chapter, we further develop the concept of an intraneuronal matrix as the basis for autonomous, self–organized neural computing, bearing in mind that at this stage such models are speculative. The intraneuronal matrix – composed of microtubules, actin filaments, and cross–linking, adaptor, and scaffolding proteins – is envisioned to be an intraneuronal computational network, which operates in conjunction with traditional neural membrane computational mechanisms to provide vastly enhanced computational power to individual neurons as well as to larger neural networks. Both classical and quantum mechanical physical principles may contribute to the ability of these matrices of cytoskeletal proteins to perform computations that regulate synaptic efficacy and neural response. A scientifically plausible route for controlling synaptic efficacy is through the regulation of neural transport of synaptic proteins and of mRNA. Operations within the matrix of cytoskeletal proteins that have applications to learning, memory, perception, and consciousness, and conceptual models implementing classical and quantum mechanical physics are discussed. Nanoneuroscience methods are emerging that are capable of testing aspects of these conceptual models, both theoretically and experimentally. Incorporating intra–neuronal biophysical operations into existing theoretical frameworks of single neuron and neural network function stands to enhance existing models of neurocognition.

As we learned in middle school science classes, inside this common variety of greens—and most other plants—are intricate circuits of biological machinery that perform the task of converting sunlight into usable energy. Or photosynthesis. These processes keep plants alive. Boston University researchers have a vision for how they could also be harnessed into programmable units that would enable scientists to construct the first practical quantum computer.

A quantum computer would be able to perform calculations much faster than the classical computers that we use today. The laptop sitting on your desk is built on units that can represent 0 or 1, but never both or a combination of those states at the same time. While a classical computer can run only one analysis at a time, a quantum computer could run a billion or more versions of the same equation at the same time, increasing the ability of computers to better model extremely complex systems—like weather patterns or how cancer will spread through tissue—and speeding up how quickly huge datasets can be analyzed.

The idea of using photosynthetic molecules from, say, a spinach leaf to power quantum computing services might sound like science fiction. It’s not. It is “on the fringe of possibilities,” says David Coker, a College of Arts & Sciences professor of chemistry and a College of Engineering professor of materials science and engineering. Coker and collaborators at BU and Princeton University are using computer simulations and experiments to provide proof-of-concepts that photosynthetic circuits could unlock new technological capabilities. Their work is showing promising early results.

Abrain is nothing if not communicative. Neurons are the chatterboxes of this conversational organ, and they speak with one another by exchanging pulses of electricity using chemical messengers called neurotransmitters. By repeating this process billions of times per second, a brain converts clusters of chemicals into coordinated actions, memories, and thoughts.

Researchers study how the brain works by eavesdropping on that chemical conversation. But neurons talk so loudly and often that if there are other, quieter voices, it might be hard to hear them.

Research into artificial intelligence (AI) network computing has made significant progress in recent years but has so far been held back by the limitations of logic gates in conventional computer chips. Through new research published in The European Physical Journal D, a team led by Aijin Zhu at Guilin University of Electronic Technology, China, introduced a graphene-based optical logic gate, which addresses many of these challenges.

The design could lead to a new generation of computer chips that consume less energy while reaching higher computing speeds and efficiencies. This could, in turn, pave the way for the use of AI in computer networks to automate tasks and improve decision-making—leading to enhanced performance, security, and functionality.

There are many advantages to microchips whose component logic gates exchange signals using light instead of electrical current. However, current designs are often bulky, somewhat unstable, and vulnerable to information loss.

The internet is speculating wildly about OpenAI’s decision to oust Sam Altman as CEO.

We’ll find out soon enough what the ousted OpenAI CEO wants to do next.

OpenAI is in discussions with Sam Altman to return to the company as its CEO, according to multiple people familiar with the matter. One of them said Altman, who was suddenly fired by the board on Friday with no notice, is “ambivalent” about coming back and would want significant governance changes. There have been some leaks on the real reason Sam Altman was fired from OpenAI’s largest investor, Microsoft, said in a statement shortly after Altman’s firing that the company “remains committed” to its partnership with the AI firm. However, OpenAI’s investors weren’t given advance warning or opportunity to weigh in on the board’s decision to remove Altman. As the face of the company and the most prominent voice in AI, his removal throws the future of OpenAI into uncertainty at a time when rivals are racing to catch up with the unprecedented rise of ChatGPT.
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TIMESTAMPS:
00:00 Vibes were off at OpenAI — Jimmy Apples.
00:55 Why Sam Altman was fired.
03:15 The Future of OpenAI and Sam Altman.
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#openai #samaltman #microsoft