Lifeboat Foundation

Imagine a universe with extremely strong gravity. Stars would be able to form from very little material. They would be smaller than in our universe and live for a much shorter amount of time. But could life evolve there? It took human life billions of years to evolve on Earth under the pleasantly warm rays from the Sun after all.

Now imagine a universe with extremely weak gravity. Its matter would struggle to clump together to form stars, planets and—ultimately—living beings. It seems we are pretty lucky to have gravity that is just right for life in our universe.

This isn’t just the case for gravity. The values of many forces and particles in the universe, represented by some 30 so-called fundamental constants, all seem to line up perfectly to enable the evolution of intelligent life. But there’s no theory explaining what values the constants should have—we just have to measure them and plug their numbers into our equations to accurately describe the cosmos.

An artificial pancreas originally developed at the University of Virginia Center for Diabetes Technology improves blood sugar control in children ages 2 to 6 with type 1 diabetes, according to a new study. Details of the clinical study and its findings have been published in the New England Journal of Medicine.

Trial participants using the artificial pancreas spent approximately three more hours per day in their target blood sugar range compared with participants in a control group who continued relying on the methods they were already using to manage their blood sugar.

The Control-IQ system, manufactured by Tandem Diabetes Care, is a diabetes management device that automatically monitors and regulates blood glucose. The artificial pancreas has an insulin pump that uses advanced control algorithms based on the person’s glucose monitoring information to adjust the insulin dose as needed.

The Eqs. (3a) and (3b) suggest two important features of the location of neutrons and the spin by switching the choice of the post-selection: (i) The first lines indicate that the neutrons are found to be localized in different paths by switching the choice of the post-selection; they are found in the path I and II by applying the post-selection \({|{\Psi ^{+}_f}\rangle }\) and \({|{\Psi ^{-}_f}\rangle }\), respectively. (ii) The lines of the second part of the equations indicate that the spin in the different paths is found to be affected by switching the choice of the post-selection; the spin in path II and I is affected by applying the post-selection \({|{\Psi ^{+}_f}\rangle }\) and \({|{\Psi ^{-}_f}\rangle }\), respectively. Note that, in both choices of the post-selection, neutron and spin are localized in different paths, i.e., the location of the cat itself and its grin are interchanged by switching the choices of the post-selection. Since measurement of the locations of the neutron and the spin in the interferometer can be carried out independently of the delayed-choice process, the picking of a direction for post-selection, the influence of the delayed-choice on the preceding measurements can be investigated. We would like to point out that the experimental proposal in a recent publication³⁵, contains a delayed choice scenario, too. The difference to the experiment presented in this report is that the authors of³⁵ suggest a setup where two properties of the same system, represented by two non-commuting observables, are separated. In contrast to that, we deal in our experiment with the separation of one property from the system itself, hereby constituting the phenomenon of disembodiment. Further we would like to point out that in their Gedanken-experiment the effect of a change in the pre-selection is discussed that in our view has no retro-causal implications.

The experiment was carried out at the S18 silicon-perfect-crystal interferometer beam line at the high flux reactor at the Institute Laue Langevin. A schematic view of the experimental set-up is shown in Fig. 2.

Year 2014 face_with_colon_three If black holes have infinitely small sizes and infinitely density this also means that string theory would also solve the infinitely small problem because now we know that infinitely small sizes exist and if that exists then so does infinite energy from super string essentially filling out the rest of the mystery of the God equation. This means that computers could be infinitely small aswell saving a ton of space aswell.

If you’ve wondered how big is a black hole? then you’ve come to the right place! Learn about the sizes of black holes and the multi-layered answer.

AI or bust. Right now, AI is what everyone is talking about, and for good reason. After years of seeing AI doled out to help automate the processes that make businesses run smarter, we’re finally seeing AI that can help the average business employee working in the real world. Generative AI, or the process of using algorithms to produce data often in the form of images or text, has exploded in the last few months. What started with OpenAI’s ChatGPT has bloomed into a rapidly evolving subcategory of technology. And companies from Microsoft to Google to Salesforce and Adobe are hopping on board.

What started with ChatGPT has bloomed into an entire subcategory of technology with Meta, AWS, Salesforce, Google, Microsoft all racing to out innovate and deliver exciting generative AI capabilities to consumers, enterprise, developers, and more. Exploring the rapid progress in the AI space.

We don’t learn by brute force repetition. AI shouldn’t either.

Despite impressive progress, today’s AI models are very inefficient learners, taking huge amounts of time and data to solve problems humans pick up almost instantaneously. A new approach could drastically speed things up by getting AI to read instruction manuals before attempting a challenge.

Continue reading “An AI Learned to Play Atari 6,000 Times Faster” »

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Researched and Written by Leila Battison.
Narrated and Edited by David Kelly.
Animations by Jero Squartini https://www.fiverr.com/share/0v7Kjv.
Incredible thumbnail art by Ettore Mazza, the GOAT: https://www.instagram.com/ettore.mazza/?hl=en.

Continue reading “What Is Beyond The Edge?” »

Empowered by artificial intelligence technologies, computers today can engage in convincing conversations with people, compose songs, paint paintings, play chess and go, and diagnose diseases, to name just a few examples of their technological prowess.

These successes could be taken to indicate that computation has no limits. To see if that’s the case, it’s important to understand what makes a computer powerful.

Continue reading “The Limits of Computing: Why Even in the Age of AI, Some Problems Are Just Too Difficult” »

Deep learning has made significant strides in text generation, translation, and completion in recent years. Algorithms trained to predict words from their surrounding context have been instrumental in achieving these advancements. However, despite access to vast amounts of training data, deep language models still need help to perform tasks like long story generation, summarization, coherent dialogue, and information retrieval. These models have been shown to need help capturing syntax and semantic properties, and their linguistic understanding needs to be more superficial. Predictive coding theory suggests that the brain of a human makes predictions over multiple timescales and levels of representation across the cortical hierarchy. Although studies have previously shown evidence of speech predictions in the brain, the nature of predicted representations and their temporal scope remain largely unknown. Recently, researchers analyzed the brain signals of 304 individuals listening to short stories and found that enhancing deep language models with long-range and multi-level predictions improved brain mapping.

The results of this study revealed a hierarchical organization of language predictions in the cortex. These findings align with predictive coding theory, which suggests that the brain makes predictions over multiple levels and timescales of expression. Researchers can bridge the gap between human language processing and deep learning algorithms by incorporating these ideas into deep language models.

The current study evaluated specific hypotheses of predictive coding theory by examining whether cortical hierarchy predicts several levels of representations, spanning multiple timescales, beyond the neighborhood and word-level predictions usually learned in deep language algorithms. Modern deep language models and the brain activity of 304 people listening to spoken tales were compared. It was discovered that the activations of deep language algorithms supplemented with long-range and high-level predictions best describe brain activity.