Lifeboat Foundation

Singularity net Ben goerzel discusses artificial and general intelligence and cosmist intelligence.

Dr. Ben Goertzel discusses artificial general, non-human and cosmist intelligences with Ed Keller at The Overview Effect Lectures, which is a series positioned as a survey of some of the key operational themes critical to post planetary and universal design.

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Equitable and accessible education in life sciences, bioengineering, and synthetic biology is crucial for training the next generation of scientists. Here the authors present the CRISPRkit, a cost-effective educational tool that enables high school students to perform CRISPR experiments affordably and safely without prior experience, using smartphone-based quantification and an automated algorithm for data analysis.

It’s about to get easier to catch and analyze a high-quality image of fast-moving molecules. Assistant Professor Ioannis Sgouralis, Department of Mathematics, and colleagues have developed an algorithm that adds a new level to microscopy: super-resolution in motion.

All-optical multiplane quantitative phase imaging design eliminates the need for digital phase recovery algorithms.

UCLA researchers have introduced a breakthrough in 3D quantitative phase imaging that utilizes a wavelength-multiplexed diffractive optical processor to enhance imaging efficiency and speed. This method enables label-free, high-resolution imaging across multiple planes and has significant potential applications in biomedical diagnostics, material characterization, and environmental analysis.

Introduction to Quantitative Phase Imaging.

Dark energy remains among the greatest puzzles in our understanding of the cosmos. In the standard model of cosmology called the Lambda-CDM, it is accounted for by adding a cosmological constant term in Einstein’s field equation first introduced by Einstein himself. This constant is very small and positive and lacks a complete theoretical understanding of why it has such a tiny value. Moreover, dark energy has some peculiar features, such as negative pressure and does not dilute with cosmic expansion, which makes at least some of us uncomfortable.

An AI model developed by scientists at King’s College London, in close collaboration with University College London, has produced three-dimensional, synthetic images of the human brain that are realistic and accurate enough to use in medical research.

The model and images have helped scientists better understand what the human brain looks like, supporting research to predict, diagnose and treat brain diseases such as dementia, stroke, and multiple sclerosis.

The algorithm was created using the NVIDIA Cambridge-1, the UK’s most powerful supercomputer. One of the fastest supercomputers in the world, the Cambridge-1 allowed researchers to train the AI in weeks rather than months and produce images of far higher quality.

An algorithm developed by Washington State University researchers can better find data anomalies than current anomaly-detection software, including in streaming data.

The work, reported in the Journal of Artificial Intelligence Research, makes fundamental contributions to artificial intelligence (AI) methods that could have applications in many domains that need to quickly find anomalies in large amounts of data, such as in cybersecurity, power grid management, misinformation, and medical diagnostics.

Being able to better find the anomalies would mean being able to more easily discover fraud, disease in a medical setting, or important unusual information, such as an asteroid whose signals overlap with the light from other stars.

QPI is a powerful technique that reveals variations in optical path length caused by weakly scattering samples, enabling the generation of high-contrast images of transparent specimens. Traditional 3D QPI methods, while effective, are limited by the need for multiple illumination angles and extensive digital post-processing for 3D image reconstruction, which can be time-consuming and computationally intensive.

In this innovative study, the research team developed a wavelength-multiplexed diffractive optical processor capable of all-optically transforming phase distributions of multiple 2D objects at various axial positions into intensity patterns, each encoded at a unique wavelength channel.

This design allows for the capture of quantitative phase images of input objects located at different axial planes using an intensity-only image sensor, eliminating the need for digital phase recovery algorithms.

The idea of the brain as a computer is everywhere. So much so we have forgotten it is a model and not the reality. It’s a metaphor that has lead some to believe that in the future they’ll be uploaded to the digital ether and thereby achieve immortality. It’s also a metaphor that garners billions of dollars in research funding every year. Yet researchers argue that when we dig down into our grey matter our biology is anything but algorithmic. And increasingly, critics contend that the model of the brain as computer is sending scientists (and their resources) nowhere fast. Is our attraction to the idea of the brain as computer an accident of current human technology? Can we find a better metaphor that might lead to a new paradigm?