Lifeboat Foundation

Can quantum science supercharge genetics? | Jim Al-Khalili for Big Think.

This interview is an episode from The Well, our new publication about ideas that inspire a life well-lived, created with the John Templeton Foundation.

Continue reading “What if quantum physics could eradicate illness? | Jim Al-Khalili for Big Think” »

For the first time TU Graz’s Institute of Theoretical Computer Science and Intel Labs demonstrated experimentally that a large neural network can process sequences such as sentences while consuming four to sixteen times less energy while running on neuromorphic hardware than non-neuromorphic hardware. The new research based on Intel Labs’ Loihi neuromorphic research chip that draws on insights from neuroscience to create chips that function similar to those in the biological brain.

The research was funded by The Human Brain Project (HBP), one of the largest research projects in the world with more than 500 scientists and engineers across Europe studying the human brain. The results of the research are published in Nature Machine Intelligence (“Memory for AI Applications in Spike-based Neuromorphic Hardware”).

The close-up shows an Intel Nahuku board, each of which contains eight to 32 Intel Loihi neuromorphic research chips. (Image: Tim Herman, Intel Corporation)

Circa 2012

In nature, you’ll find animals that undergo vast transformations, becoming almost unrecognizable in their new forms. Examples like caterpillars becoming butterflies and tadpoles becoming frogs almost look like distinct animals in the different stages of their evolution.

While this might sound amazing, all stages of these animals still belong to the same biological taxonomic rank, Animalia. This means that caterpillars don’t become plants, in their new shapes, they remain animals. That’s not what Mesodinium chamaeleon does. This single-celled organism is a unique mix of animal and plant life.

Continue reading “Mesodinium Chamaeleon Is A Unique Life Form That is Half Plant Half Animal” »

Preparedness For Emerging Diseases & Zoonoses — Dr. Maria Van Kerkhove, Ph.D., Emerging Diseases and Zoonoses Unit Head, World Health Organization, (WHO)

Dr. Maria Van Kerkhove, Ph.D., (https://www.imperial.ac.uk/people/m.vankerkhove) is an infectious disease epidemiologist who serves as the technical lead for the COVID-19 response at the World Health Organization (https://www.who.int/en/), where she develops guidance, training programs, and information products for the continuously evolving state of the pandemic, as well serving as the Emerging Diseases and Zoonoses Unit Head.

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In biological evolution, we know that it’s all about the survival of the fittest: organisms that develop genetic traits that allow them to better adapt to their physical environment are more likely to thrive, and thus pass down their winning genes to their offspring.

From the longer-beaked Galapagos Island finches studied by biologist Charles Darwin that enabled them to more effectively snatch insects, to the ability of some humans over others to digest milk, the process of natural selection results in genetic differences that give some organisms an edge over others.

New research by University of Toronto Mississauga biology assistant professor Alex N. Nguyen Ba adds an important dimension to our understanding of how genes interact in the evolutionary process.

At DeepMind, we’re embarking on one of the greatest adventures in scientific history. Our mission is to solve intelligence, to advance science and benefit humanity.

To make this possible, we bring together scientists, designers, engineers, ethicists, and more, to research and build safe artificial intelligence systems that can help transform society for the better.

Continue reading “Welcome to DeepMind: Embarking on one of the greatest adventures in scientific history” »

Deep learning models have proved to be highly promising tools for analyzing large numbers of images. Over the past decade or so, they have thus been introduced in a variety of settings, including research laboratories.

In the field of biology, deep learning models could potentially facilitate the quantitative analysis of microscopy images, allowing researchers to extract meaningful information from these images and interpret their observations. Training models to do this, however, can be very challenging, as it often requires the extraction of features (i.e., number of cells, area of cells, etc.) from microscopy images and the manual annotation of training data.

Researchers at CERVO Brain Research Center, the Institute for Intelligence and Data, and Université Laval in Canada have recently developed an artificial neural network that could perform in-depth analyses of microscopy images using simpler, image-level annotations. This model, dubbed MICRA-Net (MICRoscopy Analysis neural network), was introduced in a paper published in Nature Machine Intelligence.

A team of international scientists have performed difficult machine learning computations using a nano-scale device, named an “optomemristor.”

The chalcogenide thin-film device uses both light and electrical signals to interact and emulate multi-factor biological computations of the mammalian brain while consuming very little energy.

To date, research on hardware for artificial intelligence and machine learning applications has concentrated mainly on developing electronic or photonic synapses and neurons, and combining these to carry out basic forms of neural-type processing.

Machine learning techniques are designed to mathematically emulate the functions and structure of neurons and neural networks in the brain. However, biological neurons are very complex, which makes artificially replicating them particularly challenging.

Researchers at Korea University have recently tried to reproduce the complexity of biological neurons more effectively by approximating the function of individual neurons and synapses. Their paper, published in Nature Machine Intelligence, introduces a network of evolvable neural units (ENUs) that can adapt to mimic specific neurons and mechanisms of synaptic plasticity.

“The inspiration for our paper comes from the observation of the complexity of biological neurons, and the fact that it seems almost impossible to model all of that complexity produced by nature mathematically,” Paul Bertens, one of the researchers who carried out the study, told TechXplore. “Current artificial neural networks used in deep learning are very powerful in many ways, but they do not really match biological neural network behavior. Our idea was to use these existing artificial neural networks not to model the entire brain, but to model each individual neuron and synapse.”