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Category: biological – Page 108

Amazon and Max Planck Society announced the formation of a Science Hub—a collaboration that marks the first Amazon Science Hub to exist outside the United State… See more.

Amazon and Max Planck Society (also known as Max-Planck-Gesellschaft or MPG) today announced the formation of a Science Hub. The collaboration marks the first Amazon Science Hub to exist outside the United States and will focus on advancing artificial intelligence research and development throughout Germany.

The hub’s goal is to advance the frontiers of AI, computer vision, and machine learning research to ensure that research is creating solutions whose benefits are shared broadly across all sectors of society. To achieve that end, the collaboration will include sponsored research; open research; industrial fellowships co-supervised by Max Planck and Amazon; and community events funding to enrich the MPG and Amazon research communities.

The hub opens doors to further scientific collaboration with Max Planck Institutes (MPI), including the MPI for Intelligent Systems, the MPI for Software Systems, the MPI for Informatics, and the MPI for Biological Cybernetics.

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Incredible and somewhat frightening visions of the future will become a reality in the coming decades. According to futurologists, people of the future will gain immortality and will live in the body of a machine. Dr. Ian Pearson predicts that a person will be able to transfer his mind into a computer and one day he will go to a funeral where his previous biological body will be buried. Like anomalien.com on Facebook To stay in touch & get our latest news Cyborgization has some good sides. Let us take into account that we will be able to exchange each of…

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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.

Up next ► Where science fails, according to a physicist https://youtu.be/4hpdKQB2ruc.

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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)

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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.

Mesodinium chamaeleon, a ciliate –a group of protozoans – found in the oceans around Scandinavia and North America, was discovered in Nivå Bay (Baltic Sea) in Denmark by Øjvind Moestrup of the University of Copenhagen and his team. Other specimens have been found off the coasts of Finland and Rhode Island.

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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.

Dr. Van Kerkhove began her journey in global health given her interest in viruses and how they infect and impact both humans and animals. She received her undergraduate degree in biological sciences from Cornell University, her master’s degree in epidemiology from Stanford University, and a PhD in infectious disease epidemiology from the London School of Tropical Hygiene and Medicine where she authored her PhD on pathogenic avian influenza H5N1 in Cambodia.

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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 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 interact in the evolutionary process.

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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.

By combining creative thinking with our dedicated, scientific approach, we’re unlocking new ways of solving complex problems and working to develop a more general and capable problem-solving system, known as artificial general intelligence (AGI). Guided by safety and ethics, this invention could help society find answers to some of the most important challenges facing society today.

We regularly partner with academia and nonprofit organisations, and our technologies are used across Google devices by millions of people every day. From solving a 50-year-old grand challenge in biology with AlphaFold and synthesising voices with WaveNet, to mastering complex games with AlphaZero and preserving wildlife in the Serengeti, our novel advances make a positive and lasting impact.

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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, 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 of training data.

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

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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 to interact and emulate multi-factor biological computations of the mammalian brain while consuming very little energy.

To date, research on hardware for 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.

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