Lifeboat Foundation

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.

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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, 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.”

Evolution, the process by which living organisms adapt to their surrounding environment over time, has been widely studied over the years. As first hypothesized by Darwin in the mid 1800s, research evidence suggests that most biological species, including humans, continuously adapt to new environmental circumstances and that this ultimately enables their survival.

In recent years, researchers have been developing advanced computational techniques based on artificial neural networks, which are architectures inspired by biological neural networks in the human brain. Models based on artificial neural networks are trained to optimize millions of synaptic weights over millions of observations in order to make accurate predictions or classify data.

Researchers at Princeton University have recently carried out a study investigating the similarities and differences between artificial and biological neural networks from an evolutionary standpoint. Their paper, published in Neuron, compares the evolution of biological neural networks with that of artificial ones using psychology theory.

Researchers at Princeton University have built the world’s smallest mechanically interlocked biological structure, a deceptively simple two-ring chain made from tiny strands of amino acids called peptides.

In a paper published August 23 in Nature Chemistry, the team detailed a library of such structures made in their lab—two interlocked rings, a ring on a dumbbell, a daisy chain and an interlocked double lasso—each around one billionth of a meter in size. The study also demonstrates that some of these structures can toggle between at least two shapes, laying the groundwork for a biomolecular switch.

“We’ve been able to build a bunch of structures that no one’s been able to build before,” said A. James Link, professor of chemical and biological engineering, the study’s principal investigator. “These are the smallest threaded or interlocking structures you can make out of peptides.”

According to a new concept by LMU chemists led by Thomas Carell, it was a novel molecular species composed out of RNA and peptides that set in motion the evolution of life into more complex forms.

Investigating the question as to how life could emerge long ago on the early Earth is one of the most fascinating challenges for science. Which conditions must have prevailed for the basic building blocks of more complex life to form? One of the main answers is based upon the so-called RNA world idea, which molecular biology pioneer Walter Gilbert formulated in 1986. The hypothesis holds that nucleotides—the basic building blocks of the nucleic acids A, C, G, and U—emerged out of the primordial soup, and that short RNA molecules then formed out of the nucleotides. These so-called oligonucleotides were already capable of encoding small amounts of genetic information.

As such single-stranded RNA molecules could also combine into double strands, however, this gave rise to the theoretical possibility that the molecules could replicate themselves—i.e. reproduce. Only two nucleotides fit together in each case, meaning that one strand is the exact counterpart of another and thus forms the template for another strand.

For the first time ever, scientists have successfully grown plants in soil from the Moon.

Researchers from the University of Florida planted seeds from the Arabidopsis plant — commonly known as thale cress — into a few teaspoons worth of lunar soil collected in the late 60s and early 70s during the Apollo 11, 12 and 17 missions.

After about a week of watering and feeding, the seeds grew into and out of the soil, or lunar regolith, according to a paper detailing the experiment published Thursday in the scientific journal “Communications Biology.”

Researchers have used a widespread species of blue-green algae to power a microprocessor continuously for a year—and counting—using nothing but ambient light and water. Their system has potential as a reliable and renewable way to power small devices.

The system, comparable in size to an AA battery, contains a type of non-toxic algae called Synechocystis that naturally harvests energy from the sun through photosynthesis. The tiny electrical current this generates then interacts with an aluminum electrode and is used to power a microprocessor.

The system is made of common, inexpensive and largely recyclable materials. This means it could easily be replicated hundreds of thousands of times to power large numbers of small devices as part of the Internet of Things. The researchers say it is likely to be most useful in off-grid situations or remote locations, where small amounts of power can be very beneficial.