Lifeboat Foundation

As a robotics engineer, Yasemin Ozkan-Aydin, assistant professor of electrical engineering at the University of Notre Dame, gets her inspiration from biological systems. The collective behaviors of ants, honeybees and birds to solve problems and overcome obstacles is something researchers have developed in aerial and underwater robotics. Developing small-scale swarm robots with the capability to traverse complex terrain, however, comes with a unique set of challenges.

In research published in Science Robotics, Ozkan-Aydin presents how she was able to build multi-legged robots capable of maneuvering in challenging environments and accomplishing difficult tasks collectively, mimicking their natural-world counterparts.

“Legged robots can navigate challenging environments such as rough terrain and tight spaces, and the use of limbs offers effective body support, enables rapid maneuverability and facilitates obstacle crossing,” Ozkan-Aydin said. “However, legged robots face unique mobility challenges in terrestrial environments, which results in reduced locomotor performance.”

“If you accept that meaning is something that emerges from sufficiently complex biological machines, then the only place those machines might exist is here; then it’s correct to say that if this planet weren’t here, we’d live in a meaningless galaxy. That’s different to life. There’s a difference between life and intelligent life.”

Unique events that led to civilisation mean its demise could ‘eliminate meaning in galaxy for ever’.

An AI chatbot for ethics developed by the Allen Institute for AI has some interesting responses, to say the least.

Using machine learning, a computer model can teach itself to smell in just a few minutes. When it does, researchers have found, it builds a neural network that closely mimics the olfactory circuits that animal brains use to process odors.

Animals from fruit flies to humans all use essentially the same strategy to process olfactory information in the brain. But neuroscientists who trained an artificial neural network to take on a simple odor classification task were surprised to see it replicate biology’s strategy so faithfully.

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Continue reading “Artificial networks learn to smell like the brain” »

In this post I outline my journey creating a dynamic NFT on the Ethereum blockchain with IPFS and discuss the possible use cases for scientific data. I do not cover algorithmic generation of static images (you should read Albert Sanchez Lafuente’s neat step-by-step for that) but instead demonstrate how I used Cytoscape.js, Anime.js and genomic feature data to dynamically generate visualizations/art at run time when NFTs are viewed from a browser. I will also not be providing an overview of Blockchain but I highly recommend reading Yifei Huang’s recent post: Why every data scientist should pay attention to crypto.

W h ile stuck home during the pandemic, I’m one of the 10 million that tried my hand at gardening on our little apartment balcony in Brooklyn. The Japanese cucumbers were a hit with our neighbors and the tomatoes were a hit with the squirrels but it was the peppers I enjoyed watching grow the most. This is what set the objective for my first NFT: create a depiction of a pepper that ripens over time.

How much of the depiction is visualization and how much is art? Well that’s in the eye of the beholder. When you spend your days scrutinizing data points, worshiping best practices and optimizing everything from memory usage to lunch orders it’s nice to take some artistic license and make something just because you like it, which is exactly what I’ve done here. The depiction is authentically generated from genomic data features but obviously this should not be viewed as any kind of serious biological analysis.

Latest scientific findings suggest the ancestral Native American population does not originate in Japan, as believed by many archaeologists.

A widely accepted theory of Native American origins coming from Japan has been attacked in a new scientific study, which shows that the genetics and skeletal biology “simply does not match-up.”

The findings, published on October 12 2021, in the peer-reviewed journal PaleoAmerica, are likely to have a major impact on how we understand Indigenous Americans’ arrival to the Western Hemisphere.

In 2009—four years after it was published—I read Ray Kurzweil’s The Singularity Is Near. It is an optimistic view of the future—a future that depends on computational technology. A future of superintelligent machines. It is also a future where humans will transcend our present biological limits.

I had to read the book twice—once for the sense and once for the detail.

After that, just for my own interest, year-in, year-out, I started to track this future; that meant a weekly read through New Scientist, Wired, the excellent technology pieces in the New York Times and the Atlantic, as well as following the money via the Economist and Financial Times. I picked up any new science and tech books that came out, but it wasn’t enough for me. I felt I wasn’t seeing the bigger picture.

Circa 2019 o.o

The dream of resurrecting species like the woolly mammoth via genetic engineering is old enough that I remember reading articles about it in school 30 years ago. We may never be able to recover enough pristine genetic material from an intact woolly mammoth to make that approach feasible, but scientists working on the remains of the frozen mammoth known as Yuka have taken an incredible step nonetheless, demonstrating that at least some cell functions can remain intact after nearly 30,000 years.

Yuka, found in 2,010 is a juvenile woolly mammoth, considered to be the most intact and well-preserved mammoth ever found. That was critical to the researchers’ efforts — earlier tests in 2009 with a less-well-preserved but younger specimen at 15,000 years old yielded no positive results at all.

Continue reading “Scientists Revive 28,000-Year-Old Woolly Mammoth Cells in Mice” »

A slew of new studies now shows that the area of the brain responsible for initiating this action — the primary motor cortex, which controls movement — has as many as 116 different types of cells that work together to make this happen.

The 17 studies, appearing online Oct. 6 in the journal Nature, are the result of five years of work by a huge consortium of researchers supported by the National Institutes of Health’s Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative to identify the myriad of different cell types in one portion of the brain. It is the first step in a long-term project to generate an atlas of the entire brain to help understand how the neural networks in our head control our body and mind and how they are disrupted in cases of mental and physical problems.

“If you think of the brain as an extremely complex machine, how could we understand it without first breaking it down and knowing the parts?” asked cellular neuroscientist Helen Bateup, a University of California, Berkeley, associate professor of molecular and cell biology and co-author of the flagship paper that synthesizes the results of the other papers. “The first page of any manual of how the brain works should read: Here are all the cellular components, this is how many of them there are, here is where they are located and who they connect to.”