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Robots and AI assist in designing and building Swiss university’s ‘hanging gardens’

Architecture and construction have always been, rather quietly, at the bleeding edge of tech and materials trends. It’s no surprise, then, especially at a renowned technical university like ETH Zurich, to find a project utilizing AI and robotics in a new approach to these arts. The automated design and construction they are experimenting with show how homes and offices might be built a decade from now.

The project is a sort of huge sculptural planter, “hanging gardens” inspired by the legendary structures in the ancient city of Babylon. (Incidentally, it was my ancestor, Robert Koldewey, who excavated/looted the famous Ishtar Gate to the place.)

Begun in 2019, Semiramis (named after the queen of Babylon back then) is a collaboration between human and AI designers. The general idea of course came from the creative minds of its creators, architecture professors Fabio Gramazio and Matthias Kohler. But the design was achieved by putting the basic requirements, such as size, the necessity of watering and the style of construction, through a set of computer models and machine learning algorithms.

This Synthetic DNA Factory Is Building New Forms of Life

In this DNA factory, organism engineers are using robots and automation to build completely new forms of life.
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Ginkgo Bioworks, a Boston company specializing in “engineering custom organisms,” aims to reinvent manufacturing, agriculture, biodesign, and more.

Biologists, software engineers, and automated robots are working side by side to accelerate the speed of nature by taking synthetic DNA, remixing it, and programming microbes, turning custom organisms into mini-factories that could one day pump out new foods, fuels, and medicines.

While there are possibly numerous positive and exciting outcomes from this research, like engineering gut bacteria to produce drugs inside the human body on demand or building self-fertilizing plants, the threat of potential DNA sequences harnessing a pathological function still exists.

That’s why Ginkgo Bioworks is developing a malware software to effectively stomp out the global threat of biological weapons, ensuring that synthetic biology can’t be used for evil.

Learn more about synthetic DNA and this biological assembly line on this episode of Focal Point.

Making Tomorrow Better

I have a small YouTube channel which I create videos on clean energy and the environment. I have under 600 subs and many videos have not even hit 100 views but I am being increasingly targeted by fossil fuel activists and supporters, with personal attacks and misinformation.
I do respond to misinformation, and remove the worst comments but if anyone would like to help support me, nipping over to my channel, watching some videos and subscribing to the channel would be most appreciated.
We can show them that they are the minority, not us, and the wider the information spreads the quicker the change will be and the better life will be for everyone.
Thanks in advance and have an awesome day.


It is very likely that treatments to address the issues that cause aging & its related conditions & diseases will be within our reach in 15 to 20 years.

It is highly likely that a general realisation that these treatments are not only scientifically possible but within our reach will start to become increasingly apparent to the wider population in as little as maybe 5 years.

On this channel I will seek to hasten this realisation, & provide answers to the most common questions & concerns. I will also seek to distil the current scientific knowledge base into an easy to use action plan for those wishing to take measures to make sure they see this in good health.

To raise awareness of how close longevity treatments are.

Enhancing the workhorse: Artificial intelligence, hardware innovations boost confocal microscope’s performance

Since artificial intelligence pioneer Marvin Minsky patented the principle of confocal microscopy in 1957, it has become the workhorse standard in life science laboratories worldwide, due to its superior contrast over traditional wide-field microscopy. Yet confocal microscopes aren’t perfect. They boost resolution by imaging just one, single, in-focus point at a time, so it can take quite a while to scan an entire, delicate biological sample, exposing it light dosages that can be toxic.

To push confocal imaging to an unprecedented level of performance, a collaboration at the Marine Biological Laboratory (MBL) has invented a “kitchen sink” confocal platform that borrows solutions from other high-powered imaging systems, adds a unifying thread of “Deep Learning” artificial intelligence algorithms, and successfully improves the confocal’s volumetric resolution by more than 10-fold while simultaneously reducing phototoxicity. Their report on the technology, called “Multiview Confocal Super-Resolution Microscopy,” is published online this week in Nature.

“Many labs have confocals, and if they can eke more performance out of them using these artificial intelligence algorithms, then they don’t have to invest in a whole new microscope. To me, that’s one of the best and most exciting reasons to adopt these AI methods,” said senior author and MBL Fellow Hari Shroff of the National Institute of Biomedical Imaging and Bioengineering.

We might not know half of what’s in our cells, new AI technique reveals

Most human diseases can be traced to malfunctioning parts of a cell—a tumor is able to grow because a gene wasn’t accurately translated into a particular protein or a metabolic disease arises because mitochondria aren’t firing properly, for example. But to understand what parts of a cell can go wrong in a disease, scientists first need to have a complete list of parts.

By combining microscopy, biochemistry techniques and , researchers at University of California San Diego School of Medicine and collaborators have taken what they think may turn out to be a significant leap forward in the understanding of human cells.

The technique, known as Multi-Scale Integrated Cell (MuSIC), is described November 24, 2021 in Nature.

Artificial Intelligence Successfully Predicts Protein Interactions — Could Lead to Wealth of New Drug Targets

Research led by UT Southwestern and the University of Washington could lead to a wealth of drug targets.

UT Southwestern and University of Washington researchers led an international team that used artificial intelligence (AI) and evolutionary analysis to produce 3D models of eukaryotic protein interactions. The study, published in Science, identified more than 100 probable protein complexes for the first time and provided structural models for more than 700 previously uncharacterized ones. Insights into the ways pairs or groups of proteins fit together to carry out cellular processes could lead to a wealth of new drug targets.

“Our results represent a significant advance in the new era in structural biology in which computation plays a fundamental role,” said Qian Cong, Ph.D., Assistant Professor in the Eugene McDermott Center for Human Growth and Development with a secondary appointment in Biophysics.

Natural selection has been acting on hundreds of human genes in the last 3,000 years

Natural selection, the evolutionary process that guides which traits become more common in a population, has been acting on us for the past 3,000 years, right up to the modern day, new research suggests.

And it seems to be acting in surprising ways on complex traits encoded by multiple genes, such as those tied to intelligence, mental illness and even cancer.