Scientists might have reached the theoretical limit of how strong this particular material can get, designing the first-ever super-light carbon nanostructure that’s stronger than diamond.

The latest development in the nanoworld of carbon comes from a team that has designed something called carbon plate-nanolattices. Under a scanning electron microscope, they look like little cubes, and the math indicated that this structure would be incredibly strong, but it’s been too difficult to actually make, until now.

The team’s success was made possible by a 3D printing process called two-photon polymerization direct laser writing, which is essentially 3D printing on the level of atoms and photons.

Find out more about this technique and what the result could mean for the future of medicine, electronics aerospace and more in this Elements.

This Seeker video explains.

Anyone interested in technology and society should read Melvin Kranzberg’s Six Laws of Technology, the first of which says that “technology is neither good nor bad; nor is it neutral”.

Here’s an interesting look at how AI outsmarted antibiotic resistant bacteria (for now).

The saloon-bar version of this is that “technology is both good and bad; it all depends on how it’s used” – a tactic that tech evangelists regularly deploy as a way of stopping the conversation. So a better way of using Kranzberg’s law is to ask a simple Latin question: Cui bono? – who benefits from any proposed or hyped technology? And, by implication, who loses?

A quantum computer isn’t just a more powerful version of the computers we use today; it’s something else entirely, based on emerging scientific understanding — and more than a bit of uncertainty.

Enter the quantum wonderland with TED Fellow Shohini Ghose and learn how this technology holds the potential to transform medicine, create unbreakable encryption and even teleport information.

Can’t get enough? Here’s another video.

TED-Ed explains the science of how viruses can jump from one species to another and the deadly epidemics that can result from these pathogens.

Here’s a story that happened right here in Maryland.

At a Maryland country fair in 2017, farmers reported feverish hogs with inflamed eyes and running snouts. While farmers worried about the pigs, the department of health was concerned about a group of sick fairgoers. Soon, 40 of these attendees would be diagnosed with swine flu. How can pathogens from one species infect another, and what makes this jump so dangerous? Ben Longdon explains.

Siraj Raval has a video exploring a paper about genomics and creating reliable machine learning systems.

Deep learning classifiers make the ladies (and gentlemen) swoon, but they often classify novel data that’s not in the training set incorrectly with high confidence. This has serious real world consequences! In Medicine, this could mean misdiagnosing a patient. In autonomous vehicles, this could mean ignoring a stop sign. Machines are increasingly tasked with making life or death decisions like that, so it’s important that we figure out how to correct this problem! I found a new, relatively obscure yet extremely fascinating paper out of Google Research that tackles this problem head on. In this episode, I’ll explain the work of these researchers, we’ll write some code, do some math, do some visualizations, and by the end I’ll freestyle rap about AI and genomics. I had a lot of fun making this, so I hope you enjoy it!

Likelihood Ratios for Out-of-Distribution Detection paper: https://arxiv.org/pdf/1906.02845.pdf 

The researcher’s code: https://github.com/google-research/google-research/tree/master/genomics_ood

Digitalization leads our world in a new are of technology.

Technology in Health-care is extremely important.

Fouad Al-Noor will teach you how to build a successful medical start up step by step: so that you don‘t have to fall in the Medtech Valley of Death.

Fouad has a masters in Electric Engineering with Nanotechnology from the University of Southampton. He worked as a research assistant at Imperial College London before joining Entrepreneur First.

He wrote his thesis on paper-based medical diagnostics using image processing.

The University of California, San Francisco is developing and training an AI model that could help diagnose tears in knee cartilage, or the meniscus.  A meniscus tear can lead to long-term health challenges  and lifestyle changes, ranging from debilitation to limits on activity. One of the keys to mitigating the consequences of meniscus tears is identifying and treating tears in the meniscus early. Here’s an interesting look at the research currently going on.

While this goal is pretty simple, the path forward is rather complicated. To diagnose a torn meniscus, clinicians need to review and interpret hundreds of high-resolution 3D magnetic resonance imaging (MRI) slices showing a patient’s knee from different angles. Radiologists then assign a numerical score to indicate the presence of a tear and its severity. This labor-intensive, time-consuming process relies heavily on the skills and availability of clinical specialists, and the interpretation of the images themselves can be rather subjective.