Lifeboat Foundation

Scientists have developed a machine-learning method that crunches massive amounts of data to help determine which existing medications could improve outcomes in diseases for which they are not prescribed.

The intent of this work is to speed up drug repurposing, which is not a new concept—think Botox injections, first approved to treat crossed eyes and now a migraine treatment and top cosmetic strategy to reduce the appearance of wrinkles.

But getting to those new uses typically involves a mix of serendipity and time-consuming and expensive randomized clinical trials to ensure that a drug deemed effective for one disorder will be useful as a treatment for something else.

Scientists at DGIST in Korea, and UC Irvine and UC San Diego in the US, have developed a computer architecture that processes unsupervised machine learning algorithms faster, while consuming significantly less energy than state-of-the-art graphics processing units. The key is processing data where it is stored in computer memory and in an all-digital format. The researchers presented the new architecture, called DUAL, at the 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture.

“Today’s computer applications generate a large amount of data that needs to be processed by machine learning algorithms,” says Yeseong Kim of Daegu Gyeongbuk Institute of Science and Technology (DGIST), who led the effort.

Powerful “unsupervised” machine learning involves training an algorithm to recognize patterns in large datasets without providing labeled examples for comparison. One popular approach is a clustering algorithm, which groups similar data into different classes. These algorithms are used for a wide variety of data analyzes, such as identifying fake news on social media, filtering spam email and detecting criminal or fraudulent activity online.

Proteins are essential to cells, carrying out complex tasks and catalyzing chemical reactions. Scientists and engineers have long sought to harness this power by designing artificial proteins that can perform new tasks, like treat disease, capture carbon or harvest energy, but many of the processes designed to create such proteins are slow and complex, with a high failure rate.

In a breakthrough that could have implications across the healthcare, agriculture, and energy sectors, a team lead by researchers in the Pritzker School of Molecular Engineering at the University of Chicago has developed an artificial intelligence-led process that uses big data to design new proteins.

By developing machine-learning models that can review protein information culled from genome databases, the researchers found relatively simple design rules for building artificial proteins. When the team constructed these artificial proteins in the lab, they found that they performed chemical processes so well that they rivaled those found in nature.

Unmanned Aerial Vehicles (UAV), commonly referred to as drones, may prove to be a valuable tool in the battle against pandemics like COVID-19. Researchers at the University of Calgary, the Southern Alberta Institute of Technology (SAIT), Alberta Health Services (AHS) and Alberta Precision Laboratories (APL) are partnering with the Stoney Nakoda Nations (SNN) to deliver medical equipment and test kits for COVID-19 to remote areas, and to connect these communities to laboratories more quickly using these remotely piloted aircraft.

Access for all

“We know that testing for COVID-19 is one of our most effective tools against its spread. Many remote communities in Canada do not have easy access to testing centres and medical supplies to support rapid testing and containment. Drones can help us respond to that need,” says Dr. John Conly, MD, medical director of the W21C Research and Innovation Centre at the Cumming School of Medicine (CSM) and co-principal investigator on the project.

Some robot dancing.

Our whole crew got together to celebrate the start of what we hope will be a happier year: Happy New Year from all of us at Boston Dynamics. www.BostonDynamics.com.

Before the century is out, advances in nanotechnology, nanomedicine, AI, and computation will result in the development of a “Human Brain/Cloud Interface” (B/CI), that connects neurons and synapses in the brain to vast cloud-computing networks in real time.

That’s the prediction of a large international team of neurosurgeons, roboticists, and nanotechnologists, writing in the journal Frontiers in Neuroscience.

A Human Brain/Cloud Interface, sometimes dubbed the “internet of thoughts”, theoretically links brains and cloud-based data storage through the intercession of nanobots positioned at strategically useful neuronal junctions.

To test its experimental X-43A — an unmanned, single-use, scramjet-powered, hypersonic aircraft of which three were built — NASA piggybacked it on two other aircraft.

First was the Boeing B-52 Stratofortress, which carried under its wing the other two vehicles to an altitude at which they could be ‘drop-launched’.

Then there was the booster rocket, a modified version of a Pegasus rocket, which would accelerate the X-43A after the drop launch to a speed at which its scramjet engine could operate.

The operationalisation of these trains is a major technologcal milestone for the DMRC and the country. The 57-km Pink Line (Majlis Park–Shiv Vihar) will also have driverless operations by mid-2021, the DMRC said on Sunday.

The tiny house we’re going to discuss today won’t buy you freedom like trailer-based models, but it compensates for that with its own AI assistant. It’s smart, it’s tiny, it can be solar powered if you want, and it’s still very chic. It’s dubbed the next-generation tiny house: the Cube Two from Nestron.

You don’t have to actually live large in order to live large. Tiny houses are a good option when it comes to minimizing your footprint, downsizing costs and not sacrificing anything but space you probably wouldn’t be using either way.