Lifeboat Foundation

Why could the mongoose Rikki Tikki Tavi attack deadly snakes with impunity in Kipling’s “Jungle Book?” Because he has a uniquely mutated receptor for a brain neurotransmitter called acetylcholine, researchers at the Weizmann Institute of Science report in the Proceedings of the National Academy of Sciences.

The toxins in many snake venoms, including that of cobras, bind to the acetylcholine receptors of their victims, blocking nerve-muscle communications. Molecular biologist Sara Fuchs and her colleagues found that the acetylcholine receptor in mongooses—like that in the snakes themselves—is slightly mutated so that the venom simply bounces off the muscle cells, causing them no harm.

Circa 2018

Stem cells have been a source of much excitement in the medical community for years, as if they could be properly developed for use in humans doctors could theoretically use them to regrow missing body parts.

While the techniques have seen some success in the lab, the processes involved are complex and it will likely be some time before they become widely used in clinical practice.

Continue reading “Scientists have found a single cell that can be used to regenerate an entire animal” »

The hidden secret of artificial intelligence is that much of it is actually powered by humans. Well, to be specific, the supervised learning algorithms that have gained much of the attention recently are dependent on humans to provide well-labeled training data that can be used to train machine learning algorithms. Since machines have to first be taught, they can’t teach themselves (yet), so it falls upon the capabilities of humans to do this training. This is the secret achilles heel of AI: the need for humans to teach machines the things that they are not yet able to do on their own.

Machine learning is what powers today’s AI systems. Organizations are implementing one or more of the seven patterns of AI, including computer vision, natural language processing, predictive analytics, autonomous systems, pattern and anomaly detection, goal-driven systems, and hyperpersonalization across a wide range of applications. However, in order for these systems to be able to create accurate generalizations, these machine learning systems must be trained on data. The more advanced forms of machine learning, especially deep learning neural networks, require significant volumes of data to be able to create models with desired levels of accuracy. It goes without saying then, that the machine learning data needs to be clean, accurate, complete, and well-labeled so the resulting machine learning models are accurate. Whereas it has always been the case that garbage in is garbage out in computing, it is especially the case with regards to machine learning data.

According to analyst firm Cognilytica, over 80% of AI project time is spent preparing and labeling data for use in machine learning projects:

When doctors in a Washington hospital sought to treat the first confirmed case of the Wuhan coronavirus in the United States on Wednesday, they tapped a device called Vici that allowed them to interact with their patient not in person, but through a screen.

The telehealth device, which looks like a tablet on wheels that doctors can use to talk to patients and perform basic diagnostic functions, like taking their temperature, is one of a handful of high-tech machines that doctors, airport workers, and hotel staff are using to help contain the outbreak that has been sweeping the world since it was discovered in Wuhan, China in late December.

“Caregivers provide care within the isolation unit, but technology is allowing us to reduce the number of up-close interactions,” says Dr. Amy Compton-Phillips, chief clinical officer at Providence Regional Medical Center in Everett, Washington, where the patient is being treated. Vici, made by Santa Barbara, California-based InTouch Health, resembles a tablet on wheels, and can protect caregivers from infection.

There’s no two-ways about it, the Universe is an extremely big place! And thanks to the limitations placed upon us by Special Relativity, traveling to even the closest star systems could take millennia. As we addressed in a previous article, the estimated travel time to the nearest star system (Alpha Centauri) could take anywhere from 19,000 to 81,000 years using conventional methods.

For this reason, many theorists have recommended that humanity should rely on generation ships to spread the seed of humanity among the stars. Naturally, such a project presents many challenges, not the least of which is how large a spacecraft would need to be to sustain a multi-generational crew. In a new study, a team of international scientists addressed this very question and determined that a lot of interior space would be needed!

This mystical colorless rainbow lasted less than five minutes, just long enough to capture proof it existed

A UK-based landscape photographer recently traveled to a moor in Western Scotland to take pictures of a beautiful, solitary tree standing in the middle of the icy wasteland.

But genetic data tells a very different story.

In a trial, the scientists were capable of using electrical jolts from microelectronic controllers to make jellyfish swim not only faster but also more efficiently, according to a paper published in Science Advances today.

“We’ve shown that they’re capable of moving much faster than they normally do, without an undue cost on their metabolism,” said co-author and Stanford bioengineering PhD candidate Nicole Xu, in a statement.

Continue reading “Scientists Want to Explore Ocean With ‘Cyborg Jellyfish’” »

Swarms of microrobots will scuttle along beneath our roads and pavements, finding and fixing leaky pipes and faulty cables. Thanks to their efforts, we can avoid costly road work that costs billions of dollars each year—not to mention frustrating traffic delays.

That is, if a new project sponsored by the U.K. government is a success. Recent developments in the space seem to point towards a bright future for microrobots.