Lifeboat Foundation: Safeguarding Humanity
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Category: biotech/medical

What if Elon Musk told you that you could store your memories as a backup and then download them into a robot body. Sounds like science fiction to you, Well believe it or not it’s true and he has already launched a company called Neuralink to pursue this futuristic goal.

But how does it work and how exactly is elon musk going to pull this one-off. Well, we’ll answer these questions and take a deeper look into Neuralink and how it could change humanity forever.

The Neuralink is a small brain implant that will generate and manipulate neurons in your brain to cure health problems like addiction, blindness, depression, and other various brain-related problems.

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A maze is a popular device among psychologists to assess the learning capacity of mice or rats. But how about robots? Can they learn to successfully navigate the twists and turns of a labyrinth? Now, researchers at the Eindhoven University of Technology (TU/e) in the Netherlands and the Max Planck Institute for Polymer Research in Mainz, Germany, have proven they can. Their robot bases its decisions on the very system humans use to think and act: the brain. The study, which was published in Science Advances, paves the way to exciting new applications of neuromorphic devices in health and beyond.

Machine learning and neural networks have become all the rage in recent years, and quite understandably so, considering their many successes in image recognition, medical diagnosis, e-commerce and many other fields. Still though, this software-based approach to machine intelligence has its drawbacks, not least because it consumes so.

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The system could help physicians select the least risky treatments in urgent situations, such as treating sepsis.

Sepsis claims the lives of nearly 270,000 people in the U.S. each year. The unpredictable medical condition can progress rapidly, leading to a swift drop in blood pressure, tissue damage, multiple organ failure, and death.

Prompt interventions by medical professionals save lives, but some sepsis treatments can also contribute to a patient’s deterioration, so choosing the optimal therapy can be a difficult task. For instance, in the early hours of severe sepsis, administering too much fluid intravenously can increase a patient’s risk of death.

To help clinicians avoid remedies that may potentially contribute to a patient’s death, researchers at MIT and elsewhere have developed a machine-learning model that could be used to identify treatments that pose a higher risk than other options. Their model can also warn doctors when a septic patient is approaching a medical dead end — the point when the patient will most likely die no matter what treatment is used — so that they can intervene before it is too late.

Full Story:

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I would prefer it if the data was anonymized and handed back to the patient via an AI interface on the assessment, — Recommended actions and risks involved with each decision. It would then be up to the patient to share the data with a doctor or not, to decide how much data they want to share, and to what extent recommendations can interfere with their day to day life. I’m gonna have a glass of wine. AI: this is your 3rd glass today, do you want to know the risks associated with this decision? No. AI: ok-do you want to monitor vital health statistics in relation to drinking wine instead of water? No. AI; Do you want / Just shut up. Erase all records of my wine drinking and do not monitor this going forward. To live means to die, at least for now. Don’t touch my wine 🍷

Remote technology could save lives by monitoring health from home or outside the hospital. It could also push patients and health care providers further apart.

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Paper referenced in the video:
Predicting age by mining electronic medical records with deep learning characterizes differences between chronological and physiological age.
https://pubmed.ncbi.nlm.nih.gov/29113935/

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To watch the full interview, visit: http://www.yiddishbookcenter.org/oral…

Leonard Nimoy — Jewish actor best known for his role as Spock on the Star Trek science fiction series — explains the Jewish story behind the hand-gesture he made famous through his role as Spock in the Star Trek science fiction series.

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Researchers at the University of Texas have discovered a new way for neural networks to simulate symbolic reasoning. This discovery sparks an exciting path toward uniting deep learning and symbolic reasoning AI.

In the new approach, each neuron has a specialized function that relates to specific concepts. “It opens the black box of standard deep learning models while also being able to handle more complex problems than what symbolic AI has typically handled,” Paul Blazek, University of Texas Southwestern Medical Center researcher and one of the authors of the Nature paper, told VentureBeat.

This work complements previous research on neurosymbolic methods such as MIT’s Clevrer, which has shown some promise in predicting and explaining counterfactual possibilities more effectively than neural networks. Additionally, DeepMind researchers previously elaborated on another neural network approach that outperformed state-of-the-art neurosymbolic approaches.

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Stein Emil Vollset, the study’s lead author and Professor of Global Health at the Institute for Health Metrics and Evaluation (IHME), elaborated on the findings.

“The last time that global population declined was in the mid 14th century, due to the Black Plague,” he told IFLScience. “If our forecast is correct, it will be the first time population decline is driven by fertility decline, as opposed to events such as a pandemic or famine.”

Some countries, however, are forecasted to see an increase in population.

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