Lifeboat Foundation

Some argue that only a “Sputnik” moment will wake the American people and government to act with purpose, just as the 1957 Soviet launch of a satellite catalyzed new educational and technological investments. We disagree. We have been struck by the broad, bipartisan consensus in America to “get AI right” now. We are in a rare moment when challenge, urgency, and consensus may just align to generate the energy we need to extend our AI leadership and build a better future.

Congress asked us to serve on a bipartisan commission of tech leaders, scientists, and national security professionals to explore the relationship between artificial intelligence (AI) and national security. Our work is not complete, but our initial assessment is worth sharing now: in the next decade, the United States is in danger of losing its global leadership in AI and its innovation edge. That edge is a foundation of our economic prosperity, military power and ultimately the freedoms we enjoy.

As we consider the leadership stakes, we are struck by AI’s potential to propel us towards many imaginable futures. Some hold great promise; others are concerning. If past technological revolutions are a guide, the future will include elements of both.

Continue reading “The US is in danger of losing its global leadership in AI” »

A new FBI warning provides advice on protecting home WiFi networks from attack.

Leaving a vulnerable system unpatched can invite troubles for an organization. The issue can turn worse when the organization suffers a cyberattack that can result in, but not limited to, compromise of confidential data, DDoS attacks or stealing of customers’ details.

According to a report released by Recorded Future, it has been found that the same vulnerabilities kept showing up year-after-year. An interesting aspect of the report was that most of these vulnerabilities were found to be exploited via phishing attacks and exploit kits that specifically target flaws in Microsoft products.

O.o.

A physicist at the University of California, Riverside, has performed calculations showing hollow spherical bubbles filled with a gas of positronium atoms are stable in liquid helium.

The calculations take scientists a step closer to realizing a gamma-ray laser, which may have applications in medical imaging, spacecraft propulsion, and cancer treatment.

Continue reading “Gamma-ray laser moves a step closer to reality” »

Evolutionary stories like the grandmother hypothesis are easy to construct from mathematical models, but how well do they reflect reality?

Amidst rising hopes for using CRISPR gene editing tools to repair deadly mutations linked to conditions like cystic fibrosis and sickle cell disease, a study in Communications Biology describes a new innovation that could accelerate this work by rapidly revealing unintended and potentially harmful changes introduced by a gene editing process.

“We’ve developed a new process for rapidly screening all of the edits made by CRISPR, and it shows there may be many more unintended changes to DNA around the site of a CRISPR repair than previously thought,” said Eric Kmiec, Ph.D., director of ChristianaCare’s Gene Editing Institute and the principle author of the study.

The study describes a new tool developed at the Gene Editing Institute that in just 48 hours can identify “multiple outcomes of CRISPR-directed gene editing,” a process that typically required up to two months of costly and complicated DNA analysis.

Learn more about what’s on board the Dragon spacecraft headed to the International Space Station: https://www.nasa.gov/mission_pages/station/research/news/spx19-research

Called XLEAP, the new method will provide sharp views of electrons in chemical processes that take place in billionths of a billionth of a second and drive crucial aspects of life.

Computer scientists from Duke University and Harvard University have joined with physicians from Massachusetts General Hospital and the University of Wisconsin to develop a machine learning model that can predict which patients are most at risk of having destructive seizures after suffering a stroke or other brain injury.

A point system they’ve developed helps determine which patients should receive expensive continuous electroencephalography (cEEG) monitoring. Implemented nationwide, the authors say their model could help hospitals monitor nearly three times as many patients, saving many lives as well as $54 million each year.

A paper detailing the methods behind the interpretable machine learning approach appeared online June 19 in the Journal of Machine Learning Research.