Lifeboat Foundation

DARPA’s effort to track undersea life’s behavior as a means to detect enemy submarines has just entered its second phase. In the first phase, DARPA’s Persistent Aquatic Living Sensors (PALS) program sought to prove that sea life would respond to the presence of a submarine in a measurable way. With that seemingly confirmed, the second stage of the program will focus on developing sensors that can identify that behavior and relay a warning back to manned locations aboard a ship or onshore.

While the science is complex, the premise behind the PALS program is fairly simple. Undersea life tends to behave in a certain way when it senses the presence of a large and foreign object like a submarine. By broadly tracking the behavior of sea life, PALS aims to measure and interpret that behavior to make educated guesses about what must be causing it. In other words, by constantly tracking the behavior of nearby wildlife, PALS sensors can notice a significant change, compare it to a library of known behaviors, and predict a cause… like an enemy submarine, even if a submarine was stealthy enough to otherwise evade detection.

With enough data about how animals react to the presence of an enemy vessel as compared to how animals react to the presence of a large predator or more common undersea threat, PALS could serve as an early warning system when enemy subs approach.

An urban spaceport that floats in Tokyo Bay is designed to make space travel more accessible.

Three actions policymakers and business leaders can take today.

New developments in AI could spur a massive democratization of access to services and work opportunities, improving the lives of millions of people around the world and creating new commercial opportunities for businesses. Yet they also raise the specter of potential new social divides and biases, sparking a public backlash and regulatory risk for businesses. For the U.S. and other advanced economies, which are increasingly fractured along income, racial, gender, and regional lines, these questions of equality are taking on a new urgency. Will advances in AI usher in an era of greater inclusiveness, increased fairness, and widening access to healthcare, education, and other public services? Or will they instead lead to new inequalities, new biases, and new exclusions?

Three frontier developments stand out in terms of both their promised rewards and their potential risks to equality. These are human augmentation, sensory AI, and geographic AI.

Continue reading “Unlocking AI’s Potential for Social Good” »

The complex network of interconnected cellular signals produced in response to changes in the human body offers a vast amount of interesting and valuable insight that could inform the development of more effective medical treatments. In peripheral immune cells, these signals can be observed and quantified using a number of tools, including cell profiling techniques.

Single-cell profiling techniques such as polychromatic flow and mass cytometry have improved significantly over the past few years and they could now theoretically be used to obtain detailed immune profiles of patients presenting a number of symptoms. Nonetheless, the limited sample sizes of past studies and the high dimensionality of the patient data collected so far increase the chances of false-positive discoveries, which in turn lead to unreliable immune profiles.

Conducting studies on larger groups of patients could improve the effectiveness of these cell-profiling techniques, allowing medical researchers to gain a better understanding of the patterns associated with medical conditions. Gathering data from many patients, however, can be both expensive and time consuming.

Vertical farms subvert the space limitations of cities by allowing for portable, shipping container-esque produce farms that can operate in any urban environment.

Deep neural networks, often criticized as “black boxes,” are helping neuroscientists understand the organization of living brains.

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Robotic waiters.

This restaurant hires robots to serve food.