Lifeboat Foundation

A team of researchers at Delft University of Technology has developed a drone that flies autonomously using neuromorphic image processing and control based on the workings of animal brains. Animal brains use less data and energy compared to current deep neural networks running on GPUs (graphic chips). Neuromorphic processors are therefore very suitable for small drones because they don’t need heavy and large hardware and batteries.

The results are extraordinary: during flight the drone’s deep neural network processes data up to 64 times faster and consumes three times less energy than when running on a GPU. Further developments of this technology may enable the leap for drones to become as small, agile, and smart as flying insects or birds.

Photo of the “neuromorphic drone” flying over a flower pattern. It illustrates the visual inputs the drone receives from the neuromorphic camera in the corners. Red indicates pixels getting darker, green indicates pixels getting brighter. (Image: TU Delft)

This study shows that by enhancing internal complexity of neurons in a Hodgkin–Huxley network, similar performance to larger, simpler networks can be achieved, suggesting an alternative path for powerful AI systems by focusing on neuron complexity.

The services are necessary to maintain a domestic trusted source for strategic radiation-hardened microelectronics to meet the U.S. Department of Defense (DOD) certification to Congress, as stipulated by the fiscal 2018 National Defense Authorization Act Section 1,670, DOD officials say.

Radiation-hardened microelectronics components are necessary for manned and unmanned spacecraft operating on long-duration orbital missions in high-radiation space environments like geosynchronous orbits.

DARPA is giving seven teams $2 million to hone tools for scrutinizing the open-source code that underpins everything from banks to water systems.

The problem of intelligence — its nature, how it is produced by the brain and how it could be replicated in machines — is a deep and fundamental problem that cuts across multiple scientific disciplines. Philosophers have studied intelligence for centuries, but it is only in the last several decades that developments in science and engineering have made questions such as these approachable: How does the mind process sensory information to produce intelligent behavior, and how can we design intelligent computer algorithms that behave similarly? What is the structure and form of human knowledge — how is it stored, represented, and organized? How do human minds arise through processes of evolution, development, and learning? How are the domains of language, perception, social cognition, planning, and motor control combined and integrated? Are there common principles of learning, prediction, decision, or planning that span across these domains?

This course explores these questions with an approach that integrates cognitive science, which studies the mind; neuroscience, which studies the brain; and computer science and artificial intelligence, which study the computations needed to develop intelligent machines. Faculty and postdoctoral associates affiliated with the Center for Brains, Minds and Machines discuss current research on these questions.

A new algorithm may make robots safer by making them more aware of human inattentiveness. In computerized simulations of packaging and assembly lines where humans and robots work together, the algorithm developed to account for human carelessness improved safety by about a maximum of 80% and efficiency by about a maximum of 38% compared to existing methods.

The work is reported in IEEE Transactions on Systems, Man, and Cybernetics: Systems.

“There are a large number of accidents that are happening every day due to carelessness—most of them, unfortunately, from human errors,” said lead author Mehdi Hosseinzadeh, assistant professor in Washington State University’s School of Mechanical and Materials Engineering.

A tiny battery designed by MIT engineers could enable the deployment of cell-sized, autonomous robots for drug delivery within in the human body, as well as other applications such as locating leaks in gas pipelines.

The new battery, which is 0.1 millimeters long and 0.002 millimeters thick—roughly the thickness of a human hair—can capture oxygen from air and use it to oxidize zinc, creating a current of up to 1 volt. That is enough to power a small circuit, sensor, or actuator, the researchers showed.

“We think this is going to be very enabling for robotics,” says Michael Strano, the Carbon P. Dubbs Professor of Chemical Engineering at MIT and the senior author of the study. “We’re building robotic functions onto the battery and starting to put these components together into devices.”

This type of molecular collaboration has inspired scientists for nearly a century. Here, oxygen is the effector. It flips a protein switch, helping proteins better carry oxygen through the body. In other words, it may be possible to optimize protein functions with an alternative effector drug.

The problem? The original inspiration is wonky. Sometimes hemoglobin proteins carry oxygen. Other times they don’t. In 1965, a French and American collaboration found out why. Each protein alternates between two three-dimensional shapes—one that carries oxygen and another that doesn’t. The shapes can’t coexist in the assembled protein to carry oxygen: It’s all-or-none, depending on the presence and amount of the effector.

The new study built on these lessons to guide their AI-designed proteins.

The Monty Hall Problem.

Goat or no Goat!

Matt Hodgson reviews Why Machines Learn: The Elegant Maths Behind Modern AI by Anil Ananthaswamy.

Continue reading “Goats, sports cars and game shows: the unexpected science behind machine learning and AI” »