Lifeboat Foundation

Robotic landers and rovers have been touching down on Mars since the 1970s, but when will humanity finally set foot on the Red Planet?

Experts believe the technical challenges are nearly resolved, but political considerations make the future of any crewed mission uncertain.

NASA’s human lunar exploration program, Artemis, envisions sending people back to the Moon by 2024 and using the experience gained there to prepare for Mars.

Continue reading “Humanity on Mars? Technically possible, but no voyage on horizon” »

Recently, Tesla filed a patent called ‘Systems and methods for adapting a neural network on a hardware platform.’ In the patent, they described the systems and methods to select a neural network model configuration that satisfies all constraints.

According to the patent, the constraints mainly include an embodiment that computes a list of valid configurations and a constraint satisfaction solver to classify valid configurations for the particular platform, where the neural network model will run efficiently.

The Reason Behind the Patent.

The 400 kilogram wheeled system moves about the lab guided by LIDAR laser scanners and has an industrial robotic arm made by German firm Kuka that it uses to carry out tasks like weighing out solids, dispensing liquids, removing air from the vessel, and interacting with other pieces of equipment.

In a paper in Nature, the team describes how they put the device to work trying to find catalysts that speed up reactions that use light to split water into hydrogen and oxygen. To do this, the robot used a search algorithm to decide how to combine a variety of different chemicals and updated its plans based on the results of previous experiments.

Continue reading “This Robotic Chemist Does Over 600 Experiments a Week and Learns From Its Own Work” »

Researchers from the Max Delbrück Center for Molecular Medicine have developed a new tool that makes it easier to maximize the power of deep learning for studying genomics. They describe the new approach, Janggu, in the journal Nature Communications.

Imagine that before you could make dinner, you first had to rebuild the kitchen, specifically designed for each recipe. You’d spend way more time on preparation, than actually cooking. For computational biologists, it’s been a similar time-consuming process for analyzing genomics data. Before they can even begin their analysis, they spend a lot of valuable time formatting and preparing huge data sets to feed into deep learning models.

To streamline this process, researchers from MDC developed a universal programming tool that converts a wide variety of genomics data into the required format for analysis by deep learning models. “Before, you ended up wasting a lot of time on the technical aspect, rather than focusing on the biological question you were trying to answer,” says Dr. Wolfgang Kopp, a scientist in the Bioinformatics and Omics Data Science research group at MDC’s Berlin Institute of Medical Systems Biology (BIMSB), and first author of the paper. “With Janggu, we are aiming to relieve some of that technical burden and make it accessible to as many people as possible.”

Hello Robot’s Stretch wants to reinvent how mobile manipulators perform tasks in home environments.

A tutorial on the main types of neural networks and its applications to real-world challenges.

In the coronavirus economy, companies are adopting more automation, as they seek to cut costs and increase efficiency. There is debate about which jobs are most at risk and how soon. But climbing up the skills ladder is the best way to stay ahead of the automation wave.

Even groups that regularly disagree on labor issues said there should be significant public investment in programs that can upgrade the skills of American workers.

Circa 2016

“In 2050 there will be trillions of self-replicating robot factories on the asteroid belt,” he tells the audience at WIRED2016.

Continue reading “VIDEO: Trillions of self replicating robots will likely colonize the galaxy” »

Liquid metal machine can also be made as tiny motors. In fact, the micro- or even nanomotors that could run in a liquid environment is very important for a variety of practices such as serving as pipeline robot, soft machine, drug delivery, microfluidics system, etc. However, fabrication of such tiny motors is generally rather time and cost consumptive and has been a tough issue due to the involvement of too many complicated procedures and tools. This lab had discovered a straightforward injectable way for spontaneously generating autonomously running soft motors in large quantity Yao et al (Injectable spontaneous generation of tremendous self-fuelled liquid metal droplet motors in a moment, 2015 [ 1 ]). It was demonstrated that injecting the GaIn alloy pre-fuelled with aluminum into electrolyte would automatically split in seconds into tremendous droplet motors swiftly running here and there. The driving force originated from the galvanic cell reaction among alloy, aluminum, and surrounding electrolyte, which offers interior electricity and hydrogen gas as motion power. This finding opens the possibility to develop injectable tiny-robots, droplet machines, or microfluidic elements. It also raised important scientific issues regarding characterizing the complicated fluid mechanics stimulated by the quick running of the soft metal droplet and the gases it generated during the traveling. Our lab Yuan et al (Sci Bull 60:1203–1210, 2014 [ 2 ]) made further efforts to disclose that the self-powered liquid metal motors takes interiorly driven macroscopic Brownian motion behavior. Such tiny motors in millimeter-scale move randomly at a velocity magnitude of centimeters per second in aqueous alkaline solution, well resembling the classical Brownian motion. However, unlike the existing phenomena where the particle motions were caused by collisions from the surrounding molecules, the random liquid metal motions are internally enabled and self-powered, along with the colliding among neighboring motors, the substrate, and the surrounding electrolyte molecules. This chapter illustrates the typical behaviors of the self-powered tiny liquid metal motors.