Lifeboat Foundation

Today we’re joined by Melodie Yashar — Designer, Researcher, Technologist, co-founder of the firm Space Exploration Architecture (SEArch+), Senior Research Associate with San Jose State University Research Foundation at NASA Ames Research Center, and an Associate Researcher within the UC Davis Center for Human/Robotics/Vehicle Integration and Performance (HRVIP). She also teaches undergraduate and graduate design at Art Center College of Design and is a 2019–2020 Future Space Leaders Fellow.

Melodie’s current work focuses on the relationship of advanced software & hardware systems for spaceflight and maintains ongoing research interests in the design of augmented environments, human-machine interaction, human performance studies, and space technology development.

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A new robot created by researchers at Northwestern University looks and behaves like a tiny aquatic animal, and could serve a variety of functions, including moving things place to place, catalyzing chemical reactions, delivering therapeutics and much more. This new soft robot honestly looks a heck of a lot like a lemon peel, but it’s actually a material made up of 90% water for the soft exterior, with a nickel skeleton inside that can change its shape in response to outside magnetic fields.

These robots are very small — only around the size of a dime — but they’re able to perform a range of tasks, including walking at the same speed as an average human, and picking up and carrying things. They work by either taking in or expelling water through their soft components, and can respond to light and magnetic fields thanks to their precise molecular design. Essentially, their molecular structure is crafted such that when they’re hit by light, the molecules that make them up expel water, causing the robot’s “legs” to stiffen like muscles.

Northwestern University researchers have developed a first-of-its-kind life-like material that acts as a soft robot. It can walk at human speed, pick up and transport cargo to a new location, climb up hills and even break-dance to release a particle.

Nearly 90% water by weight, the centimeter-sized robot moves without complex hardware, hydraulics or electricity. Instead, it is activated by light and walks in the direction of an external rotating magnetic field.

Resembling a four-legged octopus, the robot functions inside a water-filled tank, making it ideal for use in aquatic environments. The researchers imagine customizing the movements of miniature robots to help catalyze different chemical reactions and then pump out the valuable products. The robots also could be molecularly designed to recognize and actively remove unwanted particles in specific environments, or to use their mechanical movements and locomotion to precisely deliver bio-therapeutics or cells to specific tissues.

Gollum in “The Lord of the Rings,” Thanos in the “Avengers,” Snoke in “Star Wars,” the Na’vi in “Avatar”—we have all experienced the wonders of motion-capture, a cinema technique that tracks an actor’s movements and translates them into computer animation to create a moving, emoting—and maybe one day Oscar-winning—digital character.

But what many might not realize is that motion capture isn’t limited to the big screen, but extends into science. Behavioral scientists have been developing and using similar tools to study and analyze the posture and movement of animals under a variety of conditions. But motion-capture approaches also require that the subject wears a complex suit with markers that let the computer “know” where each part of the body is in three-dimensional space. That might be okay for a professional actor, but animals tend to resist dressing up.

To solve the problem, scientists have begun combining motion-capture with deep learning, a method that lets a computer essentially teach itself how to optimize performing a task, e.g., recognizing a specific “key-point” in videos. The idea is to teach the computer to track and even predict the movements or posture of an animal without the need for motion capture markers.

Scientists reassemble a frog’s living cells into robotic devices — with no electronics required.

That is partly because AI businesses are not consumer-facing. Because they are mostly providers of back end hardware and software to other businesses, or, more critically, to governments, AI business will not become giant platform companies servicing billions of users.

Nina Xiang is the founder of China Money Network, a media platform tracking China’s venture and tech sectors.

In 10 years no one will remember the names of China’s artificial intelligence unicorns. While many aspects of the coming AI revolution remain unpredictable, one thing is clear: no AI company will emerge as a Big Tech brand.

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Boston Dynamics Atlas robot.

Hyundai Motor will acquire Boston Dynamics. The acquisition will be finalized at Hyundai’s December 10 board meeting. News about the deal was first reported by The Korea Economic Daily, which said the deal is for $921 million (1 trillion won). The Robot Report has also confirmed the news with a source familiar with the deal. The source said the acquisition is for about $1 billion.

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At a time when more companies are building machine learning models, Arthur.ai wants to help by ensuring the model accuracy doesn’t begin slipping over time, thereby losing its ability to precisely measure what it was supposed to. As demand for this type of tool has increased this year, in spite of the pandemic, the startup announced a $15 million Series A today.

The investment was led by Index Ventures with help from newcomers Acrew and Plexo Capital, along with previous investors Homebrew, AME Ventures and Work-Bench. The round comes almost exactly a year after its $3.3 million seed round.

As CEO and co-founder Adam Wenchel explains, data scientists build and test machine learning models in the lab under ideal conditions, but as these models are put into production, the performance can begin to deteriorate under real-world scrutiny. Arthur.ai is designed to root out when that happens.

Leveraging structure in data is key to making progress in AI, says AI prodigy Gary Marcus. A forward-looking view on Software 2.0, AI chips, robotics, and the future of AI.