Google’s parent firm, Alphabet, has long been working on multipurpose robots.
The fleet of “Everyday Robots,” as they are colloquially called, has recently been upgraded with sophisticated AI language systems so that they can better comprehend human speech.
Continue reading “Google Adds AI Language Skills To Alphabet’s Helper Robots” »
In recent years, deep learning algorithms have achieved remarkable results in a variety of fields, including artistic disciplines. In fact, many computer scientists worldwide have successfully developed models that can create artistic works, including poems, paintings and sketches.
Researchers at Seoul National University have recently introduced a new artistic deep learning framework, which is designed to enhance the skills of a sketching robot. Their framework, introduced in a paper presented at ICRA 2022 and pre-published on arXiv, allows a sketching robot to learn both stroke-based rendering and motor control simultaneously.
Someone taps your shoulder. The organized touch receptors in your skin send a message to your brain, which processes the information and directs you to look left, in the direction of the tap. Now, Penn State and U.S. Air Force researchers have harnessed this processing of mechanical information and integrated it into engineered materials that “think”.
The work, published today in Nature, hinges on a novel, reconfigurable alternative to integrated circuits. Integrated circuits are typically composed of multiple electronic components housed on a single semiconductor material, usually silicon, and they run all types of modern electronics, including phones, cars and robots. Integrated circuits are scientists’ realization of information processing similar to the brain’s role in the human body. According to principal investigator Ryan Harne, James F. Will Career Development Associate Professor of Mechanical Engineering at Penn State, integrated circuits are the core constituent needed for scalable computing of signals and information but have never before been realized by scientists in any composition other than silicon semiconductors.
Continue reading “Researchers engineer novel material capable of ‘thinking’” »
MIT researchers unveil the first open-source simulation engine capable of constructing realistic environments for deployable training and testing of autonomous vehicles. Since they’ve proven to be productive test beds for safely trying out dangerous driving scenarios, hyper-realistic virtual worlds have been heralded as the best driving schools for autonomous vehicles (AVs). Tesla, Waymo, and other self-driving companies all rely heavily on data to enable expensive and proprietary photorealistic simulators, because testing and gathering nuanced I-almost-crashed data usually isn’t the easiest or most desirable to recreate.
When the MIT Lincoln Laboratory Supercomputing Center (LLSC) unveiled its TX-GAIA supercomputer in 2019, it provided the MIT community a powerful new resource for applying artificial intelligence to their research. Anyone at MIT can submit a job to the system, which churns through trillions of operations per second to train models for diverse applications, such as spotting tumors in medical images, discovering new drugs, or modeling climate effects. But with this great power comes the great responsibility of managing and operating it in a sustainable manner—and the team is looking for ways to improve.
“We have these powerful computational tools that let researchers build intricate models to solve problems, but they can essentially be used as black boxes. What gets lost in there is whether we are actually using the hardware as effectively as we can,” says Siddharth Samsi, a research scientist in the LLSC.
To gain insight into this challenge, the LLSC has been collecting detailed data on TX-GAIA usage over the past year. More than a million user jobs later, the team has released the dataset open source to the computing community.
SUSTech.
Researchers succeeded in a 3D printing strategy to construct flexible and stretchable light-emitting devices that can be integrated with soft robots.
TABLE OF CONTENTS —————
0:00–15:11 : Introduction.
15:11–36:12 CHAPTER 1: POSTHUMANISM
a. Neurotechnology b. Neurophilosophy c. Teilhard de Chardin and the Noosphere.
TWITTER
https://twitter.com/Transhumanian.
All around smart guy Dr Goerge Church talking about genetic engineering technologies.
George Church, Ph.D. is a professor of genetics at Harvard Medical School and of health sciences and technology at both Harvard and the Massachusetts Institute of Technology. Dr. Church played an instrumental role in the Human Genome Project and is widely recognized as one of the premier scientists in the fields of gene editing technology and synthetic biology.
Continue reading “George Church, PhD: Rewriting Genomes to Eradicate Disease and Aging” »
Our approach to aligning AGI is empirical and iterative. We are improving our AI systems’ ability to learn from human feedback and to assist humans at evaluating AI. Our goal is to build a sufficiently aligned AI system that can help us solve all other alignment problems.
Our alignment research aims to make artificial general intelligence (AGI) aligned with human values and follow human intent. We take an iterative, empirical approach: by attempting to align highly capable AI systems, we can learn what works and what doesn’t, thus refining our ability to make AI systems safer and more aligned. Using scientific experiments, we study how alignment techniques scale and where they will break.
We tackle alignment problems both in our most capable AI systems as well as alignment problems that we expect to encounter on our path to AGI. Our main goal is to push current alignment ideas as far as possible, and to understand and document precisely how they can succeed or why they will fail. We believe that even without fundamentally new alignment ideas, we can likely build sufficiently aligned AI systems to substantially advance alignment research itself.
In this Robotics 24/7 Roundtable, viewers can learn how robots and software can enable retailers to improve productivity, manage their supply chains, and better serve consumers.
