AI & Robotics News and Discussions

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Yuli Ban
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Language models have demonstrated remarkable performance on a variety of natural language tasks — indeed, a general lesson from many works, including BERT, GPT-3, Gopher, and PaLM, has been that neural networks trained on diverse data at large scale in an unsupervised way can perform well on a variety of tasks.

Quantitative reasoning is one area in which language models still fall far short of human-level performance. Solving mathematical and scientific questions requires a combination of skills, including correctly parsing a question with natural language and mathematical notation, recalling relevant formulas and constants, and generating step-by-step solutions involving numerical calculations and symbolic manipulation. Due to these challenges, it is often believed that solving quantitative reasoning problems using machine learning will require significant advancements in model architecture and training techniques, granting models access to external tools such as Python interpreters, or possibly a more profound paradigm shift.

In “Solving Quantitative Reasoning Problems With Language Models” (to be released soon on the arXiv), we present Minerva, a language model capable of solving mathematical and scientific questions using step-by-step reasoning. We show that by focusing on collecting training data that is relevant for quantitative reasoning problems, training models at scale, and employing best-in-class inference techniques, we achieve significant performance gains on a variety of difficult quantitative reasoning tasks. Minerva solves such problems by generating solutions that include numerical calculations and symbolic manipulation without relying on external tools such as a calculator. The model parses and answers mathematical questions using a mix of natural language and mathematical notation. Minerva combines several techniques, including few-shot prompting, chain of thought or scratchpad prompting, and majority voting, to achieve state-of-the-art performance on STEM reasoning tasks. You can explore Minerva’s output with our interactive sample explorer!
And remember my friend, future events such as these will affect you in the future
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Yuli Ban
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And remember my friend, future events such as these will affect you in the future
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Yuli Ban
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Re: AI & Robotics News and Discussions

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And remember my friend, future events such as these will affect you in the future
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Yuli Ban
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Re: AI & Robotics News and Discussions

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And remember my friend, future events such as these will affect you in the future
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Yuli Ban
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Re: AI & Robotics News and Discussions

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And remember my friend, future events such as these will affect you in the future
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Yuli Ban
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And remember my friend, future events such as these will affect you in the future
weatheriscool
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A model that allows robots to follow and guide humans in crowded environments
https://techxplore.com/news/2022-06-rob ... ments.html
by Ingrid Fadelli , Tech Xplore
Assistance robots are typically mobile robots designed to assist humans in malls, airports, health care facilities, home environments and various other settings. Among other things, these robots could help users to find their way around unknown environments, for instance guiding them to a specific location or sharing important information with them.

While the capabilities of assistance robots have improved significantly over the past decade, the systems that have so far been implemented in real-world environments are not yet capable of following or guiding humans efficiently within crowded spaces. In fact, training robots to track a specific user while navigating a dynamic environment characterized by many randomly moving "obstacles" is far from a simple task.

Researchers at the Berlin Institute of Technology have recently introduced a new model based on deep reinforcement learning that could allow mobile robots to guide a specific user to a desired location or follow him/her around while carrying their belongings, all within a crowded environment. This model, introduced in a paper pre-published on arXiv, could help to significantly enhance the capabilities of robots in malls, airports and other public places.

"The task of guiding or following a human in crowded environments, such as airports or train stations, to carry weight or goods is still an open problem," Linh Kästner , Bassel Fatloun , Zhengcheng Shen , Daniel Gawrisch and Jens Lambrecht wrote in their paper. "In these use cases, the robot is not only required to intelligently interact with humans, but also to navigate safely among crowds."
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caltrek
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Carnegie Mellon Engineering Is Reimagining Nanosatellite Capabilities With Orbital Edge Computing
July 6, 2022

Introduction:
(EurekAlert) PITTSBURGH — Researchers at Carnegie Mellon University’s College of Engineering are setting out on a mission to reimagine the capabilities of nanosatellites in low-Earth orbit. Backed by a $7 million grant from the National Science Foundation’s (NSF) Cyber-Physical Systems (CPS) Frontiers Program, the CMU initiative will transform constellations of nanosatellites into sophisticated distributed computing platforms, building the foundation for a wide range of novel applications in public safety, defense and intelligence, carbon mapping, traffic management and precision agriculture, among others.

Today’s nanosatellites collect enormous amounts of raw data, so much that it’s impossible to downlink all of it to earth. The long loop required to beam just a portion of the data to the ground and then make sense of it also creates many latency issues.

With the team’s new approach, called orbital edge computing, researchers at CMU will work to develop computationally capable constellations of nanosatellites, equipped with machine learning techniques that extract valuable insights from data while still in orbit. This will not only reduce the amount of information being sent to earth but will build the foundation for a wide array of possible responsive applications that operate entirely from space.

The new technology will help detect the initial signs of problems before they occur, according to principal investigator Brandon Lucia, associate professor of electrical and computer engineering. For example, it could allow for monitoring suspicious activity at large-scale events like the upcoming 2028 Summer Olympics in Los Angeles or identify early signs of wildfires, enabling response teams to make mitigation efforts before forests are ablaze.

The project comprises world-leading experts in critical areas like federated learning, wireless communications, security and networking, and nanosatellite design, including Carnegie Mellon professors Gauri Joshi, Swarun Kumar, Zac Manchester and Vyas Sekar.
Read more here: https://www.eurekalert.org/news-releases/958067
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caltrek
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So, a Robot Walks Into a Nursing Home...
by Jennifer A. Kingson
July 8, 2022

Introduction:
(Axios) A 4-foot-tall droid named Pepper — preprogrammed with hundreds of jokes — is one of two robots now working at a nursing home in Roseville, Minnesota, entertaining residents and helping monitor their health.

• A sample from its sometimes-salty repertoire: "I went on a date with a Roomba last week — it totally sucked."

The big picture: Household robots are growing in utility and ubiquity, and this latest use seems to blend two trends we've noted: The availability of humanoid robots as party guests and entertainers and their use as companions and health monitors for the elderly.

Driving the news: As part of a study by the University of Minnesota Duluth, two robots (designed by SoftBank Robotics) have been deployed at a nursing home owned by Monarch Healthcare Management.

• When not delivering a standup routine, Pepper will mingle with residents, to "remind them to eat and exercise, and react to their facial expressions or tone of voice," per the Minnesota Star Tribune.
Read more here: https://www.axios.com/2022/07/08/robot ... obotics
Don't mourn, organize.

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caltrek
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Responsive Soft Robots Inspired by Sputtering Ketchup Bottle
July 8, 2022

Introduction:
(EurekAlert) A smartly designed pressure valve allows soft robots to respond to their environment without the need for computer control, reveal AMOLF researchers in their article in the journal Matter. That brings robots with natural movements and tactile responses similar to those of living organisms one step closer to reality. Such developments render soft robots more suitable for exploring rough and unknown terrain or for medical applications.
Read more here: https://www.eurekalert.org/news-releases/957996

Don't mourn, organize.

-Joe Hill
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