AI & Robotics News and Discussions

[Yuli Ban]

If you say “jump” I say “how high?” – and a new robot from UC Santa Barbara says “over 100 ft (30 m).” The research team says that’s higher than anything else has ever jumped, be it robot or animal, thanks to a unique design that multiplies its stored energy.

The strange robot looks like a toy rocket sitting atop two intersecting bicycle wheels. The “tires” of those wheels are carbon-fiber compression bows, while the spokes are rubber bands extending from a spindle running up the center.

To make the device jump, a motor drives that spindle, pulling a line that stretches the rubber bands and simultaneously compresses the carbon fiber bows. A latch mechanism releases that energy to catapult the robot into the sky.

The UC Santa Barbara researchers say the device can jump higher than 100 ft, which they estimate to be close to the limit possible with currently available materials and technology. It was clocked accelerating from 0 to 60 mph (96.6km/h) in 9 milliseconds, achieving an acceleration force of 315 G.

[Yuli Ban]

[raklian]

Yeah, I'll have to agree.

By the same vein, androids/gynoids will give us better sex.

In my very special case, I'll get my one and only waifu even she is a language model.

[TrueAnimationFan]

You guys are lame. I just want my life sized, fully in character recreations of Pixar, Disney and anime characters to hang out with.

[Yuli Ban]

[Yuli Ban]

[Yuli Ban]

[weatheriscool]

Researchers now able to predict battery lifetimes with machine learning

by Jared Sagoff, Argonne National Laboratory
https://techxplore.com/news/2022-05-bat ... chine.html

Imagine a psychic telling your parents, on the day you were born, how long you would live. A similar experience is possible for battery chemists who are using new computational models to calculate battery lifetimes based on as little as a single cycle of experimental data.

In a new study, researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory have turned to the power of machine learning to predict the lifetimes of a wide range of different battery chemistries. By using experimental data gathered at Argonne from a set of 300 batteries representing six different battery chemistries, the scientists can accurately determine just how long different batteries will continue to cycle.

In a machine learning algorithm, scientists train a computer program to make inferences on an initial set of data, and then take what it has learned from that training to make decisions on another set of data.

"For every different kind of battery application, from cell phones to electric vehicles to grid storage, battery lifetime is of fundamental importance for every consumer," said Argonne computational scientist Noah Paulson, an author of the study. "Having to cycle a battery thousands of times until it fails can take years; our method creates a kind of computational test kitchen where we can quickly establish how different batteries are going to perform."

"Right now, the only way to evaluate how the capacity in a battery fades is to actually cycle the battery," added Argonne electrochemist Susan "Sue" Babinec, another author of the study. "It's very expensive and it takes a long time."

[Yuli Ban]

[funkervogt]

[wjfox]

The rate of progress in AI is beginning to scare me...

[funkervogt]

IBM's CEO: Narrow AI is quickly improving and near a usage "tipping point," but general AI is still decades away.
https://www.zdnet.com/article/ibm-ceo-a ... cades-out/

[Tadasuke]

IBM's CEO: Narrow AI is quickly improving and near a usage "tipping point," but general AI is still decades away.
https://www.zdnet.com/article/ibm-ceo-a ... cades-out/

AI experts opinions are mutually exclusive.

[funkervogt]

More AI reasoning.

[raklian]

[Yuli Ban]

[agi]

deleted

[Yuli Ban]

Language Models Perform Reasoning via Chain of Thought

In recent years, scaling up the size of language models has been shown to be a reliable way to improve performance on a range of natural language processing (NLP) tasks. Today’s language models at the scale of 100B or more parameters achieve strong performance on tasks like sentiment analysis and machine translation, even with little or no training examples. Even the largest language models, however, can still struggle with certain multi-step reasoning tasks, such as math word problems and commonsense reasoning. How might we enable language models to perform such reasoning tasks?

In “Chain of Thought Prompting Elicits Reasoning in Large Language Models,” we explore a prompting method for improving the reasoning abilities of language models. Called chain of thought prompting, this method enables models to decompose multi-step problems into intermediate steps. With chain of thought prompting, language models of sufficient scale (~100B parameters) can solve complex reasoning problems that are not solvable with standard prompting methods.

[Yuli Ban]

[Yuli Ban]