AI & Robotics News and Discussions

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Yuli Ban
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On December 8, Peng Cheng Laboratory (PCL) and Baidu held a press conference to announce the release of PCL-BAIDU Wenxin (version Ernie 3.0 Titan), the world’s first knowledge-enhanced 100-billion-scale pretrained language model and largest Chinese-language monolithic model. PCL-BAIDU Wenxin achieves state-of-the-art results on more than 60 natural language processing (NLP) tasks and significantly advances more than 30 benchmarks in zero-shot and few-shot learning.

PCL-BAIDU Wenxin is based on Peng Cheng’s industry-leading Pengcheng Cloud Brain II computing power system and Baidu’s PaddlePaddle deep learning platform, and its 260 billion parameters exceed GPT-3’s total by 50 percent. PCL-BAIDU Wenxin aims to solve common model bottlenecks such as poor generalization ability, strong reliance on expensive manually labelled data and high application cost; and simplify the development of artificial intelligence (AI) applications.

At the presser, PCL Director and Chinese Academy of Engineering academician Wen Gao said the large model is crucial for continuing technological development and innovation and will enable more industries to benefit from the power of AI.
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Yuli Ban
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Re: AI & Robotics News and Discussions

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Not a day passes without a fascinating snippet on the ethical challenges created by “black box” artificial intelligence systems. These use machine learning to figure out patterns within data and make decisions – often without a human giving them any moral basis for how to do it.

Classics of the genre are the credit cards accused of awarding bigger loans to men than women, based simply on which gender got the best credit terms in the past. Or the recruitment AIs that discovered the most accurate tool for candidate selection was to find CVs containing the phrase “field hockey” or the first name “Jared”.

More seriously, former Google CEO Eric Schmidt recently combined with Henry Kissinger to publish The Age of AI: And Our Human Future, a book warning of the dangers of machine-learning AI systems so fast that they could react to hypersonic missiles by firing nuclear weapons before any human got into the decision-making process. In fact, autonomous AI-powered weapons systems are already on sale and may in fact have been used.

Somewhere in the machine, ethics are clearly a good idea.
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Yuli Ban
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Large language models (e.g., GPT-3) have many significant capabilities, such as performing few-shot learning across a wide array of tasks, including reading comprehension and question answering with very few or no training examples. While these models can perform better by simply using more parameters, training and serving these large models can be very computationally intensive. Is it possible to train and use these models more efficiently?

In pursuit of that question, today we introduce the Generalist Language Model (GLaM), a trillion weight model that can be trained and served efficiently (in terms of computation and energy use) thanks to sparsity, and achieves competitive performance on multiple few-shot learning tasks. GLaM’s performance compares favorably to a dense language model, GPT-3 (175B) with significantly improved learning efficiency across 29 public NLP benchmarks in seven categories, spanning language completion, open-domain question answering, and natural language inference tasks.
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raklian
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Yuli Ban wrote: Sat Dec 11, 2021 6:51 am
A pre-, pre-, pre-taste for the Singularity to come.
To know is essentially the same as not knowing. The only thing that occurs is the rearrangement of atoms in your brain.
weatheriscool
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Machine learning speeds up vehicle routing

by Becky Ham, Massachusetts Institute of Technology
https://techxplore.com/news/2021-12-mac ... uting.html
Waiting for a holiday package to be delivered? There's a tricky math problem that needs to be solved before the delivery truck pulls up to your door, and MIT researchers have a strategy that could speed up the solution.

The approach applies to vehicle routing problems such as last-mile delivery, where the goal is to deliver goods from a central depot to multiple cities while keeping travel costs down. While there are algorithms designed to solve this problem for a few hundred cities, these solutions become too slow when applied to a larger set of cities.

To remedy this, Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in Civil and Environmental Engineering and the Institute for Data, Systems, and Society, and her students have come up with a machine-learning strategy that accelerates some of the strongest algorithmic solvers by 10 to 100 times.

The solver algorithms work by breaking up the problem of delivery into smaller subproblems to solve—say, 200 subproblems for routing vehicles between 2,000 cities. Wu and her colleagues augment this process with a new machine-learning algorithm that identifies the most useful subproblems to solve, instead of solving all the subproblems, to increase the quality of the solution while using orders of magnitude less compute.

Their approach, which they call "learning-to-delegate," can be used across a variety of solvers and a variety of similar problems, including scheduling and pathfinding for warehouse robots, the researchers say.
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Yuli Ban
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Re: AI & Robotics News and Discussions

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Eureka Robotics, a tech spin-off from Nanyang Technological University, Singapore (NTU Singapore), has developed a technology, called Dynamis, that makes industrial robots nimbler and almost as sensitive as human hands, able to manipulate tiny glass lenses, electronics components, or engine gears that are just millimeters in size without damaging them.

This proprietary force feedback technology developed by NTU scientists was previously demonstrated by the Ikea Bot which assembled an Ikea chair in just 20 minutes. The breakthrough was first published in Science in 2018 and went viral on the internet when it could match the dexterity of human hands in assembling furniture.

NTU Associate Professor Pham Quang Cuong, Co-founder of Eureka Robotics, said they have since upgraded the software technology, which will be made available for a large number of industrial robots worldwide by Denso Wave, a market leader in industrial robots, which is part of the Toyota Group.

Clients purchasing the latest robots sold by Denso Wave will have an option to include this new technology as part of the force controller, which reads the force detected by a force sensor on the robot's wrist and applies force accordingly: Apply too little force and the items may not be assembled correctly while applying too much force could damage the items.
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caltrek
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Trial Identifies Five-drug Combo for ‘Ultra high risk’ Bone Marrow Cancer
December 12, 2021

https://www.eurekalert.org/news-releases/browse

Introduction:
(Institute of Cancer Research via EurekAlert!) A combination of five existing drugs keeps cancer at bay for longer in patients with a highly aggressive type of bone marrow cancer, a major new trial reveals.

The five-drug cocktail, along with a stem cell transplant, allowed people with ultra-high risk multiple myeloma to live longer before their disease progressed than those who received the standard of care.

The ‘MUK Nine OPTIMUM’ trial used a highly innovative methodology to open the door to the first tailored approach for people with the highest risk forms of myeloma – whose outcomes from treatment are currently poor.

It is also the latest study to demonstrate the benefit of combining drugs with different mechanisms of action to combat cancer’s ability to evolve and become resistant to treatment.
Further extract:
OPTIMUM is the first ‘digital comparator’ trial for multiple myeloma
caltrek's comment: Correct me if I am wrong, but I presume AI, or at least computers, would be used as part of the "digital"
comparison process.
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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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Bringing a new robot to market is exciting: new capability, new hardware, new services. The problem is when you get to software, where everything feels harder and takes longer than you think it should. Like Tesla’s full self-driving, which has all the hardware and intelligence it needs — with the possible exception of LIDAR — but is perpetually just ... about ... to ... arrive ... and even so, was recently savaged by Consumer Reports as buggy and ineffective.

Hardware is necessary, but software provides the animating intelligence that allows it to do useful, efficient, and safe work.

That’s why Nader Elm, CEO of autonomous drone company Exyn Technologies, compared robots today to the iPhone before the App Store: the hardware’s there, but the software layer is immature.

Amazon’s hard at work on that.

And the early results are impressive, with easily 1000X cheaper training that is helping robot makers bring products to market 300X faster ... and enabling entire new capabilities.
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weatheriscool
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Moving toward the first flying humanoid robot
https://techxplore.com/news/2021-12-humanoid-robot.html
by Ingrid Fadelli , Tech Xplore

Researchers at the Italian Institute of Technology (IIT) have recently been exploring a fascinating idea, that of creating humanoid robots that can fly. To efficiently control the movements of flying robots, objects or vehicles, however, researchers require systems that can reliably estimate the intensity of the thrust produced by propellers, which allow them to move through the air.

As thrust forces are difficult to measure directly, they are usually estimated based on data collected by onboard sensors. The team at IIT recently introduced a new framework that can estimate thrust intensities of flying multibody systems that are not equipped with thrust-measuring sensors. This framework, presented in a paper published in IEEE Robotics and Automation Letters, could ultimately help them to realize their envisioned flying humanoid robot.

"Our early ideas of making a flying humanoid robot came up around 2016," Daniele Pucci, head of the Artificial and Mechanical Intelligence lab that carried out the study, told TechXplore. "The main purpose was to conceive robots that could operate in disaster-like scenarios, where there are survivors to rescue inside partially destroyed buildings, and these buildings are difficult to reach because of potential floods and fire around them."
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