AI & Robotics News and Discussions

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Yuli Ban
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Azure AI milestone: Microsoft KEAR surpasses human performance on CommonsenseQA benchmark
Last month, our Azure Cognitive Services team, comprising researchers and engineers with expertise in AI, achieved a groundbreaking milestone by advancing commonsense language understanding. When given a question that requires drawing on prior knowledge and five answer choices, our latest model— KEAR, Knowledgeable External Attention for commonsense Reasoning—performs better than people answering the same question, calculated as the majority vote among five individuals. KEAR reaches an accuracy of 89.4 percent on the CommonsenseQA leaderboard compared with 88.9 percent human accuracy. While the CommonsenseQA benchmark is in English, we follow a similar technique for multilingual commonsense reasoning and topped the X-CSR leaderboard.

Although recent large deep learning models trained with big data have made significant breakthroughs in natural language understanding, they still struggle with commonsense knowledge about the world, information that we, as people, have gathered in our day-to-day lives over time. Commonsense knowledge is often absent from task input but is crucial for language understanding. For example, take the question “What is a treat that your dog will enjoy?” To select an answer from the choices salad, petted, affection, bone, and lots of attention, we need to know that dogs generally enjoy food such as bones for a treat. Thus, the best answer would be “bone.” Without this external knowledge, even large-scale models may generate incorrect answers. For example, the DeBERTa language model selects “lots of attention,” which is not as good an answer as “bone.”

On the other hand, expert systems with lots of rules and domain knowledge and little data have failed to deliver their promise of AI that understands and reasons more like people do. We revisit the rules and knowledge approach and find that deep learning models and knowledge can be organically combined via an external attention mechanism to achieve breakthroughs in AI. With KEAR, we specifically equip language models with commonsense knowledge from a knowledge graph, dictionary, and publicly available machine learning data.
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weatheriscool
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Machine learning used to predict synthesis of complex novel materials
https://phys.org/news/2021-12-machine-s ... rials.html
by Northwestern University
Machine learning enables materials discovery. Credit: Northwestern University

Scientists and institutions dedicate more resources each year to the discovery of novel materials to fuel the world. As natural resources diminish and the demand for higher value and advanced performance products grows, researchers have increasingly looked to nanomaterials.

Nanoparticles have already found their way into applications ranging from energy storage and conversion to quantum computing and therapeutics. But given the vast compositional and structural tunability nanochemistry enables, serial experimental approaches to identify new materials impose insurmountable limits on discovery.

Now, researchers at Northwestern University and the Toyota Research Institute (TRI) have successfully applied machine learning to guide the synthesis of new nanomaterials, eliminating barriers associated with materials discovery. The highly trained algorithm combed through a defined dataset to accurately predict new structures that could fuel processes in clean energy, chemical and automotive industries.

"We asked the model to tell us what mixtures of up to seven elements would make something that hasn't been made before," said Chad Mirkin, a Northwestern nanotechnology expert and the paper's corresponding author. "The machine predicted 19 possibilities, and, after testing each experimentally, we found 18 of the predictions were correct."

The study, "Machine learning-accelerated design and synthesis of polyelemental heterostructures," will be published December 22 in the journal Science Advances.
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Yuli Ban
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CEO of Serve Robotics, Ali Kashani, joins Yahoo Finance to talk about the goal of replacing 5% of food delivery with robots.

- So one thing we've learned during the pandemic is that not only have traditional businesses had to adapt, but the whole concept of delivery, whether it be food, retail delivery, it is adapting, and technology is leading the way. We want to bring back into the stream, Ali Kashani, Serve Robotics CEO and former head of Postmates X at Uber, it's good to have you back. In fact, the last time we talked about this issue, the goal was to replace I think it was 5% of food delivery with the robots. And let's just face the facts, your robots are so darn cute, those little bots that you're testing. When is this really going to take place? Because many of us in New York City are tired of dodging the bicyclists who are going to run us over when they run the red lights.
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Yuli Ban
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Robot 509 from Starship Technologies is both patient and skittish. The autonomous machine, which resembles a Yeti cooler crossed with a Waymo minivan, moves around like a mammal near the bottom of the food chain. It freezes up in crowds and, even when utterly alone, scoots forward in halting spurts, seemingly suspicious of fallen leaves.

A few days after Thanksgiving, Robot 509 ferries some cargo across the campus of James Madison University in Harrisonburg, Va. It stops on a deserted road, waiting for about a minute before finally scooting across, then trundles over a stretch of pavement and some railroad track before coming to a full stop.

Rachael Haberstroh, a James Madison administrator whose office is a long walk from most of the campus’s cafes, comes over and taps on her phone. “It’s supposed to play me a song,” she says, watching skeptically as the entire top of the robot swings open. As 509 serenades her with Adele’s Easy on Me, she reaches in and grabs a large, iced Starbucks drink.
Image
James Madison University has about 50 Starship Technologies autonomous robots rolling across campus. PHOTOGRAPHER: GREG KAHN FOR BLOOMBERG BUSINESSWEEK
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weatheriscool
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Using game theory to thwart multistage privacy intrusions when sharing data
https://phys.org/news/2021-12-game-theo ... ivacy.html
by Thamarasee Jeewandara , Phys.org
Biomedical data is widely collected in the field of medicine, although sharing such data can raise privacy concerns about the re-identification of seemingly anonymous records. Risk assessment frameworks for formal re-identification can inform decisions on the process of sharing data, and current methods focus on scenarios where data recipients use only one resource to identify purposes. However, this can affect privacy where adversaries can access multiple resources to enhance the chance of their success. In a new report now in Science Advances, Zhiyu Wan and a team of scientists in electrical engineering and computer engineering and biomedical informatics in the U.S. represented a re-identification game using a two-player Stackelberg game of perfect information to assess risk. They suggest an optimal data-sharing strategy based on a privacy-utility trade-off. The team used experiments with large-scale genomic datasets and game theoretic models to induce adversarial capabilities to effectively share data with low re-identification risk.
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Yuli Ban
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New footage of a lifelike android fitted with cutting-edge artificial intelligence reacting to engineers invading its “personal space” has spooked the internet.

Britain-based tech company Engineered Arts this month revealed a scarily realistic robot with lifelike facial features and expressions that can “develop interaction” with people. The android, nicknamed Ameca, uses high resolution cameras for eyes to scan its surroundings and react accordingly.

Now available for purchase or rent for an undisclosed sum, the relatable android has a grey face and movable arms and is billed as “the perfect humanoid robot platform for human-robot interaction.”

It can smile, routinely blink its eyes, gasp in shock, scratch its nose — or even have a staring contest with an owner, just for the heck of it — along with plenty more hi-tech features.

According to the company, the can “strike an instant rapport with anybody” due to its personlike nature.

“Human-like artificial intelligence needs a human-like artificial body,” Engineered Arts wrote of Ameca.
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Yuli Ban
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Yuli Ban
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Researchers in China say they have achieved a world first by developing a machine that can charge people with crimes using artificial intelligence.
The AI “prosecutor” can file a charge with more than 97 per cent accuracy based on a verbal description of the case, according to the researchers.
The machine was built and tested by the Shanghai Pudong People’s Procuratorate, the country’s largest and busiest district prosecution office.
The technology could reduce prosecutors’ daily workload, allowing them to focus on more difficult tasks, according to Professor Shi Yong, director of the Chinese Academy of Sciences’ big data and knowledge management laboratory, who is the project’s lead scientist.
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Nanotechandmorefuture
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Re: AI & Robotics News and Discussions

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Yuli Ban wrote: Mon Dec 27, 2021 2:47 am Image
It would be nice to see that in action. I will say that would probably be freaky in a way.
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Lurking
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Nanotechandmorefuture wrote: Wed Dec 29, 2021 1:51 am
Yuli Ban wrote: Mon Dec 27, 2021 2:47 am Image
It would be nice to see that in action. I will say that would probably be freaky in a way.
It seems to be a joke
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