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AI system learns to keep warehouse robot traffic running smoothly
Inside a giant autonomous warehouse, hundreds of robots dart down aisles as they collect and distribute items to fulfill a steady stream of customer orders. In this busy environment, even small traffic jams or minor collisions can snowball into massive slowdowns. To avoid such an avalanche of inefficiencies, researchers from MIT and the tech firm Symbotic developed a new method that automatically keeps a fleet of robots moving smoothly. Their method learns which robots should go first at each moment, based on how congestion is forming, and adapts to prioritize robots that are about to get stuck. In this way, the system can reroute robots in advance to avoid bottlenecks.
The rarest dog breed in the United States is a puffin hunter
More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Only around 1,500 Norwegian Lundehunds existed in the world in 2022. Breakthroughs, discoveries, and DIY tips sent six days a week. Golden retrievers, poodles, and German shepherds are all instantly recognizable dog breeds . But these are only a fraction of the 202 pooch types officially recognized by the American Kennel Club (AKC).
The most detailed 3D map of the universe EVER: Scientists unveil stunning 'CT scan' capturing 47 MILLION galaxies
Ritzy Bay Area town torn apart after teacher's daughter, 16, was behind wheel when four friends died in high-speed crash... then she posted a TikTok video that poured fuel on the flames Two CIA officers killed in Mexico when their car skidded off ravine and exploded after meeting about bust of'largest ever drug lab' Nancy Guthrie sheriff's appalling past revealed: Beat handcuffed suspect so badly he needed intensive care, used VILE language about woman and lied in sworn statement Trump confronts Xi as US forces seize Chinese ship carrying mysterious'gift' to Iran New'Hollywood dose' pill: A-listers hooked on'youth elixir' that dermatologists say is anti-ageing, shrinks pores, smooths wrinkles... and even banishes rosacea Days after we got engaged, the love of my life told me he'd killed a man and buried him in a bog. I reported him to police... but then I made this irreversible mistake Life-threatening cantaloupe recall in four states upgraded to FDA's highest risk level... 'reasonable probability of death' Fury as murderer marries pen pal behind bars... as teenage victim's mom says: 'I'm serving a life sentence without my son' Insiders claim failed AI rollout could be to blame for Tim Cook's departure from Apple - as one says'the AI era requires a different kind of leadership' Ark of the Covenant's final resting place pinpointed by archaeologists as fresh search begins AMANDA PLATELL: Why Sarah Ferguson - with the ghost of Princess Diana at her side - is ready to sensationally blow up the Royal Family. She knows ALL their secrets... Team USA Olympics star Noah Lyles slammed for'horrible' reaction to his wife's wedding dress reveal In honour of the Queen's (purple!) reign: Kate mirrors late monarch's colourful wardrobe and wears her pearl earrings and necklace US troops board second tanker as Iran is accused of breaking ceasefire'numerous times' How to lose weight when perimenopause sabotages your metabolism: I'm a trainer but when I hit 46, I piled on the pounds overnight. The new'posh' drug that's easier to order than Uber Eats - and why all my middle-class friends have ditched booze and cocaine for it: JANA HOCKING Autistic woman, 24, worked hard to build independent life for herself... now she's PARALYZED thanks to selfishness of stranger Even Cameron Diaz admits she's a dirty mess. I'll get hate for saying it, but we're all thinking the same thing about THAT wrinkled forehead: CAROLINE BULLOCK The most detailed 3D map of the universe EVER: Scientists unveil stunning'CT scan' capturing 47 MILLION galaxies READ MORE: The universe is expanding'too fast' and scientists don't know why The largest and most detailed 3D map of the universe ever created has been unveiled, bringing an end to a five-year-long scientific marathon.
How you like THAT? Blackpink's Jennie collaborates with Beats on Special Edition Onyx Black Headphones - and gives fans a sneak peek at a brand-new song
Ritzy Bay Area town torn apart after teacher's daughter, 16, was behind wheel when four friends died in high-speed crash... then she posted a TikTok video that poured fuel on the flames Two CIA officers killed in Mexico when their car skidded off ravine and exploded after meeting about bust of'largest ever drug lab' Nancy Guthrie sheriff's appalling past revealed: Beat handcuffed suspect so badly he needed intensive care, used VILE language about woman and lied in sworn statement Trump confronts Xi as US forces seize Chinese ship carrying mysterious'gift' to Iran New'Hollywood dose' pill: A-listers hooked on'youth elixir' that dermatologists say is anti-ageing, shrinks pores, smooths wrinkles... and even banishes rosacea Days after we got engaged, the love of my life told me he'd killed a man and buried him in a bog. I reported him to police... but then I made this irreversible mistake Ark of the Covenant's final resting place pinpointed by archaeologists as fresh search begins Fury as murderer marries pen pal behind bars... as teenage victim's mom says: 'I'm serving a life sentence without my son' Insiders claim failed AI rollout could be to blame for Tim Cook's departure from Apple - as one says'the AI era requires a different kind of leadership' Life-threatening cantaloupe recall in four states upgraded to FDA's highest risk level... 'reasonable probability of death' AMANDA PLATELL: Why Sarah Ferguson - with the ghost of Princess Diana at her side - is ready to sensationally blow up the Royal Family. She knows ALL their secrets... Team USA Olympics star Noah Lyles slammed for'horrible' reaction to his wife's wedding dress reveal In honour of the Queen's (purple!) reign: Kate mirrors late monarch's colourful wardrobe and wears her pearl earrings and necklace US troops board second tanker as Iran is accused of breaking ceasefire'numerous times' How to lose weight when perimenopause sabotages your metabolism: I'm a trainer but when I hit 46, I piled on the pounds overnight. The new'posh' drug that's easier to order than Uber Eats - and why all my middle-class friends have ditched booze and cocaine for it: JANA HOCKING Autistic woman, 24, worked hard to build independent life for herself... now she's PARALYZED thanks to selfishness of stranger How you like THAT? Blackpink's Jennie collaborates with Beats on Special Edition Onyx Black Headphones - and gives fans a sneak peek at a brand-new song And now Jennie has joined forces with Beats on a brand new pair of headphones. The singer, who is part of the South Korean girl group, Blackpink, has unveiled an Onyx Black pair of the Beats Solo 4 - JENNIE Special Edition.
Israeli soldiers and settlers kill 11 Palestinians across Gaza, West Bank
'This is an apartheid regime' Israeli soldiers and settlers have killed at least 11 Palestinians across Gaza and the occupied West Bank, according to Palestinian officials and local media, in the latest bloodshed to occur during a "ceasefire" announced in October. In Gaza, at least seven Palestinians were killed in a series of Israeli attacks, including a child who died from injuries sustained days earlier, while 21 were reported on Tuesday to have been injured over a 24-hour period. Another Palestinian man was later killed on Tuesday in an Israeli drone attack near the Sheikh Nasser neighbourhood, east of Khan Younis. In northern Gaza, a Palestinian woman was killed when Israeli naval forces shelled tents sheltering displaced families northwest of Beit Lahiya. Verified video obtained by Al Jazeera showed the body of Abdullah Dawas, a child wrapped in white cloth for burial, after he succumbed to injuries 10 days after being shot in the head near al-Fakhoura clinic in northern Gaza's Jabalia refugee camp.
Interview with Xinwei Song: strategic interactions in networked multi-agent systems
In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. We hear from Xinwei Song about the two main research threads she's worked on so far, plans to expand her investigations, and what inspired her to study AI. Could you start with a quick introduction - where are you studying, and what is the topic of your research? My research primarily focuses on strategic interactions in networked multi-agent systems. Could you give us an overview of the research you've carried out so far during your PhD? My research to date consists of two main threads, which complement each other in exploring strategic interactions from different perspectives.
'Probably' doesn't mean the same thing to your AI as it does to you
'Probably' doesn't mean the same thing to your AI as it does to you When a human says an event is "probable" or "likely," people generally have a shared, if fuzzy, understanding of what that means. But when an AI chatbot like ChatGPT uses the same word, it's not assessing the odds the way we do, my colleagues and I found. We recently published a study in the journal NPJ Complexity that suggests that, while large language model AIs excel at conversation, they often fail to align with humans when communicating uncertainty . The research focused on words of estimative probability, which include terms like "maybe," "probably" and "almost certain." By comparing how AI models and humans map these words to numerical percentages, we uncovered significant gaps between humans and large language models.
A model for defect identification in materials
In biology, defects are generally bad. But in materials science, defects can be intentionally tuned to give materials useful new properties. Today, atomic-scale defects are carefully introduced during the manufacturing process of products like steel, semiconductors, and solar cells to help improve strength, control electrical conductivity, optimize performance, and more. But even as defects have become a powerful tool, accurately measuring different types of defects and their concentrations in finished products has been challenging, especially without cutting open or damaging the final material. Without knowing what defects are in their materials, engineers risk making products that perform poorly or have unintended properties.
Causal models for decision systems: an interview with Matteo Ceriscioli
How do you go about integrating causal knowledge into decision systems or agents? We sat down with Matteo Ceriscioli to find out about his research in this space. This interview is the latest in our series featuring the AAAI/SIGAI Doctoral Consortium participants. Could you start by telling us a bit about your PhD - where are you studying, and what's the broad topic of your research? The idea is to integrate causal knowledge into agents or decision systems to make them more reliable.
What I've learned from 25 years of automated science, and what the future holds: an interview with Ross King
What I've learned from 25 years of automated science, and what the future holds: an interview with Ross King We're excited to launch our new series, where we're speaking with leading researchers to explore the breakthroughs driving AI and the reality of the future promises - to give you an inside perspective on the headlines. Our first interviewee is Ross King, who created the first robot scientist back in 2009. He spoke to us about the nature of scientific discovery, the role AI has to play, and his recent work in DNA computing. Automated science is a really exciting area, and it feels like everyone's talking about it at the moment - e.g. But you've been working in this field for many years now. In 2009 you developed Adam, the first robot scientist to generate novel scientific knowledge. Could you tell me some more about that? So the history goes back to before Adam.