Abstract-- Healthcare workers (HCWs) encounter challenges in hospitals, such as retrieving medical supplies quickly from crash carts, which could potentially result in medical errors and delays in patient care. Robotic crash carts (RCCs) have shown promise in assisting healthcare teams during medical tasks through guided object searches and task reminders. Limited exploration has been done to determine what communication modalities are most effective and least disruptive to patient care in real-world settings. T o address this gap, we conducted a between-subjects experiment comparing the RCC's verbal and non-verbal communication of object search with a standard crash cart in resuscitation scenarios to understand the impact of robot communication on workload and attitudes toward using robots in the workplace. Our findings indicate that verbal communication significantly reduced mental demand and effort compared to visual cues and with a traditional crash cart. Although, frustration levels were slightly higher during collaborations with the robot compared to a traditional cart. These research insights provide valuable implications for human-robot teamwork in high-stakes environments.

The Atlantic CEO's new book,, examines his complicated relationship with the sport. On this week's episode of, he talks about the ways tech is helping him become a better runner. To most of the world, Nicholas Thompson is known as an editor, an AI enthusiast, or something of a LinkedIn influencer. But the former WIRED editor in chief, who is now CEO of The Atlantic, is often better known to colleagues as . On Tuesday, Thompson is releasing . As the title suggests, it's a book about his commitment to running--Thompson runs a ridiculously fast marathon and holds the American 50K record for the 45-49 age group. Ultimately, though, the book examines the complicated relationship between the sport, Thompson, and his father, who first took him on a run when he was just 5 years old. Tech obsessives, of course, will also get their fix: includes plenty of science-backed training guidance and documents Thompson's experience training with elite Nike coaches. On this week's episode of, I talked to Thompson (who was also my first boss; he hired me as an intern at WIRED in 2008) about his book, the interplay between running and addiction, and what he thinks AI can do for runners for writers. It is a joy to be here with you at Condé Nast at WIRED. I loved coming up those elevators. I love seeing you as the editor in chief. I'm thrilled that you're here. We're going to start this conversation the way we start all of them, which is with a little warmup, some rapid-fire questions. In honor of your new book,, I'm gonna make them entirely running themed. I mean, if your listeners don't wanna hear about running Trail run or track run? Worst running injury you've ever had. The one you wish people would stop talking to you about. You only need to run a 20-miler before a marathon. What do you need to run? Why do people die at mile 20? Because they only train for [marathons] with 20-mile-runs. I generally prefer people, but then you have to schedule it. Backup sport of choice if you could never run again.

Real-world time series are influenced by numerous factors and exhibit complex non-stationary characteristics. Non-stationarity can lead to distribution shifts, where the statistical properties of time series change over time, negatively impacting model performance. Several instance normalization techniques have been proposed to address distribution shifts in time series forecasting. However, existing methods fail to account for shifts within individual instances, leading to suboptimal performance. To tackle inner-instance distribution shifts, we propose two novel point-level methods: Learning Distribution (LD) and Learning Conditional Distribution (LCD). LD eliminates internal discrepancies by fitting the internal distribution of input and output with different parameters at different time steps, while LCD utilizes neural networks to predict scaling coefficients of the output. We evaluate the performance of the two methods with various backbone models across public benchmarks and demonstrate the effectiveness of the point-level paradigm through comparative experiments.

AI means too many (different) things to too many people. We need better ways of talking – and thinking – about it. Cue, Drew Breunig, a gifted geek and cultural anthropologist, who has come up with a neat categorisation of the technology into three use cases: gods, interns and cogs. "Gods", in this sense, would be "super-intelligent, artificial entities that do things autonomously". In other words, the AGI (artificial general intelligence) that OpenAI's Sam Altman and his crowd are trying to build (at unconscionable expense), while at the same time warning that it could be an existential threat to humanity. AI gods are, Breunig says, the "human replacement use cases".

The advanced machine is about enhancing the quality of life for those who need assistance the most. Ariana Grande at the Fourth Annual Academy Museum Gala held at Academy Museum of Motion Pictures on Oct. 19, 2024 in Los Angeles, California. SOMETHING'WICKED': "Wicked" star Ariana Grande is uncertain about artificial intelligence after her co-star Cynthia Erivo felt insulted by fan edits of the poster for the upcoming musical. TECH INTERFERENCE: TikTok parent company ByteDance has confirmed it terminated an intern over the summer for allegedly sabotaging the training of an artificial intelligence model. A woman walks to cast her ballot after filling it in a privacy booth while voting in the gubernatorial election in Newark, New Jersey, on Nov. 2, 2021.

The owner of TikTok has sacked an intern for allegedly sabotaging an internal artificial intelligence project. ByteDance said it had dismissed the person in August after they "maliciously interfered" with the training of artificial intelligence (AI) models used in a research project. Thanks to the video-sharing app TikTok and its Chinese counterpart, Douyin, which rank among the world's most popular mobile apps, ByteDance has risen to become one of the world's most important social media companies. Like other big players in the tech sector, ByteDance has raced to embrace generative AI. Its Doubao chatbot earlier this year took over from the competitor Baidu's Ernie in the race to produce a Chinese rival to OpenAI's ChatGPT.

"The individual was an intern with the [advertising] technology team and has no experience with the AI Lab," ByteDance said in a statement. "Their social media profile and some media reports contain inaccuracies." Its commercial online operations, including its large language AI models, were unaffected by the intern's actions, the company added. ByteDance also denied reports that the incident caused more than 10m of damage by disrupting an AI training system made up of thousands of powerful graphics processing units (GPU). Aside from firing the person in August, ByteDance said it had informed the intern's university and industry bodies about the incident.

When Vinod Khosla had a skiing accident in 2011 that led to an ACL injury in his knee, doctors gave conflicting opinions over his treatment. Frustrated with the healthcare system, the leading venture capitalist proffered, in a hotly debated article, that AI algorithms could do the job better than doctors. Since then, Khosla's firm has invested in a number of robotics and medtech companies, including Rad AI, a radiology tech company. The self-professed techno-optimist still stands by his assertions a decade later. "Almost all expertise will be free in an AI model, and we'll have plenty of these for the benefit of humanity," he told TIME in an interview in August.

Sensor setups of robotic platforms commonly include both camera and LiDAR as they provide complementary information. However, fusing these two modalities typically requires a highly accurate calibration between them. In this paper, we propose MDPCalib which is a novel method for camera-LiDAR calibration that requires neither human supervision nor any specific target objects. Instead, we utilize sensor motion estimates from visual and LiDAR odometry as well as deep learning-based 2D-pixel-to-3D-point correspondences that are obtained without in-domain retraining. We represent the camera-LiDAR calibration as a graph optimization problem and minimize the costs induced by constraints from sensor motion and point correspondences. In extensive experiments, we demonstrate that our approach yields highly accurate extrinsic calibration parameters and is robust to random initialization. Additionally, our approach generalizes to a wide range of sensor setups, which we demonstrate by employing it on various robotic platforms including a self-driving perception car, a quadruped robot, and a UAV. To make our calibration method publicly accessible, we release the code on our project website at http://calibration.cs.uni-freiburg.de.

Code-completion systems offering suggestions to a developer in their integrated development environment (IDE) have become the most frequently used kind of programmer assistance.1 When generating whole snippets of code, they typically use a large language model (LLM) to predict what the user might type next (the completion) from the context of what they are working on at the moment (the prompt).2 This system allows for completions at any position in the code, often spanning multiple lines at once. Potential benefits of generating large sections of code automatically are huge, but evaluating these systems is challenging. Offline evaluation, where the system is shown a partial snippet of code and then asked to complete it, is difficult not least because for longer completions there are many acceptable alternatives and no straightforward mechanism for labeling them automatically.5 An additional step taken by some researchers3,21,29 is to use online evaluation and track the frequency of real users accepting suggestions, assuming that the more contributions a system makes to the developer's code, the higher its benefit. The validity of this assumption is not obvious when considering issues such as whether two short completions are more valuable than one long one, or whether reviewing suggestions can be detrimental to programming flow. Code completion in IDEs using language models was first proposed in Hindle et al.,9 and today neural synthesis tools such as GitHub Copilot, CodeWhisperer, and TabNine suggest code snippets within an IDE with the explicitly stated intention to increase a user's productivity.