Existing benchmarks evaluating biases in large language models (LLMs) primarily rely on explicit cues, declaring protected attributes like religion, race, gender by name. However, real-world interactions often contain implicit biases, inferred subtly through names, cultural cues, or traits. This critical oversight creates a significant blind spot in fairness evaluation. We introduce ImplicitBBQ, a benchmark extending the Bias Benchmark for QA (BBQ) with implicitly cued protected attributes across 6 categories. Our evaluation of GPT-4o on ImplicitBBQ illustrates troubling performance disparity from explicit BBQ prompts, with accuracy declining up to 7% in the "sexual orientation" subcategory and consistent decline located across most other categories. This indicates that current LLMs contain implicit biases undetected by explicit benchmarks. ImplicitBBQ offers a crucial tool for nuanced fairness evaluation in NLP.

Food Does the Grocery Cart Actually Need a Makeover? The rolling basket we dump our food in hasn't changed much in almost a century, and for good reason--it works. But meddling tech gurus think they know better. In the past few decades there have been numerous incremental changes to grocery stores, like the crazed proliferation of snacks and frozen food, security cameras tracking anything that moves, and self-checkout robots flashing in panic because they can't detect your Twix bar in the bag. Carts remain the open-ceiling prison cells on wheels they were 50 years ago, and baskets don't look much different either.

Abstract-- High degree-of-freedom dual-arm robots are becoming increasingly common due to their morphology enabling them to operate effectively in human environments. However, motion planning in real-time within unknown, changing environments remains a challenge for such robots due to the high dimensionality of the configuration space and the complex collision-avoidance constraints that must be obeyed. In this work, we propose a novel way to alleviate the curse of dimensionality by leveraging the structure imposed by shared joints (e.g. First, we build two dynamic roadmaps (DRM) for each kinematic chain (i.e. Then, we show that we can leverage this structure to efficiently search through the composition of the two roadmaps and largely sidestep the curse of dimensionality. Finally, we run several experiments in a real-world grocery store with this motion planner on a 19 DoF mobile manipulation robot executing a grocery fulfillment task, achieving 0.4s average planning times with 99.9% success rate across more than 2000 motion plans.

Stunning before-and-after photos show the seven most dramatic changes in Trump's controversial White House makeover She was a respected Teacher of the Year finalist... until she lost everything when Charlie Kirk was killed. Inside Andrew's family summit: How Fergie wailed and'melted down' at title loss, Beatrice and Eugenie were'blindsided' and now daughters' assets face'ethics check' to avoid more scandal: BARBARA DAVIES I have no sympathy for Britney Spears. What if her latest stunt had killed a kid? It's time to admit the truth about this public menace: KENNEDY'Nazi texts' leakers UNMASKED: Alleged White House saboteurs are finally exposed... and so is their twisted motive for destroying political prodigy Extraordinary story behind GM's decision to ax much-loved CarPlay... and sinister reason ALL manufacturers will follow What is Charcot-Marie-Tooth disease... the devastating condition that killed 9-1-1 Nashville actor Isabelle Tate Bijou Phillips files to change daughter's name after ex Danny Masterson's rape conviction Treasure hunters seeking Nazi gold worth £200MILLION believe they have'found the real thing' after'monumental' discovery under remains of SS palace'brothel' Former Gambino mob boss'Sammy the Bull' Gravano reveals the truth behind the NBA betting scandal My wife won't get a job and I feel broken trying to provide for our family. Hold on, says DEAR CAROLINE... that's bad enough but your letter raises a MUCH bigger red flag I got the body of my dreams at 51 by following 9 simple rules, says beauty guru ROSIE GREEN.

Wegmans is testing AI-powered Caper Carts at four New York locations, allowing shoppers to track spending in real time and skip checkout lines with automatic item detection technology.

The Cybertruck answers a question no one in the auto industry even thought to ask: what if there was a truck that a Chechen warlord couldn't possibly pass up – a bulletproof, bioweapons-resistant, road rage-inducing street tank that's illegal to drive in most of the world? Few had seen anything quite like the Cybertruck when it was unveiled in 2019. Wrapped in an "ultra-hard, 30X, cold-rolled stainless steel exoskeleton", the Cybertruck was touted as the ultimate doomsday chariot – a virtually indestructible, obtuse-angled, electrically powered behemoth that can repel handgun fire and outrun a Porsche while towing a Porsche, with enough juice leftover to power your house in the event of a blackout. At the launch, Tesla's CEO, Elon Musk, said the truck could tackle any terrain on Earth and possibly also on Mars – and all for the low, low base price of 40,000. "Sometimes you get these late-civilization vibes [that the] apocalypse could come along at any moment," Musk said.

-- Mobile manipulation robots are continuously advancing, with their grasping capabilities rapidly progressing. However, there are still significant gaps preventing state-of-the-art mobile manipulators from widespread real-world deployments, including their ability to reliably grasp items in unstructured environments. T o help bridge this gap, we developed SHOPPER, a mobile manipulation robot platform designed to push the boundaries of reliable and generalizable grasp strategies. We develop these grasp strategies and deploy them in a real-world grocery store - an exceptionally challenging setting chosen for its vast diversity of manipulable items, fixtures, and layouts. Additionally, we provide an in-depth analysis of our latest real-world field test, discussing key findings related to fundamental failure modes over hundreds of distinct pick attempts. Through our detailed analysis, we aim to offer valuable practical insights and identify key grasping challenges, which can guide the robotics community towards pressing open problems in the field. I. INTRODUCTION Grasping and placing of a large diversity of novel items is a fundamental problem in mobile manipulation, necessary for robots to be useful in real-world settings like the home. Significant progress has been made over the past decade, showing mobile manipulators grasping a diversity of items in lab settings. However, many grasping works abstract away different parts of the robot stack, leading to assumptions that do not hold in the real-world (e.g. Furthermore, few works have (1) been able to make the jump to the real world, or (2) exhibited reliability close to necessary for real-world deployment. This is reflected in the dearth in widespread deployments of commercial mobile manipulators.

A trained Large Language Model (LLM) contains much of human knowledge. Yet, it is difficult to gauge the extent or accuracy of that knowledge, as LLMs do not always ``know what they know'' and may even be actively misleading. In this work, we give a general method for detecting semantic concepts in the internal activations of LLMs. Furthermore, we show that our methodology can be easily adapted to steer LLMs toward desirable outputs. Our innovations are the following: (1) we use a nonlinear feature learning method to identify important linear directions for predicting concepts from each layer; (2) we aggregate features across layers to build powerful concept detectors and steering mechanisms. We showcase the power of our approach by attaining state-of-the-art results for detecting hallucinations, harmfulness, toxicity, and untruthful content on seven benchmarks. We highlight the generality of our approach by steering LLMs towards new concepts that, to the best of our knowledge, have not been previously considered in the literature, including: semantic disambiguation, human languages, programming languages, hallucinated responses, science subjects, poetic/Shakespearean English, and even multiple concepts simultaneously. Moreover, our method can steer concepts with numerical attributes such as product reviews. We provide our code (including a simple API for our methods) at https://github.com/dmbeaglehole/neural_controllers .

Picking diverse objects in the real world is a fundamental robotics skill. However, many objects in such settings are bulky, heavy, or irregularly shaped, making them ungraspable by conventional end effectors like suction grippers and parallel jaw grippers (PJGs). In this paper, we expand the range of pickable items without hardware modifications using bimanual nonprehensile manipulation. We focus on a grocery shopping scenario, where a bimanual mobile manipulator equipped with a suction gripper and a PJG is tasked with retrieving ungraspable items from tightly packed grocery shelves. From visual observations, our method first identifies optimal grasp points based on force closure and friction constraints. If the grasp points are occluded, a series of nonprehensile nudging motions are performed to clear the obstruction. A bimanual grasp utilizing contacts on the side of the end effectors is then executed to grasp the target item. In our replica grocery store, we achieved a 90% success rate over 102 trials in uncluttered scenes, and a 67% success rate over 45 trials in cluttered scenes. We also deployed our system to a real-world grocery store and successfully picked previously unseen items. Our results highlight the potential of bimanual nonprehensile manipulation for in-the-wild robotic picking tasks. A video summarizing this work can be found at youtu.be/g0hOrDuK8jM

Former Obama-Biden fundraiser Allison Huynh joins'Jesse Watters Primetime' to discuss her'divorce' from the Democratic Party and why she is auctioning off her million-dollar Obama'Hope' poster. An ex-Obama fundraiser who helped raise millions in donations for his campaign announced that she is "divorcing" the Democratic Party and plans to vote for Trump in the upcoming election. "Like any divorce, there's not just one thing, there's a series of things that led up to it," Allison Huynh said on "Jesse Watters Primetime" Wednesday. Huynh, who created Willow Garage, a company that created robotics and AI systems which were later sold to Google, along with her then-husband Google programmer Scott Hassan, helped raise millions of dollars for the Obama campaign in 2008 by hosting elaborate " 50,000- and 100,000-per-plate dinners," for Silicon Valley giants, the New York Post reported. Allison Huynh attends FORMS Opening Reception Presented by Gagosian and Jeffrey Deitch at Miami Design District on December 5, 2023, in Miami, Florida.