Environment Animals Wildlife Birds A giant-footed bird showed up in a Massachusetts backyard. The purple gallinule found its way north through unusual winds. Breakthroughs, discoveries, and DIY tips sent every weekday. A winter storm blew an unexpected visitor from the south into a backyard in New Bedford, Massachusetts--a purple gallinule (). These gorgeously colored birds with shockingly large feet, live in marshes from the southeastern United States through South America.

Local news stations are often considered to be reliable sources of non-politicized information, particularly local concerns that residents care about. Because these stations are trusted news sources, viewers are particularly susceptible to the information they report. The Sinclair Broadcast group is a broadcasting company that has acquired many local news stations in the last decade. We investigate the effects of local news stations being acquired by Sinclair: how does coverage change? We use computational methods to investigate changes in internet content put out by local news stations before and after being acquired by Sinclair and in comparison to national news outlets. We find that there is clear evidence that local news stations report more frequently on national news at the expense of local topics, and that their coverage of polarizing national topics increases.

Hallucination has emerged as the most vulnerable aspect of contemporary Large Language Models (LLMs). In this paper, we introduce the Sorry, Come Again (SCA) prompting, aimed to avoid LLM hallucinations by enhancing comprehension through: (i) optimal paraphrasing and (ii) injecting [PAUSE] tokens to delay LLM generation. First, we provide an in-depth analysis of linguistic nuances: formality, readability, and concreteness of prompts for 21 LLMs, and elucidate how these nuances contribute to hallucinated generation. Prompts with lower readability, formality, or concreteness pose comprehension challenges for LLMs, similar to those faced by humans. In such scenarios, an LLM tends to speculate and generate content based on its imagination (associative memory) to fill these information gaps. Although these speculations may occasionally align with factual information, their accuracy is not assured, often resulting in hallucination. Recent studies reveal that an LLM often neglects the middle sections of extended prompts, a phenomenon termed as lost in the middle. While a specific paraphrase may suit one LLM, the same paraphrased version may elicit a different response from another LLM. Therefore, we propose an optimal paraphrasing technique to identify the most comprehensible paraphrase of a given prompt, evaluated using Integrated Gradient (and its variations) to guarantee that the LLM accurately processes all words. While reading lengthy sentences, humans often pause at various points to better comprehend the meaning read thus far. We have fine-tuned an LLM with injected [PAUSE] tokens, allowing the LLM to pause while reading lengthier prompts. This has brought several key contributions: (i) determining the optimal position to inject [PAUSE], (ii) determining the number of [PAUSE] tokens to be inserted, and (iii) introducing reverse proxy tuning to fine-tune the LLM for [PAUSE] insertion.

The seafood processing industry provides fertile ground for robotics to impact the future-of-work from multiple perspectives including productivity, worker safety, and quality of work life. The robotics research challenge is the realization of flexible and reliable manipulation of soft, deformable, slippery, spiky and scaly objects. In this paper, we propose a novel robot end effector, called HASHI, that employs chopstick-like appendages for precise and dexterous manipulation. This gripper is capable of in-hand manipulation by rotating its two constituent sticks relative to each other and offers control of objects in all three axes of rotation by imitating human use of chopsticks. HASHI delicately positions and orients food through embedded 6-axis force-torque sensors. We derive and validate the kinematic model for HASHI, as well as demonstrate grip force and torque readings from the sensorization of each chopstick. We also evaluate the versatility of HASHI through grasping trials of a variety of real and simulated food items with varying geometry, weight, and firmness.

In the wake of the May 2018 mass shooting that resulted in 10 deaths at Santa Fe (Texas) High School, the Santa Fe Independent School District looked at all possible options to improve school safety within reasonable financial constraints. The district considered the idea of technology to enhance its approximately 750 cameras with facial recognition but did not immediately see a workable solution -- for reasons of cost, and concerns about shaky accuracy that could lead to false positives, says Kip Robins, director of technology for Santa Fe ISD, which has about 4,500 students. The district ultimately contracted with a company called AnyVision, which demonstrated its Better Tomorrow product, an artificial-intelligence-based application that plugs into an existing camera network and provides the ability to do surveillance based on a certain face, body or object. School districts or other end users can create a watch list to keep an eye out for potential pedophiles, for example, or someone known to be mentally unstable. The Santa Fe ISD's solution is part of a growing cadre of software offerings that use artificial intelligence to power through reams of data and notice certain predetermined visual information – whether it's someone's face, or a certain license plate, or simply human movement in a place and time where there shouldn't be any.

We consider the problem of designing an artificial agent capable of interacting with humans in collaborative dialogue to produce creative, engaging narratives. In this task, the goal is to establish universe details, and to collaborate on an interesting story in that universe, through a series of natural dialogue exchanges. Our model can augment any probabilistic conversational agent by allowing it to reason about universe information established and what potential next utterances might reveal. Ideally, with each utterance, agents would reveal just enough information to add specificity and reduce ambiguity without limiting the conversation. We empirically show that our model allows control over the rate at which the agent reveals information and that doing so significantly improves accuracy in predicting the next line of dialogues from movies. We close with a case-study with four professional theatre performers, who preferred interactions with our model-augmented agent over an unaugmented agent.