Existing benchmarks for evaluating long-context language models (LCLMs) primarily focus on long-context recall, requiring models to produce short responses based on a few critical snippets while processing thousands of irrelevant tokens. We introduce LongProc (Long Procedural Generation), a new benchmark that requires both the integration of highly dispersed information and long-form generation. LongProc consists of six diverse procedural generation tasks, such as extracting structured information from HTML pages into a TSV format and executing complex search procedures to create travel plans. These tasks challenge LCLMs by testing their ability to follow detailed procedural instructions, synthesize and reason over dispersed information, and generate structured, long-form outputs (up to 8K tokens). Furthermore, as these tasks adhere to deterministic procedures and yield structured outputs, they enable reliable rule-based evaluation. We evaluate 17 LCLMs on LongProc across three difficulty levels, with maximum numbers of output tokens set at 500, 2K, and 8K. Notably, while all tested models claim a context window size above 32K tokens, open-weight models typically falter on 2K-token tasks, and closed-source models like GPT-4o show significant degradation on 8K-token tasks. Further analysis reveals that LCLMs struggle to maintain long-range coherence in long-form generations. These findings highlight critical limitations in current LCLMs and suggest substantial room for improvement. Data and code available at: https://princeton-pli.github.io/LongProc

Sensory substitution is an effective approach for displaying stable haptic feedback to a teleoperator under time delay. The finger is highly articulated, and can sense movement and force in many directions, making it a promising location for sensory substitution based on kinesthetic feedback. However, existing finger kinesthetic devices either provide only one-degree-of-freedom feedback, are bulky, or have low force output. Soft pneumatic actuators have high power density, making them suitable for realizing high force kinesthetic feedback in a compact form factor. We present a soft pneumatic handheld kinesthetic feedback device for the index finger that is controlled using a constant curvature kinematic model. \changed{It has respective position and force ranges of +-3.18mm and +-1.00N laterally, and +-4.89mm and +-6.01N vertically, indicating its high power density and compactness. The average open-loop radial position and force accuracy of the kinematic model are 0.72mm and 0.34N.} Its 3Hz bandwidth makes it suitable for moderate speed haptic interactions in soft environments. We demonstrate the three-dimensional kinesthetic force feedback capability of our device for sensory substitution at the index figure in a virtual telemanipulation scenario.

Rapid advancements in artificial intelligence (AI) have sparked growing concerns among experts, policymakers, and world leaders regarding the potential for increasingly advanced AI systems to pose catastrophic risks. Although numerous risks have been detailed separately, there is a pressing need for a systematic discussion and illustration of the potential dangers to better inform efforts to mitigate them. This paper provides an overview of the main sources of catastrophic AI risks, which we organize into four categories: malicious use, in which individuals or groups intentionally use AIs to cause harm; AI race, in which competitive environments compel actors to deploy unsafe AIs or cede control to AIs; organizational risks, highlighting how human factors and complex systems can increase the chances of catastrophic accidents; and rogue AIs, describing the inherent difficulty in controlling agents far more intelligent than humans. For each category of risk, we describe specific hazards, present illustrative stories, envision ideal scenarios, and propose practical suggestions for mitigating these dangers. Our goal is to foster a comprehensive understanding of these risks and inspire collective and proactive efforts to ensure that AIs are developed and deployed in a safe manner. Ultimately, we hope this will allow us to realize the benefits of this powerful technology while minimizing the potential for catastrophic outcomes.

This study investigates the interplay among social demographics, built environment characteristics, and environmental hazard exposure features in determining community level cancer prevalence. Utilizing data from five Metropolitan Statistical Areas in the United States: Chicago, Dallas, Houston, Los Angeles, and New York, the study implemented an XGBoost machine learning model to predict the extent of cancer prevalence and evaluate the importance of different features. Our model demonstrates reliable performance, with results indicating that age, minority status, and population density are among the most influential factors in cancer prevalence. We further explore urban development and design strategies that could mitigate cancer prevalence, focusing on green space, developed areas, and total emissions. Through a series of experimental evaluations based on causal inference, the results show that increasing green space and reducing developed areas and total emissions could alleviate cancer prevalence. The study and findings contribute to a better understanding of the interplay among urban features and community health and also show the value of interpretable machine learning models for integrated urban design to promote public health. The findings also provide actionable insights for urban planning and design, emphasizing the need for a multifaceted approach to addressing urban health disparities through integrated urban design strategies.

The pricing of housing properties is determined by a variety of factors. However, post-pandemic markets have experienced volatility in the Chicago suburb area, which have affected house prices greatly. In this study, analysis was done on the Naperville/Bolingbrook real estate market to predict property prices based on these housing attributes through machine learning models, and to evaluate the effectiveness of such models in a volatile market space. Gathering data from Redfin, a real estate website, sales data from 2018 up until the summer season of 2022 were collected for research. By analyzing these sales in this range of time, we can also look at the state of the housing market and identify trends in price. For modeling the data, the models used were linear regression, support vector regression, decision tree regression, random forest regression, and XGBoost regression. To analyze results, comparison was made on the MAE, RMSE, and R-squared values for each model. It was found that the XGBoost model performs the best in predicting house prices despite the additional volatility sponsored by post-pandemic conditions. After modeling, Shapley Values (SHAP) were used to evaluate the weights of the variables in constructing models.

All qualified applicants will receive due consideration for employment without any discrimination. All applicants will be evaluated solely on the basis of their ability, competence and their proven capability to perform the functions outlined in the corresponding role. We promote and support a diverse workforce across all levels in the company.

This is a rush transcript of "Tucker Carlson Tonight" on February 9, 2022. This copy may not be in its final form and may be updated. It would be pretty fascinating to see the Democratic Party's latest internal polling on COVID restrictions. We haven't seen it, but it must have been pretty awful, apocalyptic, because something spooked them bad. Over the course of less than a week, the same people who have systematically turned America into a quarantine camp suddenly out of nowhere started calling in unison for medical freedom. Suddenly, they sound like Bobby Kennedy, Jr., pretty much all of them, even the whiny hypochondriacs at "The Atlantic" Magazine, those neurotic cat owners who've turned COVID hysteria into a religion are now calling for a total abandonment of all corona restrictions. Open everything, "The time to end pandemic restrictions is now." Believe it or not, that was the headline on "The Atlantic's" website today. So if even "The Atlantic" has given up on corona restrictions, obviously the pandemic is over. You should know this virus was killed not by science, but by the midterm elections. It turns out the only real cure for COVID-19 is the political ambition of the Democratic Party. Yes, every upside has a downside. It means that pasty NPR listeners are going to emerge from their apartment for the first time in two years, they will be loose on the streets. You're going to see them at Whole Foods again, shuffling along with their tote bags, looking bewildered and annoyed. That's bad, but it's still worth it, anything to make the insanity go away, we're celebrating. But we're also looking forward, and the question is, how do we guarantee that nothing like this ever happens again? How do we prevent future mass hysteria events in the United States?

It'd be pretty fascinating to see the Democratic Party's latest internal polling on COVID restrictions. We haven't seen it, but it must have been pretty awful, apocalyptic, because something spooked them bad. Over the course of less than a week, the same people who have systematically turned America into a quarantine camp suddenly, out of nowhere, started calling in unison for medical freedom. Suddenly, they sounded like Bobby Kennedy Jr., pretty much all of them. Even the whiny hypochondriacs at The Atlantic Magazine, those neurotic cat owners who've turned COVID hysteria into a religion are now calling for a total abandonment of all Coronavirus restrictions. Believe it or not, that was the headline on The Atlantic's website today.

My name is Arjun Santhanakrishnan, and I am a junior at Naperville North high school in Illinois. I have a passion for almost anything science-related and fields of study regarding Artificial Intelligence. When I have free time, I love to read and talk with my friends. I enjoy Artificial Intelligence because of all the different facets and possibilities. Attending the Inspirit AI program over the summer was a great experience overall and educationally.

IBM Data and AI general manager Rob Thomas discusses AI being incorporated into the workforce. The labor market may be humming right now, but there may be a dark cloud looming ahead. Over the course of the next decade, up to 800 million jobs globally could disappear due to advances in artificial intelligence and robotics, according to research from the McKinsey Global Institute, a top consulting firm. An estimated one-third of the 2030 workforce in the U.S. may need to learn new skills and find work in new occupations. The changes won't hit the country equally.