What is in the model? Abstract For many scientific questions, understanding the underlying mechanism is the goal. To help investigators better understand the underlying mechanism, variable selection is a crucial step that permits the identification of the most associated regression variables of interest. A variable selection method consists of model evaluation using an information criterion and a search of the model space. Here, we provide a comprehensive comparison of variable selection methods using performance measures of correct identification rate (CIR), recall, and false discovery rate (FDR). We consider the BIC and AIC for evaluating models, and exhaustive, greedy, LASSO path, and stochastic search approaches for searching the model space; we also consider LASSO using cross validation. We perform simulation studies for linear and generalized linear models that parametrically explore a wide range of realistic sample sizes, effect sizes, and correlations among regression variables. We consider model spaces with a small and larger number of potential regressors. The results show that the exhaustive search BIC and stochastic search BIC outperform the other methods when considering the performance measures on small and large model spaces, respectively. These approaches result in the highest CIR and lowest FDR, which collectively may support long-term efforts towards increasing replicability in research.

Episodic Future Thinking (EFT) is an intervention that involves vividly imagining personal future events and experiences in detail. It has shown promise as an intervention to reduce delay discounting - the tendency to devalue delayed rewards in favor of immediate gratification - and to promote behavior change in a range of maladaptive health behaviors. We present EFTeacher, an AI chatbot powered by the GPT-4-Turbo large language model, designed to generate EFT cues for users with lifestyle-related conditions. To evaluate the chatbot, we conducted a user study that included usability assessments and user evaluations based on content characteristics questionnaires, followed by semi-structured interviews. The study provides qualitative insights into participants' experiences and interactions with the chatbot and its usability. Our findings highlight the potential application of AI chatbots based on Large Language Models (LLMs) in EFT interventions, and offer design guidelines for future behavior-oriented applications.

Large-scale generative language and vision-language models (LLMs and VLMs) excel in few-shot in-context learning for decision making and instruction following. However, they require high-quality exemplar demonstrations to be included in their context window. In this work, we ask: Can LLMs and VLMs generate their own prompt examples from generic, sub-optimal demonstrations? We propose In-Context Abstraction Learning (ICAL), a method that builds a memory of multimodal experience insights from sub-optimal demonstrations and human feedback. Given a noisy demonstration in a new domain, VLMs abstract the trajectory into a general program by fixing inefficient actions and annotating cognitive abstractions: task relationships, object state changes, temporal subgoals, and task construals. These abstractions are refined and adapted interactively through human feedback while the agent attempts to execute the trajectory in a similar environment. The resulting abstractions, when used as exemplars in the prompt, significantly improve decision-making in retrieval-augmented LLM and VLM agents. Our ICAL agent surpasses the state-of-the-art in dialogue-based instruction following in TEACh, multimodal web agents in VisualWebArena, and action anticipation in Ego4D. In TEACh, we achieve a 12.6% improvement in goal-condition success. In VisualWebArena, our task success rate improves over the SOTA from 14.3% to 22.7%. In Ego4D action forecasting, we improve over few-shot GPT-4V and remain competitive with supervised models. We show finetuning our retrieval-augmented in-context agent yields additional improvements. Our approach significantly reduces reliance on expert-crafted examples and consistently outperforms in-context learning from action plans that lack such insights.

LOS ANGELES – A few days after a gunman killed 19 children and two teachers in Uvalde, Texas, a year ago, taser-maker Axon Enterprise floated the idea of a "nonlethal" drone for schools that could be activated by AI-powered surveillance. It caused a stir -- prompting the company's own AI ethics advisory board to quit in protest and highlighting growing unease about the ethics and effectiveness of security tools being marketed aggressively by technology firms to U.S. schools. "I had to have my secretary screen out the calls from all these companies," said Rita Bishop, former superintendent of the school system in the city of Roanoke, Virginia, recalling sales pitches for everything from drones to AI-powered surveillance cameras and weapons detectors. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

We examined multiple deep neural network (DNN) architectures for suitability in predicting neurotransmitter concentrations from labeled in vitro fast scan cyclic voltammetry (FSCV) data collected on carbon fiber electrodes. Suitability is determined by the predictive performance in the "out-of-probe" case, the response to artificially induced electrical noise, and the ability to predict when the model will be errant for a given probe. This work extends prior comparisons of time series classification models by focusing on this specific task. It extends previous applications of machine learning to FSCV task by using a much larger data set and by incorporating recent advancements in deep neural networks. The InceptionTime architecture, a deep convolutional neural network, has the best absolute predictive performance of the models tested but was more susceptible to noise. A naive multilayer perceptron architecture had the second lowest prediction error and was less affected by the artificial noise, suggesting that convolutions may not be as important for this task as one might suspect.

Once you trust a self-driving car with your life, you pretty much will trust Artificial Intelligence with anything--Dave Waters. Keith Kirkpatrick is the author of an interesting CACM article on self-driving cars. It is titled "Still Waiting For Self-Driving Cars" and appears in the news section of this month's issue. Today we discuss why it has been so difficult to get self-driving cars started. Over the past decade, technology and automotive pundits have predicted the "imminent" arrival of fully autonomous vehicles that can drive on public roads without any active monitoring or input from a human driver.

The 89 people who work at Buffer, a company that makes social-media management tools, are used to having an unconventional employer. Everyone's salary, including the CEO's, is public. All employees work remotely; their only office closed down six years ago. And as a perk, Buffer pays for any books employees want to buy for themselves. So perhaps it is unsurprising that last year, when the pandemic obliterated countless workers' work-life balance and mental health, Buffer responded in a way that few other companies did: It gave employees an extra day off each week, without reducing pay--an experiment that's still running a year later. "It has been such a godsend," Essence Muhammad, a customer-support agent at Buffer, told me. Miraculously--or predictably, if you ask proponents of the four-day workweek--the company seemed to be getting the same amount of work done in less time. It had scaled back on meetings and social events, and employees increased the pace of their day. Nicole Miller, who works in human resources at Buffer, also cited "the principle of work expanding to the time you give it": When we have 40 hours of work a week, we find ways to work for 40 hours.

A few simple tricks can make one's time with computer-based speech-to-text engines much more pleasant -- and productive, says David Weiss, MD. Weiss, a radiologist with the Carilion Clinic in Roanoke, Va., has made an avocation of optimizing his interactions with the speech recognition system that turns his words into reports. He shared his wisdom during his turn in a diverse RSNA 2011 session on practical informatics for radiologists. The goal, he said, is to keep one's eyes on the screen as much as possible while maximizing speed and accuracy. Here are a few of his suggestions.