As cities around the world aim to improve walkability and safety, understanding the irregular and unpredictable nature of pedestrian behavior has become increasingly important. This study introduces a data-driven framework for modeling chaotic pedestrian movement using empirically observed trajectory data and supervised learning. Videos were recorded during both daytime and nighttime conditions to capture pedestrian dynamics under varying ambient and traffic contexts. Pedestrian trajectories were extracted through computer vision techniques, and behavioral chaos was quantified using four chaos metrics: Approximate Entropy and Lyapunov Exponent, each computed for both velocity and direction change. A Principal Component Analysis (PCA) was then applied to consolidate these indicators into a unified chaos score. A comprehensive set of individual, group-level, and contextual traffic features was engineered and used to train Random Forest and CatBoost regression models. CatBoost models consistently achieved superior performance. The best daytime PCA-based CatBoost model reached an R^2 of 0.8319, while the nighttime PCA-based CatBoost model attained an R^2 of 0.8574. SHAP analysis highlighted that features such as distance travel, movement duration, and speed variability were robust contributors to chaotic behavior. The proposed framework enables practitioners to quantify and anticipate behavioral instability in real-world settings. Planners and engineers can use chaos scores to identify high-risk pedestrian zones, apprise infrastructure improvements, and calibrate realistic microsimulation models. The approach also supports adaptive risk assessment in automated vehicle systems by capturing short-term motion unpredictability grounded in observable, interpretable features.

With rapid urbanization in recent decades, traffic congestion has intensified due to increased movement of people and goods. As planning shifts from demand-based to supply-oriented strategies, Intelligent Transportation Systems (ITS) have become essential for managing traffic within existing infrastructure. A core ITS function is traffic forecasting, enabling proactive measures like ramp metering, signal control, and dynamic routing through platforms such as Google Maps. This study assesses the performance of statistical, machine learning (ML), and deep learning (DL) models in forecasting traffic speed and flow using real-world data from California's Harbor Freeway, sourced from the Caltrans Performance Measurement System (PeMS). Each model was evaluated over 20 forecasting windows (up to 1 hour 40 minutes) using RMSE, MAE, and R-Square metrics. Results show ANFIS-GP performs best at early windows with RMSE of 0.038, MAE of 0.0276, and R-Square of 0.9983, while Bi-LSTM is more robust for medium-term prediction due to its capacity to model long-range temporal dependencies, achieving RMSE of 0.1863, MAE of 0.0833, and R-Square of 0.987 at a forecasting of 20. The degradation in model performance was quantified using logarithmic transformation, with slope values used to measure robustness. Among DL models, Bi-LSTM had the flattest slope (0.0454 RMSE, 0.0545 MAE for flow), whereas ANFIS-GP had 0.1058 for RMSE and 0.1037 for flow MAE. The study concludes by identifying hybrid models as a promising future direction.

This paper presents DEEGITS (Deep Learning Based Heterogeneous Traffic State Measurement), a comprehensive framework that leverages state-of-the-art convolutional neural network (CNN) techniques to accurately and rapidly detect vehicles and pedestrians, as well as to measure traffic states in challenging scenarios (i.e., congestion, occlusion). In this study, we enhance the training dataset through data fusion, enabling simultaneous detection of vehicles and pedestrians. Image preprocessing and augmentation are subsequently performed to improve the quality and quantity of the dataset. Transfer learning is applied on the YOLOv8 pretrained model to increase the model's capability to identify a diverse array of vehicles. Optimal hyperparameters are obtained using the Grid Search algorithm, with the Stochastic Gradient Descent (SGD) optimizer outperforming other optimizers under these settings. Extensive experimentation and evaluation demonstrate substantial accuracy within the detection framework, with the model achieving 0.794 mAP@0.5 on the validation set and 0.786 mAP@0.5 on the test set, surpassing previous benchmarks on similar datasets. The DeepSORT multi-object tracking algorithm is incorporated to track detected vehicles and pedestrians in this study. Finally, the framework is tested to measure heterogeneous traffic states in mixed traffic conditions. Two locations with differing traffic compositions and congestion levels are selected: one motorized-dominant location with moderate density and one non-motorized-dominant location with higher density. Errors are statistically insignificant for both cases, showing correlations from 0.99 to 0.88 and 0.91 to 0.97 for heterogeneous traffic flow and speed measurements, respectively.

Deep Learning (DL) models have been popular nowadays to execute different speech-related tasks, including automatic speech recognition (ASR). As ASR is being used in different real-time scenarios, it is important that the ASR model remains efficient against minor perturbations to the input. Hence, evaluating efficiency robustness of the ASR model is the need of the hour. We show that popular ASR models like Speech2Text model and Whisper model have dynamic computation based on different inputs, causing dynamic efficiency. In this work, we propose SlothSpeech, a denial-of-service attack against ASR models, which exploits the dynamic behaviour of the model. SlothSpeech uses the probability distribution of the output text tokens to generate perturbations to the audio such that efficiency of the ASR model is decreased. We find that SlothSpeech generated inputs can increase the latency up to 40X times the latency induced by benign input.

Half of college students are likely already using ChatGPT to cheat, experts have estimated. They warn the revolutionary AI has created a cheating epidemic that poses a huge threat to the integrity of academia. 'At present, well over half of students are likely using AI tools to cheat the education system in exams or essays, but it wouldn't surprise me if that number were already higher.' Could educators resort to written tests to deal with AI cheating? He added: 'If educators make the mistake of ignoring the threat of AI-based cheating, I can honestly see more than 90 percent of students cheating in this way [in future].' OpenAI's new GPT-4 update (GPT-3 and GPT-4 are the models which underlie ChatGPT) is able to get 90 percent on a huge number of exams, including the American bar exam.

ChatGPT has become a global obsession in recent weeks, with experts warning its eerily human replies will put white-collar jobs at risk in years to come. But questions are being asked about whether the $10billion artificial intelligence has a woke bias. This week, several observers noted that the chatbot spits out answers which seem to indicate a distinctly liberal viewpoint. Elon Musk described it as'concerning' when the program suggested it would prefer to detonate a nuclear weapon, killing millions, rather than use a racial slur. The chatbot also refused to write a poem praising former President Donald Trump but was happy to do so for Kamala Harris and Joe Biden. And the program also refuses to speak about the benefits of fossil fuels.

"We have the ability to build technologies into the physical spaces where health care is delivered to help cut the rate of fatal errors that occur today due to the sheer volume of patients and the complexity of their care," said Arnold Milstein, a professor of medicine and director of Stanford's Clinical Excellence Research Center (CERC). Milstein, along with computer science professor Fei-Fei Li and graduate student Albert Haque, are co-authors of a Nature paper that reviews the field of "ambient intelligence" in health care -- an interdisciplinary effort to create such smart hospital rooms equipped with AI systems that can do a range of things to improve outcomes. For example, sensors and AI can immediately alert clinicians and patient visitors when they fail to sanitize their hands before entering a hospital room. AI tools can be built into smart homes where technology could unobtrusively monitor the frail elderly for behavioral clues of impending health crises. And they prompt in-home caregivers, remotely located clinicians and patients themselves to make timely, life-saving interventions.

Artificial intelligence (AI) has been tapped to revolutionize operations across industries. Researchers are using algorithms to more aptly predict wildfires across the western US. Earlier this year, an AI system identified an existing rheumatoid arthritis medication that could be repurposed to treat COVID-19 patients. In a recent paper, researchers illustrate various ways these technologies could be used to enhance patient care in the hospitals of tomorrow. "We have the ability to build technologies into the physical spaces where health care is delivered to help cut the rate of fatal errors that occur today due to the sheer volume of patients and the complexity of their care," said Arnold Milstein, a professor of medicine and director of Stanford's Clinical Excellence Research Center (CERC) in a Stanford report.

IBM's journey toward cognitive computing is well documented in the media. Cognitive computing is loosely defined as a simulating of human thought processes in a computerized model. It's a self-learning system that mimics the way the human brain works. Cognitive computing is an evolution of the artificial intelligence work that started in the 1960's. Many people are familiar with IBM's cognitive computing product, called Watson, through the famous Jeopardy episode where Watson won the game in 2011.

As regular readers know, I've been writing quite a bit over the last year about the opportunities and challenges associated with bringing advances in data and digital to bear on the discovery and development of impactful new medicines. I've been struck by the potential of many of these powerful approaches, tools, and techniques, but underwhelmed by the drooling that's often accompanied them. An important theme of this column has been that despite what seems like exceptional potential, the impact of data science and digital on drug discovery and development to date has been conspicuously limited. This may reflect the extravagant expectations around big data, which has become viewed as a self-evident religion (preached by managerialist consultants), rather than as a potentially useful tool that must rigorously prove itself in context, as I recently discussed. I've also examined the impact of cultural factors (and how the culture of data science differs from that of pharma), here; the challenge of AI black boxes, here; and the importance of understanding the difference between invention and implementation (here).