Introduction: Substance use disorders (SUDs) have emerged as a pressing public health crisis in the United States, with adolescent substance use often leading to SUDs in adulthood. Effective strategies are needed to prevent this progression. To help in filling this need, we develop a novel and the first-ever absolute risk prediction model for cannabis use disorder (CUD) for adolescent or young adult cannabis users. Methods: We train a Bayesian machine learning model that provides a personalized CUD absolute risk for adolescent or young adult cannabis users using data from the National Longitudinal Study of Adolescent to Adult Health. Model performance is assessed using 5-fold cross-validation (CV) with area under the curve (AUC) and ratio of the expected to observed number of cases (E/O). External validation of the final model is conducted using two independent datasets. Results: The proposed model has five risk factors: biological sex, delinquency, and scores on personality traits of conscientiousness, neuroticism, and openness. For predicting CUD risk within five years of first cannabis use, AUC and E/O, computed via 5-fold CV, were 0.68 and 0.95, respectively. For the same type of prediction in external validation, AUC values were 0.64 and 0.75, with E/O values of 0.98 and 1, indicating good discrimination and calibration performances of the model. Discussion and Conclusion: The proposed model is the first absolute risk prediction model for an SUD. It can aid clinicians in identifying adolescent/youth substance users at a high risk of developing CUD in future for clinically appropriate interventions.

Rylan Schaeffer, Brando Miranda, and Sanmi Koyejo won a NeurIPS 2023 outstanding paper award for their work Are Emergent Abilities of Large Language Models a Mirage?. In their paper, they present an alternative explanation for emergent abilities in large language models. We spoke to Brando about this work, their alternative theory, and what inspired it. This is a good and hard question to answer cleanly because the word emergence has been around in science for a while. For example, in physics, when you reach a certain number of uranium atoms you can make a bomb, but with fewer than that you can't.

ChatGPT, Google Gemini, and other tools like them are making artificial intelligence available to the masses. We can now get all sorts of responses back on almost any topic imaginable. These chatbots can compose sonnets, write code, get philosophical, and automate tasks. However, while you can just type anything you like into ChatGPT and get it to understand you. There are ways of getting more interesting and useful results out of the bot.

ChatGPT and tools like it have made AI available to the masses. We can now get all sorts of responses back on almost any topic imaginable. These bots can come up with sonnets, code, philosophy, and more. However, while you can just type anything you like into ChatGPT and get it to understand you, there are ways of getting more interesting and useful results out of the bot. This "prompt engineering" is becoming a specialized skill of its own.

In this article, we will be going through what is Dueling Double Deep Q Learning and how to implement it in Tenroflow. Dueling Double Deep Q learning is the combination of Dueling Deep Q Learning and Double Deep Q Learning. Let's try to understand what is Dueling Deep Q learning and Double Deep Q Learning. One of the drawbacks of the DQN algorithm is that it overestimates the true rewards; the Q-values think the agent is going to obtain a higher return than what it will obtain in reality. This overestimation is due to the presence of Max of Q value for the next state in the Q learning update equation.

Microsoft 365's new Productivity Score feature could produce unwanted effects on its clients's ... [ ] performance. A few days ago, Forbes broke the news that Microsoft unveiled a new feature of its 365 services software that allows employers to secretly monitor and "score" their staff on productivity. On the surface, this is an obvious up-sale tactic by Microsoft to make clients further dependent upon its eco-system. Also obvious are the many privacy concerns that arise from employer surveillance. Yet, what's less obvious, to most, are the negative implications such slick snooping could have on the company's performance: via its corporate and risk cultures.

Artificial Intelligence (AI) is a technology that fuels machines with human intelligence -- machines that have AI capabilities can automate manual tasks and learn on the go just like humans. Such automation gets repetitive and time-consuming tasks under the AI-powered systems that learn with time and can eventually carry out critical tasks and make decisions on their own. Such unique potential drove the transportation businesses to start investing into AI technology to improve revenue and stay ahead of their competitors. Transportation industry has just begun to apply AI in critical tasks however the reliability and safety in transport are still under question. Major challenges in transport like safety, capacity issues, environmental pollution, reliability etc. provide a huge opportunity for AI innovation.

Optimization of hyper-parameters in reinforcement learning (RL) algorithms is a key task, because they determine how the agent will learn its policy by interacting with its environment, and thus what data is gathered. In this work, an approach that uses Bayesian optimization to perform a two-step optimization is proposed: first, categorical RL structure hyper-parameters are taken as binary variables and optimized with an acquisition function tailored for such variables. Then, at a lower level of abstraction, solution-level hyper-parameters are optimized by resorting to the expected improvement acquisition function, while using the best categorical hyper-parameters found in the optimization at the upper-level of abstraction. This two-tier approach is validated in a simulated control task. Results obtained are promising and open the way for more user-independent applications of reinforcement learning.

A well-calibrated estimator for the conditional probabilities should obey this equation. Once we have derived a statistical classifier, we need to validate it on some test data. This data should be different from that used to train the classifier, otherwise skill scores will be unduly optimistic. This is known as cross-validation. The confusion matrix expresses everything about the accuracy of a discrete classifier over a given database and you can use it to compose any possible skill score. Here, we are going to cover two that are rarely seen in the literature, but are nonetheless important for reasons that will become clear.

A well-calibrated estimator for the conditional probabilities should obey this equation. Once we have derived a statistical classifier, we need to validate it on some test data. This data should be different from that used to train the classifier, otherwise skill scores will be unduly optimistic. This is known as cross-validation. The confusion matrix expresses everything about the accuracy of a discrete classifier over a given database and you can use it to compose any possible skill score. Here, we are going to cover two that are rarely seen in the literature, but are nonetheless important for reasons that will become clear.