INTRODUCTION: Interpersonal problems are a transdiagnostic risk factor for the development and maintenance of various psychiatric disorders, including anxiety and depression. Interventions that address these interpersonal challenges could therefore play a crucial role in enhancing mental health. This study aims to evaluate the efficacy and feasibility of a transdiagnostic group intervention, the Kiesler Circle Training… →
CONCLUSIONS: The effectiveness of the Embedding packages was hampered by the COVID-19 pandemic. However, our base case analysis shows that Embedding could be cost-effective for this patient population, but this was subject to significant structural uncertainty. This suggests that while implementation initiatives can be highly cost-effective in this population, more robust evidence or further incentivisation… →
Competitive programming has long served as a benchmark for assessing problem-solving and coding skills. These challenges require advanced computational thinking, efficient algorithms, and precise implementations, making them an excellent testbed for evaluating AI systems. While early AI models like Codex demonstrated strong capabilities in program synthesis, they often relied on extensive sampling and heuristic-based selection,… →
In many modern Python applications, especially those that handle incoming data (e.g., JSON payloads from an API), ensuring that the data is valid, complete, and properly typed is crucial. Pydantic is a powerful library that allows you to define models for your data using standard Python-type hints and then automatically validate any incoming data against… →
The study examines the concept of agency, defined as a system’s ability to direct outcomes toward a goal, and argues that determining whether a system exhibits agency is inherently dependent on the reference frame used for assessment. By analyzing essential properties of agency, the study contends that any evaluation of agency must consider the perspective… →
CONCLUSION: The findings of this study show high acceptability of the e-learning training by participants, indicating a potential for implementation, if kept concise, self-paced, asynchronous, and with a stronger focus on HL. The training helped GPs reflect on their practices, enhance HL knowledge, and improve patient communication strategies, leading some to adopt new techniques in… →
Yann LeCun, Chief AI Scientist at Meta and one of the pioneers of modern AI, recently argued that autoregressive Large Language Models (LLMs) are fundamentally flawed. According to him, the probability of generating a correct response decreases exponentially with each token, making them impractical for long-form, reliable AI interactions. While I deeply respect LeCun’s work… →
In this tutorial, we will build an advanced AI-powered research agent that can write essays on given topics. This agent follows a structured workflow: Planning: Generates an outline for the essay. Research: Retrieves relevant documents using Tavily. Writing: Uses the research to generate the first draft. Reflection: Critiques the draft for improvements. Iterative Refinement: Conducts… →
Large language models (LLMs) struggle with precise computations, symbolic manipulations, and algorithmic tasks, often requiring structured problem-solving approaches. While language models demonstrate strengths in semantic understanding and common sense reasoning, they are not inherently equipped to handle operations that demand high levels of precision, such as mathematical problem-solving or logic-based decision-making. Traditional approaches attempt to… →
Mathematical reasoning remains one of the most complex challenges in AI. While AI has advanced in NLP and pattern recognition, its ability to solve complex mathematical problems with human-like logic and reasoning still lags. Many AI models struggle with structured problem-solving, symbolic reasoning, and understanding the deep relationships between mathematical concepts. Addressing this gap requires… →