Although large language models (LLMs) have demonstrated impressive reasoning capabilities across general domains, their effectiveness in real-world clinical practice remains limited. This is likely due to their insufficient exposure to real-world clinical data during training, as such data is typically not included due to privacy concerns. To address this, we propose enhancing the clinical reasoning capabilities of LLMs by leveraging real-world clinical data. We constructed reasoning-intensive questions from a nationwide sepsis registry and fine-tuned Phi-4 on these questions using reinforcement learning, resulting in C-Reason. C-Reason exhibited strong clinical reasoning capabilities on the in-domain test set, as evidenced by both quantitative metrics and expert evaluations. Furthermore, its enhanced reasoning capabilities generalized to a sepsis dataset involving different tasks and patient cohorts, an open-ended consultations on antibiotics use task, and other diseases. Future research should focus on training LLMs with large-scale, multi-disease clinical datasets to develop more powerful, general-purpose clinical reasoning models.

We introduce a novel distributed source seeking framework, DIAS, designed for multi-robot systems in scenarios where the number of sources is unknown and potentially exceeds the number of robots. Traditional robotic source seeking methods typically focused on directing each robot to a specific strong source and may fall short in comprehensively identifying all potential sources. DIAS addresses this gap by introducing a hybrid controller that identifies the presence of sources and then alternates between exploration for data gathering and exploitation for guiding robots to identified sources. It further enhances search efficiency by dividing the environment into Voronoi cells and approximating source density functions based on Gaussian process regression. Additionally, DIAS can be integrated with existing source seeking algorithms. We compare DIAS with existing algorithms, including DoSS and GMES in simulated gas leakage scenarios where the number of sources outnumbers or is equal to the number of robots. The numerical results show that DIAS outperforms the baseline methods in both the efficiency of source identification by the robots and the accuracy of the estimated environmental density function.

An increasing number of reports raise concerns about the risk that machine learning algorithms could amplify health disparities due to biases …

Back in 2018, the American Civil Liberties Union found out that Amazon's Rekognition, face surveillance technology used by police and courting departments across the US, shows AI bias. During the test, the software incorrectly matched 28 members of Congress with the mugshots of people who have been arrested for committing a crime, and 40% of the false matches were people of color. Following mass protests wherein Amazon's employees refused to contribute to AI tools that reproduce facial recognition bias, the tech giant has announced a one-year moratorium on law enforcement agencies using the platform. The incident has stirred new debate about bias in artificial intelligence algorithms and made companies search for new solutions to the AI bias paradox. In this article, we'll dot the i's, zooming in on the concept, root causes, types, and ethical implications of AI bias, as well as list practical debiasing techniques shared by our AI consultants that worth including in your AI strategy.

As teachers and administrators strive to improve student performance and graduation rates, they're increasingly leveraging new Educational Technology (EdTech) to deliver a higher quality learning experience. To gain a competitive advantage, EdTech market players are integrating advanced technologies such as augmented reality (AR), virtual reality (VR), artificial intelligence (AI), robotics, and Blockchain that are set to be the largest revenue contributors to the education sector in the coming years. In the UAE, 1.2 million school and university students started their e-learning journey a year ago with the onset of the pandemic, which has fueled the surge of EdTech startups. The EdTech sector has been gaining significant momentum, leading to an acceleration of investments in 2020. For instance, the regional EdTech companies raised almost $4m in March last year.

Shouldn't young people know about the most important change force that will influence their lives and livelihoods? That's why every high school should offer a course on artificial intelligence (AI). Or, better yet, incorporate a set of competencies into graduation requirements that ensure that every young person understands the technology drivers and the implications for the economy and society. The prevalence of AI has increased dramatically in the last few years. Most people are unaware that AI is a key technology behind personal assistants (Alexa, Siri, Google), autonomous vehicles, predictive analytics (Amazon and Netflix recommendations) and medical diagnostics just to name a few.

There's no doubt that customer experience is absolutely essential for brand survival. AI and analytics will increasingly be deployed to support the customer experience, as well as being the principal means to deliver it. That makes trust and transparency every bit as important as technology in achieving success. So what are the components of customer experience? Personalisation is one key element.

There's no doubt that customer experience is absolutely essential for brand survival. AI and analytics will increasingly be deployed to support the customer experience, as well as being the principal means to deliver it. That makes trust and transparency every bit as important as technology in achieving success. So what are the components of customer experience? Personalisation is one key element.