This workshop paper presents a critical examination of the integration of Generative AI (Gen AI) into the academic writing process, focusing on the use of AI as a collaborative tool. It contrasts the performance and interaction of two AI models, Gemini and ChatGPT, through a collaborative inquiry approach where researchers engage in facilitated sessions to design prompts that elicit specific AI responses for crafting research outlines. This case study highlights the importance of prompt design, output analysis, and recognizing the AI's limitations to ensure responsible and effective AI integration in scholarly work. Preliminary findings suggest that prompt variation significantly affects output quality and reveals distinct capabilities and constraints of each model. The paper contributes to the field of Human-Computer Interaction by exploring effective prompt strategies and providing a comparative analysis of Gen AI models, ultimately aiming to enhance AI-assisted academic writing and prompt a deeper dialogue within the HCI community.

We implement a Physics-Informed Neural Network (PINN) for solving the two-dimensional Burgers equations. This type of model can be trained with no previous knowledge of the solution; instead, it relies on evaluating the governing equations of the system in points of the physical domain. It is also possible to use points with a known solution during training. In this paper, we compare PINNs trained with different amounts of governing equation evaluation points and known solution points. Comparing models that were trained purely with known solution points to those that have also used the governing equations, we observe an improvement in the overall observance of the underlying physics in the latter. We also investigate how changing the number of each type of point affects the resulting models differently. Finally, we argue that the addition of the governing equations during training may provide a way to improve the overall performance of the model without relying on additional data, which is especially important for situations where the number of known solution points is limited.

Computer vision is the ability to extract meaning and intent out of visual elements, such as faces, objects, scenes and activities. Our company, Deepomatic, a computer vision company founded in Paris, recently launched in the North American market. Our proprietary technologies, Deepomatic Studio and Deepomatic Run, provide companies with the tools – both in the form of software and managed services – to build, operate and deploy their own enterprise-level artificial intelligence applications. In the European market, we work with global organizations, including Airbus, Belron, and the Compass Group, on a number of use cases, from automated checkout to smart CCTV. In the North American market, we are focused on enabling the augmented worker to achieve a more seamless workflow through computer vision technology in industries including insurance, telecommunications and quick serve restaurants (QSR).

Novel and high-performance medical image classification pipelines are heavily utilizing ensemble learning strategies. The idea of ensemble learning is to assemble diverse models or multiple predictions and, thus, boost prediction performance. However, it is still an open question to what extent as well as which ensemble learning strategies are beneficial in deep learning based medical image classification pipelines. In this work, we proposed a reproducible medical image classification pipeline for analyzing the performance impact of the following ensemble learning techniques: Augmenting, Stacking, and Bagging. The pipeline consists of state-of-the-art preprocessing and image augmentation methods as well as 9 deep convolution neural network architectures. It was applied on four popular medical imaging datasets with varying complexity. Furthermore, 12 pooling functions for combining multiple predictions were analyzed, ranging from simple statistical functions like unweighted averaging up to more complex learning-based functions like support vector machines. Our results revealed that Stacking achieved the largest performance gain of up to 13% F1-score increase. Augmenting showed consistent improvement capabilities by up to 4% and is also applicable to single model based pipelines. Cross-validation based Bagging demonstrated to be the most complex ensemble learning method, which resulted in an F1-score decrease in all analyzed datasets (up to -10%). Furthermore, we demonstrated that simple statistical pooling functions are equal or often even better than more complex pooling functions. We concluded that the integration of Stacking and Augmentation ensemble learning techniques is a powerful method for any medical image classification pipeline to improve robustness and boost performance.

Often while developing Natural Language Processing models, we find it difficult to find relevant data. Previously, while developing our Intent Classifier, we used the CLINC150 Dataset that had 100 samples for 150 different classes. But, what if we needed even more samples? One more similar scenario was when I was working on a contextual assistant with Rasa. While creating the training data from scratch, I'd have to imagine different samples for each intent or ask my friends for some help.

AI and its applications in healthcare have matured considerably over the last several years. AI technologies are more widely available to healthcare providers than ever before and they present tremendous opportunities across healthcare, but especially in highly complex robotic-assisted procedures that demand an exceptional amount of insight and foresight from physicians. Unfortunately, there is still a belief that AI, as a result of its maturation, will inevitably replace the role of the physician, though this couldn't be further from the truth. In fact, AI empowers physicians with new knowledge and capabilities that improve clinical decision making and help to achieve more positive patient outcomes. It transfers their attention from thinking technically about maneuvering a wire through the heart to thinking more holistically about case strategy and how to achieve the best possible outcome.

If you've been keeping up with us, you know that our core goal is to deliver extreme agility with no limits, allowing everyone to innovate. And if you're new here, now you know what OutSystems is all about. A future with no limits is one where intelligent tools augment the work of users with the expertise learned from millions of anonymized code patterns. At outsystems.ai, we're building that future with the ultimate goal of making app development 100x faster, while enabling pro developers and business people to deliver robust, high-quality applications with different levels of complexity. That's why we're happy to announce the general availability of our next-generation AI-Assisted Development capability, which is built right into Service Studio -- the OutSystems development environment.

Computer vision is the ability to extract meaning and intent out of visual elements, such as faces, objects, scenes and activities. Our company, Deepomatic, a computer vision company founded in Paris, recently launched in the North American market. Our proprietary technologies, Deepomatic Studio and Deepomatic Run, provide companies with the tools – both in the form of software and managed services – to build, operate and deploy their own enterprise-level artificial intelligence applications. In the European market, we work with global organizations, including Airbus, Belron, and the Compass Group, on a number of use cases, from automated checkout to smart CCTV. In the North American market, we are focused on enabling the augmented worker to achieve a more seamless workflow through computer vision technology in industries including insurance, telecommunications and quick serve restaurants (QSR).

"Augmented workforce" is a term we hear increasingly often these days, but the concept is hardly new. Think, for example, of the cotton mills of the Industrial Revolution. Scores of looms were driven not by the millworkers, who had other tasks, but by a series of steam-powered belts, resulting in a larger production output, higher quality, and new ways of working that combined humans and machines. However, as the cotton mill example shows, it's important not just that technology delivers benefits like these, but that it does so in concert with the human workforce. That's where AI can really take off – when you combine it with people, you create an outcome that is greater than the sum of its parts.

Invariances to translation, rotation and other spatial transformations are a hallmark of the laws of motion, and have widespread use in the natural sciences to reduce the dimensionality of systems of equations. In supervised learning, such as in image classification tasks, rotation, translation and scale invariances are used to augment training datasets. In this work, we use data augmentation in a similar way, exploiting symmetry in the quadruped domain of the DeepMind control suite (Tassa et al. 2018) to add to the trajectories experienced by the actor in the actor-critic algorithm of Abdolmaleki et al. (2018). In a data-limited regime, the agent using a set of experiences augmented through symmetry is able to learn faster. Our approach can be used to inject knowledge of invariances in the domain and task to augment learning in robots, and more generally, to speed up learning in realistic robotics applications.