The role of data annotation in healthcare AI is pivotal. High-quality data labeling and annotation directly impact the accuracy of AI training data and the reliability of AI use cases in healthcare. From diagnosing diseases using medical imaging to drug discovery and remote patient monitoring, annotated datasets form the backbone of modern healthcare AI systems. In this article, we will explore the data annotation techniques used for healthcare AI applications, highlight the latest use cases, and address some common questions about medical data annotation. What is Data Annotation in Healthcare AI? Data annotation is the process of labeling or tagging data (text, images, audio, or video) to make it understandable for AI models. In healthcare, it involves annotating complex datasets like medical images, electronic health records (EHRs), and clinical trial data to train AI systems. For instance, AI models for cancer detection require annotated datasets of X-rays or MRIs to identify tumors accurately. Without proper annotation, models fail to deliver precise results. 1. Chatbots for Clinical and Patient Support Use Case AI-powered healthcare chatbots are transforming patient care by: Booking appointments Analyzing symptoms Offering mental health support Answering post-surgery queries Annotation Techniques To train chatbots for healthcare, annotation experts use...

NVIDIA has taken a major step in supporting the open-source community by fully releasing the source code of its PhysX and Flow GPU-accelerated libraries under the permissive BSD-3 license. While the CPU version of PhysX has been open-source since 2018, this latest release includes the long-awaited GPU simulation kernels, enabling developers to access over 500 CUDA-based modules powering advanced real-time physics simulations. PhysX is a well-established physics engine used in hundreds of games and integrated into popular game engines like Unreal Engine, Unity, and REDengine. It handles real-time rigid body dynamics, collisions, and physical interactions in interactive applications. Now, with full GPU acceleration available, developers can implement even more realistic and efficient simulations in their projects. In addition to PhysX, NVIDIA has also open-sourced the GPU shader implementation of Flow, its sparse grid-based fluid simulation system used for visual effects like smoke, fire, and gas. Also included is Blast, a destruction simulation library used for dynamic breakable environments in games and simulations. “We’re excited to share that the latest update to the PhysX SDK now includes all the GPU source code, fully licensed under BSD-3! With over 500 CUDA kernels powering features such as rigid body dynamics, fluid simulation, and...

Hello! I have been enrolled at Stanford and have been taking their courses online. Here are my few cents on the ones I have taken so far. CS224n – Natural Language Processing with Deep Learning (Prof. Manning) Difficulty: 4/5 (Moderate) What to expect:  Get exposed to State-of-the-Art (SoTA) Deep Learning techniques applied to NLP. Key topics:  Question and Answering Text Summarization Parts of Speech tagging Sequence-to-Sequence models Transformers Gives you a very good overview of where NLP is headed, homeworks are challenging but allow you to implement latest neural architectures to solve various language problems. My class project: BertQA (99* stars on github) – Won Best Project Award in the class CS231n – Convolutional Neural Networks for Visual Recognition (Prof. Li and Justin Johnson) Difficulty: 4/5 (Moderate) What to expect:  Extensive overview of latest trends in Computer Vision techniques across different domains and applications –  Discriminative models Unsupervised techniques Neural Architecture layers and intutions  Segmentation Generative Techniques Style Transfer Homeworks are the best part of the class which allow you to implement a variety of Neural Layers and get in-depth intuition of how deep learning actually works. I would suggest some familiarity with matrix calculus and probability for this course. My class...

Like most people, the notion of eating insects grosses me out. Of course we probably eat small bugs or bug parts all the time without knowing it. I read a theory somewhere that vegetarian Indians used to get sufficient protein partially from the bugs they accidentally ate that were in the grains. Before you say don’t knock it before you try it, I have eaten a “properly” cooked bug before. It was a grub made in a supposedly old regional style in Ecuador—it’s a thing they do for tourists. It tasted kind of like bacon but too fatty for me (don’t get me wrong, I like to eat fats, but mixed into foods not in a chunky format). I’ll stick to vertebrates. But more importantly, why are some people out there so dead set to reprogram me to like eating bugs? Well one front seems to be coming from Europe. Recently the EU approved mealworms in food, and they don’t even have to have explicit labeling about the mealworms. (Mealworms are the larval stage of certain beetles.) Rumor is Europe has regressed on their tech and expertise for preventing insect infestation of grain storage. Instead of trying to solve the...

Human reasoning naturally operates through abstract, non-verbal concepts rather than strictly relying on discrete linguistic tokens. However, current LLMs are limited to reasoning within the boundaries of natural language, producing one token at a time through predefined vocabulary. This token-by-token approach not only restricts the expressive capacity of the model but also limits the breadth of reasoning paths it can explore, especially in ambiguous or complex scenarios. Standard Chain-of-Thought (CoT) methods exemplify this limitation, forcing the model to commit to a single path at each step. In contrast, human cognition is more flexible and parallel, allowing for simultaneous consideration of multiple ideas and delaying verbalization until concepts are fully formed. This makes human reasoning more adaptable and robust in dealing with uncertainty. To address these limitations, researchers have proposed transitioning from token-based reasoning to reasoning within a continuous concept space, representing reasoning steps as token embeddings combinations. This approach allows models to explore multiple reasoning trajectories in parallel and integrate richer conceptual representations. Prior studies have demonstrated the potential of manipulating hidden states to influence reasoning outcomes or introduce latent planning. However, applying continuous-space reasoning to larger models presents challenges. In models under 7B parameters, shared weights between input and...