LiDAR (Light Detection & Ranging) data is essential for applications that need precise 3D spatial information, such as autonomous driving, robotics, geospatial analysis, and infrastructure mapping. It enables machines to perceive objects in three-dimensional space, capturing their shapes, sizes, and distances with high precision for safe and reliable operation. This article covers introduction to LiDAR… The post LiDAR Data: A Comprehensive Guide to Annotation and AI Integration appeared first on Cogitotech ..read more

Artificial intelligence has emerged as a transformative tool in healthcare, with the potential to transform medical diagnostics, research, treatment planning, and patient care. Generative AI, a promising subset of AI, holds immense potential to support clinical practices in healthcare, from automating administrative tasks to generating synthetic patient data. It has the capacity to detect signs,… The post Generative AI in Healthcare: Innovations, Challenges, and the Role of High-Quality Data appeared first on Cogitotech ..read more

Large language models (LLMs) are rapidly growing in number, capability, and popularity—from ChatGPT bringing them into the mainstream to DeepSeek now making waves in the tech world with its low-cost, high-performance features—alongside many other influential models. Trained on a massive amount of data, LLMs can generate text, write computer code, interpret images and audio, and… The post Data Labeling for LLMs: The Key to Safer and More Effective AI Models appeared first on Cogitotech ..read more

In recent years, artificial intelligence has significantly impacted various medical fields, including ophthalmology. AI enhances capabilities in screening, diagnosis, treatment planning, and patient management for ophthalmic diseases like glaucoma, age-related macular degeneration (AMD), and diabetic retinopathy. A key factor driving these advancements is the meticulous process of data annotation—labeling of ophthalmic data to train models.… The post The Role of Data Annotation in AI-Driven Ophthalmology appeared first on Cogitotech ..read more

The complexity and increasing volume of healthcare data drive the adoption of artificial intelligence (AI) in the field. AI is already being applied in several areas, such as diagnosis, treatment recommendations, research, patient engagement, and administrative tasks in medical institutions and life sciences companies. While advanced AI tools have the potential to perform healthcare tasks… The post Inside the Medical AI Innovation Hub: How Cogito Tech Produces FDA- and HIPAA-Compliant Data appeared first on Cogitotech ..read more

PropTech, short of property technology, leverages digital technology and platforms to simplify real estate research, transaction, management, etc. Through online booking portals and payments, it also integrates with fintech. PropTech saves considerable time and effort for buyers and sellers by optimizing different processes. As shared by Fortune, the PropTech market was at $36.55 billion in… The post How Smart Data Solutions are Powering the PropTech Industry? appeared first on Cogitotech ..read more

Facial recognition technology (FRT) relies on image annotation to accurately detect human facial features in images or videos. This involves using landmark detection or keypoint annotations to label facial traits, such as the eyes, nose, ear, and mouth. Without accuracy in training data, computer vision models cannot process and detect facial features. This is similar… The post Image Annotation: A Critical Component of Model Training appeared first on Cogitotech ..read more

The advancement of AI in recent years is transformative in many respects, including one which holds much promise for the delivery of important health benefits: the healthcare sector. In such a scenario early disease detection is potentially an area where AI is likely to make a difference. With the application of AI, diagnosis and predictions… The post The Role of Data Annotation in Training AI for Early Disease Detection appeared first on Cogitotech ..read more

Machine learning (ML) models require enormous amounts of high-quality annotated data for training. Getting the data labeled quickly and accurately is not easy. And if you are thinking of doing it yourself (in-house), well, manually labeling is time-consuming and labor-intensive. Since the data labeling is considered a foundation step for a successful AI model. Businesses… The post Mastering Data Labeling: A Practical Guide appeared first on Cogitotech ..read more

Machine learning (ML) models require enormous amounts of high-quality annotated data for training. Getting the data labeled quickly and accurately is not easy. And if you are thinking of doing it yourself (in-house), well, manually labeling is time-consuming and labor-intensive. Since the data labeling is considered a foundation step for a successful AI model. Businesses… The post Data Labeling for AI Model Success: Why Accuracy and Efficiency Matter? appeared first on Cogitotech ..read more