In 2020, 1.7 MB of data was created every second by people. And in the same year, we produced close to 2.5 quintillion data bytes every day in 2020. Data scientists predict that by 2025, people will generate close to 463 exabytes of data daily. However, not all the data can be used by businesses […]
In-House or Outsourced Data Annotation – Which Gives Better AI Results? Read More »
We are witnessing an era in which AI is also being used by fraudsters. This makes it extremely difficult for users to detect suspicious activity. Frauds are costing the industry billions, with estimates suggesting a staggering $300 billion+ in damages for Americans alone. This is where Natural Language Processing comes in, allowing insurance companies and
The Role of NLP in Insurance Fraud Detection and Prevention Read More »
What is Data Annotation [2025 Updated] – Best Practices, Tools, Benefits, Challenges, Types & more Need to know the Data Annotation basics? Read this complete Data Annotation guide for beginners to get started. Table of Contents Download eBook Get My Copy Curious about how cutting-edge AI systems like self-driving cars or voice assistants achieve their
Precision healthcare stems from precise diagnosis. Since allopathy is evidence-based, this precision boils down to the most accurate and up-to-date recording of symptoms and any minute data that could aid in strengthening the diagnosis. Such data and inferences were earlier recorded and managed in paper-based files stored offline. Digitization paved the way for EHR data
What is Longitudinal Patient Data? Exploring Its Impact and Challenges in Healthcare Read More »
Louisville, Kentucky, and New York, New York, USA, March 4, 2025: Shaip, a global leader in AI-driven data solutions, has announced the availability of its extensive Electronic Health Records (EHR) and Physician Dictation Speech datasets via the Protege Training Data Platform. By making its meticulously curated datasets available on the Protege platform, Shaip enables AI
Facial recognition has become a key pillar of present security systems in smartphone authentication, banking, and surveillance. However, with the increasing application of facial recognition, the likelihood of spoofing attacks rises, whereby imposters use artificial biometric inputs to bypass face recognition systems. Anti-spoofing technologies have emerged as the most effective remedy to this problem by
What is Anti-Spoofing and Its Techniques for Liveness Detection in Face Recognition? Read More »
If you are active in the AI space, then you must be familiar with NLP, which stands for Natural Language Processing. NLP is changing how machines can interact with and understand human language. This is a huge deal, especially in regions like India, where there are 20+ official languages and 19,000+ dialects. By leveraging NLP,
Multimodal AI brings together knowledge from varying resources like text, pictures, audio, and video, thus being able to provide richer and more thorough insights into a given scene. In this sense, the approach is distinct from older models which focus only on one type of data. Mixing different streams of data provides multimodal AI with
What are the Top Multimodal AI Applications and Use Cases? Read More »
In simple terms, retrieval-augmented fine-tuning, or RAFT, is an advanced AI technique in which retrieval-augmented generation is joined with fine-tuning to enhance generative responses from a large language model for specific applications in that particular domain. It allows the large language models to provide more accurate, contextually relevant, and robust results, especially for targeted sectors
Large Multimodal Models (LMMs) are a revolution in artificial intelligence (AI). Unlike traditional AI models that operate within a single data environment such as text, images, or audio, LMMs are capable of creating and processing multiple modalities simultaneously. Hence the generation of outputs with context-aware multimedia information. The purpose of this article is to unravel
