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 ..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 ..read more

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 ..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 ..read more

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 ..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 ..read more

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 ..read more

RAG (Retrieval-Augmented Generation) is a recent way to enhance LLMs in a highly effective way, combining generative power and real-time data retrieval. RAG allows a given AI-driven system to produce contextual outputs that are accurate, relevant, and enriched by data, thereby giving them an edge over pure LLMs. RAG optimization is a holistic approach that ..read more

Large Language Models (LLMs) such as GPT-4 and Llama 3 have affected the AI landscape and performed wonders ranging from customer service to content generation. However, adapting these models for specific needs usually means choosing between two powerful techniques: Retrieval-Augmented Generation (RAG) and fine-tuning. While both these approaches enhance LLMs, they are articulate towards different ..read more

Imagine you have an x-ray report and you need to understand what injuries you have. One option is you can visit a doctor which ideally you should but for some reason, if you can’t, you can use Multimodal Large Language Models (MLLMs) which will process your x-ray scan and tell you precisely what injuries you ..read more