One common application of machine learning is seen in generative AI and Large Language Models (LLMs) that are the driving force behindAI content generatorsand tools likeAI content summarizers. LLMs use a type of
In the realm of machine learning (ML), a knowledge graph is a graphical representation that captures the connections between different entities. It consists of nodes, which represent entities or concepts, and edges, which represent the relationships between those entities. Google coined the term know...
What is LLM Temperature? LLM temperature is a parameter that influences the language model’s output, determining whether the output is more random and creative or more predictable. A higher temperature will result in lower probability, i.e more creative outputs. A lower temperature will result in...
such as ChatGPT and other highly capable LLMs, do not demonstrate cognitive abilities on par with humans and cannot generalize across diverse situations. ChatGPT, for example, is designed for natural language
Get to Know ChatGPT, LLMs What are machine learning and deep learning? Machine learningis a critical technique that enables AI to solve problems. Despite common misperceptions (and misnomers in popular culture), machines do not learn. They store and compute — admittedly in increasingly complex ...
Tokenization: Text data is segmented into pieces and numerically embedded in a way the model can work with. Tokenization is critical for understanding language context. Multimodal: Multimodal LLMs can handle text and images. An example of this is using the LLM to caption an image. Examples...
Large language models (LLMs) LLMsare an application of ML, a type of AI that can learn from and make decisions based on data. These models use deep learning techniques to understand context, nuance, and semantics in human language. LLMs are considered “large” due to their complex archite...
AI transparency and explainable AI:AI transparency refers to the openness and clarity of how AI systems work to ensure that their operations, decision-making processes, and outcomes are understandable and interpretable by humans. This is crucial for building trust in AI applications and addressing con...
As the number of organizations moving their ML projects to production is growing, the need to build reliable, scalable architecture has become a more pressing... AI in 2025: is it an agentic year? 2024 was the GenAI year. With new and more performant LLMs and a higher number of projects...
All of this helps generative AI tools build and use large language models (LLM) that communicate with human beings. Data scientists have used NLP to build virtual assistants like Siri, chatbots, language translation services, and text summarization tools. Types of AI AI systems are categorized ba...