NSAi’s Post

Introduction: The entire evolution so far in the filed is a perfect example of how deep we went into it, and GPT-44x from Amazons stands as evidence for this. This advanced language model represents a major improvement in natural language processing (NLP) and the potential for an exciting range of new capabilities that organizations can use across verticals.

I am thrilled to share a groundbreaking concept that has the potential to redefine the landscape of artificial intelligence interaction – Prompt Engineering. In the ever-evolving field of AI, the way we communicate with machines is at the core of innovation, and prompt engineering is the key to unlocking new possibilities. I have a little example of reaching AI boundaries, may help you : - [ you have a strong foundation in natural language processing (NLP) and machine learning, which allows you to understand the underlying mechanisms of AI models and to write prompts that are tailored to their specific capabilities.] * I invite you to join the conversation and contribute your insights to this exciting journey. Let's explore the potential of prompt engineering together and pave the way for a future

The Fundamentals of AI course from IBM has been a really insightful way to learn about AI. Developing a greater comprehension of natural language processing (NLP) and machine learning has changed my perspective on and interactions with artificial intelligence (AI) technology. With IBM Watson Studio, I was able to gain practical skills that would help me create my own AI models.

Natural Language Processing (NLP). The field of computer science known as "natural language processing," or more precisely, "artificial intelligence," is focused on enabling computers to comprehend spoken and written language in a manner that is similar to that of humans.

Natural Language Processing (NLP) has dramatically transformed how we interact with technology, making it possible for machines to understand and respond to human language. This evolution is not just a technological marvel but also a fascinating journey that raises profound questions about communication, intelligence, and the future of human-machine relationships.

Characteristics of AI 1. Machine Learning: AI systems can learn from data and improve over time. 2. Problem-Solving: AI can solve complex problems using algorithms and reasoning. 3. Natural Language Processing (NLP): AI can understand and generate human language. 4. Autonomous Systems: AI can operate independently, making decisions without human intervention.

Could you explain what a pre-trained model is and how it is used in transfer learning? A pre-trained model is a machine learning model that has been trained on a large dataset for a specific task, such as image classification, natural language processing (NLP), or object detection, before being made available for further use. These models are trained on massive amounts of data and computational resources, often using powerful hardware like GPUs or TPUs, to learn meaningful patterns and representations from the data. Here's the link: https://lnkd.in/eWdN7Qc5

Visual Question Answering (VQA) is a research area at the intersection of computer vision and natural language processing (NLP). It involves developing systems that can answer questions about images. The goal is to enable a computer to understand both the visual content of an image and the textual question posed about it, providing a coherent answer that may involve reasoning or inference. AIMER Society - Artificial Intelligence Medical and Engineering Researchers Society #intern #visualquestionanswering #ai

Which AI technology excites you most? Machine Learning: Predictive algorithms learning from data Natural Language Processing (NLP): Computers understanding human language Robotic Process Automation (RPA): Automating routine for creative freedom