Executive’s Guide to Leveraging Generative AI for Competitive Advantage

Generative AI for Competitive Advantage

Generative AI Wiki

This Generative AI Wiki is your go-to resource for concise information on the cutting-edge concepts of generative AI and its related fields. Explore bite-sized explanations of key terms in this rapidly evolving landscape, gain insights into their significance, and discover how they are implemented in Foundation Models and LLM operations.

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Attention Mechanism

Attention Mechanism

The attention mechanism, drawing inspiration from human cognition, has changed AI's data processing, enhancing language translation models by focusing on key data segments for improved understanding and text generation.

Autonomous AI Agents

Autonomous AI Agents

Autonomous AI agents are programs that sense their environment and make decisions independently to achieve specific goals without human intervention.

Chain-of-Thought Prompting

Chain-of-Thought Prompting

The chain-of-thought(CoT) prompting method enables LLMs to explain their reasoning while enhancing their computational capabilities and understanding of complex problems.

Deepfake AI

Deepfake AI

Deepfakes are synthetic media created using advanced AI, particularly deep learning techniques like GANs, to replace a person's likeness in an image or video, offering revolutionary content creation possibilities but also posing challenges to media integrity, privacy, and security.

Diffusion Models

Diffusion Models

Diffusion models are probabilistic generative models designed to convert random noise into meaningful data samples, resembling the distribution of the training data.

Foundation Models

Foundation Models

Foundation Models

Foundation models are large AI models trained using vast quantities of unstructured data to handle various downstream tasks.

Generative AI

Generative AI

Gen AI is an advanced AI technology that creates new content like text, audio, video, synthetic data, and images.

Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs)

GANs are a framework for training two neural networks, a generator, and a discriminator, to generate realistic and diverse data.

Generative Agents

Generative Agents

Generative agents are computational software agents capable of simulating believable human behavior to respond to environmental changes.

Goal-based AI Agents

Goal-based AI Agents

AI entities that strategize actions based on future outcomes to meet defined objectives, pivotal in Generative AI tasks.

Grounding in Large Language Models (LLMs) and AI

Grounding in Large Language Models (LLMs) and AI

Explore the vital role of grounding in AI and Large Language Models (LLMs), a key process for ensuring accurate, relevant, and context-sensitive AI outputs. Dive into techniques, importance, and applications for grounding AI models, making them more effective in real-world scenarios.

Hallucination Phenomena

Hallucination Phenomena

Sometimes, a machine learning model makes predictions or generates data that isn't grounded in its training data. Imagine teaching a computer to recognize cats by showing it pictures. If it starts to say that it sees cats in images without cats, that's a hallucination. The computer is seeing things that aren't there. This can happen when the model is too complex or hasn't been appropriately trained, leading to incorrect or nonsensical outputs.

LLM Evaluation Parameters

LLM Evaluation Parameters

Large Language Model (LLM) evaluation refers to the process of assessing the performance of LLMs specialized in understanding, generating, and interacting with human language on the basis of specific parameters

LLM Optimization Parameters

LLM Optimization Parameters

Gain insights into the essential parameters for optimizing Large Language Models (LLMs). Explore how LLM optimization parameters such as temperature, top-p,top-k and stop sequences help.

LLMOps

LLMOps

LLMOps is an emerging and specialized domain of MLOps that focuses on operationalizing large language models(LLMs) at scale.

Multimodal AI

Multimodal AI

Multimodal AI refers to AI systems that can understand, interpret, and generate multiple data types, such as text, images, sound, and more.

Prompt Engineering

Prompt Engineering

Prompt engineering is a new field focussed on designing and refining transformer-based LLMs with specific text prompts to guide them for the most accurate outcomes.

Sustainable AI: Ensuring a Greener Future

Sustainable AI: Ensuring a Greener Future

Explore the concept of Sustainable AI, where technological innovation meets environmental consciousness. Learn how it minimizes ecological footprints, maximizes efficiency, and balances innovation with ecological responsibility.

Task-Driven Autonomous AI Agent

Task-Driven Autonomous AI Agent

A task-driven autonomous AI agent operates independently to achieve defined goals, adapt priorities, learn from previous actions, and execute without human intervention.

Transformers in AI

Foundation Models

Transformers in AI

A transformer model is a neural network that learns context and thus meaning by tracking relationships in sequential data like the words in this sentence.

Zero, Few Shot and One Shot Learning

Zero, Few Shot and One Shot Learning

Zero-shot, few-shot, and one-shot learning techniques allow ML models to predict based on limited labeled data.