This content originally appeared on Level Up Coding - Medium and was authored by Akash Mahale
Understanding Generative AI is becoming increasingly essential in today’s rapidly evolving technological landscape. Generative AI is empowering smart systems to create content — be it text, images, or audio format simulating human-like creativity. As organizations across various sectors embrace it, knowing its key concepts and terms is crucial for anyone looking to learn or leverage Gen AI. To better understand it, I have curated a list of glossaries with an example so it becomes easier to understand it practically along with technical definitions.
Here is a simple, engaging example to explain how generative AI could enhance customer experience, marketing, and sales for a shoe manufacturing company:
Example:
A shoe manufacturing company wants to boost its sales, shoe designs, and customer experience. The company produces high-quality sneakers and footwear for all purposes. To stand out in the market, they decide to use Generative AI to improve their customer experience and drive sales.
Here’s how the company will leverage Gen AI:
1. Personalized Recommendations:
When customers visit the website or the app, the AI analyzes their browsing behavior and past purchases. It will then generate personalized shoe recommendations, suggesting styles, sizes, and colors that match the customer’s preferences. If a customer recently bought running shoes, the AI may recommend sports accessories or the latest shoe technology, like a new lightweight running sneaker or an environment-friendly material-made shoe with a better grip.
2. Custom Shoe Design:
The company will use a Generative AI design tool that will let customers create their custom shoe designs. Customers can choose from different patterns, colors, and materials, and the AI will automatically generate 3D visualizations of their unique designs. This will enhance engagement, as customers can see how their personalized shoe will look before making a purchase.
3. Virtual Try-On:
Using AI-powered augmented reality (AR), customers can virtually try on shoes from their homes. The AI scans their feet, makes calculations, and generates a real-time image showing how different shoe styles would look. This makes the buying process easier and more engaging.
4. Customer Support and Insights:
The company will have an AI chat assistant available 24/7 to answer questions about sizing, product availability, orders, or delivery times. It will also analyze customer feedback from reviews, sentiments, and the current market to identify trends, allowing the company to improve its products based on real-time insights.
5. Marketing Campaigns:
Gen AI will help the company plan personalized marketing campaigns. For example, if a customer frequently buys eco-friendly products, the AI will send them a campaign showcasing the company’s sustainable line of shoes made from recycled materials. It may give them offers and discounts as rewards for contributing to sustainable development, etc.
So with this example let us start with the glossary:
Generative AI
Definition:
Generative AI refers to artificial intelligence models that generate new content, such as text, images, and audio, based on the data they were trained on. These models use machine learning techniques, particularly deep learning techniques, to learn data patterns and produce new, similar outputs.
Example:
In the shoe company case, Generative AI is used to design and recommend custom shoes. For example, when a customer wants to order a unique shoe, the AI model can generate various design options, such as color combinations or material patterns, based on past designs customer preferences, and purchase history. The customer can adjust these designs in real-time with AR features, and the AI will keep generating new visuals until the customer is satisfied with the final look. This ability to generate original, tailored designs helps the company offer a personalized shopping experience, making it easier and more engaging for customers to visualize their perfect shoes.
Deep learning
Definition:
Deep learning is a subset of machine learning that uses neural networks with multiple layers to mathematically model complex patterns in data. It is suited for tasks like image recognition, natural language processing, and generating new content in the same way how the human brain processes and generates new information.
Example:
Deep learning powers the AI’s ability to understand customer preferences and generate custom shoe designs. The AI model analyzes large amounts of data, including shoe trends, materials, customer behaviors, and past choices, using deep learning to detect patterns. This helps generate design suggestions that are personalized and likely to appeal to each customer.
Large Language Model (LLM)
Definition:
A Large Language Model (LLM) is a type of AI model pre-trained on massive amounts of text data to understand language and can generate human-like responses, answer questions, and produce creative content based on input prompts. LLMs rely on a technique called unsupervised learning during pre-training, where they learn language patterns and relationships without explicit task-based labels.
Example:
The LLM model can be used to generate personalized product descriptions and marketing content. For instance, when a customer customizes a shoe, the LLM model can generate a detailed description of the unique features of that shoe, such as the material quality of the shoe, the purpose of the shoe, durability, rates, etc. Additionally, LLM can be integrated into customer support chatbots to provide real-time responses. For example, if a customer asks, “Recommend shoes for running?” the LLM-powered AI would generate a response based on the customer’s profile and preferences, enhancing the overall shopping experience.
Generative Adversarial Network (GAN)
Definition:
A Generative Adversarial Network (GAN) is a type of neural network architecture that consists of two models: a generator and a discriminator. The generator creates fake data, while the discriminator tries to distinguish between real and fake data. These two models are trained together in a hand-in-hand scenario, with the generator improving its ability to create realistic data and the discriminator getting better at detecting fakes.
Example:
A GAN could be used to generate realistic shoe recommendations based on customer preferences. The generator part of the GAN would create shoe designs by combining various styles, colors, and patterns. Meanwhile, the discriminator would evaluate whether these designs look like authentic, high-quality shoes that customers would buy. As the model improves, it could also create entirely new styles that have never been made before but are still in line with current fashion trends. The company could use these designs to enhance its product line and offer unique, AI-generated shoe designs to its customers.
Multi-Modal LLM:
Definition:
A Multi-Modal LLM is an advanced LLM model capable of processing and generating content across multiple types of data, such as text, images, and audio. This capability allows the model to understand and create more complex interactions by integrating different forms of information.
Example:
Imagine a customer using Gen AI to find new formal office shoes. They upload a photo of a shoe and ask, “Can you please find similar?” The Multi-Modal LLM analyzes both the image and the text query, retrieving and recommending shoes from inventory that match the style in the photo. This seamless integration of visual and textual data enhances the customer experience by making it easier to discover products in inventory that align with their preferences.
Embedding and Vectors
Definition:
Embeddings are simplified numerical representations of data, like words or images, that capture their meanings. Vectors are the mathematical forms of these embeddings, allowing the AI to compare and understand data based on their relationships.
Example:
When a customer searches for “comfortable running shoes” the AI converts this phrase into an embedding — a vector that encapsulates the meaning of the query. It then compares this vector with other shoe embeddings in the database to find the best matches, such as models specifically designed for comfort and running. This process allows the AI to deliver highly relevant product recommendations.
Prompt Engineering
Definition:
A prompt is simply the text or input given to an AI model to trigger a specific response. For example, a prompt can be a question, a sentence, an image, or even a more detailed description, depending on the type of content or information desired. Prompt engineering is the process of crafting effective input prompts to direct generative AI models in producing the desired output. Well-designed prompts help guide the AI to generate relevant, accurate, or creative responses by providing clear instructions.
Example:
Prompt engineering would be used to generate personalized marketing messages or product recommendations. If a customer prefers eco-friendly shoes, a carefully crafted prompt like, “Suggest an eco-friendly, durable, formal shoes ” will guide the AI to highlight shoes made from sustainable materials, which can be used for offices or any formal occasions.
Prompt Defence:
Definition:
To effectively detect and defend against prompt jailbreaking attacks in GenAI applications, implementing a robust content filter is crucial. This filter should scrutinize the inputs fed into LLMs to ensure they comply with supported intents and reject the rest.
Example:
When a customer engages with the company’s GenAI for custom shoe design, the AI’s prompt defense system checks the input for appropriateness. If a user tries to inject inappropriate commands or queries, like “Create a shoe that promotes violence,” the content filter should immediately stop and reject it. This keeps the interaction safe and focuses on positive, creative designs, ensuring a secure and enjoyable experience for all customers while maintaining the integrity of the AI’s responses and nontoxicity.
Hallucination:
Definition:
In the context of Generative AI, hallucination refers to instances when an AI model generates content that is incorrect, misleading, or entirely fabricated based on wrong information. This can occur due to limitations in the training data or the model’s understanding of context. The most common reasons include incorrect model assumptions, overfitting, poor testing, and lack of access to real-time information.
Example:
When a customer asks AI about the features of a new running shoe, the AI might mistakenly claim, “This shoe is made with 100% recycled materials,” even if that’s not true. This occurs when the model preassumes that the customer is interested in recycled shoes only. Such hallucinations can mislead customers about product specifications. To combat this, the company can encourage customer feedback on AI responses, enabling the team to refine the model and enhance accuracy.
Bias:
Definition:
In Generative AI, bias refers to systematic and unfair prejudices in model outputs that arise from imbalances and inaccuracies in the training data. This can lead to skewed representations of stereotypes, affecting the fairness and reliability of the AI’s responses. This happens when the training data is not inclusive enough of the reality. Other technical reasons can be unprocessed, unclean data, biased sampling, or other factors that may lead to an incomplete dataset.
Example:
If the company’s AI is trained predominantly on data featuring a specific demographic, it may recommend shoe styles that appeal mostly to that group, neglecting the preferences of other groups. For instance, if most training data includes running shoes for men, the AI might overlook innovative designs for other genders. To address this, the training data should be well diversified, and the model should be thoroughly tested and incorporated with customer feedback, ensuring that the AI recognizes and respects a wider range of styles and preferences, leading to a more inclusive shopping experience for all.
Retrieval-Augmented Generation (RAG):
Definition:
Retrieval-Augmented Generation (RAG) is a technique that combines the capabilities of Generative AI with information retrieval. It involves retrieving relevant data from a knowledge base or external sources to enhance the context and accuracy of generated responses, allowing the model to provide more informed and relevant outputs.
Example:
When a customer inquires about the latest trends in shoe technology, the company’s AI can use RAG to first retrieve up-to-date information from the company’s database of recent innovations. It then generates a response like, “For the last 3 months, shoes with lightweight materials and smart sensors are trending in sneaker design which are comfortable and durable as well” ensuring the information is not only relevant but also current. By integrating this real-time data, the AI enhances the customer experience, providing insights that help shoppers make informed decisions while showcasing the company’s commitment to innovation.
Ethical and Responsible AI Maturity Model
Definition:
The Ethical and Responsible AI Maturity Model is a framework that helps organizations assess and improve their practices in developing and deploying Gen AI responsibly. It evaluates different levels of maturity based on criteria such as fairness, transparency, accountability, and compliance with ethical and legal guidelines.
Example:
The shoe company can use the Ethical and Responsible AI Maturity Model to evaluate its GenAI systems, ensuring they adhere to ethical standards. For instance, they might start at a basic level, where they simply monitor AI outputs for bias. As they progress, they implement regular audits and establish a diverse team to review AI decisions. By reaching higher maturity levels, the company not only enhances trust with customers but also promotes a culture of responsibility and fairness in AI development, ensuring that their technology serves all customers fairly without biases.
Explainable AI (XAI):
Definition:
Explainable AI (XAI) refers to methods and techniques that make the decisions and processes of GenAI systems understandable to humans. It aims to provide clarity on how Gen AI models arrived at their conclusions, ensuring transparency and trust. By demystifying AI operations, XAI helps users grasp the rationale behind specific outputs, making the technology more accessible and accountable.
Example:
When a customer asks why a particular shoe was recommended by the AI, the system provides a clear explanation: “This shoe is recommended because you previously searched for running shoes and rated lightweight models highly in previous purchases.” By offering this insight, the company helps customers understand the AI’s reasoning, fostering trust and making them feel more confident in their choices. This transparency enhances customer satisfaction and encourages more informed purchasing decisions.
Conclusion:
With this, we have explored essential glossary terms in Generative AI. With prominent models such as OpenAI’s GPT-4, Google’s Gemini, and Meta’s LLaMA and many such LLMs, organizations are increasingly leveraging Generative AI to address a wide range of applications, from content creation and customer support to personalized marketing and data analysis. As businesses seek innovative solutions, they are focusing on practical use cases that enhance efficiency while ensuring the principles of fairness, inclusion, and security are adhered to. I hope this glossary serves as a solid foundation to start the journey of learning and using Generative AI. That’s it from this blog of mine, suggestions are most welcomed.
Complete Generative AI Glossary with examples was originally published in Level Up Coding on Medium, where people are continuing the conversation by highlighting and responding to this story.
This content originally appeared on Level Up Coding - Medium and was authored by Akash Mahale
Akash Mahale | Sciencx (2024-09-26T23:03:10+00:00) Complete Generative AI Glossary with examples. Retrieved from https://www.scien.cx/2024/09/26/complete-generative-ai-glossary-with-examples/
Please log in to upload a file.
There are no updates yet.
Click the Upload button above to add an update.