What is Generative AI? – Complete Definition With Examples

The roots of generative AI can be traced back to the very beginning of AI research. The concept of machine learning emerged in 1950 paving the way for data-based algorithms. Following that, Joseph Weizenbaum develops the first chatbot in history ‘ELIZA’ in the 1960’s. The turning point started in the 2000’s. We are experiencing a massive wave of generative AI-enabled services. This brings us to the question: what is generative AI?. Generative AI has marked its footprints in various fields like art, design, healthcare, education, consulting, and more.

Let’s get into the details of what generative AI is, use cases, applications, and risk factors. 

What is Generative AI? 

“Generative AI is not just about copying the world; it’s about understanding the world and using that understanding to create new things.” – Demis Hassabis, DeepMind, co-founder

Generative AI is a class of AI systems capable of autonomously producing new content resembling human-generated data. Many see it as a tool for creating fresh content by learning from existing data. For example, Generative Adversarial Networks (GANs) are a common type. One network generates content, like images, while another checks its authenticity, improving the results gradually.

The most recent generative AI to go viral is Google’s AI Overviews, which launched mid-May 2024. Google’s AI Overviews feature is designed to deliver organized summaries of search results, swiftly analyzed and crafted by generative AI. Nonetheless, OpenAI’s GPT series maintains its position as a pioneer in natural language generation, delivering models for tasks such as text generation, language translation, and conversational AI. Google’s native generative AI chatbot, Gemini, has entered the ring to compete with ChatGPT, the longstanding player.

Generative AI according to industry experts

Various industries have been exploring what is generative AI and finding ways to explain it clearly to the general public. Below we have have listed a few you might want to look into, 

• Generative AI, as defined by IBM Research, refers to deep-learning models that leverage extensive datasets, such as the entirety of Wikipedia, to acquire knowledge and generate entirely new outputs. These outputs are statistically probable creations, crafted by learning the “style” of existing content and applying it to generate original material. In essence, generative AI mimics the style or characteristics of the input data to produce innovative and unique outputs, akin to learning the traits of a particular artistic style and using that knowledge to design something entirely new.
• Accenture emphasizes that generative AI is frequently developed using “foundation models” trained on large volumes of unlabeled data. This self-supervised learning method enables the models to perform a wide range of tasks with minimal customization
• According to McKinsey & Company, generative AI refers to algorithms (such as ChatGPT) capable of producing new content, including audio, code, images, text, simulations, and videos.
• Amazon Web Services explains that generative artificial intelligence (generative AI) is a type of AI capable of producing new content and ideas, such as conversations, stories, images, videos, and music. These AI technologies aim to emulate human intelligence in nontraditional computing tasks, including image recognition, natural language processing (NLP), and translation.

From these definitions, generative AI can be understood as a type of artificial intelligence that uses deep-learning models trained on extensive datasets to create new and original content. Essentially, it leverages the learned patterns and knowledge from the input data to generate innovative and unique outputs.

Generative AI Models 

Generative Adversarial Networks ( GANs) 

Generative Adversarial Networks (GANs) involve two neural networks in a continuous competitive loop, much like a game. The generator acts like an artist, creating realistic and novel data (such as images and text) based on the training data it receives. The discriminator, on the other hand, functions as an art critic, evaluating the generated data to distinguish it from real data.

During the training process, the generator refines its creations based on feedback from the discriminator, which tries to detect fake data. This iterative battle helps both networks improve: the generator becomes better at producing data that closely resembles real-world examples, while the discriminator sharpens its ability to spot fakes.

As a result, GANs can generate high-quality data for a variety of applications, including creating photorealistic images, developing new artistic styles, generating realistic simulations for fields like autonomous vehicle training, and even composing music. However, training GANs can be complex, requiring careful balancing of the generator and discriminator.

Transformer Based Models 

Unlike traditional models that process data sequentially, transformers are better at understanding complex relationships within data through a mechanism called “self-attention. It analyzes how different parts of the data influence each other. 

This deep understanding empowers transformer models to generate creative and coherent outputs, such as producing new text formats like poems, code, or scripts, and creating or manipulating images. Some famous generative AI models leveraging transformers include Generative Pre-trained Transformers (GPTs) like OpenAI’s GPT-3 and DALL-E.

Transformers have benefits such as faster analysis because they can process data in parallel. They’re good at understanding complex relationships in data, even between distant elements. They can handle different tasks well, like generating text or images.

However, transformers also have limitations. They can sometimes learn biases from the data they’re trained on, leading to incorrect or biased results. Understanding how transformers arrive at their outputs can be difficult. 

Diffusion Models 

Diffusion models are a new addition to generative AI, celebrated for their ability to create realistic images. These models take a counterintuitive approach compared to other generative models. Rather than generating images from scratch, they begin with a real image and gradually introduce noise, essentially converting it into random static. 

During training, the diffusion model is exposed to various images along with their corresponding noisy versions at different stages. It learns to predict the denoising step, transitioning from a blurry to a clear image. With each step, the noise decreases, revealing more detailed image features.They offer a diverse range of outputs, allowing for the generation of images with specific variations. Additionally, they boast a more stable training process compared to some other generative models like GANs, reducing issues such as mode collapse.

Variational Autoencoders (VAEs)

VAEs are built upon autoencoders, which compress and then reconstruct data, with the added innovation of introducing a latent space that captures key features of the input data. Unlike regular autoencoders, VAEs aim to learn a probability distribution over the latent space, allowing for more diverse outputs during generation. 

The training process involves two key components: an encoder and a decoder. The encoder transforms input data into a representation in the latent space, while the decoder reconstructs the original data. VAEs can generate new data by sampling random points from the learned latent space distribution and feeding them into the decoder to produce novel variations resembling the training data. 

Despite training complexities, VAEs offer valuable advantages, such as interpretability and controllable generation, making them suitable for tasks like image or music generation.

Unimodal Models 

Unimodal models are essential components of generative AI, dedicated to processing a single type of data for both input and output. They specialize in processing specific data formats, such as text, images, or audio, and generating new outputs within the same modality. 

Unimodal models offer simplicity and efficiency in design and training compared to multi-modal models, allowing for highly optimized performance within their specific domains. However, they are limited in scope, lacking the ability to leverage information from other data types.

Multimodal Models 

Multimodal models combine different types of info like text, images, sound, and video to understand things better and make outputs that mix elements from various sources. By merging data from different modalities, these models can achieve enhanced comprehension, improved performance, and the generation of creative outputs. 

Firstly, by pairing visual data with text descriptions, they facilitate a deeper understanding of processed information. Secondly, leveraging the strengths of each data type allows multimodal models to overcome limitations inherent in individual modalities; for example, text descriptions can clarify ambiguities in images. Lastly, multimodal models are better in generating creative outputs by seamlessly merging elements. These abilities highlight the adaptability and promise of multimodal models across diverse applications within generative AI.

Artificial Intelligence vs. Generative AI

Artificial Intelligence: AI or artificial intelligence is a machine-powered algorithm that is trained to perform functions associated with the human mind faster and more precisely. It collaborates with data sources and mimics human intelligence to produce content that resonates with human behavior. 

Generative AI: Now, generative AI is one step ahead of AI (artificial intelligence). AI goes deeper into generative AI. Generative AI can be considered a specific application of AI. It utilizes many of the same underlying techniques and algorithms but with a distinct focus on content creation. It does not simply mimic user inputs like the previous artificial intelligence model. Generative AI can produce absolute raw and new content. 

Text-Based Learning Models and Their Training Methods

Text-based machine learning models, such as language models, work by learning patterns and relationships within textual data to generate or understand text. They can be trained using various techniques, but one of the most common approaches is supervised learning. Here’s a high-level overview of how text-based machine-learning models are trained:

1. Data Collection: The first step involves gathering a large corpus of text data relevant to the desired task. This can include books, articles, websites, social media posts, or any other text source.
2. Data Preprocessing: The collected text data is then preprocessed to clean and prepare it for training. This can involve removing unnecessary characters or symbols, tokenization (splitting the text into individual words or subwords), and applying techniques like stemming or lemmatization to normalize the vocabulary.
3. Feature Extraction: To represent the text numerically, various feature extraction techniques can be used. For example, the bag-of-words representation assigns a unique number to each word in the vocabulary and counts the occurrences of each word in a text sample. Other approaches, like word embeddings (e.g., Word2Vec or GloVe), assign dense vector representations to words in a continuous vector space, capturing semantic relationships.
4. Model Training: Once the data is preprocessed and the features are extracted, the training process begins. Supervised learning involves training a model using labeled examples, where the input data (e.g., a sentence) is associated with a corresponding target (e.g., a sentiment label). The model is optimized to learn the statistical patterns and relationships between the input and output through an iterative process.
5. Model Evaluation and Validation: After training, the model’s performance is evaluated using a separate dataset called the validation set. This helps assess how well the model generalizes to new, unseen data and allows for fine-tuning of hyperparameters or model architecture if needed.
6. Deployment and Testing: Once the model is deemed satisfactory, it can be deployed for inference on new, unseen text data. This involves applying the trained model to make predictions or generate text based on the task it was trained for.

What are Some Shortcomings of Generative AI?

While generative AI has made significant advancements, it also has some shortcomings. Here are a few common limitations:

1. Lack of Contextual Understanding: Generative AI models can struggle with understanding context and delivering accurate responses. They often generate responses based on patterns and statistical correlations in the training data without truly comprehending the meaning or intent behind the input.
2. Over-reliance on Training Data: Generative AI models heavily rely on the data they are trained on. Biases or inaccuracies present in the training data can be reflected in the generated outputs, perpetuating existing biases or misinformation.
3. Lack of Common Sense Reasoning: Generative AI models may struggle with common sense reasoning and logical inference. They can produce outputs that are factually incorrect or nonsensical, as they do not possess real-world experiences or a genuine understanding of the world.
4. Difficulty in Handling Ambiguity: Ambiguous input can pose challenges for generative AI models. They may struggle to disambiguate and produce coherent responses, leading to outputs that are vague or irrelevant.
5. Ethical and Misuse Concerns: The power of generative AI can also raise ethical concerns. There is a risk of misuse, such as generating misleading or harmful content, deepfakes, or spam. Safeguards and responsible use of generative AI are crucial to mitigate these risks.
6. Resource-Intensive Training and Inference: Training generative AI models often requires significant computational resources and time. Additionally, generating outputs from these models can be computationally demanding, limiting their real-time applications or accessibility on low-powered devices.

How Does Generative AI Work? 

“Generative AI has the power to unlock new forms of human expression and creativity. It will push the boundaries of what’s possible and help us imagine and build a better future.” – Satya Nadella, Microsoft CEO

After discovering what generative AI is, most people become interested in how it functions. Generative AI produces data through the use of models that are trained on large datasets. These models, often based on neural networks, learn patterns and relationships within the data they are exposed to during the training process. Once trained, generative AI can generate new data samples that mimic the patterns of the original dataset.

Example: 

Let’s consider a generative AI model trained on a dataset of handwritten digits (0-9). During training, the model learns the features and variations present in the dataset. Once trained, it can generate new, realistic-looking handwritten digits that were not part of the original dataset.

For instance, if the model encounters a sample of the digit ‘8’ during training, it learns the typical strokes, curves, and variations associated with the digit. After training, the generative AI model could then produce new, synthetic examples of the digit ‘8’ that were not present in the original dataset.

In summary, generative AI produces data by learning and replicating patterns from the input dataset, enabling it to create synthetic data that resembles the original and can be used for various applications, including image generation, text completion, and more.

Here’s a step-by-step breakdown:

Step 1: Data Collection 

The first step is data collection. Generative AI gathers massive datasets from the existing content in the desired domain. 

The data collection process runs on a variety of sources including books, articles, websites, social media posts, and anything available in the cloud. Data preparation and cleaning are the crucial focus at this step.

Step 2: Data Cleaning and Processing

The generative AI model then identifies and collects relevant data from various sources. To bring you the best answer from the prompt, it cleans data and arranges it into a format. The process may involve converting various file formats, aligning timestamps, and ensuring proper structure. 

Then, the data goes through a few more cleaning processes like noise removal, bias detection and mitigation, consistency checks, and validation.

Step 3: Model Training

Now the actual magic begins! Here, a specific type of AI model, often a deep learning model like a neural network, is chosen.

This model is fed the prepped data and starts learning its subject matter. It analyzes patterns, relationships, and statistical probabilities within the data, understanding how words connect, images are structured, or music unfolds.

Training involves feeding the model the data repeatedly and fine-tuning its internal parameters through algorithms like backpropagation to minimize errors and improve its learning.

Step 4: Content Generation

“Generative AI is the future of content creation. It will democratize the ability to create high-quality content, making it accessible to everyone.”- Andrew Ng, Landing AI founder

Once trained, the model is ready to create! You provide it with a prompt or seed, which can be a text phrase, an image sample, or any relevant input in the chosen domain. The model then leverages its previously learned knowledge to generate new content. 

This generation process can be iterative, with the model refining its output based on feedback or additional inputs. It leads to increasingly personalized results.

Step 5: Content Refining and Optimizing

Generative AI isn’t a one-and-done process. The models and generated content are constantly evaluated for accuracy, quality, and desired originality. If you make even minor changes to your prompt, generative AI might produce very different results compared to the previous. 

Techniques like human validation, comparison with ground-truth data, and error analysis are used to identify areas for improvement. Based on these evaluations, the model can be further trained with additional data or adjusted in its parameters to generate better outputs over time.

Generative AI Applications and Examples 

“Generative AI is still in its early stages, but it has the potential to revolutionize many aspects of our lives, from the way we create art and music to the way we design products and develop drugs.” – Jeff Dean, Google Senior Fellow, and Chief AI Scientist

The application of generative AI is diverse and it’s rapidly growing with new inventions.  Here’s an explanation of 8 generative AI applications across various domains:

1. Text Applications

The use of generative AI in text applications is the most commonly adopted application. This usage includes generating articles, blog posts, social media content, emails, marketing copy, product descriptions, scripts, code, emails, letters, poems, code, scripts, musical pieces, emails, letters, etc.

Some more variants include translating text from one language to another accurately and efficiently. You can also use generative AI text applications to summarize long texts into shorter summaries. 

Tools: Jasper, Writesonic, Copy.ai, Sudowrite, Rytr, Copysmith

2. Conversational Applications

These applications use advanced natural language processing and machine learning algorithms. For example, they use GPT-3. They generate human-like responses and engage users in dynamic, relevant conversations. 

These applications closely mimic human communication. They can provide personalized and context-aware interactions across various domains. These include customer service, virtual assistants, and interactive content creation.

Chatbots are considered primarily conversational AI applications as they are designed to simulate conversation with humans, understand user input, generate responses, and maintain context throughout the interaction. 

Tools: Bard, Kore, Cohere Generate, Avaamo, Yellow AI, Fireflies

3. Visual Applications

This advanced artificial intelligence can transform creative fields as well. It generates realistic images, artwork, and designs from textual descriptions or abstract concepts. This technology extends its reach to video generation. It enables the creation of videos with lifelike visuals and animations. This opens up new possibilities in the realm of digital content creation.

Additionally, generative AI plays a crucial role in photo editing. It offers tools to enhance images, remove imperfections, and apply artistic effects. This contributes to the refinement of visual content.

Tools: Midjourney, DALL-E, Synthesia, StyleGAN, Canva, Nightcafe

4. Audio Applications

Generative AI promotes advancements in AI-powered audio generation. This includes converting speech to text for documentation, accessibility, and searchability.  Audio applications also include composing original music pieces in various styles. 

It sets you all free from complex software usage. Moreover, the time-consuming process of audio generation is now faster than ever. Thanks to generative AI. 

Tools: AudioCraft, Play.ht, Otter.ai, Transcribe, Murf.ai, Soundraw

5. Customer Service Applications

This is another application that has tremendously utilized generative AI. Many customer engagement platforms incorporate AI-enabled services. It helps provide automated support and answer customer queries effectively.

Several companies and industries can provide 5X enhanced customer services. Generative AI-supported chatbots offer personalized recommendations and solutions based on individual needs. This results in higher customer satisfaction. 

Tools: REVE Chat, Zigpoll, Yuma AI, MeyaGPT

6. Personalized Learning Applications

Personalized lessons improve the effectiveness of education. Generative AI tools can help to design personalized courses and lesson plans. The education comes paired with technologies making learning more fun. 

Generative AI can also help teachers in preparing their sessions better. For example, they can prepare quizzes, exercises, or questions for exams. They can prepare tailored study guides for individual students.

Tools: Duolingo, Mondly, ELSA Speak

7. Software Coding Applications

Generative AI has also revolutionized the field of software coding. It offers innovative approaches to streamline and automate the development process.  Developers can use natural language interfaces to interact with the code. For example, with models like GPT-3. This enables a more intuitive and efficient coding experience. 

Generative AI helps generate code snippets and automate repetitive tasks. It can even propose solutions to complex programming challenges. This technology has the potential to enhance collaboration among developers. It can understand and generate code in various programming languages. 

However, developers need to remember that this may not produce flawless code. Thus, it will need their major involvement in software coding. 

Tools: ChatGPT, Auto-GPT, GitHub Copilot, AlphaCode

Generative AI Use Cases 

AI has been part of every growing sector in recent years. Generative AI is growing fast replacing more creative forms of mundane tasks. The new disruptive opportunities are making industries grow and improve productivity, with forward-thinking businesses leveraging AI business plan generators to capitalize on this trend. Let’s look at some use cases,

1. Healthcare Facilities

Previously, the healthcare industry faced several challenges to streamline its tasks. Generative AI came up with various solutions to assist healthcare industries in boosting their productivity. Some generative AI-backed healthcare facility task involves, 

• Designing new molecules for targeted therapies and predicting their effectiveness.
• Tailored treatment plans based on individual genetic data and medical history.
• Automating diagnosis and disease detection at early stages.
• Supporting doctors and nurses with scheduling appointments, managing patient records, and answering FAQs with the help of virtual assistance. 

2. Consulting Services

Generative AI in consulting is used in more than one way. It helps make reports adjustable and information representation reports for organizations in addition to other things. It also empowers data-driven insights. Structured data enables consultants to be more confident about their decisions. Some more benefits are, 

• Preparing tailored proposals to specific client needs and preferences.
• Creating simulations for analyzing potential outcomes and mitigation strategies.
• Summarizing complex information and automating report writing.

3. Retail Sector

In retail generative AI emerged as a ground-breaking technology. It has simplified ground operations, automated regular tasks, and assisted creative sides. Imagine having a shopping experience so good that you get back to the same retail every time. Generative AI can create such experiences for retail sectors. 

• Generating customized product recommendations and offering unique design options.
• Predicting demand and managing stock levels to avoid product shortages.
• Optimizing pricing strategies based on market trends and customer behavior.
• Creating personalized marketing campaigns and chatbot interactions.

4. Travel Industry

The travel industry is booming in 2024 and travelers are looking forward to having unparalleled travel experiences.  This industry is heavily using generative AI to take travel and tourism one step ahead.  Whether it’s an AI-powered travel assistant or booking assistance, generative AI is all on board to create memorable travel experiences. 

• Suggest tailored itineraries and experiences based on user preferences.
• Optimizing pricing based on real-time demand
• Generating itineraries and booking flights, hotels, and activities.
• Answering customer questions, providing recommendations, and assisting with bookings with virtual travel assistance. 

5. Marketing

Generative AI has been very promising to marketing sectors. From content to visual creation, AI is everywhere. Traditional marketing used to rely on human predictions. Marketers had to handle many time-consuming tasks manually. Generative AI has automated many of those monotonous tasks.

• Creating catchy ad copy, product descriptions, and marketing materials.
• Generating engaging posts and tailoring content for different platforms.
• Targeting specific customer segments with tailored messaging.
• Analyzing customer data to predict behavior and personalize marketing strategies.

6. Supply Chain

Supply chain management can also leverage generative AI. It trains supply chain data to provide accurate information. It facilitates the automation of routine tasks, freeing human resources for more strategic decision-making. The only challenge faced is the supply chain business model. Overly complex models lack transparency which might hinder AI-driven insights.

• Planning efficient routes and schedules for transportation and delivery.
• Identifying potential equipment failures and scheduling preventative maintenance.
• Accurately predicting product demand to optimize inventory and production.
• Identifying anomalies and potential fraud in supply chain operations.

7. Customer Support and Services

Conversational AI is the most commonly used generative AI in customer services and contact centers. It helps to improve real-time interaction. Chatbots have been revolutionary for these industries. Customer services can leverage both live chat and chatbot technology to provide a seamless experience.

• Providing 24/7 support, answering questions, and resolving simple issues.
• Understanding customer sentiment and identifying areas for improvement.
• Tailoring interactions to individual customer needs and preferences.
• Predicting potential problems and proactively addressing them before they impact customers.

The Risks of Generative AI

As generative AI brings us into the future where machines mimic human creativity, the landscape is both promising and precarious. The risks associated with thing technology cannot be ignored. There is a high risk that people can use generative AI for malicious purposes. We already have examples including deepfake videos, fake news, and other forms of misinformation.

As the system can reproduce from existing data. This raises concerns about exposing sensitive data and confidential details. There is also the risk of adversarial attacks. A slight modification to input data can lead to unexpected and potentially harmful outputs.

Generative AI systems are becoming more complex. Understanding how and why certain decisions or outputs are generated can be challenging. This lack of transparency can make it difficult to hold the system accountable for its actions.

FAQs

What is generative AI?

Generative AI is a type of artificial intelligence (AI) that can create new data, like text, images, or code by learning patterns from existing data and using them to generate entirely new content.

How do Large Language Models (LLMs) relate to generative AI?

Large language models (LLMs) are a subset of deep learning models vital for generative AI, especially for generating text. By being trained on extensive text datasets, they develop a remarkable ability to comprehend and predict language patterns. This skill allows them to create realistic and coherent text, whether it’s poems, code,or answering questions. 

What are some of the ethical considerations surrounding generative AI?

As with any advanced technology, generative AI brings ethical considerations. Concerns such as the ownership of AI-generated content, the risk of plagiarism, and the potential for spreading misinformation must all be addressed as this field evolves.

End Note

Generative AI is increasingly becoming popular. It incorporates our regular lives and many industry sectors to fine-tune the task process. All we need is to recognize the best uses of generative AI and minimize risk factors. Striking a balance between innovation and responsibility is important to utilize the full potential of generative AI. It’s indeed a powerful tool.