RESAB Engineering

What Is Generative AI, Why Does It Matter?

Generative AI, a form of artificial intelligence, can create diverse content, including text, images, audio, and synthetic data.

Generative AI, a form of artificial intelligence, can create diverse content, including text, images, audio, and synthetic data. Recently, there has been significant excitement surrounding generative AI due to user-friendly interfaces that enable the rapid production of high-quality text, graphics, and videos within seconds.

It’s important to note that generative AI has been around since the 1960s, with early implementations in chatbots. However, the real breakthrough came in 2014 with the introduction of Generative Adversarial Networks (GANs), a type of machine learning algorithm. GANs allowed generative AI to create convincingly realistic images, videos, and audio of actual people.

This newfound capability has opened up numerous opportunities, such as improving movie dubbing and creating rich educational content. Nevertheless, it has also raised concerns about deep fakes, which are digitally manipulated images or videos, and potential cybersecurity threats, including deceptive requests that convincingly mimic an employee’s superior.

Two recent advancements have been pivotal in bringing generative AI into the mainstream: transformers and the revolutionary language models they’ve enabled. Transformers are a type of machine learning that enables the training of increasingly large models without the need to label all the data beforehand. This has allowed models to be trained on vast amounts of text, resulting in deeper and more context-aware responses. Additionally, transformers introduced a novel concept called attention, which enables models to understand the relationships between words across not just sentences, but also pages, chapters, and books, as well as in contexts such as code, proteins, chemicals, and DNA.

Evolution of Generative AI

How Does Generative AI Work?

Generative AI begins with a prompt, which can take the form of text, images, videos, designs, musical notes, or any input that the AI system can process. Various AI algorithms then generate new content in response to the prompt, including essays, problem solutions, or realistic simulations created from images or audio.

In the past, early generative AI versions required data submission through APIs or other complex processes. Developers had to work with specialized tools and write applications using programming languages like Python. However, pioneers in generative AI are now designing more user-friendly experiences that allow users to describe their requests in plain language. After the initial response, users can further customize the results, providing feedback on the desired style, tone, and other elements to refine the generated content.

Generative AI Models

Generative AI models combine different AI algorithms to represent and process content. For example, to generate text, various natural language processing techniques transform raw characters (letters, punctuation, and words) into sentences, parts of speech, entities, and actions, which are then represented as vectors using various encoding techniques. Similarly, images are transformed into different visual elements, also expressed as vectors. It’s important to note that these techniques can inadvertently capture biases, racism, deception, and exaggeration present in the training data.

Once the representation is established, a specific neural network is applied to generate new content in response to a query or prompt. Techniques such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), which consist of encoder and decoder components, are suitable for generating realistic human faces, synthetic data for AI training, or simulations of specific individuals.

Recent advancements in transformer-based models like Google’s Bidirectional Encoder Representations from Transformers (BERT), OpenAI’s GPT, and Google AlphaFold have further expanded the capabilities of neural networks. These models can not only encode language, images, proteins, and other content but also generate new content based on the encoded information.

How Neural Networks Are Transforming Generative AI?

Generative AI has a history dating back to the 1960s, but it has experienced significant growth and transformation in recent years. Early generative AI implementations relied on rule-based systems and expert systems, using explicitly crafted rules to generate responses and data sets. However, the development of neural networks, based on how the human brain works, marked a turning point.

Neural networks, introduced in the 1950s and 1960s, initially faced limitations due to inadequate computational power and small data sets. It wasn’t until the mid-2000s, with the advent of big data and improvements in computer hardware, that neural networks became practical for generating content.

The field progressed even further when researchers found a way to run neural networks in parallel across graphics processing units (GPUs), which were originally used in the gaming industry for rendering video games. Machine learning techniques like Generative Adversarial Networks (GANs) and transformers, introduced in the last decade, played a significant role in the recent advancements in AI-generated content.

Google Bard vs Chat GPT

Dall-E, ChatGPT, and Bard

Dall-E, ChatGPT, and Bard are well-known generative AI interfaces.


Trained on a large dataset of images and their associated text descriptions, Dall-E is a multimodal AI application that identifies connections across multiple media, such as vision, text, and audio. It was built using OpenAI’s GPT implementation in 2021. A more capable version, Dall-E 2, was released in 2022, enabling users to generate imagery in various styles based on user prompts.


This AI-powered chatbot, based on OpenAI’s GPT-3.5, gained widespread attention in November 2022. Unlike earlier versions of GPT, ChatGPT allows for interactive and fine-tuned text responses through a chat interface, integrating the conversation’s history into its results. Microsoft’s investment in OpenAI led to the integration of GPT into its Bing search engine.


Google pioneered transformer AI techniques for processing language, proteins, and other types of content. While it open-sourced some of these models, it did not release a public interface for them. However, Microsoft’s integration of GPT into Bing prompted Google to rush the launch of a public-facing chatbot, Google Bard, based on its LaMDA family of large language models. Google encountered challenges following Bard’s initial release, but it has since introduced a new version built on its most advanced LLM, PaLM 2.

Use Cases for Generative AI

Generative AI can find applications in various fields and use cases, thanks to its ability to create different types of content. Some notable use cases for generative AI include:

  • Implementing chatbots for customer service and technical support.
  • Using deepfakes to mimic people or specific individuals.
  • Enhancing movie dubbing and educational content translation.
  • Generating emails.
  • Responses, dating profiles, resumes, and term papers.
  • Creating photorealistic art in specific styles.
  • Improving product demonstration videos.
  • Suggesting new drug compounds for testing.
  • Designing physical products and architectural prototypes.
  • Optimizing chip designs.
  • Composing music in different styles and tones.

Benefits of Generative AI

Generative AI offers several advantages across different industries and applications. These benefits include:

  • Automating the manual content creation process.

  • Reducing the effort required to respond to emails.

  • Enhancing responses to specific technical queries.

  • Creating realistic representations of people.

  • Summarizing complex information into coherent narratives.

  • Simplifying the content creation process in specific styles.

Limitations of Generative AI

While generative AI holds promise, there are several limitations to consider, particularly in its early stages:

  • Difficulty in identifying the source of content.
  • Challenges in assessing the bias of original sources.
  • Realistic-sounding content makes it harder to detect inaccuracies.
  • Complexity in fine-tuning the AI for new circumstances.
  • Potential for results to exhibit bias, prejudice, and hatred.
  • Concerns related to ethics, trustworthiness, and accuracy.

Attention: Transformers Bring New Capability

In 2017, Google introduced transformers, a novel neural network architecture that significantly improved the efficiency and accuracy of natural language processing and other tasks. This approach, based on the concept of “attention,” mathematically describes how words relate to and modify each other. Transformers revolutionized tasks such as language translation, enabling higher accuracy in less training time compared to previous neural networks. They also uncovered hidden relationships within data that were too complex to express or discern manually.

Transformer architecture has evolved rapidly since its introduction, leading to the development of Large Language Models (LLMs) like GPT-3 and enhanced pre-training techniques, such as Google’s BERT.

Concerns Surrounding Generative AI

The rise of generative AI has raised several concerns related to the quality of results, misuse, and potential disruption of existing business models. These concerns include:

  • Generation of inaccurate and misleading information.
  • Challenges in trusting results without knowing their source and provenance.
  • Potential for new forms of plagiarism that disregard content creators’ rights.
  • Disruption of existing business models reliant on search engine optimization and advertising.
  • Increased susceptibility to fake news.
  • Easier generation of AI-created fakes, leading to potential social engineering cyberattacks.

Examples of Generative AI Tools

Generative AI tools cover various modalities, including text, imagery, music, code, and voices. Some notable AI content generators include:

  • Text generation tools like GPT, Jasper, AI-Writer, and Lex.
  • Image generation tools such as Dall-E 2, Midjourney, and Stable Diffusion.
  • Music generation tools like Amper, Dadabots, and MuseNet.
  • Code generation tools including CodeStarter, Codex, GitHub Copilot, and Tabnine.
  • Voice synthesis tools like Descript, Listnr, and
  • AI chip design tool companies like Synopsys, Cadence, Google, and Nvidia.
Use cases of generative AI

Use Cases for Generative AI, by Industry

Generative AI technologies have the potential to impact a wide range of industries and use cases. They can enhance and streamline various processes, leading to improved efficiency and innovation. Here are some ways generative AI applications could influence different industries:

  • Finance can use generative AI to improve fraud detection by analyzing transactions within the context of an individual’s history.
  • Legal firms can leverage generative AI for contract design and interpretation, evidence analysis, and argument generation.
  • Manufacturers can use generative AI to identify defective parts more accurately by combining data from cameras, X-rays, and other metrics.
  • Film and media companies can produce content more efficiently and translate it into different languages using actors’ voices.
  • The medical industry can employ generative AI for more efficient identification of promising drug candidates.
  • Architectural firms can use generative AI for quicker design and adaptation of prototypes.
  • Gaming companies can utilize generative AI for designing game content and levels.

Generative AI: FAQs

  1. Joseph Weizenbaum developed one of the earliest examples of generative AI with the Eliza chatbot in the 1960s.
  2. Ian Goodfellow introduced Generative Adversarial Networks (GANs) in 2014, a crucial milestone in generative AI.
  3. Recent advancements in Large Language Models (LLMs) by OpenAI and Google have played a significant role in the latest generative AI developments.

Generative AI has the potential to automate tasks traditionally performed by humans, including content writing, graphic design, and customer service, which could lead to job displacement in some areas.

Building a generative AI model involves encoding content representations efficiently and fine-tuning the model for specific use cases, followed by training on relevant datasets.

Training a generative AI model involves optimizing its parameters for a particular application and fine-tuning it based on relevant training data.

Generative AI is revolutionizing creative work by enabling artists, designers, and creators to explore and refine their ideas, generate variations, and democratize aspects of the creative process.

The future of generative AI involves improving user experiences, building trust in results, customizing generative AI for specific applications, and integrating generative AI capabilities into various tools and workflows.

Final Word

Generative AI has the potential to revolutionize content creation, enhance efficiency, and open up new possibilities across various industries. While it offers numerous benefits, it also presents challenges related to accuracy, bias, and ethical considerations. As generative AI continues to evolve, it will play a pivotal role in shaping the future of technology and creative endeavors.

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