What is Generative AI? A Beginner’s Guide in 2024

​​Generative AI, or GenAI, is a term we hear often. It’s changing our lives in many ways, from text generation to video production. But what is generative AI, anyway? It’s an advanced evolution of traditional AI, offering enhanced capabilities to change how we create content.

In this guide, Designveloper will help you demystify generative AI, from its popular use cases to future trends. Also, we’ll showcase some GenAI giants, like ChatGPT, which lay the solid foundation for this realm. So, are you ready to discover the exciting world of this tech? Let’s get started!

Understanding Generative AI

In this section, we’ll discover the fundamentals of GenAI.

What is Generative AI?

Generative AI is a cutting-edge field within AI that leverages machine learning techniques to generate data. 

Unlike traditional AI, which excels at analyzing and understanding existing information, generative AI takes it a step further. Particularly, GenAI devours massive amounts of data – text, images, code, music – and uses that knowledge to generate fresh and original outputs. 

For example, if a GenAI model is trained using a collection of cat images, it can produce new images resembling cats, despite never having encountered them previously. This is a significant departure from traditional AI models, which would typically just classify the images as cats or not cats.

Generative AI is not just limited to images. It can generate a wide variety of data types, including text, music, speech, and even 3D models of objects. This makes it a powerful tool for a wide range of applications, from content creation to design and beyond.

4 Main Use Cases of Generative AI

You’ve resolved the question: “What is Generative AI?” and discovered how it differs from traditional AI. But in what use cases is this advanced AI applied?

McKinsey reported that the influence of Generative AI on productivity could add up to USD 4.4 trillion to the global economy. Its impact spreads to all industry sectors, especially banking, high-tech, and life sciences. But 75% of its value lies in the following use cases as follows: 

Marketing & Advertising

In this industry, GenAI is used to create personalized content and targeted advertising campaigns. Numerous tools (e.g., GetResponse or HubSpot) integrated with GenAI can support various marketing and advertising tasks, such as content creation, campaign optimization, and customer segmentation. This then helps improve engagement and conversion rates. 

Software Development

Generative AI is a game-changer in software development. It aids in brainstorming new ideas and suggesting code. GenAI coding tools like GitHub Copilot, for example, can predict coding sequences and brainstorm coding solutions. This not only saves you much time in coding but also shifts your role from only coding to strategic decision-making.

Research & Development (R&D)

GenAI is increasingly used in R&D across various industries, especially in automotive and manufacturing. According to McKinsey, GenAI has a big potential in R&D in these realms, with 75% of respondents trying at least one GenAI tool in their R&D processes. 

This advanced tech benefits every step of the product development process. From concept recommendations based on existing design to regulatory approvals, GenAI can handle with ease. Through data collection and analytics, it helps you make informed, yet quick decisions to address such challenges as innovation delays or resource allocation. This allows your business to expedite the work of designing and developing the product without any errors. 

Customer Operations

GenAI is revolutionizing every aspect of customer operations, from boosting customer service productivity to improving chat channels. For example, GenAI can automate the analysis of conversations for context. This allows it to generate human-like, contextually relevant responses, significantly improving how you handle customer inquiries. Further, it could tailor suggestions based on customer data or past behavior, thereby fostering customer experience.

Benefits of Generative AI

With widespread applications across various fields, generative AI’s benefits are vast and transformative. Here are some immense pros it can provide:

Automating Content Creation

One of the most significant benefits of generative AI is its ability to automate content creation tasks. This includes generating text, images, music, and even code. By learning patterns and structures from existing data, generative AI can produce new, unique content, significantly reducing the time and effort required in manual content creation.

Enhancing Creativity and Innovation

GenAI acts as a powerful partner in the creative process. One study showed that this tech can boost human creative productivity by 25% and increase the value of creative outputs by 50%. GenAI achieves this by suggesting novel ideas and perspectives, pushing the boundaries of innovation. For example, in design, GenAI can propose unique concepts based on user-defined parameters, sparking new possibilities.

Collecting, Processing, and Managing Data

GenAI plays a crucial role in data management tasks. Particularly in data processing, this advanced AI tech can elevate automation up to 90%. Additionally, GenAI can create synthetic data to train machine learning models, especially when dealing with small or imbalanced datasets. This synthetic data preserves the statistical properties of real data but protects sensitive information, addressing privacy concerns.

Boosting Productivity and Job Satisfaction

Studies show a clear link between GenAI and improved productivity and job satisfaction. 71% of users report feeling more productive, while one NBER study found a 13.8% productivity increase at a Fortune 500 company alongside increased customer satisfaction and reduced employee turnover.

GenAI achieves this in several ways. It automates manual and repetitive tasks, freeing employees to focus on more complex and creative work. These tasks can include scheduling meetings, managing routine emails, or even predicting future outcomes based on historical data. By reducing the workload and allowing employees to focus on more engaging aspects of their jobs, GenAI leads to increased productivity and job satisfaction.

Challenges of Generative AI

Generative AI, while promising, is not without its challenges. So before using GenAI in your business routines, you should consider the following issues:

Data Bias

GenAI models learn from the dataset they are trained with. However, if this training dataset contains biased information, the model will mirror these biases in its results. This can lead to issues with both the accuracy and controllability of the generated content. 

For instance, a language model trained on biased text might produce discriminatory or offensive language, while an image generator might create images that reinforce harmful stereotypes. This is also why most companies see inaccuracy as the top concern when using this tech.

Safe and Ethical Considerations

The ability of generative AI to create realistic content raises several ethical concerns. For example, deepfakes – hyper-realistic AI-generated images or videos – can be used to spread misinformation or commit fraud. 

Similarly, AI-generated text can be used to produce fake news or deceptive marketing content. This has promoted nearly half of the organizations to develop a governance framework and regulatory requirements that guide the ethical and safe use of GenAI.

That said, 55% of workers still leverage unapproved GenAI apps. Meanwhile, 40% are using banned tools at work just because these GenAI tools can advance their careers, expedite their workflows, and reduce the workflow.

Future Trends of Generative AI

The global market size of GenAI is estimated to reach US 967.65 billion by 2032, with a staggering CAGR of 39.6% during the forecast period (2024-2032). This growth is mainly driven by advancements in natural language processing and a higher demand for product commercialization. 

Along with the soaring of the GenAI market, we also observe the emergence of trends that can shape the future of this advanced tech. Understanding the key trends is crucial as it allows us to anticipate the direction of technological advancements and align our strategies accordingly. 

But what is generative AI’s tendency in upcoming years? Let’s explore!

Multimodal AI

Multimodal AI is an AI system that can handle diverse types of data or data from multiple modalities such as text, voice, images, videos, and sensor data. This allows such AI models to understand the various kinds of information presented to them. 

By combining information from multiple sources, multimodal AI can also improve accuracy and enhance decision-making in tasks like object identification, sentiment interpretation, and more.

Google Gemini is a typical example of this trend. This multimodal AI model can process and understand inputs from different sources, making it more versatile and adaptable to different situations.

Small Language Models

When you use Microsoft Copilot or ChatGPT to answer complex questions, you’re approaching large language models (LLMs). While large language models (LLMs) have grabbed headlines for their impressive capabilities, a new trend is emerging: small language models (SLMs).

Unlike their large counterparts, SLMs are AI models trained on a significantly smaller dataset. This makes them less computationally expensive to train and run, allowing them to operate on devices with lower processing power. Imagine a large LLM as a vast library containing countless books, while an SLM is a curated collection focusing on a specific topic.

While LLMs boast impressive versatility, their complexity comes at a cost. This is where SLMs shine. Accordingly, their smaller size makes them ideal for deployment on edge devices like smartphones or wearables, bringing GenAI capabilities closer to everyday users. Also, When trained on specific data sets, they excel in tasks relevant to that domain, offering potentially higher accuracy and efficiency compared to a broad LLM.

Today, tech giants like Microsoft and Meta have released SMLs to enhance efficiency and scalability, for example, Llama 2 or Phi-2.

Top 5 Popular Generative AI Examples

Having unlocked all the secrets behind the question: “What is generative AI?”, you may now be curious about which models dominate this industry. The top 5 positions belong to these models:

ChatGPT

ChatGPT is a language model developed by OpenAI. It’s designed to produce text resembling human communication. The primary purpose of ChatGPT is to engage in conversation with users, answer questions, and provide information in a natural, conversational manner. 

It’s been used in various applications, including customer service bots and interactive fiction. ChatGPT can also understand and generate images, making it a versatile tool in the realm of AI.

Due to these standout capabilities, many applications now integrate ChatGPT to support users with their work. For example, MailChimp, an email marketing platform, uses ChatGPT to generate personalized email content, search, retrieve, and interact with MailChimp data seamlessly. This not only saves time in developing email marketing campaigns but also provides a more custom experience for the customer.

Microsoft Copilot

Microsoft Copilot, formerly known as BingChat, is an AI-powered chatbot developed by Microsoft. It integrates with Bing’s search engine and provides a paragraph summary in response to your queries. 

Users can ask Copilot questions, upload images, and even request AI-generated images. In addition to answering questions, Copilot can make recommendations, compose a love song, suggest exciting night activities, plan balanced diets, and more. 

Besides, it’s integrated into various Microsoft 365 apps, enhancing productivity and creativity. For example, Copilot can help you create a new presentation from a prompt or Word file in Microsoft PowerPoint or automate complex data analysis tasks in Excel. 

Google Gemini

Developed by Google DeepMind, Google Gemini is another powerful LLM with a focus on informative and comprehensive responses. Need help researching a topic, summarizing complex information, or even writing different creative text formats? Gemini can be your AI research assistant, providing insightful and well-structured text outputs.

Besides, it can understand and operate across different types of data beyond text, including audio, video, and image. Accordingly, it can generate images based on specific prompts or descriptions. 

Like Copilot, Google Gemini integrates Google apps like Docs, Gmail, and more as extensions. This allows users to access a wide range of services and information directly from Gemini.

DALL-E

DALL-E is another generative AI model developed by OpenAI. It’s the first AI-powered system that uses deep learning neural networks trained on large datasets to generate unique images from textual descriptions. DALL-E also integrates an assessment system to verify the precision of the resulting image.

For this reason, DALL-E has become the right arm of those working for creative industries like design and advertising in automating the process of image creation.

Speechify 

If you wonder what generative AI software is the best for voice creation, Speechify is a decent option. This tool uses advanced AI techniques to transform text into human-like voiceovers. Accordingly, it allows users to generate high-quality, natural-sounding content.

What sets Speechify apart is its versatility. It supports 40+ languages, plus 100+ high-quality AI voices. 

But that’s not all. Speechify also offers a unique feature: AI Voice Cloning. This enables you to build high-quality AI clones of human voices within seconds. For this reason, this tool is suitable for people with any level of experience, whether a beginner content creator or a seasoned pro. 

Build Generative AI with Designveloper

As the global generative AI market is projected to reach a worth of $266 billion by 2032, the pressure to adopt this wonderful technology is increasing. Here at Designveloper, for a while now, we’ve embraced the AI advancements and offered solutions with the help of Generative AI to help businesses grow and optimize their performance. By combining the best innovations of the academic and industrial worlds, our team of AI specialists develops tools capable of creating new, original texts, images, music, etc.

Generative AI is changing the world and we are happy to be a part of it. We possess a broad range of experience across various industries that enable us to come up with appropriate solutions for our clients. We appreciate the fact that Generative AI is complex and it is our primary responsibility to ensure that our clients find it easy to use.

How We Incorporate Generative AI into Project Management

Introducing Generative AI in our team has proven to be a game-changer for improving project management efficiency. This AI technology has changed how we carry out tasks to better ways, thus increasing our efficiency.

Due to its capacity to create new data out of other data, generative AI has been transforming the manner in which projects are managed. They assist us in managing certain processes in order to prevent waste of our team’s time on repetitive jobs. For instance, we employ machine intelligences to sum up text, explain concepts, propose and create texts based on templates, write code for data queries, and verify the quality of content in tickets and documents.

We understand and appreciate that data security is something that must be given top priority. That is why, AI has to be integrated securely and meets the requirements of the data protection legislation. Even if some large companies decide that the cost of incorporating AI is prohibitive, here at Designveloper, we understand the advantages it presents in the long term. There are always ways to make these effective solutions more easily available to our clients.

Successful Cases

At Designveloper, we have witnessed the impact that can be created by generative AI ourselves.

• Document Summarization: Our team employ the use of AI chat such as ChatGPT, Gemini, and Microsoft Copilot to make summaries of lengthy documents. This has in particular helped to reduce greatly the time required on document review so we can attend to more significant issues.
• Drafting Specifications and Documents: Generative AI has positively contributed to writing specs and docs. It also assists to make us consistent and accurate, thereby being able to deliver excellent results each time.
• Code Explanation and Assistance: AI helps our developers in comprehend the codebase and in seeking help when deriving at the code. This has helped us create better code and spend less time tracing bugs.
• Test Case Generation: Using Generative AI, we have made the process of test case generation automatic. This has not only facilitated our testing but also expanded the scope of the test, making the software more effective.
• Text Editing Tasks: Our web ops team for Lumin specializes in utilizing Generative AI for complex text editing work. For example, they’ve employed it into coming up with codes for sitemap extraction and SEO for static page.
• GitHub Copilot: In the recent past, we have tried incorporating GitHub Copilot to help in coding. First impression is good and there are numerous advantages that this can offer.

These successful cases prove our efforts to apply Generative AI to strengthen our project management. At Designveloper, we are not only consumers of the AI revolution but active contributors to the industry’s development.

Final Words

We’ve explored the question, “What is generative AI?” and its immense potential in creating new, unique content, from images to text and beyond. You also learn how popular GenAI models work and how key trends like Multimodal AI contribute to the growth of this field. 

Generative AI is not just for tech experts. Even beginners can dive in and start experimenting. So why wait? Dive in and see what you can create! To further understand about GenAI, keep reading our blog posts.