According to the survey, 73 percent of Indians and 49 percent of Australians use Generative AI tools.
We are sure you have used these tools once.
Generative AI has elevated artificial intelligence to a new level and given it a new avatar. Still, many people are unaware of this tool’s capabilities. Most people who use this technology tool still need to understand the basics of this attractive technology.
Are you also thinking about what this AI is? If yes, stay for a few minutes with us.
We are going to explore the world of Generative AI in a few minutes. Here, we will discuss the basics of this AI. You will also explore the benefits and other crucial elements of this technology in this article.
So, tech lovers let’s dive into our main point without wasting time.
Basic of Generative AI
ChatGPT comes with an introduction to Generative AI or Gen AI in the year 2022. After the ChatGPT evolution, we have noticed the introduction of many similar tools. Gemini (Bard), Bing AI, and more are a few examples of those tools.
You may already be aware of what Generative AI is in case you have ever used any of these tools.
It is a subset of Artificial intelligence (AI) that uses the generative AI model for developing images, texts, videos, and other types of content. These models generate these outputs in response to particular prompts. It means that you need to enter the prompt or text to ask this tool to generate the content.
Generative AI depends on the intelligent machine learning model known as deep learning. This model simulates the human brain’s process of learning and decision-making.
These models operate by identifying and encoding the pattern in enormous data. Later, they use the same information to understand your request or question and respond with the relevant new content.
Are you interested in knowing how these models work in detail? If yes, you should definitely read the next section.
How does Generative AI work?
Traditionally, Machine Learning Models were focused on classifying data points. These models tried to find out the relationship between known and unknown factors. For example, they look at the pictures (known data) and map them to words (unknown factors).
Generative AI took one step further by introducing some other features. It works in three phases in most cases mentioned below:
1. Training
Generative AI starts working with the evolution of a foundational model. It is a deep learning model that serves as a basis for countless types of Generative AI applications. Large Learning Model (LLM) is the most common model created for image generation, video generation, and other content generation.
Developers train a deep learning algorithm on huge volumes of data to create a foundational model. The algorithm does and evaluates millions of predicting exercises during training. It tries to predict the next element under the sequences, like text, images, or even code.
This training result is a neural network of parameters that can develop content autonomously in response to output or prompts.
2. Tuning
Training involves making the foundational model that knows everything but can’t give the exact and accurate result. Tuning this model to a particular content generation task is necessary to achieve more accurate outcomes. The following are the two ways in which tuning can be performed:
Fine-tuning
The process of fine-tuning includes training a pre-trained model on new data to make it suitable for a particular task. The model receives queries and prompts and corresponds with the answer in the desired format. Let us understand it with a concise example.
Suppose a development team is producing a customer service chatbot. In that case, the team will create thousands of documents including labelled questions and desired answers. Then, the team will upload those documents to the model.
1. Reinforcement learning with human feedback (RLHF)
RLHF is another technique that uses feedback from humans to train the AI models to let them learn more effectively. It often involves people like you to score different outputs in the same response. However, it is as simple as you chat with an AI model.
2. Generation, evaluation, more tuning
The foundational model updates itself much less frequently, even after 18 months. Developers and users continually access their Generative AI tools for further tuning to make them more accurate and reliable. Have you ever noticed two outputs on ChatGPT when you give it a prompt? It’s because of the same reason.
Retrieval Augmented Generative (RAG) is another option to improve these tool’s performance. It serves as a structure for expanding the base model to incorporate additional sources beyond the training data. RAG ensures that your Gen AI tool always produces the most correct and accurate information.
Top Abilities of Generative AI
As discussed before, Generative AI has the potential to create various forms of content. If you are still interested in knowing what they can generate, you should take a look below:
1. Text
Generative AI tools can generate comprehension and contextually relevant text. You can ask them to generate any form of written content, like blogs, brochures, reports, articles, emails, and more. They can also perform repetitive writing tasks such as drafting summaries of documents.
These tools use machine learning models to generate new text based on patterns learned from existing text data. Markov Chains, RNNs, and transformers are some examples of those models used to generate new text.
2. Image and video
Tools like DALL-E and Midjourney can create real-looking images or original art. Apart from generating images, they can perform image editing or enhancement tasks. You can also perform image-to-image translation and transfer with these tools. They use machine learning algorithms like VAEs or GANs to create new pictures that look similar to real-world images.
Besides generating images, you can also use these tools to create animation from text prompts. On top of that, these tools can apply special effects to any video instantly and affordably. These tools again use deep learning methods to generate new videos by predicting frames based on the last one.
3. Coding
Gen AI is also capable of performing coding. It can create original codes and autocomplete code snippets. You can translate your existing code into another programming language and summarise the code functionality. This capability allows you to instantly debug, refract, and prototype the application while offering a natural language interface for coding tasks.
4. Sound
With the Generative ai model, you can also generate audio content and natural-sounding speech. The same technology can generate original music that mirrors the structure and sound of professional compositions.
5. Design art
Generative ai models can also assist you in graphic design by generating unique works of art. You can generate dynamic environment graphics and characters with this model. It can also create special effects for virtual simulation and video games.
Benefits of Generative AI
Gen AI has the potential to provide you with countless advantages. Apart from performing countless tasks, it can offer many perks. Let us take a look at the top benefits of this model.
1. Boost research
Generative AI can drastically enhance research and innovation because it allows you to discover new trends and patterns that may not have been transparent before. For example, these systems can be used in the pharma industry to generate protein sequences.
2. Dynamic personalization
Generative AI models can boost personalization by analysing user preferences and generating personalised content in content creation and recommendation systems. This can lead to a more personalised and engaging user experience (UX).
3. Optimise business process
With the Generative AI model, you can optimise your business process using Machine Learning (ML) and AI applications across all your business operations. It would be best to apply this technology across all lines of your business, like customer services, finance, sales, marketing, and more.
Limitation of Generative AI
Generative AI often includes several limitations that can be harmful to some users. So, let us explore the top limitation of this technology:
1. Data training
Data training can significantly influence the capability of Gen AI. For example, a model that has been trained on news articles with gender biases can generate content that highlights those biases.
2. Inability to create new ideas
Most Generative AI models are not able to produce new and creative ideas because they are developed on pre-existing data and rules. Hence, they are not capable of thinking out of the box or breaking rules since they are not trained in that way.
3. Outdated information
Most generative AI models are trained on data with outdated information. You might be shocked to know that ChatGPT is also among those models. Therefore, it results in outdated information or an inability to answer queries about current events. Some models didn’t even make this date transparent to the users.
Conclusion
Generative AI has pushed artificial intelligence to a new height. It has provided AI tools with the capability to produce text, images, videos, sound, and more. This model offers countless benefits. However, this technology often has many limitations, but we can expect it to overcome these limitations in the future.