What is Generative AI ? Everything you Need to Know as beginner

What is Generative AI ? Everything you Need to Know as beginner

Before going deep into the generative AI. Lets first understand what AI is and where generative AI fits into this umbrella. There are lot of buzz around AI now a days. Its happening due to the popular AI based tools like ChatGPT or Google bard. So Now Lets see what is AI first.

AI means artificial intelligence. According to the google, “AI is the science of making machines that can think like humans.” In simple words, AI is the field which focuses on developing the ability to think inside machines like humans using algorithms. Now AI is a very broad umbrella, which consists of so many fields like machine learning, deep learning etc and one of them are generative AI. Now Lets see what is generative AI and where it fits into this umbrella.

Introduction to GenAI

As i said machine learning, which is a subfield of AI, is a program or system that trains a model from input data and helps to make accurate predications. There are two most common machine learning models exists supervised machine learning and unsupervised machine learning.

Deep learning is the subset of machine learning. Deep learning is a type of machine learning that uses artificial neural networks, allowing them to process more complex patterns than machine learning. Artificial neural networks are inspired by the human brain. Generative AI is the subset of deep learning. Bit confusing right, To clarify just check the below image once.

I hope now we are on the same page. Now we finally understood, where generative AI fits into this AI umbrella. Now lets see how generative AI works behind the scene.

Understanding the Basics of GenAI

As we have seen, Gen AI is a subset of deep learning, which means it uses artificial neural networks, which can process both labeled and unlabeled data which specializes in creating new data from it. In simple words,

  • Gen AI is a type of artificial intelligence that creates new content based on what it has learned from existing content.

  • The process of learning from existing content is called training and results in the creation of a statistical model.

  • when given a prompt, GEN AI uses the model to predict what an expected response might be and this generates new content.

There are different types of models which we can create using the help of generating AI. Some of them are listed below.

Model types of generative AI -

  • Text to text

  • Text to image

  • Text to video

  • Text to 3D

  • Text to task.

Now we know what is GenAI and different types of models it can provide. Now lets see what power these models behind the scenes and how it works.

How GenAI Works

To grasp how generative AI models work, Understanding the architecture of the Generative AI is essential. Most generative AI models, including the popular GPT-3, are based on the deep neural networks. These networks are consists of multiple layers of interconnected artificial neurons that process and analyse data.

Generative AI architecture are often organized in a way that allow them to take row data as input and transform it into the desired output like text, image, music or other forms of data. These models are highly complex and involve complex mathematical operations.

For example, let's consider the architecture of GPT-3:

  • Input Layer: GPT-3 starts with an input layer that receives the initial prompt or input data provided by the user.

  • Transformer Layers: The core of GPT-3 is the transformer architecture, comprising multiple layers of transformers. These transformers process and transform the input data, allowing the model to understand and generate text based on the input provided by the user.

  • Output Layer: After processing the input, GPT-3 generates an output, which can be text, code, or any content relevant to the input.

This architectural design enables GPT-3 and similar models like google bard to generate high-quality text that appears remarkably human-like. Now we have seen how these modals works basically. Its time to see the real world applications of GenAI and how it can help.

Real World Applications of GenAI

GenAI has found its applications in wide range of industries, revolutionizing the way task has performed. Best example of this is the content creation field. From single prompt you can produce a whole blog on a certain topic or you can generate speech for you next conference in fraction of seconds.

Here are the list of some of the industries in which GenAI is going to change the way we work.

  1. Health care : This is the industry in which GenAI can perform very crucial role and save many lives as well. Here are some examples -

    1. Report Imaging : GenAI models can assist in the interpretation of the medical reports. Helping doctors detect and diagose the problem.

    2. Drug Discovery: These models can speed up the drug discovery process by simulating molecular interactions and predicting potential drug candidates.

  2. Marketing : In the field of marketing, GenAI is changing the way we do PR for our products and services. Here is one simple example.

    1. Personalization : GenAI can help you to create personalized content based on the audience. It can help you to create recommendations and advertisements for users based on their preferences and behavior.
  3. Content Generation : Content creation is the field which is influenced by the GenAI most. Here is some examples.

    1. Content Writing : Models like GPT3, Bard and Dall-E have changed the way we create content. Now you just have to describe your imagination using simple english prompts and your imagination will turn into reality.

      Gen AI models can automate the creation of articles, reports, and essays by just providing your prompts.

    2. Code Generation: These models can generate code snippets and assist developers in writing software.

  4. Language Translation: Generative AI models can translate text from one language to another, breaking down language barriers and facilitating global communication.

Ethical considerations

The rise of Generative AI have sparked important discussions around ethics and societal impact. As we are using these models in our day to day life, concerns regarding misinformation, deep fakes and privacy has arisen.

Additionally, There are ethical questions surrounding the responsible use of generative AI. Its very crucial to ensure that this technology should be used for beneficial purposes and prevent this to be used in destructive manner. Some of the key ethical consideration include -

Deepfakes: The technology can be used to create convincing deepfake videos, images or even audios which raise concerns about the manipulation of content for malicious purposes. Which can harm someone weather financially or emotionally.

Misinformation : This technology can also be used to create false or misleading information, which can have real-world consequences. To prevent it, we need more strong fact-checking and content verification mechanisms.

Privacy : These models pose a significant threat to privacy. Because it uses users input data which may contain personal information to improve its models. To prevent it, governments and other stackholders should come together and make guidelines for the data use.

Now we have seen what all the ethical considerations are there while working with these models. Now lets see what are all the challanges and opportunities in this field.

Challenges and Opportunities

While Generative AI has made remarkable progress, it still faces significant challenges and holds exciting possibilities for the future.

Challenges -

  • Energy Efficiency: These models requires heavy computational resources for training and running large models. Which can be environmentally taxing. Future developments may focus on making AI more energy-efficient.

  • Bias Mitigation: Addressing and reducing bias in Generative AI models is an very big ongoing challenge. This involves improving the training data and fine-tuning processes to minimize biased outputs.

Opportunities -

  • Multi-Modal Models: The future of Generative AI will likely involve models that can handle multiple modalities, such as text and images, enabling more complex and versatile tasks.

  • Interdisciplinary Applications: Generative AI is expected to find applications across diverse domains, from art and entertainment to scientific research and education.

  • Improved Fine-Tuning: Future models may offer better control and fine-tuning mechanisms to ensure that the generated content aligns with user intent and ethical standards.

  • Human-AI Collaboration: The future could see increased collaboration between humans and AI, with AI acting as a creative tool and assistant in various professions.

Conclusion

In conclusion, Generative AI is a innovative technology that has the potential to reshape the way we interact with artificial intelligence its going to the way we work.

Understanding its architecture, data requirements, content generation processes, ethical implications, and real-world applications is essential for anyone interested in the field. As Generative AI continues to advance, it's crucial to use this technology responsibly. With a better understanding of this technology and by navigating its challenges and opportunities, we can create a significant impact in human life.

Thank you :