What Actually Generative AI is?
Generative AI is a smart computer program that can create new things like stories, pictures, songs, videos, or even computer code. It learns by looking at a lot of examples, like how people talk, write, draw, or make music. After learning, it can use that knowledge to make something brand new, like writing a poem or drawing a picture.
This kind of AI is different from regular AI, which usually just answers questions or makes guesses. Generative AI’s special skill is creating things that look or sound like they were made by people. It helps in many areas like making art, helping with homework, designing products, or talking like a virtual assistant.
What Gen AI can do?
Generative AI tools can:
- Respond to prompts and questions
- Create images or video
- Summarize and synthesize information
- Revise and edit content
- Generate creative works like musical compositions, stories, jokes, and poems
- Write and correct code
- Manipulate data
- Create and play games
Capabilities can vary significantly by tool, and paid versions of generative AI tools often have specialized functions. For example, Chat GPT 4o, allows users to interact with the AI using voice and images rather than just text and create and train enclosed GPTs to perform specific functions.
Some popular tools:
This list is not comprehensive but features some of the most widely used generative AI tools.
- Chatbots and Text Generators (Bing Chat , ChatGPT , Copilot).
- Research Tools (Open Knowledge Maps , Research Rabbit , Semantic Scholar).
- Image Generators (DALL-E2 , Midjourney).
- Code Generators (GitHub Copilot , Codacy).
- Research Tools (Elicit , Discuss Genie).
Ai vs Gen Ai:
Ai is the broader concept of making machines more human-like. It includes everything from smart assistants like Alexa, chatbots, and image generators to robotic vacuum cleaners and self-driving cars. Generative AI is a subset that generates new content meaningfully and intelligently.
GenAI supporters have claimed that generative techniques represent a significant step toward artificial general intelligence (AGI): AI that possesses all the intellectual capabilities humans possess, including reasoning, adaptability, self-improvement and understanding. Despite GenAI’s impressive results, we are likely many technological advances short of that happening. While GenAI excels at interpreting and generating content at one level of abstraction, it still struggles when parsing context across multiple levels of abstraction, resulting in various omissions and errors that are easily spotted by humans.
History:
Generative AI emerged in the late 2010s with advancements in deep learning, particularly with models like Generative Adversarial Networks (GANs) and transformers. Advances in cloud computing have made generative AI commercially viable and available since 2022
How these Ai work?
Generative AI systems today are mostly built using very smart computer models called large language models (LLMs). These models can make new things like stories, pictures, or computer code just by reading a short message or question from a person.
For example, if someone types “write a story about a dragon,” the AI can create a brand new story using what it has learned before. People already use these AI tools to help them make resumes, business plans, drawings, or fix computer problems.
Experts believe that in the future, this kind of AI will work with people like a helpful teammate. But it’s still important for humans to watch and guide the AI, especially at work. This way, we can make sure it works safely, makes good choices, and doesn’t cause problems.
On the technical side, generative AI uses special methods like GANs and transformers to learn and create. There are other helpful tools too, like VAEs (which make new samples from old ones) and NeRFs (which help make very real-looking 2D or 3D pictures).
GANs are made up of two neural networks: a generator and a discriminator. The two networks compete with each other, with the generator creating an output based on some input and the discriminator trying to determine if the output is real or fake. The generator then fine-tunes its output based on the discriminator’s feedback, and the cycle continues until it stumps the discriminator.
Transformer models, like ChatGPT, (which stands for Chat Generative Pretrained Transformer), create outputs based on sequential data (like sentences or paragraphs) rather than individual data points. This approach helps the model efficiently process context and is why it’s used to generate or translate text.
Limitations of Gen Ai:
Despite their advancements, generative AI systems can sometimes produce inaccurate or misleading information. They rely on patterns and data they were trained on and can reflect biases or inaccuracies inherent in that data. Other concerns related to training data include
Security
Data privacy and security concerns arise if proprietary data is used to customize generative AI models. Efforts must be made to ensure that the generative AI tools generate responses that limit unauthorized access to proprietary data. Security concerns also arise if there is a lack of accountability and transparency in how AI models make decisions.
Creativity
While generative AI can produce creative content, it often lacks true originality. The creativity of AI is bounded by the data it has been trained on, leading to outputs that may feel repetitive or derivative. Human creativity, which involves a deeper understanding and emotional resonance, remains challenging for AI to replicate fully.
Cost
Training and running generative AI models require substantial computational resources. Cloud-based generative AI models are more accessible and affordable than trying to build new models from scratch.
Explainability
Due to their complex and opaque nature, generative AI models are often considered black boxes. Understanding how these models arrive at specific outputs is challenging. Improving interpretability and transparency is essential to increase trust and adoption.
Algorithmic models to train GenAI
There are also GANs (Generative Adversarial Networks), which use two AI one to create things and one to check if they look real. They challenge each other to improve. A very new and advanced method is called KAN (Kolmogorov-Arnold Network). It works differently by directly connecting input to output without needing the normal encoder and decoder it’s still being tested but may be helpful in predicting things like weather or the stock market.
Each of these smart methods works better for different kinds of input like writing, pictures, music, code, or data. For example, diffusion models are great at making realistic photos and faces. GANs also create images but are harder to train. Both GANs and VAEs are also used to make fake data that helps train other AIs. KANs, though still new, might help with more complex ideas.
At first, using AI wasn’t easy you had to upload data in tricky ways and write special code using languages like Python. But now, AI tools are much easier. You can simply type what you want in plain language, and the AI understands it. Then you can even ask it to change the result like making it more funny, more serious, or using different words just by giving feedback. This makes generative AI more useful and fun for everyone.
Best practices for using generative AI
The best practices for using generative AI vary depending on the modalities, workflow and desired goals. That said, it is always important to consider factors such as accuracy, transparency and ease of use when working with generative AI. The following practices serve as a guide:
- Clearly label all generative AI content for users and consumers.
- Assess the cost/benefit tradeoffs compared with other tools.
- Vet the accuracy of generated content using primary sources, where applicable.
- Consider how bias might get woven into generated AI results.
- Double-check the quality of AI-generated code and content using other tools.
- Learn the strengths and limitations of each generative AI tool.
- Familiarize yourself with common failure modes in results and work around them.
- Vet new applications with subject matter experts to identify problems.
- Implement guardrails to mitigate issues with trust and security.
Future of Gen AI:
1. Hyper-Personalized Content Creation
Generative AI will create customized text, music, videos, and designs for individuals. Imagine movies or games that adapt their storylines to each viewer’s mood or personality.
2. AI-Assisted Creativity
Rather than replacing artists, Gen AI will act as a co-creator. Designers, writers, and developers will collaborate with AI to generate ideas faster and refine creative work.
3. Next-Generation Education
Students will have AI tutors that generate lessons, quizzes, and explanations tailored to their learning style. This could make personalized education accessible to everyone.
4. Smarter Software Development
AI tools will soon generate complete applications — from interface to backend based on simple text prompts. Coding may shift from “writing code” to “designing logic in natural language.”
5. Synthetic Data for AI Training
Generative AI will produce realistic synthetic data to train other AIs safely without privacy issues. This is especially useful for healthcare, finance, and autonomous driving systems.
Concluion:
The relationship between hype and reality can be a delicate balance, especially when it comes to introducing new concepts or technology. While there is a lot of buzz surrounding generative AI, its impact should not be underestimated. Few advancements in technology have had such a rapid and transformative effect, even on itself. Its rapid pace of change can be seen in the vast scope of ChatGPT. Therefore, it is crucial to act quickly. As we move forward, it is important to establish trust and transparency in order to fully utilize the potential of Gen AI in the economy, business, and society.
