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  • Writer's pictureDeepali Salwan

The Innovative Frontier of Generative AI

The tech world was taken by storm last November as Microsoft-backed OpenAI revealed the chatbot ChatGPT to the world. Even if you are not remotely related to the tech space, you simply cannot ignore the buzz around ChatGPT and the impact it has created since its launch. In fact, just a mere two months after its launch, reports came out saying that ChatGPT reached a whopping 100 million monthly active users in the first month of the year making it the fastest-growing consumer application ever!


As ChatGPT soared in popularity, Google's dominance in search was in grave danger. To keep up, its AI team came up with its own AI chatbot, Google BARD in March, though OpenAI arrived earlier in the game.


Reinforcement Learning from Human Feedback-the backbone of ChatGPT


But what exactly is ChatGPT and how does it work?


To put it simply, ChatGPT is built on transformer architecture and trained on millions of conversations from various sources. It uses supervised machine learning and reinforcement learning techniques. OpenAI uses a technique known as Reinforcement Learning from Human Feedback(RLHF). It involves incorporating human feedback into the model's training loop to minimize the generation of harmful, untruthful, or biased outputs.


The timeline


GPT (Generative Pre-Trained Transformer) was initially introduced by OpenAI in 2018 and serves as the basis for ChatGPT. The first version, GPT-1, had 117 million parameters to work with and utilized a deep learning technique known as transformers. GPT-2 came a year later with improvements and had

1.5 billion parameters. Then, came its successor GPT-3 with 175 billion parameters in 2020. Just a few months back in March, GPT-4 was released and has been made publicly available (limited capacity) through ChatGPT Plus. GPT-4 has been the talk of the town ever since.


What exactly is Generative AI?


ChatGPT is just one (rather popular) example of a subset of AI models called Generative AI.


If you are not that exposed to the tech scene, you may ask- what is Generative AI? Generative AI refers to a branch of artificial intelligence that generates new content in the form of images, videos, texts, and code in response to prompts.


Inside look


There are multiple types of generative models. Let us have a look:


● Generative Adversarial Networks (GANs): GANs have two neural networks- a generator and a discriminator, while The generator generates new samples based on the inputs, while the discriminator tries to distinguish between the generated samples and real samples from the training data.




● Variational Autoencoders (VAEs): Researchers point out that VAEs consist of an encoder network that maps input data to a latent space and a decoder network that reconstructs the input data from the latent space. By sampling from the learned latent space, VAEs can generate new data samples that resemble the training data.



● Transformers: Transformers are a type of neural network architecture that has been widely used in generative AI models. They were introduced in a groundbreaking paper called "Attention Is All You Need" in 2017.



Diffusion models are a class of generative AI models that simulate a diffusion process, gradually transforming a simple initial distribution into a target data distribution through a series of steps. By employing denoising autoencoders and invertible neural networks, these models learn to generate high-quality samples. During training, the models maximize the likelihood, and in the generation phase,they run a reverse diffusion process to generatecoherent and realistic samples. Generative AI can be a game-changer


Generative AI can revolutionize various industries by enabling machines to mimic human creativity and generate realistic outputs. A Gartner report says that by 2025, 30% of outbound marketing messages from large enterprises will be synthetically generated (a huge jump from less than 2% in 2022).


Image source: Gartner Generative AI has already disrupted content creation and its penetration will deepen in the future. By using tools like DALL-E 2 (again from OpenAI), and Midjourney, anybody can produce high-quality text, images, videos, and music. This can impact fields like advertising, marketing, and even journalism. Generative AI can venture into other creative fields and help designers and creatives by generating ideas, prototypes, and designs as well. It is quite obvious that the chatbot and virtual assistant space will never be the same after ChatGPT and its competitors burst into the scene. We can expect more natural and contextual interactions with chatbots now.


Generative AI can assist in the arena of healthcare and medicine too. It can aid in medical image analysis, pathology, and genomics research. Even in financial modeling and forecasting, Ge, anybody can produce high-quality text, images, videos, and music. This can impact fields like advertising, marketing, and even journalism. Generative AI can venture into other creative fields and help designers and creatives by generating ideas, prototypes, and designs as well. It is quite obvious that the chatbot and virtual assistant space will never be the same after ChatGPT and its competitors burst into the scene. We can expect more natural and contextual interactions with chatbots now.



Risks


While the advantages of Generative AI are numerous, great benefits often come coupled with risks as well. One such risk of Generative AI that is being talked about a lot these days is its potential for malicious use like the creation of highly realistic deep fake videos or deceptive content aimed at spreading misinformation and manipulating public opinion. Another concern that is being raised with Generative AI is the potential infringement of intellectual property rights in areas of originality and ownership. As generative models can be trained on vast amounts of personal data, ethical concerns surrounding privacy are also something to look after.


If such risks are properly addressed through responsible development and regulation, we can harness the positive aspects of generative AI while mitigating potential harm.





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