Types of Generative AI You Should Know

I am pretty sure you will agree with me when I say that artificial intelligence has gone through different cycles of hype, and one of them turns out to be generative AI. One of the most crucial turning points is ChatGPT. Right from churning out essays to sharing jokes, editing images, the concept has been considered as one of the breakthroughs. As the title implies, the following post focuses on what generative AI is, why it is needed in the first place and of course, you will be well-acquainted with different types of generative AI.

Generative AI, short for Generative Artificial Intelligence, is a rare type of AI that has seamless potential to develop a new range of content and ideas, whether you are willing to have a conversation or create amazing stories, edit images, videos, and of course, listen to your favourite music. The concept, right from the start, seemed to have gained lots and lots of traction since it manages to not just learn human language but also assist in dealing with the latest and complicated programming languages, and not just tech; take any subject, be it art, chemistry or biology, generative AI can assist with all your concerns and queries. And do you know what the most amazing aspect of generative AI technology is? It successfully reuses what it knows to solve new problems. For instance, the tech itself happens to learn English and can create an amazing poem from the words and vocab it has learned, and trust me, all this is quite legit and not vague. No wonder several organisations are using the concept of generative AI to take care of different purposes such as chatbots, media creation, product development, and design.

Now, many of you have this confusion regarding AI and generative AI. Yes, many of you find the concepts being used interchangeably and more or less the same, which is not true! Artificial Intelligence is wider than you think, and it is all about making machines more human-like. So the crux lies in the least involvement of humans, yes, like no human intervention required, all thanks to smart assistants such as Alexa, chatbots, and image generators, to robotic vacuum cleaners and self-driving cars. So generative AI can be considered as a subset.

When was Generated AI developed?

Joseph Weizenbaum created the first generative AI in the 1960s as part of the Eliza chatbot. As a full-fledged technology, though the concept emerged in the late 2010s, it does feature great advancements in terms of deep learning, especially with the help of different models, including Generative Adversarial Networks (GANs) and transformers.

The concept heavily relies on a sophisticated machine learning model, which is another significant name considered for deep learning model algorithms. However, the model assists well in simulating as well as easing learning and decision-making procedures, which were pretty hard earlier because humans were supposed to do that manually.

Time to understand their working, these models are supposed to identify as well as encode the different patterns, as well as relationships, and all this needs to be done for tons and tons of data, and further, the information is used to understand users’ natural language requests or questions and respond with relevant new content.

It's been more than a decade since everyone has been gushing over the concept of AI. In a way, it is a safe bet to say that ChatGPT has thrust AI into worldwide headlines and has successfully lured humans to a great extent. The overall operation of generative AI can be successfully divided into three phases:

Training- This phase is meant to create a successful foundation model that has the potential to serve as the basis of different gen AI applications.
Tuning- The next phase is to tailor the foundation model.
Generation, evaluation and retuning - This step is considered to successfully assess the gen application’s output and thus results in enhanced quality and accuracy.

No wonder the concept of generative AI is taking off like never before. I mean, who would have thought it would be easy-peasy to create high-quality text in seconds and that too overnight? ChatGPT is considered one of the fastest-growing consumer apps in history. Time to focus on the core benefits offered by generative AI.

Benefits of Generative AI

Would you believe me if I say that generative AI has the potential to drive a 7 per cent (or almost $7 trillion) increase in global gross domestic product (GDP) and assist in lifting productivity growth by 1.5 per cent, and this I am talking about a span of ten years. However, I would like to mention a few key points regarding the benefits offered by generative AI.

Accelerated Research

Generative AI algorithms have the potential to explore as well as analyse extremely complex and complicated data in several ways, which makes it pretty easy for researchers to spot the latest trends as well as patterns that might not be very apparent. So what do these algorithms do? They can successfully summarise different kinds of content, outline adequate solution paths and even assist in brainstorming ideas. What’s more to ask for? It is possible to develop highly detailed documentation, especially using research notes. No wonder generative AI ensures the enhancement of research and innovation. You might find a high demand for generative AI in the pharma industry to successfully generate as well as optimise sequences and enhance drug discovery.

Enhanced Customer Experiences

Generative AI has the potential to naturally respond to different human conversations and successfully serve as a tool that can work wonders in regard to enhancing customer services and ensuring absolute personalisation of customer workflows. For example, I am sure you have seen AI-powered chatbots, voicebots, and of course, virtual assistants, which turn out to be way more accurate in terms of response and make sure to achieve first-contact resolution. Also, these chatbots can provide curated offers and assist when there is a need to communicate in a more personalised way.

Optimised Business Procedures

Another significant benefit offered by generative AI turns out to be that businesses can optimise business procedures. Yes, irrespective of the business or the industry vertical, the tech can be applied anywhere and everywhere, be it engineering, marketing, customer service, finance or even sales. Here are certain examples that you must be interested in knowing about generative AI.

Extract and summarise data from any source
Evaluate as well as optimise different scenarios.
Generate synthetic data.

Increased Employee Productivity

Different Generative AI models have the potential to augment employee workflows as well as act in the most efficient manner for everyone within the organisation. Right from searching to creating a human-like way. Here’s how Generative AI as a concept can boost productivity.

Absolute support for a creative range of tasks, and this is created depending on a wide range of inputs and constraints. It is also feasible to optimise an existing bunch of designs depending on human feedback as well as specified constraints.
It is possible to generate new software code suggestions that can work wonders in conducting a different bunch of app development tasks.
It highly supports management by generating relevant reports, summaries, and projections.
Generate new sales scripts, email content, and blogs for marketing teams.

Types of Generative AI Models

Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) assist machines in developing a new and realistic batch of data, and this is usually done by learning from different examples as well. The type of generative AI model was introduced by Ian Goodfellow and his entire team back in 2014, and everything’s changed. They have succeeded in transforming the way computers are able to generate images, videos, music and much more.

Earlier, traditional models were supposed to recognise and classify data; now, they have accepted a clear way to generate new content that resembles real-world data. Right from art to gaming, healthcare and data science, these approaches have made absolute wonders like never before. This type of generative AI usually comprises two main models, especially for creating realistic synthetic data.

Generator model
The generator is a deep neural network that requires random noise as input, especially if you are willing to generate realistic data samples in regard to images and text. The type successfully learns about all underlying patterns by successfully adjusting all its internal parameters, and the ultimate objective is to produce relevant samples that can be classified as real.
Discriminator model
This type of model acts as a binary classifier, so here you are bound to find out absolute real and generated data. It is possible to enhance the overall classification ability via training, and what’s more amazing here is that all fake samples can be detected in a more accurate manner. The model mainly uses convolutional layers or other relevant architectures.

Variational Autoencoders (VAEs)

Variational autoencoders (VAEs) are another main kind of generative model that makes the most of machine learning technology to successfully generate new data in the form of variations. And moreover, it is possible to successfully perform different tasks, including denoising.

Variational autoencoders are no different from any other autoencoders. In comparison to traditional autoencoders, you will find different applications of variational autoencoders.

Generative modelling - The core benefit of choosing VAEs is that they feature the absolute ability to generate new data samples which are similar to the training data, but this doesn’t mean they are identical.
Anomaly detection - VAEs help in identifying deviations from this distribution as anomalies. So aspects such as fraud detection, network security, and predictive maintenance are well taken care of.
Data imputation and denoising - This turns out to be VAE’s one of the strongest points, where data can be reconstructed even if it has several missing and noisy parts. With the help of learned latent distribution, all the noise can be removed from corrupted data. So in a way, this does become pretty valuable.
Semi-supervised learning - In such scenarios, VAEs have the potential to enhance classifier performance by using the latent space, and this is done to successfully capture underlying data structures, leading to highly enhanced learning procedures, it doesn’t matter if the data is limited.
Latent space manipulation - VAEs have the potential to offer a highly structured and constant latent space, which can be manipulated for developing a wide range of apps. Some of the common examples to pick here include image editing and specific features, by seamlessly navigating the latent space.

Autoregressive Models

Another interesting type of generative model, which you need to be well acquainted with, is the autoregressive model. The model has been known as a testament to the sequential nature of certain types of data, like text or music. These models have a proven track record for generating high amounts of data, taking one element into account at a time. The condition here is that the context needs to be provided by the preceding elements, and a sample should be taken from this distribution to generate tons of new data.

One of the most well-known autoregressive models is Generative Pre-trained Transformer (GPT), which has already succeeded by revolutionising several areas, including modelling an generating coherent text. Does this mean their overall impact is limited to this? Of course not! They are instrumental in music composition and other sequence-based generation tasks. The core aspect here is the unique structure and approach, which means it offers an amazing ability to produce coherent and relevant outputs.

Recurrent Neural Networks (RNNs)

Now I am pretty sure many of you have been pondering how chatbots understand your question and answer accordingly. The secret weapon is the type of generative AI, which is a Recurrent Neural Network (RNN). Now this is different from a standard neural network; this excels in taking care of different tasks, such as image recognition, and even the following type has a strong superpower, which is its memory. The internal memory enables accurate analysis of sequential data.

I mean, imagine how it would be while in the middle of having a conversation, you need to remember what was being said earlier again and again. It’s bizarre and insane, well, RNNs can successfully analyse sequences like speech or text. Some of the core aspects that make RNNs worth considering include: internal memory, sequential data processing, Contextual understanding, Dynamic processing and a lot more.

Reinforcement Learning for Generative Tasks

Last but certainly not least, one is reinforcement learning of generative tasks. This type of generative AI has successfully represented a paradigm shift in creating generative tasks. So, here an agent is involved that can generate data. The crux lies in a continuous approach in terms of action, feedback and adaptation. No wonder high-quality outputs are created.

Best Practices in Generative AI

Always begin with internal app development and try to focus more on high optimisation and enhanced employee productivity.
Make sure you succeed in establishing clear communication so that users know they are communicating with AI.
Do not forget to implement guardrails so there is no scope for unauthorised access to sensitive data. It is advisable to mask data and remove personally identifiable information (PII).
Make sure to develop automated and manual testing processes for better results, and by doing so, you will get more control over expected outcomes as well as responses.
Lastly, the more your customers have better experiences, the more you are likely to succeed.

Final Words

So good so far, I am extremely sorry to burst your bubble in advance, but there is no such technology that doesn’t carry any limitations, and generative AI is definitely no longer an exception. Here are some of the common challenges that might occur when using a generative AI app:

The app doesn’t identify the source of content.
At times, things can be extremely challenging.
Realistic-sounding content can be troublesome.
It can be difficult to work in new circumstances.
The app lacks, especially in the context of original thought.

No wonder misleading information, fake citations, and copyright violations are some of the major concerns, especially when you are using GenAI tools. These tools can successfully hallucinate and offer results that are not grounded in prompts. The worst-case scenarios could be where companies are open and transparent regarding the usage of AI models they have been developing and marketing. This does compel enterprises to pay licensing fees if they exceed a threshold of usage or compete with the developer.

Above all, hackers are coming away with more creative ways to launch attacks. Of course, gen AI tools do possess the potential to detect these vulnerabilities, but to an extent.

Despite all these concerns, what if I say the future of generative AI is pretty bright and rosy? It’s true! Already, generative AI has become a known term that was once a bizarre concept inculcated straight out of Sci-Fi, and has become an integral part of our everyday lives.

So that’s all for now! I hope you did find the following post worth taking into account. Generative AI, on and all, is here to stay, no two ways about it, so ignoring it might be one of the most foolish decisions you have taken. In addition to all this, if you still have any concerns or doubts, feel free to mention them in the comment section below. I hope you can share the following post among your peers and help us spread the word. Wish you good luck!