The future of AI. Exploring the next generation of generative models

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If you follow the tech world, you will know that Generative AI is the hottest topic. We hear so much about ChatGPT, DALL-E, etc.

Recent advances in Generative AI will dramatically change the way we continue to approach content creation and the rate of growth of AI tools across industries. In its Artificial Intelligence Market Size, Share and Trends Analysis report, Grand View Research stated:

“The global artificial intelligence market size was estimated at USD 136.55 billion in 2022 and is projected to expand at a CAGR of 37.3% from 2023 to 2030.”

Day by day, more and more organizations from different industries or backgrounds are looking to improve themselves using Generative AI.

Generative AI is an algorithm used to generate new and unique content such as text, audio, code, images, and more. As AI evolves, Generative AI has the potential to dominate various industries, helping them perform tasks that humans once thought impossible.

Generative AI is already creating art that can emulate artists like Van Gogh. The fashion industry could potentially use generative intelligence to create new designs for their next line. Interior designers can use generative intelligence to build someone their dream home in days instead of weeks and months.

Generative AI is fairly new, a work in progress, and still needs time to perfect itself. However, apps like ChatGPT have set the bar high and we should expect more innovative apps to be released in the coming years.

The role of generative AI

There are no specific limits to what generative AI can do, as mentioned before, it’s still a work in progress. However, as of today, we can categorize it into 3 parts.

  1. Production of new content/information.
  2. This can range from creating a new blog, a video tutorial, or some fancy new art for your wall. However, it can also help in the development of a new drug.

  3. Replace repetitive tasks.
  4. Generative AI can take over the tedious and repetitive tasks of employees, such as email. letters, presentation summaries, coding and other types of operations.

  5. Personalized data.
  6. Generative AI can create content for specific customer experiences, and this can be used as data to drive success, ROI, marketing techniques and customer engagement. Using consumer behavior patterns, companies will be able to differentiate effective strategies and methods.

Below is an example of one of the generative AI models, Diffusion Models.

Diffusion model

A diffusion model is designed to learn the underlying structure of a database by mapping it into a lower-dimensional hidden space. Latent diffusion models are a type of deep generative neural network developed by the CompVis group at LMU Munich and Runway.

Diffusion is when you slowly add or diffuse noise to a compressed hidden representation and create an image that is just noise. However, the diffusion model goes in the opposite direction and performs the reverse process of diffusion. The noise is gradually reduced from the image in a controlled manner, so the image gradually looks like the original.

The future of AI.  Exploring the next generation of generative models
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Generative AI has been widely adopted by many organizations in various industries. It has allowed them to adopt the tools to fine-tune their current processes and methods and make them more efficient. For example:


If it’s creating a new article, a new image to post on the site, or a cool video. Generative AI has taken the media industry by storm, allowing them to produce effective content at a faster pace and reduce their cost. Personalized content has allowed organizations to take their customer engagement to the next level by providing a more effective customer retention strategy.


AI tools such as Intelligent Document Processing (IDP) for KYC and AML processes. However, generative AI has allowed financial institutions to take their customer analytics even further, identifying new consumer spending patterns and identifying potential problems.


Generative AI can help with images such as X-rays and CT scans to provide more accurate visualization, better definition of images, and faster detection of diagnoses. For example, the use of tools such as image-to-image transformation using GANs (Generative Adversarial Networks) has allowed healthcare professionals to gain a deeper understanding of a patient’s current medical condition.

With something great comes bad, right? The rise of generative AI has led to governments being able to control the use of generative AI tools.

For some time now, the AI ​​field has been open for organizations to do what they want. However, it was only a matter of time before someone stepped in and created firm regulations around AI. Many are concerned about the control of generative AI models and how this will affect the socioeconomics, as well as other issues such as intellectual property and privacy violations.

The main challenges that generative artificial intelligence currently faces in terms of governance are:

  • Data Privacy – Generative AI models require a lot of data to be able to successfully export accurate results. Data privacy is a challenge that all AI companies and tools face due to the potential misuse of sensitive information.
  • Ownership – Intellectual property rights for any content or information generated by generative AI is still an open debate. Some may say that the content is unique, while others may say that the content created by the text is adapted from various internet sources.
  • Quality. With the large volume of data being fed into generative artificial intelligence models, the number one concern will be examining the quality of the data and then the accuracy of the resulting output. Fields like medicine are areas of great concern because dealing with misinformation can have a big impact.
  • Bias – When we consider data quality, we also need to assess potential bias in the training data. This can lead to discriminatory results, which makes AI unpleasant in the eyes of many.

Generative AI still has a lot of work to do before it is positively embraced by everyone. These AI models need better understanding of human speech from different cultural backgrounds. For us common sense, talking to someone comes naturally to us, however, this is not so common for AI systems. They struggle to adapt to different circumstances because they are programmed to be trained on factual information.

It will be interesting to see what role generative AI will play in the future. We’ll have to wait and see.

Nisha Arya is a data scientist, freelance technical writer, and community lead at KDnuggets. He is particularly interested in providing Data Science career advice or tutorials and theory based knowledge on Data Science. He also wants to explore the different ways Artificial Intelligence can/can contribute to human longevity. An enthusiastic learner looking to expand his technology knowledge and writing skills while helping to lead others.

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