Deep Learning and AI

Generative AI Use Cases 2024 - Deep Learning, Life Science, Content Creation

May 9, 2024 • 6 min read


Why is Generative AI so Popular?

Generative AI has emerged as a powerful tool across various domains, including deep learning, healthcare, life sciences, and content creation. LLMs, multi-modal models, and other AI algorithms capable of generating content have become indispensable in various workloads.

This never start at 0% has not only changed those drafting emails or writing articles, but even the most technical. With Generative AI, our workloads can get a head start just by talking to a highly trained, highly creative chatbot or by a purpose trained algorithm. In this blog, we’ll explore how generative AI is transforming these fields, unlocking new possibilities and driving innovation.

Using Generative AI to Train AI

You might be thinking, “Generative AI models essentially trained using deep learning via neural networks. How can generative AI impact deep learning?”

Here some interesting ways that data scientists, AI practitioners, and deep learning enthusiasts can utilize Generative AI to accelerate or improve their workflow:

Explaining Code

Recently, one of our backend developers used ChatGPT to explain a block of code by prompting “can you explain this algorithm so I can tell the marketing guy?”

Generative AI can help translate complex technical texts or even blocks of code into simpler language. This can be applied both ways: developers reading dense technical papers can use generative AI for summarization. Or it can be used by the developers to effectively explain their implementations to investors in less technical jargony language.

Coding Assistant

Generative AI models have gotten better and better at helping developers code simple things. You can fine-tune models on a specific coding language alongside the coding language documentation to help you find the appropriate algorithms to approach a problem.

Copilots have been the topic of discussion for quite some time now helping code developers not just with auto-complete but ideation of an entire algorithm.

Synthetic data

Synthetic data is extremely useful to train AI models when data is nonexistent, private, niche, and or imbalanced. For instance, when working with limited data, albeit from sensitive, risky, or expensive to collect data, generative AI can definitely help generate new samples that resemble the original data distribution.

Non-Existent: Generative AI models can create additional training data by generating new data points similar to the real ones. This way, the AI model gets more exposure to different situations and becomes better at making accurate predictions or decisions.

How Generative AI Changed Life Science Research

The development and study of drugs and molecules are a huge part of researching and addressing the world’s most challenging problems. Generative AI has the potential to transform the life sciences industry by reducing costs, improving efficiency and accuracy, and assisting in report generation.

Protein Prediction, Reconstruction, & Drug Discovery,

In the field of life science, generative AI models are trained using a dataset of molecules with specific characteristics. Scientists and researchers can prompt the models to create new molecules that meet particular criteria, providing a valuable initial step in drug development. This process helps save time and reduce the expenses associated with parsing through thousands of potential (but not so potential) drug candidates.

This ties along with the recently released AlphaFold 3 which dropped just this past Wednesday, which accurately predicts ligands, protein targets, and other biomolecules. AlphaFold has been well known in the industry for revolutionizing protein reconstruction and has become a staple in molecular biologists toolbelts. AlphaFold can be illustrated as the first generative AI model for protein prediction even if the term generative AI hadn’t been coined during its discovery.

Simple Summarization

Going through extensive scientific journals can be a time intensive. Scientists can employ Large Language Model (LLM) generative AI to condense lengthy research papers into concise summaries. This approach provides scientists with a quick overview of research methodologies and outcomes before delving deeply into a particular paper. Furthermore, these generative AIs can facilitate the creation of scientific papers from scratch. By instructing the AI to outline or generate brief summaries of findings, scientists can receive initial drafts of their research reports, reducing human effort and expediting the process.

Generative AI for Content Creation

Generative AI has delivered itself as a new tool for creatives to use. In the realm of 3D content creation, it revolutionizes the way virtual environments, characters, and objects are designed and brought to life. These advancements open up new creative horizons and improve efficiency across various creative and technical processes.


While models like Stable Diffusion, Midjourney, and DALLE-2 may not be flawless, they can serve as sources of inspiration when creating new artistic assets. Artists can use text-to-image models to overcome creative blocks, ensuring they never find themselves without ideas. These models can even be provided with a specific art style, allowing them to generate images closely resembling that style.

3D Modeling

Emerging generative AI models are now in development to convert text descriptions into 3D models. Although this technology is relatively new, it holds great promise. Soon, these generative AI models could assist architects and urban planners in creating complex building layouts, generating accessible designs, or providing initial, simplified 3D models for designers to work from. By employing generative AI to spark creative ideas, design iterations can be significantly accelerated, valuable in fields like automotive and product design.

The Impact of Generative AI

The advancements brought by training these deep learning models to create something original deliver immeasurable advantages. By leveraging generative AI, individuals and creatives gain the power to avoid starting from a blank slate and enhance their productivity. The ability to let your language or any input turn into creativity and originality redefines what’s achievable with artificial intelligence.

Nonetheless, implementing these solutions is no straightforward task and demands substantial computational resources. AI models such as ChatGPT rely on thousands of servers to support their functionality, ensuring that anyone can engage with and benefit from these tools. If you're looking to train your own model using your specific data to yield customized results, SabrePC offers expertise in delivering the appropriate workstations, servers, or clusters to meet your Deep Learning and AI requirements.


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