Why is Generative AI so Popular?

Over the past year, advancements in the AI industry have been made possible by the emergence of Generative AI, especially DALLE-2 and ChatGPT. These two brought to light the power of feeding deep learning models with massive amounts of data and immensely complex training algorithms to give them the ability to be creative. Generative AI enables users to generate new content based on inputs like text, images, sounds, animation, 3D models, and more.

Text-to-image models like DALLE-2 inspired open-source models like Stable Diffusion and Midjourney to surpass the expectations of computer-generated art. It brought the untapped potential of artificial intelligence to the forefront of technological development.

Chatbot-like Generative AI like ChatGPT inspires the continued development of more text-based Large Language Models (LLMs) like GPT4, LLAMA 2, and others. These LLMs are changing the landscape of productivity from the consumer all the way to the enterprise setting. We will explore how generative AI impacts these 3 industries: Deep Learning, Life Science, and 3D Content Creation.

Generative AI in Deep Learning

You might be thinking, “Generative AI models are trained via deep learning. How can generative AI impact deep learning?” Training deep learning models requires extensive data to make precise predictions and produce the intended results. Data is gold. With only so much real-world data available on highly specific topics, using generative AI to create synthetic data increases the capabilities of training a robust model.

Synthetic data is extremely useful to train AI models when data is nonexistent, private, niche, and or imbalanced.

• 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.
• Private/Sensitive: Real data can contain sensitive information, like medical history, making it difficult to share or use for training. Generative AI models can parse through said data and generate statistically accurate and new representations of the dataset, avoiding private information leaks and ensuring the security of sensitive information.
• Specialized Scenarios: When it's challenging or risky to gather real-world data, such as in medical procedures, industrial operations, or autonomous driving simulation generative AI can generate synthetic data that replicates these situations. This enables the training of models in a secure and controlled setting, without the need for actual data collection in potentially hazardous or complex environments.
• Data Imbalances: It's common for real-world data to have imbalances, reflecting the realities of the world. Synthetic data can be valuable for evaluating various scenarios, helping to detect and address potential problems or biases. For instance, if there's a bias in the data where most doctors are labeled as male, synthetic data can be used to balance this representation by creating more examples of underrepresented classes, thus preventing future deep-learning models from perpetuating such biases.

Generative AI in Life Science and Healthcare

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.

• 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 identifying potential drug candidates.
• Analyzing and Producing Text: 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 in Media and Entertainment

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.

• Inspiration: 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.