Evolution of AIGC Technology and Its Applications in Life Sciences

Introduction – The session focuses on AIGC technologies and their applications in life sciences, covering text‑to‑image, text‑to‑text, and molecular generation, with basic technical background for audiences new to AIGC.

1. Evolution of Artificial Intelligence – Since 2012 deep learning has driven rapid AI progress; the release of GPT‑4 in 2023 propelled AIGC into the spotlight. Three stages are identified: (1) supervised deep models for vision and speech, (2) self‑supervised large models trained on massive text corpora, and (3) large models fine‑tuned with prompts, adapters, or reinforcement learning using only a small amount of high‑quality data.

2. AIGC Technology Evolution – AIGC generates content (text, images, video, molecules) from prompts. Representative generative models include GAN (2014), VAE, Flow, Diffusion, and Auto‑Regressive models, each with distinct strengths and limitations.

3. Text‑to‑Image Techniques – Mainstream methods discussed are DALL·E 2 (multimodal training and autoregressive generation), Stable Diffusion (iterative denoising), and ControlNet (conditioning generation on external control signals).

4. Text Generation Technologies – The GPT series evolution is outlined: GPT‑1, GPT‑2, GPT‑3, GPT‑3.5 (including Code‑davinci‑002 and InstructGPT), ChatGPT, and GPT‑4, highlighting model scaling, instruction fine‑tuning, and emerging capabilities such as image input and longer context.

5. Emergence of Large Models – As model parameters increase, performance initially scales linearly but eventually exhibits a sudden jump, a phenomenon known as emergence.

6. AI in Drug Discovery – AI can accelerate target identification, molecular design, and screening. Molecular generation is categorized into small‑molecule generation, peptide generation, and protein generation, with algorithms such as MCMG (reinforcement‑learning‑based), ResGen (3D pocket‑aware), and Delete (fragment growth/linker design).

7. Challenges and Opportunities in Molecular Generation – Challenges include limited data, difficulty of realistic 3D molecule generation, and the need for human‑in‑the‑loop iterative design. Opportunities arise from self‑supervised pre‑training, advances in protein‑structure prediction, and improving reinforcement‑learning and diffusion‑based generative models.

8. Thoughts & Outlook – Generative AI may be a necessary step toward AGI because it unifies many task types and requires large models and data to reach a critical threshold where capabilities suddenly improve. Future possibilities include AI‑driven science, intelligent robotics, and transformative applications across NLP, search, and office automation.

9. About Carbon‑Silicon Intelligence – The company aims to merge life sciences with AI, offering the DrugFlow platform for AI‑driven drug discovery, AI‑assisted design services, and core technologies such as deep generative models, reinforcement learning, and AutoML to boost efficiency and success rates in pharmaceutical R&D.

Overall, the presentation provides a comprehensive overview of AIGC progress, technical foundations, practical applications in drug discovery, and strategic insights for future development.