# Synthetic data Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/synthetic-data
> Markdown URL: https://aitinkerers.org/technologies/synthetic-data.md
> Technology record last updated: 2026-03-05T10:20:11Z
> Generated: 2026-09-20T21:49:16Z

Artificially generated information that statistically mirrors real-world data, used primarily for AI model training, robust testing, and critical privacy compliance.

Synthetic data is computer-generated information designed to replicate the statistical properties and patterns of real-world data without containing any personally identifiable information (PII). This capability solves major logistical and ethical issues: it provides an on-demand, limitless supply of data for training data-hungry AI models, such as those using Generative Adversarial Networks (GANs) or Transformer models. Industries like finance use it to simulate rare fraud scenarios for anti-money laundering solutions, while healthcare uses it to accelerate drug development without violating patient confidentiality (HIPAA/GDPR). This is a high-growth sector: Gartner predicts 75% of businesses will use generative AI to create synthetic customer data by 2026.

- Official technology site: https://www.ibm.com/topics/synthetic-data
- Public AI Tinkerers demos and talks: 3
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Clinical Events Prediction engine to accelerate pharma trials](https://nyc.aitinkerers.org/talks/rsvp_drBRHOh35QY)

KolateAI provides patient-level Clinical Events Prediction to accelerate pharma trials and real-world studies. With our foundation models trained on clinical studies delivered through our Co-pilot, we predict major clinical events (drug response, adverse events, study end-points). This enables pharmas to target the best-performing patient segments and pro-actively manage study trajectory.

- Event context: AI Tinkerers July Meetup — 2024-07-24 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_drBRHOh35QY

### [LearnQuantum: AI Native Physics Engine](https://sf.aitinkerers.org/talks/rsvp_L724yA7lINQ)

LearnQuantum's AI-native physics engine translates natural language into precise physical simulations across multiple scales, from quantum to cosmic. Built on existing AI frameworks and architectures, including large language models (LLMs), it parametrizes and quantizes physical laws to produce complex simulations that ensure physically consistent results. This tool also generates synthetic data and optimizes complex systems. By facilitating rapid hypothesis testing, LearnQuantum accelerates scientific discovery and extends AI capabilities in physical reasoning, with potential revolutionary impacts in fields such as drug discovery and quantum computing. During my presentation, I will demonstrate the simplicity of generating real-time physics simulations from various prompts and managing complex physical data across different modalities, including interactive, static, and video generations.

- Event context: AI Tinkerers - San Francisco - Summer Edition - July 2024 — 2024-07-12 — San Francisco
- Public talk page: https://sf.aitinkerers.org/talks/rsvp_L724yA7lINQ

### [Synthetic Data](https://chicago.aitinkerers.org/talks/rsvp_9ztaE1LcoTY)

Synthetic data can push your models to the next level, but what is it and how do you get it? This demo will showcase some strategies I've used in my own projects to generate instructions and other datasets.

- Event context: AI Tinkerers Chicago February Meetup — 2024-02-20 — Chicago
- Public talk page: https://chicago.aitinkerers.org/talks/rsvp_9ztaE1LcoTY

## Related Technologies

- [GPT-3](https://aitinkerers.org/technologies/gpt-3) ([Markdown](https://aitinkerers.org/technologies/gpt-3.md)) — 191 public demos
- [GPT-4](https://aitinkerers.org/technologies/gpt-4) ([Markdown](https://aitinkerers.org/technologies/gpt-4.md)) — 529 public demos
- [BERT](https://aitinkerers.org/technologies/bert) ([Markdown](https://aitinkerers.org/technologies/bert.md)) — 179 public demos
- [BLOOM](https://aitinkerers.org/technologies/bloom) ([Markdown](https://aitinkerers.org/technologies/bloom.md)) — 115 public demos
- [Chinchilla](https://aitinkerers.org/technologies/chinchilla) ([Markdown](https://aitinkerers.org/technologies/chinchilla.md)) — 1 public demo
- [Co-pilot](https://aitinkerers.org/technologies/co-pilot) ([Markdown](https://aitinkerers.org/technologies/co-pilot.md)) — 2 public demos
- [Foundation models](https://aitinkerers.org/technologies/foundation-models) ([Markdown](https://aitinkerers.org/technologies/foundation-models.md)) — 5 public demos
- [Generative AI](https://aitinkerers.org/technologies/generative-ai) ([Markdown](https://aitinkerers.org/technologies/generative-ai.md)) — 45 public demos
- [LearnQuantum](https://aitinkerers.org/technologies/learnquantum) ([Markdown](https://aitinkerers.org/technologies/learnquantum.md)) — 2 public demos
- [Llama-2](https://aitinkerers.org/technologies/llama-2) ([Markdown](https://aitinkerers.org/technologies/llama-2.md)) — 227 public demos
- [Machine Learning](https://aitinkerers.org/technologies/machine-learning) ([Markdown](https://aitinkerers.org/technologies/machine-learning.md)) — 20 public demos
- [PaLM 2](https://aitinkerers.org/technologies/palm-2) ([Markdown](https://aitinkerers.org/technologies/palm-2.md)) — 116 public demos
- [Prompt Engineering](https://aitinkerers.org/technologies/prompt-engineering) ([Markdown](https://aitinkerers.org/technologies/prompt-engineering.md)) — 28 public demos
- [Quantum Computing](https://aitinkerers.org/technologies/quantum-computing) ([Markdown](https://aitinkerers.org/technologies/quantum-computing.md)) — 1 public demo
- [RAG](https://aitinkerers.org/technologies/rag) ([Markdown](https://aitinkerers.org/technologies/rag.md)) — 147 public demos
- [RoBERTa](https://aitinkerers.org/technologies/roberta) ([Markdown](https://aitinkerers.org/technologies/roberta.md)) — 118 public demos