Protopia AI
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About
Protopia AI offers Stained Glass Transform (SGT), a technology that converts sensitive data into stochastic representations to enhance AI accuracy, protect privacy, and boost compute utilization. It's designed for use with various deep learning models and is significantly faster than other data protection methods. The company works with enterprise clients, helping them integrate SGT into their AI pipelines and solve data privacy challenges across different use cases. Protopia also offers Stained Glass Engine, enabling customers to build SGT capabilities for their applications and retain complete data ownership.
Platform
Task
Features
• data protection for llms
• ai data privacy and security
• sgt for model deployment
• sgt for model training
FAQs
How is Protopia AI's solution different from data masking solutions?
Protopia AI's solution is fundamentally different from data masking solutions. We don't scan data to mask anything. For inference, our software runs as an optimization step, creating a transform that lets the model use transformed data for accurate predictions without accessing original data. No data scanning is involved.
Can SGT be used with any deep-learning model?
Yes, SGT is model-agnostic and can be applied to various deep-learning models, including LLMs and computer vision models.
How does SGT modify embeddings and apply transformations?
SGT applies transformations after the initial input embedding layer, ensuring minimal disruption. Tokenization and embedding transformations are handled by SGT, and only transformed embeddings are sent to the model. The model remains unchanged.
How fast is Stained Glass Transform?
SGT is orders of magnitude faster (900x-15,000x faster) than similar techniques. It adds milliseconds of latency.
How does SGT impact model accuracy?
SGT minimizes impact on accuracy. In many cases, models trained with SGT-transformed data achieve near-identical performance to plain-text counterparts.
How much computational overhead is required?
The computational overhead of applying Stained Glass Transform™ is minimal. This is made possible by our patented technology which runs as an optimization stage at the end of model training.
How do you empirically evaluate the effectiveness of SGT on prompts when the data is fully obfuscated?
The stochastic representation of data is fully compatible with the target model, so all existing metrics such as accuracy, precision, perplexity, etc. can be calculated exactly the same as without SGT. When the content of the obfuscated prompts are important, Protopia AI employs a unique identifier which the model provider can use to request access to the originals from the data owner.This allows for accurate measurement of model performance without exposing the raw data and maintaining data sovereignty These techniques are fully compatible with fine-tuning scenarios.
How does SGT address the risk of data leakage or theft?
Protopia AI's SGT safeguards data used by AI models. SGT transformations render data unintelligible to humans but still understandable by the target model, adding an extra layer of protection.
Can SGT protect data used in RAG applications?
Yes, SGT can integrate easily into existing RAG pipelines. The retrieval mechanism remains unchanged, and compiled prompts are transformed before leaving the enterprise.
How does SGT handle fine-tuning of models with sensitive data?
After creating a Stained Glass Transform for a foundation LLM, that model can be fine-tuned using protected data. Because SGT’s outputs are embeddings fully compatible with its corresponding base model, fine-tuning a foundation model looks exactly the same as without SGT, except that the data is first transformed.
How does SGT compare to encryption?
SGT complements encryption. Encryption protects data in transit and at rest; SGT safeguards data when used by AI models.
Is this encryption? Is there a key?
No, the transformed data is not encrypted, so there is no key and no decryption process.
I have a lot of data my customers want to access in order to validate their model, but I cannot currently offer them my data because of the sensitive nature of the data, can you help with that?
Yes, we can enable you to provide customized versions of your data that do not expose all the information in each data record for customers or 3rd party AI service providers that want to validate their ML models with your data.
Do I (the customer) need to send you (Protopia AI) my data to transform?
No, we never input any customer’s data. Transformations are done within the customer’s own data ingestion pipeline.
Do I have to expose my neural network model to you?
No, Protopia's solution works within your enterprise's infrastructure. Neither the model nor data needs to be exposed.
Job Opportunities
Artificial Intelligence & Machine Learning Solutions Engineer
Protopia AI's Stained Glass Transform (SGT) secures sensitive data for AI applications, improving accuracy and privacy while boosting compute utilization. It's model-agnostic and significantly faster than encryption.
Education Requirements:
PhD/MS in Computer Science or Electrical and Computer Engineering with specialization of machine learning, computer vision, NLP, speech recognition
Experience Requirements:
Experience with training deep learning models and deploying to real applications
Experience with large data sets: image and video, text, speech
Experience with speech processing, text analysis, image and video analysis
Effective presentation ability and communication
Analytical, logical and critical-thinking skills
Other Requirements:
Experience with Python, C++
Experience with PyTorch, numpy
Responsibilities:
Interface directly with enterprise clients and their ML engineers
Take ownership of ensuring client’s success with their use cases
Communicate effectively to key internal stakeholders
Work with a team of world class engineers and scientists
Occasional on-site work with enterprise clients
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Applied Scientist
Protopia AI's Stained Glass Transform (SGT) secures sensitive data for AI applications, improving accuracy and privacy while boosting compute utilization. It's model-agnostic and significantly faster than encryption.
Education Requirements:
Ph.D./MS in Computer Science or Electrical and Computer Engineering, specializing in NLP and deep learning
Experience Requirements:
Experience with deploying distributed systems for training deep learning models
Experience with developing NLP systems, specifically with language modeling
Experience with fine-tuning Large Language Models (LLM) on external datasets
Hands-on experience with using HuggingFace APIs
4 Years Experience with Python and 2 Years of Professional Experience with PyTorch, NumPy
Other Requirements:
Speed optimization/ optimization of memory consumption for LLM
Proven ability to impact products with cutting-edge research technology
Experience with downstream NLP models
Experience with using Kubernetes
Experience training LLM’s such as GPT, BERT, LaMDA, and LLaMA
Responsibilities:
Develop novel methods to obfuscate data for common NLP tasks
Interface with Large Language Models
Develop tools and metrics for handling machine learning experiments
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Sr. Product/Program Manager
Protopia AI's Stained Glass Transform (SGT) secures sensitive data for AI applications, improving accuracy and privacy while boosting compute utilization. It's model-agnostic and significantly faster than encryption.
Experience Requirements:
Proven experience as a Product Manager, preferably in the Generative AI, Data Governance, or Data Security space
Strong understanding of data science, machine learning, and privacy principles
Experience with product management tools and methodologies (e.g., Agile, Scrum)
Track record of influencing key stakeholders/collaborators
Knowledge of enterprise software development and deployment cycles
Responsibilities:
Product Development
Prioritize and manage the product backlog
Oversee the product lifecycle
Customer Focus
User Experience
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