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Software Engineering Daily

Ethical GPTs with Amruta Moktali

Thu Jul 27 2023
Generative AIGPT ModelsChat TPTLanguage ModelsPrivacy ConcernsData PrivacyAI ResponsibilityCompliance

Description

Generative pre-trained transformer models (GPT models) have countless applications and are being rapidly deployed across a wide range of domains. Using GPT models without appropriate safeguards could lead to leakage of sensitive data, highlighting the need for privacy and data protection. SkyFlow GPT privacy vault prevents sensitive data from reaching GPTs. Generative AI and its importance are discussed, along with the impact of chat TPT and the potential of generative AI to reduce mundane tasks for developers. The training process of generative AI models is explained, including the use of language models like GPT. Privacy concerns with language models are addressed, and various approaches to protecting sensitive data in language models are explored. The chapter also covers ways to protect data privacy in AI and LLMs, as well as the importance of using AI responsibly and compliance with regulations.

Insights

Generative pre-trained transformer models (GPT models)

GPT models have countless applications and are being rapidly deployed across a wide range of domains. Using GPT models without appropriate safeguards could lead to leakage of sensitive data, highlighting the need for privacy and data protection.

Generative AI and its Importance

Generative AI differs from other AI approaches. The importance of using generative AI in a safe and ethical way is emphasized.

Chat TPT and its Impact

Chat TPT came on the radar in late 2020 when GPT-3 was introduced. The interface of chat GPT was groundbreaking and had a big impact on how AI is used. The explosion of AI companies and products can be attributed to factors like improved compute power, availability of data, and user-friendly interfaces.

Generative AI Models and Training

Generative AI models are based on deep learning architectures. Language models like GPT are forms of generative AI that create texts. To train these models, a large amount of information is fed to them in the form of numbers or vectors.

Privacy Concerns with Language Models

LLMs are a concern because they are currently unsupervised. Italy and Samsung have implemented temporary bans on chat TBT due to GDPR violations and sharing of proprietary code. Privacy is a major issue with LLMs, as sensitive information can be leaked.

Protecting Sensitive Data in Language Models

Fully redacting or replacing sensitive data with synthetic data before training or fine-tuning the model can solve the problem of leakage of sensitivity. Redaction requires using stand-ins to inform the model about the type of information that was redacted.

Protecting Data Privacy in AI and LLMs

There are three key ways to use data with AI and LLM without compromising sensitive data: tokenization, reversible pseudo-anonymization, and prompt filtering. Tokenization involves replacing sensitive data with standardized tokens before sending it to the model.

Using AI Responsibly and Compliance

Conversational interfaces are commonly used for experimenting with privacy. Redacting or pseudo-anonymizing information before it reaches the model is important. Tokenization and detokenization are used to protect data.

Chapters

  1. Generative pre-trained transformer models (GPT models)
  2. Generative AI and its Importance
  3. Chat TPT and its Impact
  4. Generative AI Models and Training
  5. Privacy Concerns with Language Models
  6. Protecting Sensitive Data in Language Models
  7. Protecting Data Privacy in AI and LLMs
  8. Using AI Responsibly and Compliance
Summary
Transcript

Generative pre-trained transformer models (GPT models)

00:00 - 08:08

  • GPT models have countless applications and are being rapidly deployed across a wide range of domains.
  • Using GPT models without appropriate safeguards could lead to leakage of sensitive data, highlighting the need for privacy and data protection.
  • SkyFlow GPT privacy vault prevents sensitive data from reaching GPTs.

Generative AI and its Importance

00:00 - 08:08

  • Generative AI differs from other AI approaches.
  • The importance of using generative AI in a safe and ethical way is emphasized.

Chat TPT and its Impact

07:40 - 15:26

  • Chat TPT came on the radar in late 2020 when GPT-3 was introduced.
  • The interface of chat GPT was groundbreaking and had a big impact on how AI is used.
  • The explosion of AI companies and products can be attributed to factors like improved compute power, availability of data, and user-friendly interfaces.
  • OpenAI's UI made AI accessible to everyone, especially with the familiarity of chat interfaces.
  • Generative AI has the potential to significantly impact developers by reducing mundane tasks and allowing them to focus on more interesting work.
  • However, it is important to use generative AI responsibly with proper controls and considerations.

Generative AI Models and Training

14:57 - 23:34

  • Generative AI models are based on deep learning architectures.
  • Language models like GPT are forms of generative AI that create texts.
  • To train these models, a large amount of information is fed to them in the form of numbers or vectors.
  • The models learn to understand context and generate the next word or character based on what has been given to them.
  • Language models can be fine-tuned by providing additional data and context for specific tasks.
  • There is no human involvement in the training phase, which raises privacy concerns.
  • Information given to language models can be used at any point in time, even if it was not initially generating answers.

Privacy Concerns with Language Models

23:27 - 30:55

  • LLMs are a concern because they are currently unsupervised.
  • Italy and Samsung have implemented temporary bans on chat TBT due to GDPR violations and sharing of proprietary code.
  • Privacy is a major issue with LLMs, as sensitive information can be leaked.
  • One approach to privacy is running private LMs within your own cloud infrastructure, isolating the data but still giving it sensitive information.
  • This approach solves the problem of not sharing data with others, but has limitations in terms of cost and preventing data leakage within the enterprise.
  • Running your own LM cluster requires expensive resources and a dedicated team, while also limiting access control and flexibility with different models.
  • Another approach is using fully redacted or synthetic data to prevent sensitivity leakage during training or fine-tuning of models.

Protecting Sensitive Data in Language Models

30:27 - 37:47

  • Fully redacting or replacing sensitive data with synthetic data before training or fine-tuning the model can solve the problem of leakage of sensitivity.
  • Redaction requires using stand-ins to inform the model about the type of information that was redacted.
  • Synthetic data generated using AI can introduce bias and is not real, which affects the quality of models.
  • Tokenization and stand-ins can be used instead of synthetic data to provide context and ensure real answers from the model without compromising individual experiences.
  • Fully redacting sensitive data may solve some problems but loses contextual information, reducing the power of the model.
  • Companies focused on protecting chat TPT use Chrome extensions to monitor pasted content, but this approach can be bypassed by using competitors or running open AI APIs locally.
  • Blocking access to powerful tools like GPT will lead people to find ways around it, so it's better to give them safe access.
  • Using filters instead of firewalls allows employees to use GPT while removing sensitive information and replacing it with real information for a useful response.
  • One-way redaction, one-way pseudo-anonymization with tokens, and reversible pseudo-anonymization are three approaches for keeping things private yet useful in training models.

Protecting Data Privacy in AI and LLMs

37:17 - 44:56

  • There are three key ways to use data with AI and LLM without compromising sensitive data: tokenization, reversible pseudo-anonymization, and prompt filtering.
  • Tokenization involves replacing sensitive data with standardized tokens before sending it to the model.
  • Reversible pseudo-anonymization involves pseudo anonymizing the data before sending it to the model and then re-identifying it when it comes back.
  • Prompt filtering filters out sensitive data from the prompt before reaching the LOM and re-identifies the identified data in the response.
  • Governance and policies play a role in ensuring that the right person sees the right information by masking or blocking certain data based on access permissions.
  • Localization and data residency are important considerations for privacy, as models trained in one location may handle data from different locations differently based on policies.
  • SkyFlow is working on a privacy platform that allows for de-identification of data going into LLMs, both for training models and for input during inference.

Using AI Responsibly and Compliance

44:28 - 51:44

  • Conversational interfaces are commonly used for experimenting with privacy.
  • Redacting or pseudo-anonymizing information before it reaches the model is important.
  • Tokenization and detokenization are used to protect data.
  • An extensive access control layer helps apply policies and governance when re-identifying data.
  • A data dictionary allows customization of sensitive fields beyond standard categories like PII, PCI, or PHI.
  • A niche health LLM is being built using hospital data that has been de-identified and certified.
  • Companies without deep pockets can use a privacy layer to protect sensitive customer information while using public algorithms or LLMs.
  • Starting with existing APIs or open source LLMs is common for companies experimenting in the space.
  • Compliance with regulations is more complicated in LLMs due to the aggregation of data in new models.
  • Deleting or forgetting data in an LLM is currently a challenge without blowing up the model.
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