EFFECTED USERS: Anyone who uses AI, or plans to

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Imagine you’re crafting an email, seeking the perfect phrase, and you consult an AI assistant. In moments, it delivers a polished suggestion. But have you considered where your input goes? Could your confidential information be stored, analyzed, or even used to train future AI models?

In our digital age, artificial intelligence offers remarkable convenience, yet it raises pressing questions about data privacy. How can we harness AI’s benefits without compromising our personal or organizational information?

Here with me to talk about these concerns is Suman Kanuganti, a visionary technologist and entrepreneur. As the co-founder of Personal.ai, Suman champions the idea of AI that serves as a true extension of oneself—empowering users with control over their data and digital identity. 

SUMMARY:

Chapters & Topics:

Discussion on AI Privacy Concerns
In Episode 74 of DIY Cyber Guy, David W. Schropfer highlighted the critical issue of privacy in relation to artificial intelligence, warning that users may unknowingly share more personal information than intended. He provided examples of how AI tools, such as ChatGPT, can process sensitive data when crafting communications. Suman Kanuganti, co-founder of personal.ai, joined the conversation to discuss privacy concerns for novice AI users.

Data Retention Policies and Consumer Choices
Suman Kanuganti highlighted the importance of understanding data retention policies when using AI services, noting that consumers often face a choice between free offerings and paid subscriptions that guarantee privacy. David W. Schropfer provided an example of Google, explaining how free users have limited control over data retention, while subscribers can opt for stricter privacy measures. Both emphasized the need for consumers to be aware of these trade-offs.

• The importance of understanding the value exchange when using AI services.

The Role of Data in AI Development
David W. Schropfer highlighted that data is vital for AI development, as it helps systems learn and improve over time. Suman Kanuganti questioned whether users are merely benefiting from AI or actively contributing to its development. He pointed out that big tech companies often compartmentalize user data for their own benefit, raising concerns about privacy and control.

• The differences between user-generated AI and traditional AI models.
• The role of personal.ai in empowering users to control their data.

Personal AI Development and Decision-Making
Suman Kanuganti explained the concept of a personal AI that augments individual cognition by utilizing personal knowledge for decision-making. This AI would assist users in crafting responses, such as emails, by considering their historical context and decision-making patterns. David W. Schropfer emphasized the importance of understanding user behavior in generating appropriate responses.

Integration of Language Models
David W. Schropfer and Suman Kanuganti explored how large language models (LLMs) can be integrated with private language models (PLMs). Suman highlighted that while LLMs provide general knowledge accessible to everyone, PLMs are essential for applying personal and institutional knowledge in decision-making processes. They noted that cognitive decisions are still required, particularly in professional contexts like law and finance.

Discussion on Privacy and Personal AI
David W. Schropfer and Suman Kanuganti engaged in a conversation about privacy policies related to personal AI, with Suman asserting that users retain ownership of their data and models. He emphasized that their data will never be used to train other models, providing a sense of security. Suman also discussed the company’s approach to developing AI solutions tailored for businesses, which will eventually expand to small businesses and consumers.

• Privacy concerns related to AI usage and data retention policies.