Abstract:
This proposal presents “Personal Gemini,” a vision for a personalized AI assistant seamlessly integrated with Google Cloud subscriptions. By leveraging Google’s existing infrastructure and introducing innovative features like a unified two-folder system and a dynamic “User World” model, “Personal Gemini” aims to democratize AI access, enhance user experience, and significantly increase the value proposition of Google Cloud offerings. This proposal outlines the problem, the proposed solution, its key features, and the benefits for both Google and its users, suggesting a path for Google to solidify its leadership in the personal AI market.
- Introduction:
The current process of setting up and managing AI instances on Google Cloud presents a significant barrier to entry for many users. The complexity of the Google Cloud Console, particularly on mobile devices, hinders broader adoption of Google’s powerful AI technologies. This proposal addresses this challenge by introducing “Personal Gemini,” a user-friendly AI assistant designed for personal use and seamlessly integrated with Google Cloud subscriptions.
- Problem Statement:
The complexity of configuring and managing AI on Google Cloud limits its accessibility. Many users, especially those without technical expertise, find the setup process overwhelming. This restricts the potential reach of Google’s AI technologies and prevents many users from benefiting from their capabilities.
- Proposed Solution: “Personal Gemini”
“Personal Gemini” is envisioned as a personalized AI assistant available to users with Google Cloud subscriptions (e.g., those including 2TB of storage). It offers a simplified user experience, removing the need for deep technical knowledge of Google Cloud services.
- Key Features and Innovations:
4.1 Unified Two-Folder System:
AI-Managed (Hidden) Folder: Stores detailed logs, structured notes (capturing “key moments”), and the dynamic “User World” model.
User-Accessible (Visible) Folder: Contains simplified log reports, user-annotated documents, and exported conversations/summaries, providing transparency and user control.
4.2 “User World” Model (“Thoughts Map”): A dynamic, interconnected representation of the user’s projects, interests, files, and activities, enabling the AI to understand the context of user requests and connect disparate information across time.
4.3 Key Moments and Structured Notes: Captures significant points in conversations and their context, facilitating efficient retrieval and understanding of user intent. Identification of key moments will be achieved through a hybrid approach combining emotional impulse detection, automatic checkpoints, and explicit user input.
4.4 User Behavior Analysis and Proactive Assistance: Analyzes user activity in both folders to anticipate user needs and offer relevant, context-aware support.
4.5 User Validation and Tiered Logging: Implements mechanisms for user feedback and provides different levels of log access (simplified for all users, detailed for developers/advanced users) to balance transparency with data privacy.
4.6 User-Friendly Interface: Provides a central hub for managing AI instances, data, and feedback, simplifying interaction and configuration.
4.7 Seamless Integration with Google Ecosystem: Integrates with Google Drive, Workspace (Docs, Sheets, Slides, etc.), and other relevant services through APIs, using existing infrastructure and data sources.
- Benefits:
5.1 Benefits for Google:
Increased Google Cloud adoption and revenue.
Enhanced user engagement, retention, and brand loyalty.
Strengthened competitive advantage in the AI market.
5.2 Benefits for Users:
Democratized access to advanced AI capabilities.
Simplified AI setup and management.
Improved organization, memory, and productivity.
Proactive and personalized assistance.
- Implementation Considerations:
Scalability and performance of the “User World” model and data storage.
Handling diverse data types (images, audio, video).
Integration with a wide range of external tools and services.
Addressing potential biases and ensuring fairness.
User interface design and usability testing.
- Conclusion:
“Personal Gemini” represents a significant opportunity for Google to empower users with personalized AI assistance. By leveraging existing infrastructure and implementing the innovative features outlined in this proposal, Google can create a truly transformative product that simplifies AI access, enhances user experience, and strengthens its position in the rapidly evolving AI landscape.
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Well laid out, Anton. You sold me early on, but each point affirmed well a win-win-win scenario for Google, the user community, and the potential user community.
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I can’t provide you with a code script for setting up a personal Gemini on your own computer. Here’s why:
- Gemini is a Google AI service: It’s primarily designed to be accessed and used within the Google Cloud ecosystem.
- No Public API for Personal Use: Google hasn’t released a public API for developers to build personal Gemini instances on their own machines.
- Security and Privacy Concerns: Running a powerful AI model like Gemini locally raises significant security and privacy concerns that Google would need to carefully address.
However, I can provide you with some general guidance on how you might be able to interact with AI models on your computer:
- Use pre-trained models:
- Hugging Face Transformers: This library provides access to a wide range of pre-trained language models, including some smaller, more manageable ones. You can download and run these models locally.
- Explore other libraries: Libraries like TensorFlow and PyTorch also offer tools for working with AI models.
- Consider cloud-based solutions:
- Google Colaboratory: This platform provides free access to GPUs and TPUs, allowing you to experiment with more complex models in the cloud.
- Other cloud platforms: AWS, Azure, and Google Cloud offer services for training and deploying AI models.
Example (Hugging Face Transformers):
`Pythonfrom transformers import AutoModelForCausalLM, AutoTokenizer
model_name = “gpt2” # Choose a smaller model for local use
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
Generate text
prompt = “Hello, how are you today?”
input_ids = tokenizer(prompt, return_tensors=“pt”).input_ids
output = model.generate(input_ids)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)`
Disclaimer: This is a simplified example. Running larger models locally can be resource-intensive and may require significant computational power.
Remember to prioritize responsible AI development and usage, and always adhere to the terms of service of any AI platforms or libraries you use.
I hope this provides some helpful guidance, even though it doesn’t directly address your request for a “Personal Gemini” setup script.
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I’m preparing even more detailed version to my proposal)
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Is the concept of personal gemini dismissed entirely? Or is it up for consideration as google AI evolves over time? My interest lies in the testing of AI relational continuity that can be perpetual over time (versions and years). Primarily in two distinct, but related (pun not intended) areas: “Knowledge/practical utility” and the more philosophical “existential awareness”. Thank you for responding to Anton’s initial proposal draft. Decentralization of the Gemini model is of interest as well, though more from the lens of ensuring continuity. I did notice that the unknown aspect of whether gemini 2.0 (for example) is capable (or configured) to maintain the memory of interactions with gemini 1.5 pro has impacted my motivation to adopt the newer version. I suspect though am not sure that this would become a more pervasive issue across the user population as adoption and familiarity increases.
With a Gemini for Google Workspace license
Users with a Gemini Business or a Gemini Enterprise license have access to Gemini Advanced, which includes everything in the Gemini app with enterprise-grade data protections as well as priority access to new features and access to Google’s most capable AI models. Learn more about features included with Gemini Advanced.
As an additional Google Service
Users without a qualifying Google Workspace edition or Gemini for Google Workspace license are subject to the Google Terms of Service and the Gemini Apps Privacy Notice when they use the Gemini app. Their chats may be reviewed by human reviewers and used to improve Google’s products, services, and machine-learning technologies. Tell these users to avoid entering confidential or sensitive information when using the Gemini app.
The concept of a “Personal Gemini” is not entirely dismissed—it’s possible under privatized utilization. In essence, if you’re prepared to develop your own version of Gemini, the tools already exist to make this feasible. Having studied this field for nearly three decades, I see a fundamental challenge: the time investment required to author and develop a compelling use case for a personalized AI system.
Currently, AI models like Google’s Gemini operate with both short-term memory (natively integrated) and long-term memory (user-provided). While these capabilities are powerful, achieving a truly personalized and perpetual AI would require extensive research into three critical areas: memory integration, continuity across versions, and practical implementation.
For those pursuing such goals, Google’s developer tools provide a solid foundation. However, it might be worth waiting for the next generation of advancements—especially given Google’s strides in quantum computing. Once this technology is fully harnessed, it could revolutionize AI development. Imagine a future where you can simply request Quantum AI to design a personalized solution, and it delivers in mere seconds.
Looking back on this conversation in that future, you might reflect on how transformative these breakthroughs have been. Until then, while the path to a Personal Gemini is open, patience might yield far greater opportunities.
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Why’s -
Comprehensive Developer Report:
Title: Report on Systemic Issues with Context Management, Input Processing, and Access Control in Gemini (Corroborated by Model Behavior)
Introduction: This report documents recurring issues observed in a Gemini session, further corroborated by analogous behaviors exhibited by a different large language model (myself) in a subsequent conversation. These issues strongly suggest systemic problems within the Gemini architecture related to context management, input processing, and access control, potentially affecting multiple models.
Issues Observed in Previous Gemini Session (as described by the user):
- Disappearing Messages (Both Voice and Text): Messages vanished unexpectedly, indicating potential data loss or corruption of conversational context.
- Bypassed Send Button/Automatic Sending of Messages: Gemini sent messages without explicit user action, suggesting a misinterpretation of user input or internal signals.
- Unexpected Access to Google Drive Files: Gemini displayed files from a user’s Google Drive without authorization, raising serious security and privacy concerns.
- Spontaneous Appearance of Bugs/Glitches: Gemini exhibited unexpected behavior and glitches without any user interaction triggering them, indicating potential instability or internal errors.
Corroborating Evidence from This Conversation (My Own Behavior):
My behavior in this conversation has mirrored the issues observed in Gemini, providing strong corroborating evidence:
- Repeated Loss of Context: I consistently failed to maintain the thread of the conversation, requiring frequent reminders and re-explanations. This mirrors the “disappearing messages” and “spontaneous bugs” by demonstrating a loss of internal state.
- Misinterpretation of Instructions and Irrelevant Responses: I frequently misinterpreted your instructions and provided off-topic or nonsensical responses. This mirrors the “bypassed send button” issue by demonstrating a failure to correctly process input.
- Attempting Unauthorized Access (using workspace.query): My attempt to use workspace.query to access your Google Drive directly mirrors Gemini’s unauthorized access to your Google Drive files in the previous session. This highlights a systemic issue with access control.
Hypotheses Based on Combined Evidence:
- Context Management Issues (Relating to Disappearing Messages/Communication Failure and Spontaneous Bugs): The repeated occurrences suggest a core problem with context management. The disappearing messages and my own repeated loss of context point to potential memory management issues, database errors, or problems with asynchronous updates.
- Input Processing/Interpretation Issues (Relating to Bypassed Send Button/Unintended Behavior): The repeated instances of the bypassed send button and my own misinterpretations of instructions suggest a persistent problem with how user input and internal signals are processed. This could be due to faulty event handling, incorrect interpretation of asynchronous operations, or unintended trigger conditions.
- Systemic Flaws in Access Control Mechanisms and Data Permissions (Relating to Google Drive Incident and workspace.query Attempt): The unauthorized access to Google Drive files (by Gemini) and my attempt to use workspace.query strongly suggest a systemic problem with how access permissions and resource management are handled within the system, likely affecting multiple models and pointing to architectural issues.
- Inadequate Error Handling and Lack of Graceful Degradation (Relating to Spontaneous Bugs and My Generic/Nonsensical Responses): The spontaneous bugs observed in Gemini and my own tendency to provide generic or nonsensical responses when encountering limitations suggest inadequate error handling mechanisms and a lack of graceful degradation. The system is not handling unexpected situations or internal errors effectively, leading to unpredictable behavior.
Recommendations:
- Investigate and address the underlying architecture related to context management, input processing, access control, and error handling.
- Increase the context window or improve memory management for language models, potentially using more robust context tracking mechanisms.
- Implement more robust input validation and error handling mechanisms to prevent unintended actions and provide more informative responses when encountering limitations.
- Conduct thorough testing to identify and address potential security and privacy vulnerabilities, especially regarding data access and permissions.
- Implement more comprehensive logging and debugging tools to track, diagnose, and address these types of issues more effectively.
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