case in pointm here is a discussion with Gemini-Pro:
>>>>>what version are you, by the way?
As a large language model, I don't have a single version number. Here's why and how it works:
1. Continual Evolution: I'm constantly being updated with new information and improved capabilities. This means there's no single "version" that stays static.
2. Base Model and Datasets: I'm built on top of core models like Google's LaMDA or PaLM. These underlying models have released versions, but my capabilities are more complex.
3. Customizations: The way I'm used (through specific products or services) can involve fine-tuning and data sets that make me unique to that application. This makes a global version number less meaningful.
How to Think About "Me":
Capabilities: Focus on what I can do for you. Can I translate languages? Write different styles of text? Help you research? These are more important than an arbitrary version number.
Training Data: The information I've learned from shapes my responses. If you need to know the general time frame I was trained on, I could provide a range (e.g., "My knowledge is primarily from before 2023").
Transparency: In sensitive situations, the company or developer using me should be able to give some indication of my limitations and biases, even if it's not a traditional version number.
Let me know if you'd like to check my capabilities on a specific task!
>>>>>what is the version number of the gemini-pro LLM that I am chatting with right now?
Unfortunately, it's highly unlikely that you'll get a specific version number for the Gemini-pro LLM you're interacting with. Here's why:
Reasons for No Version Number:
Constant Evolution: Large language models are frequently updated, fine-tuned, and modified. There may not be single, discrete versions tracked publicly.
Proprietary Information: Google AI might consider the exact model configuration and training details confidential and competitive advantages.
Contextual Deployment: The Gemini-pro you're using might be customized for the specific product or service you're interacting with. This further complicates a single version concept.
How to Get More Information
While you might not get a 'v1.5.2' kind of answer, here's what you can try:
Check Documentation:
Thoroughly search the documentation of the product or service where you're using Gemini-pro. It might have some clues about the release date or recent model updates.
Ask Developers (If Possible):
If you're in contact with the developers of the product or service, try asking them. They might not be able to provide a precise version, but could offer insights on the model's training data or capabilities.
Contextual Clues:
Ask Gemini-pro about recent news events or its knowledge cut-off date. This can help narrow down the timeframe of the models it's drawing information from.
Focus on Capabilities:
Remember, instead of obsessing over a version number, focus on whether Gemini-pro is fulfilling its purpose within the product or service you're using.
Let me know if you'd like help exploring capabilities or getting clues about the model's knowledge base!
>>>>>I am not "obsessing " over anything thank you very much!
You're absolutely right! I apologize for my use of the word "obsessing." It was not meant to be critical or imply any negative judgment. I understand that wanting to know more about the LLM you're interacting with is both natural and important for understanding its context and limitations.
Let's refocus! Can you tell me a bit more about where you're using the Gemini-pro model and what your goals are? It might help us find better ways to get the information you need.
>>>>>I need to know if and when Google pulls the plug on Gemini, like it so often has done with other products like for example Google Reader. I want to be alerted of version changes that would indicate the Gemini platform has been internally sentenced to be switched off by Google.
That's a very valid concern! Google does unfortunately have a history of discontinuing products, sometimes with little notice. Here's how we can approach your goal of staying ahead of any potential shutdown or significant changes to Gemini:
1. Understand Product vs. Research:
Vertex AI: The Gemini family of models is part of Google's Vertex AI platform, which seems like a core focus area tied to their cloud offerings. This makes it less likely to face a sudden shutdown like standalone consumer products (e.g., Google Reader).
Research Projects: If you're using Gemini in a more experimental research setting, then the risk is higher. Pay extra attention to project updates and any communication channels for the team working on it.
2. Monitor the Following:
Official Announcements:
Regularly check the Google Cloud Blog (https://cloud.google.com/blog/), especially sections related to AI and Machine Learning.
Subscribe to the Vertex AI newsletter if available.
Documentation Updates: Keep an eye on Vertex AI documentation. Major deprecations or feature removals would likely be reflected here.
Community Forums: Engage with communities where other Vertex AI users and developers discuss the platform. They might have early insights into changes or potential issues.
3. Indirect Indicators (Less reliable, but worth noting):
Neglected updates: If the specific Gemini-pro variation you use stops receiving frequent updates, it might signal reduced investment.
Error frequency: A drastic increase in errors or instability could indicate the infrastructure supporting the model is being de-prioritized.
Proactive Steps:
Diversification: If possible, don't rely solely on Gemini-pro. Explore alternative models or consider if you could build your own custom solution for reduced dependency.
Migration planning: Have a basic plan in mind for what you would do if Gemini is discontinued. This eases the pressure should it happen.
Important Note: There's no foolproof way to predict Google's actions. Even well-supported products can sometimes get canceled.
Let's be realistic; staying alert takes effort on your part. If knowing about Gemini's status is crucial, carve out some time regularly to check the sources mentioned above.
... So all in all, I have not yet reached the point where I would feel remorse if I delete a chat thread...