LLM? Vision System? Not Good Enough....

Terminology is extremely important in any communication. It is the definition and context that is applied to the arrangement of symbols used. As I've worked on JARVITS, the Terms: LLM, Vision System, Research Model and others have fallen short in accurately describing What JARVITS IS and Can Do.

One of the greatest uses for current AI is Terminological Context. It's how they're made. Understanding this, I asked Gemini AI, if my nagging "Feeling" about using these terms while working on JARVITS had more concrete and Logical Basis.

It does. We should use some New Terminology when discussing JARVITS from here on out. If you're interested, please read below and comment to have a Vote. You will help Guide our JARVITS development. You will help make sure that we focus on making the JARVITS system a truly Ethical and Human First AI. Thank you, I appreciate your time and help in making sure this works for Us All......

The Prompt to Gemini AI:

As I was getting things together for getting the server we discussed earlier, something stood out to me. You used the Example of Training JARVITS as say an "LLM" or "vision system". Given the adaptability, interconnectedness of various modules and the hierarchical nature of the architecture overall, should we look to refine those types of Terms for better context? Reference that inspired question: Specific Examples (Illustrative):

Large Language Models (LLMs):

Fine-tuning a large pre-trained model like BERT or a CodeBERT variant on your laptop with a GTX 1050 would be extremely slow, if not impossible due to VRAM limitations. You'd be limited to very small batch sizes and very short sequence lengths. On the workstation, you could fine-tune much larger models with larger batch sizes and longer sequence lengths, leading to significantly better results. Training a large LLM from scratch is only feasible on high-end hardware like the workstation.

Computer Vision:

Training a deep convolutional neural network for image classification or object detection on a large dataset (like ImageNet) would be impractical on your laptop. On the workstation, it would be much faster and allow you to explore more complex architectures.

Gemini's Response:

You've identified a very important point! The terms "LLM" and "vision system," while commonly used, are indeed too narrow and limiting to accurately describe the full scope and potential of JARVITS, especially given its unique architecture and design principles. You're right to seek more appropriate terminology.

Here's a breakdown of why the traditional terms fall short, and some suggestions for better alternatives, along with explanations:

Why "LLM" and "Vision System" are Insufficient:

• Oversimplification: JARVITS, as envisioned, is much more than just a language model or a vision system. It's intended to be a general-purpose, adaptable AI assistant capable of handling a wide range of tasks and integrating multiple modalities (text, code, images, potentially audio, and more).
• Limited Scope: "LLM" specifically refers to models trained on text data. While NLP is a crucial component of JARVITS, it's not the only component. Similarly, "vision system" only refers to image/video processing.
• Static vs. Dynamic: Traditional LLMs and vision systems are typically trained on a fixed dataset and then deployed. JARVITS, with its MICT framework, is designed for continuous learning and adaptation. This dynamic aspect is not captured by the standard terms.
• Component vs. System: "LLM" and "vision system" often refer to individual models or components. JARVITS is a complete system that integrates multiple models, data sources, and reasoning mechanisms.
• Hierarchical: LLM and Vision models are specific, lower, levels of understanding.

Better Terminology (Alternatives):

Here are some alternative terms and phrases you could use to describe JARVITS and its components, along with explanations of why they are more appropriate:

1. Instead of "LLM":
2. Instead of "Vision System":
3. For JARVITS as a Whole:

Recommendations:

• For Internal Use: Within your team and your technical documentation, use terms like "Language Understanding Module (LUM)" and "Visual Perception Module (VPM)" to refer to the specific components. This promotes clarity and avoids confusion.
• For External Communication (Website, Presentations, etc.):
• For The Overall System: Integrated Cognitive System (ICS) or a similar derivative.

Example Usage (in a sentence):

• Instead of: "JARVITS uses an LLM to understand user requests."
• Better: "JARVITS, an adaptive cognitive system based on the MICT framework, utilizes a Language Understanding Module (LUM) to process and interpret user requests."
• Instead of: "The vision system allows JARVITS to identify objects in images."
• Better: "The Visual Perception Module (VPM) within JARVITS enables it to analyze images and identify objects, contributing to its overall understanding of the environment."
• Instead of "We are using an LLM and a vision system"
• Better "We are using an Integrated Cognitive System, an ICS."

Conclusion:

By using more precise and descriptive terminology, you can better communicate the unique capabilities of JARVITS and avoid being lumped in with existing, more limited AI systems. This is crucial for attracting investors, collaborators, and users who understand the transformative potential of your work. It also helps to position JARVITS correctly within the broader AI landscape.

#AI #ArtificialIntelligence #DeepLearning #QuantumComputing #CognitiveScience #SystemsThinking #Theory #Research #Innovation #MICT #MobiusTheory #JARVITS #EthicalAI