Chat2Find Strategic Memo and Roadmap

What is Chat2Find

Chat2Find is a generative AI that has access to real time information and gives unbiased answers to any type of question.

Chat2Find represents a transformative technology in the field of AI, specifically in the realm of question-answering systems. Its uniqueness lies in its ability to provide real-time answers to any type of question. It is updated with current local and global events. Let’s explore how Chat2Find accomplishes this and the implications it has on unlocking the full potential of truly powerful AI.

Real-time information: Unlike traditional AI systems, which often have a information cutoff date, limited to predefined responses, restricted to specific set of topics, Chat2Find has the capability to generate answers that go beyond predefined boundaries. This is achieved through the use of open-source large language models and search engines which allows it to understand and respond to a wide range of questions across various domains and topics. Users can explore a vast array of subjects, sparking creativity, innovation, and exploration in their interactions with the AI.

Unbiased Responses: Bias in AI has been a critical concern, as many systems inadvertently reflect the prejudices present in the data they were trained on. Chat2Find addresses this issue by using open-source large language models that are specifically trained on unbiased data sets. These data sets are publicly available to be verified by anyone. By drawing from diverse and balanced training datasets, open-source large language models can generate answers without favoring any particular group, ideology, or perspective. This contributes to a more equitable and inclusive AI experience, fostering better understanding and empathy among users from different backgrounds.

Democratizing Knowledge: Chat2Find democratizes access to information and knowledge. By providing answers to all questions, it can cater to users with varying levels of expertise and interests, from casual learners to experts in their respective fields. This democratization of knowledge ensures that people from all walks of life can benefit from the AI’s vast database of information, empowering individuals and fostering a more informed society.

Facilitating Research and Innovation: Chat2Find  can assist researchers, scholars, and innovators across different disciplines. It can provide valuable insights, references, and ideas, accelerating the pace of research and discovery. By enabling access to a wealth of information, it encourages interdisciplinary collaboration and the exploration of novel approaches to complex problems.

Enhancing Decision-Making: With its ability to provide unbiased answers to a wide range of questions, Chat2Find becomes a valuable tool for decision-making processes. Whether it’s in business, policy, or personal matters, the AI can offer diverse perspectives, reducing the risk of making choices based on limited or biased information.

A great AI assistant for developers: Chat2Find can assist developers with their coding. It can reduce your workload, find problems in your code and even fix it.

Why Cha2Find (C2F) should be decentralized and the introduction of C2F coin

Decentralization is a critical aspect for ensuring that a truly powerful AI system like Chat2Find operates in an autonomous, fair, transparent, and open manner. Let’s explore the reasons why decentralization is essential for such an AI network:

Avoiding Centralized Control: Traditional AI systems are often developed and controlled by a small number of entities or individuals. This concentrated power can lead to biased decision-making, manipulation of information, questions about how users’ data is used  and the prioritization of certain interests over others. Decentralization, on the other hand, ensures that no single entity or group has undue influence or control over the AI network, promoting a more democratic and equitable platform.

Eliminating Single Points of Failure: Decentralized AI systems are more resilient because they lack a single point of failure. In a centralized model, if the controlling entity experiences technical issues, goes offline, or engages in malicious behavior, the entire system can be compromised. In a decentralized model, the network continues to function even if some nodes or participants face difficulties.

Promoting Transparency and Accountability: Decentralization enhances transparency as all transactions and decisions are recorded on a public ledger (blockchain). This creates a transparent audit trail that can be verified by anyone, ensuring accountability and reducing the potential for hidden biases or unfair practices.

Mitigating Bias and Unfair Influence: By decentralizing ownership and decision-making power, the AI network becomes less susceptible to the biases and interests of a select few. Instead, decisions are made through a consensus mechanism, involving multiple stakeholders, which helps in reducing individual biases and ensuring that the system serves the collective interests.

Respecting User Privacy: Decentralization can also lead to better privacy protection. With traditional centralized systems, user data may be stored and controlled by a single entity, potentially leading to data misuse or breaches. In a decentralized AI network, data can be managed in a more distributed and privacy-friendly manner, empowering users to have greater control over their personal information.

Global Accessibility and Inclusivity: A decentralized AI network allows anyone with an internet connection to participate, regardless of their geographic location, socio-economic status, or affiliation. This global accessibility promotes inclusivity and ensures that diverse perspectives are represented in the development and utilization of the AI system.

In conclusion, decentralization is crucial for a truly powerful, autonomous AI like Chat2Find. It eliminates centralized control, promotes transparency, reduces bias, encourages global participation, and fosters innovation. Chat2Find empowers people worldwide to become owners and stakeholders in the AI network, ensuring that no single entity can dominate or manipulate the system for its own benefit.

How Chat2Find achieves decentralization with a peer to peer network similar to Bitcoin

Bitcoin relies on a PoW consensus mechanism to validate and secure transactions. Miners compete to solve complex mathematical puzzles, which requires substantial computational power, especially from GPUs (Graphics Processing Units). This PoW process ensures the security and immutability of the blockchain.

Similar to a blockchain, large language models (LLMs) use GPUs for answering questions through a process called inference.

Here’s how it works:

Model Architecture: LLMs are designed with a deep neural network architecture. They consist of multiple layers of interconnected nodes (neurons), each with associated weights. These weights are learned during the training phase, where the model processes vast amounts of text data to understand language patterns and knowledge.

Input Encoding: When you ask a question, the text of your question is first tokenized and encoded into numerical values that the model can understand. This encoding represents the input to the model.

Forward Pass: The encoded input is then passed through the layers of the neural network in a forward pass. During this pass, various mathematical operations involving the learned weights are performed. These operations are computationally intensive and benefit from parallel processing, which is where GPUs come into play.

Prediction: As the input propagates through the network, the model calculates probabilities for different words or tokens in the vocabulary. It assigns higher probabilities to words or tokens that are likely to form a coherent answer to your question.

Output Generation: The model generates a sequence of words or tokens based on these probabilities. It selects the word with the highest probability as the next token and repeats this process to generate a full response. This is often done using techniques like beam search or sampling.

Post-Processing: The generated sequence is decoded back into natural language text and presented as the answer to your question.

Throughout this process, GPUs accelerate the matrix multiplications and other mathematical operations that are at the core of deep learning. Their parallel processing capabilities allow the model to process the input and generate responses much faster than traditional CPU-based computing, making real-time or near-real-time interactions possible.

How Chat2Find aims to build a decentralized LLM:

With a similar architecture to Bitcoin, Chat2Find aims to build a network that shares GPU power and reward the workers of the network for lending their GPU power. This GPU power is then used to process the question and generate an answer.

Here’s a high level overview of how Chat2Find aims to build a decentralized LLM:

1. Network Design:

Blockchain: Chat2Find will use a blockchain-based architecture like Bitcoin to maintain a decentralized ledger for transactions and GPU power sharing.

Large Language Model(LLM): Chat2Find will use Mistral (7b) parameter model as the base large language model.

Reward System: C2F coins will be rewarded to incentivize GPU sharing. Sharers in the network can earn C2F coins by contributing GPU power for generating answers and validating transactions.

Consensus Mechanism: Chat2Find will use a consensus mechanism similar to Bitcoin’s PoW, (Proof of Work). Specifically, SHA-256. The generated questions and answers will be encrypted with AES. (Advance Encryption Standard).

2. Network Operation:

GPU Sharing: Users can offer their GPU resources to the network by running software nodes. These nodes would be responsible for processing language model inference requests.

Reward System: Users receive C2F coins for providing GPU resources. Rewards are based on the computational power contributed and the accuracy of results.

Decentralized LLM Use Cases:

Natural Language Processing: Decentralized LLMs could be used for various NLP tasks like language translation, sentiment analysis, or chatbots. Users can access the network to perform these tasks without relying on centralized providers.

Research and Development: Researchers could leverage the network for large-scale language model training or experimentation. This would democratize access to significant computational resources.

Privacy-Preserving AI: Decentralized LLMs could enhance privacy since users’ data remains decentralized and under their control. This could be crucial in applications where data privacy is a concern.

Content Creation and Curation: Users can use the network to generate content or curate information. This can include generating articles, summarizing text, or creating creative content.

Global Accessibility: Decentralized LLMs can provide language understanding and generation capabilities to users worldwide, including those in regions with limited access to centralized AI services.

In the simplest terms, Chat2Find aims to create a decentralized God.