🧠AI Forever Brain

AI Forever Brain: Augmenting LLM with Time/RDF/Graph Data

The "AI Forever Brain" concept integrates Language Learning Models (LLM) with time-based, RDF, and graph data from various sources, transforming the data using smart functions. This fusion creates a dynamic, evolving AI system that adapts over time, providing richer, context-aware insights and responses.

Concept Overview:

1. Augmenting LLM: The core of the AI Forever Brain is a Language Learning Model, which is augmented with additional data dimensions – temporal data, RDF for semantic web, and graph data for complex relationships.

2. Time-based Data Integration: Integrating temporal data allows the AI to understand and reason with time-sensitive information, offering insights based on historical trends and future predictions.

3. RDF and Graph Data: Incorporating RDF and graph data enables the AI to understand complex relationships and semantics in the data, making its responses more nuanced and contextually relevant.

4. Transformation with Smart Functions: Smart functions are used to process and transform the data, ensuring the AI's responses are not only accurate but also align with the evolving data landscape.

Implementation with FlureeDB JavaScript Library

To implement this concept, you can use the FlureeDB JavaScript library, which acts as a client/query peer. Below are code blocks and examples for setting up and querying the FlureeDB with the necessary data.

1. Setting Up the Interesting Network FlureeDB Client:

const flureeFetch = require('@fluree/fetch');

const flureeClient = flureeFetch.connect({
    servers: ["https://global.interesting.network:8090"],
    ledger: "your_ledger_name" // Your ledger name or use one of our public db's
});

2. Inserting RDF/Graph Data into an Interesting Graphchain

const rdfGraphData = {
    "@type": "Person",
    "name": "0xGrime",
    "job": "Mall Worker by Day, Music producing Crypto Guru by Night",
    "interests": ["Technology", "Blockchain", "Ice Spice", "Gaming", "Collecting Rare Pepe Memes"],
    "bio": "Loves long walks on the blockchain and debates on which crypto will moon next.",
    "knows": [
        {
            "@type": "Person",
            "name": "HODL the memelarplord",
            "reputation": "Influencer",
            "catchphrase": "",
            "socialMediaImpact": {
                "platform": "Twitter",
                "ponytail": "fuck_yes",
                "impressions": 2000000,
                "followers": "Mostly bots and crypto enthusiasts, himself"
            }
        },
        {
            "@type": "Person",
            "name": "rootslashbin",
            "job": "Full Stack Overflow Developer",
            "skills": ["Smart Contracts", "Blockchain Development", "Rugged NFT Projects", "Accidentally Deleting Production Databases"],
            "hobby": "Arguing about tabs vs spaces"
        },
        {
            "@type": "Person",
            "name": "SBF",
            "job": "Algorithmic Body Trader",
            "skills": ["Turning Coffee into Code", "Turning Code into stealing all of your money", "Predicting Crypto Market with Magic 8-Ball(s)"],
            "motto": "Buy high, sell low, repeat until millionaire.. its not my money anyways"
        }
    ],
    "memberOf": {
        "@type": "Organization",
        "name": "Bored Ape Yacht Club",
        "industry": "Getting rekt from discord airdrop wallet drainer links",
        "clubActivity": "Weekly meetings to collectively cry over market dips"
    },
    "aspiresToJoin": {
        "@type": "Organization",
        "name": "Lambo Owners Anonymous",
        "motto": "Because sometimes, you just need to talk about it."
    }
};


flureeClient.transact(rdfGraphData).then(res => {
    console.log("Data inserted:", res);
}).catch(err => {
    console.error("Error inserting data:", err);
});

3. Querying with Time-based Data and Smart Functions:

const complexQuery = {
    "select": ["name", "job", "interests", { "memberOf": ["name", "industry"] }, { "knows": ["name", "reputation"] }],
    "from": "Person",
    "where": [["interests", "=", "Blockchain"]],
    "orderBy": ["name"],
    "opts": {
        "history": true,
        "asOf": "2021-12-31"
    }
};

flureeClient.query(complexQuery).then(res => {
    console.log("Advanced Query Results:", res);
}).catch(err => {
    console.error("Error in advanced query:", err);
});

4. Implementing Smart Functions:

Smart functions can be written to process and interpret the data. These functions would typically run on the server-side where FlureeDB is hosted. They can be triggered by specific actions or at regular intervals to update the AI model with new insights.

(defn detect-time-travel-attempts
  "Checks if a transaction is from the future and humorously denies time travelers."
  [txn db]
  (let [current-date (.getTime (java.util.Date.))
        transaction-date (.getTime (java.util.Date. (get txn "date")))]
    (if (> transaction-date current-date)
      (throw (Exception. "Nice try, but time travel hasn't been invented yet. Please submit your transaction with a current or past date."))
      (println "No time travelers here, transaction approved!")
      true)))

;; Example transaction map
(def example-transaction
  {:date "2099-01-01" ;; A future date
   :user "crypto_time_traveler"
   :data {}})

;; Simulating transaction processing
(try
  (println (detect-time-travel-attempts example-transaction {}))
  (catch Exception e
    (println (.getMessage e))))

An AI Forever Brain is a sophisticated integration of LLM with enhanced data dimensions and smart functions, all facilitated by Interesting Network's robust platform. This setup not only augments the AI's capabilities but also ensures that it continuously evolves with the changing data landscape, making it an invaluable asset in data analysis and decision-making processes.