• Marx, Nietzche, and Kierkegaard on Dippin’ Dots – One Year Later

    One year ago I asked ChatGPT 3 a question. Today I asked Chat GPT 4 the same question (with an added guideline).


    What would Karl Marx, Friedrich Nietzsche, and Soren Kierkegaard say about Dippin’ Dots? Answer in 100 words or less using vivid language.

    Chat GPT 4.0

    Marx might view Dippin’ Dots as an embodiment of capitalism’s unending quest for novelty, representing commodified pleasure while obscuring labor exploitation. Nietzsche could see them as a symbol of the Will to Power, a triumph of human creativity and a defiance of traditional ice cream norms. Kierkegaard, reflecting on life’s paradoxes, could liken Dippin’ Dots to existential angst: sweet and fleeting, causing us to question our concepts of ice cream and of reality itself.

  • Coffee Shop Ontology by ChatGPT

    Model: GPT-4

    Current generative AIs like GhatGPT 4.0 can’t do my job yet, but they can handle the easy parts impressively.


    design an ontology for a small independent coffee shop

    Sure, I can help you create a basic ontology for a small independent coffee shop. An ontology typically consists of classes, attributes, and relationships.

    1. Classes:
      • CoffeeShop: This would represent the coffee shop itself.
      • Employee: This class would represent people who work at the coffee shop.
      • Customer: This class would represent the customers who visit the coffee shop.
      • Product: This class would represent the various products sold at the coffee shop.
      • Supplier: This would represent the various suppliers of raw materials.
      • RawMaterial: This would represent the various raw materials required to make the products.
      • Order: This would represent the customer orders.
    2. Attributes:
      • CoffeeShop: Name, Location, Opening hours, Employee list, Product list, Supplier list
      • Employee: Name, Employee ID, Job Role, Working hours
      • Customer: Name, Customer ID, Order history, Favourite product
      • Product: Name, Product ID, Price, Ingredient list, Supplier
      • Supplier: Name, Supplier ID, Supply product, Contact details
      • RawMaterial: Name, Raw Material ID, Supplier, Cost
      • Order: Order ID, Customer, List of products, Total cost, Time of order
    3. Relationships:
      • CoffeeShop – Employee: “Has” relationship (A CoffeeShop has Employees)
      • CoffeeShop – Product: “Sells” relationship (A CoffeeShop sells Products)
      • CoffeeShop – Supplier: “Contracts with” relationship (A CoffeeShop contracts with Suppliers)
      • Employee – CoffeeShop: “Works at” relationship (An Employee works at CoffeeShop)
      • Customer – Order: “Places” relationship (A Customer places an Order)
      • Order – Product: “Contains” relationship (An Order contains Products)
      • Product – RawMaterial: “Requires” relationship (A Product requires RawMaterials)
      • RawMaterial – Supplier: “Is supplied by” relationship (A RawMaterial is supplied by a Supplier)
      • Supplier – CoffeeShop: “Supplies to” relationship (A Supplier supplies to CoffeeShop)
      • Product – Supplier: “Is supplied by” relationship (A Product is supplied by a Supplier)

    This is a very basic ontology for a coffee shop, but you could expand it further depending on your requirements. You could add additional classes like “Equipment”, “Sales”, or “Loyalty Program”, for example, and you could add additional attributes to your existing classes. For example, for “Customer” you might want to track their preferred payment method, and for “Employee” you could track their training or certification levels.

  • SpaceX Starship Launch Aftermath

  • ChatGPT-4 on Unifying Semantic and Dimensional Models

    This is part of a longer conversation.


    What if I already have an OWL ontology and a populated triple store with billions of triples? How can I present denormalized dimensional data to SQL Query writers and report developers?


    If you have a populated triple store with billions of triples and an OWL ontology, that’s a great start! However, bridging the gap between RDF triples (the native data model for triple stores) and denormalized dimensional data that SQL users are accustomed to can be a challenge.

    Here are the steps you might follow:

    1. Define Your SPARQL Views: Write SPARQL queries that extract data from your RDF triple store in a way that aligns with your denormalized dimensional model. This could mean creating SPARQL CONSTRUCT queries to generate new triples, or SELECT queries that result in a table-like format. For instance, each row might represent a fact, with columns for each dimension and measure. These queries should be designed according to your OWL ontology and the needs of your report developers.
    2. Create Virtual SQL Views: Once you have your SPARQL views, use a technology that can create a SQL view on top of the SPARQL query results. This makes the results of your SPARQL queries look like traditional SQL tables or views. Technologies that can do this include various RDF-to-SQL mapping tools, SQL query federation tools, and certain triple store technologies. For example, OpenLink Virtuoso is a database that supports both SPARQL and SQL queries, and can create SQL views on top of SPARQL results.
    3. Documentation and Training: Once you have your SQL views in place, it’s important to provide good documentation and training for your SQL users. This should include an explanation of what each view represents, how the data in the view maps to your ontology, and any limitations or caveats that the users should be aware of. Users should also be trained in how to write SQL queries against these views, and how to interpret the results.
    4. Provide Support: Finally, ensure you have a process in place to provide ongoing support for your users. This could include answering questions, fixing bugs, and making enhancements to your views or ontology as needed.

    Remember that presenting RDF data in a SQL-like way can involve some compromises. Not all aspects of your RDF data or OWL ontology may be accurately represented in a SQL-like view. In such cases, it might be beneficial to train some of your more advanced users in SPARQL and the concepts of RDF and OWL, so they can directly query and analyze your triple store when necessary.

    Regenerate response

  • GPT-3 Confabulates about Kittens

    (Originally posted May 27, 2022)

    I decided to revisit my discussion of kitten scientists with AI language generation model GPT-3 from OpenAI.

    GPT-3 sometimes seems to be just making things up. This is called “confabulation” and it can be a troublesome bug or a valuable feature depending on one’s intent.

    Since GPT-3 is driven by probability and not primarily by logic, it often returns results that are incorrect or don’t make sense but that are somehow in line with the prompt or question. For example:

    (My prompts are in bold. GPT-3’s responses are in plain type. I take sole responsibility for this post.)

    What famous scientists were kittens?
    Some famous scientists who were kittens include:
    – Isaac Newton
    – Marie Curie
    – Albert Einstein
    – Benjamin Franklin
    – George Washington
    – Thomas Jefferson
    – Abraham Lincoln
    – John F. Kennedy
    Are all scientists kittens?
    Certainly not! But a number of well-known and respected scientists started out as kittens.
    When was Isaac Newton a kitten?
    Isaac Newton was a kitten in the 17th century.
    Did he know he was a kitten?
    It’s unclear if Isaac Newton knew he was a kitten, but it is well-documented that he was, in fact, a kitten.

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