{"id":7103,"date":"2025-08-02T13:31:20","date_gmt":"2025-08-02T10:31:20","guid":{"rendered":"https:\/\/store.nit.bg\/the-chat-bot-that-doesnt-think\/"},"modified":"2025-08-03T14:25:19","modified_gmt":"2025-08-03T11:25:19","slug":"the-chat-bot-that-doesnt-think","status":"publish","type":"post","link":"https:\/\/store.nit.bg\/en\/the-chat-bot-that-doesnt-think\/","title":{"rendered":"The chat-bot that doesn’t think"},"content":{"rendered":"

[vc_row][vc_column][vc_column_text]<\/p>\n

Most people believe that ChatGPT is the artificial intelligence. They are wrong. Calling ChatGPT “artificial intelligence” is like calling the microwave “cooking” – it’s a single appliance, not the entire kitchen. <\/p>\n

ChatGPT really took the world by storm for a few years, which is why many now consider chatbots to be artificial intelligence itself. In fact, it is just one particular tool built on a single technique – nothing more. <\/p>\n

1\ufe0f\u20e3 What is “artificial intelligence” (AI)?<\/h3>\n
\nMachine Learning (ML)<\/strong><\/summary>\n

Learning from data without writing rules for it. Example. <\/p>\n<\/details>\n

\nNatural Language Processing (NLP)<\/strong><\/summary>\n

Understands and writes human language. Example. <\/p>\n<\/details>\n

\nComputer Vision (CV)<\/strong><\/summary>\n

“Sees” and analyzes images. Example: Face ID unlocks you even with a hat and glasses because it has studied millions of faces from different angles. <\/p>\n<\/details>\n

\nRobotics<\/strong><\/summary>\n

Moves and works in the real world. Example: the robot in the Amazon warehouse picks up boxes with millimetre precision and doesn’t get tired. <\/p>\n<\/details>\n

\nExpert systems<\/strong><\/summary>\n

Apply ready-made rules. Example: software in a hospital inputs symptoms and outputs the most likely diagnosis according to medical protocols. <\/p>\n<\/details>\n

2\ufe0f\u20e3 From AI to ChatGPT – a 7-step “ladder descent”<\/h3>\n
    \n
  1. Artificial Intelligence (AI)<\/strong>
    \nthe whole science, from a chess computer to a self-driving drone.<\/li>\n
  2. Neural Networks (NN)<\/strong>
    \nmathematical “neurons” that together capture complex shapes such as a face or handwriting.<\/li>\n
  3. Deep Learning (DL)<\/strong>
    \nhundreds of layers of neurons – the more, the finer the details it recognizes.<\/li>\n
  4. Transformers (2017)<\/strong>
    \narchitecture that reads an entire sentence at once and connects a word from the beginning with one from the end.<\/li>\n
  5. Large Language Models (LLMs)<\/strong>
    \nbillions of parameters “read” the entire Internet; GPT-4 is an example.<\/li>\n
  6. GPT-4<\/strong>
    \nthe exact engine hidden behind ChatGPT – more powerful, but without the chat-wrapper.<\/li>\n
  7. ChatGPT<\/strong>
    \nnice chat window, added conversation memory and safety filters.<\/li>\n<\/ol>\n

    3\ufe0f\u20e3 What happens “under the hood” of ChatGPT?<\/h3>\n
    \nLearning as a child – two phases<\/strong><\/summary>\n

    Phase 1:<\/strong> Reads the whole internet without labels – learns grammar, facts and style.
    \nPhase 2:<\/strong> People show good and bad answers – model adjusts to be helpful and polite.<\/p>\n<\/details>\n

    \nTransformer in 3 sentences<\/strong><\/summary>\n

    1) Divides text into small chunks (tokens).
    \n2) Each chunk “looks at” all the others (self-atttention).
    \n3) Adds position codes to know the order.<\/p>\n<\/details>\n

    \nGenerating the response<\/strong><\/summary>\n

    Does not search a database but predicts the next word word by word. If there is no information – makes it up in a confident tone. <\/p>\n<\/details>\n

    4\ufe0f\u20e3 Issues everyone should know about<\/h3>\n
    \nHallucinations<\/strong><\/summary>\n

    He makes up scientific papers and cites them with DOIs. Always check a second opinion. <\/p>\n<\/details>\n

    \nLoss of context<\/strong><\/summary>\n

    After a few paragraphs I forget the beginning. Break long tasks into parts. <\/p>\n<\/details>\n

    \nFixed context<\/strong><\/summary>\n

    GPT-4o remembers ~300 pages. Don’t hand it a whole book at once. <\/p>\n<\/details>\n

    \nRepeatability<\/strong><\/summary>\n

    Often chooses the same “random” number. Raise the temperature for variety. <\/p>\n<\/details>\n

    5\ufe0f\u20e3 How to Use ChatGPT Smartly – 5 Quick Rules<\/h3>\n
      \n
    1. Verify<\/strong> what is important in a reliable source – confidence does not mean truth.<\/li>\n
    2. Break<\/strong> complex tasks into steps – the chat-bot loses thread on large blocks.<\/li>\n
    3. Give context last<\/strong> – that’s what you remember best.<\/li>\n
    4. Use for drafts<\/strong> – translation, ideas, structure; then edit.<\/li>\n
    5. Don’t rely on critical topics<\/strong> – law, medicine, finance – without human verification.<\/li>\n<\/ol>\n

      6\ufe0f\u20e3 Advanced mini-dictionary<\/h3>\n
      \n
      AI<\/strong>
      \nA big umbrella over all smart machines, from YouTube recommendations to driverless cars.<\/div>\n
      AGI<\/strong>
      \nThe Hollywood assistant who can cook, heal and go out for coffee – more fiction.<\/div>\n
      ML<\/strong>
      \nThe computer looks at examples and learns the rules itself – without a human typing “if-then”.<\/div>\n
      DL<\/strong>
      \nHundreds of layers of artificial neurons – the more the finer the detail it picks up.<\/div>\n
      NLP<\/strong>
      \nFrom “translates word for word” to “understands irony and context”, this is NLP.<\/div>\n
      CV<\/strong>
      \nTurns pixels into sense: face, car, cat, tumor on x-ray.<\/div>\n
      LLM<\/strong>
      \nGPT-4 without the chat wrapper – the brain that never sleeps and has read the entire Internet.<\/div>\n
      GPT<\/strong>
      \nThe exact model of OpenAI – the engine that powers ChatGPT.<\/div>\n
      RLHF<\/strong>
      \nPeople appreciate the responses; the model is being adjusted to be more polite and helpful.<\/div>\n
      RAG<\/strong>
      \nFirst search documents, then write – reduce fabrications.<\/div>\n
      GAN<\/strong>
      \nOne AI paints a fake picture, the other catches it – after 1000 rounds they become almost real.<\/div>\n
      CNN<\/strong>
      \nFilters that look for edges, shapes, eyes – so your phone recognizes your face.<\/div>\n
      RNN\/LSTM<\/strong>
      \nRemember the order of words or notes – useful for weather forecasting or music.<\/div>\n
      CoT<\/strong>
      \nMakes the AI think out loud, “First… then… therefore…” – more precise answers.<\/div>\n
      VC<\/strong>
      \nYou speak to the computer in English “make me a button” – it writes the code.<\/div>\n
      ASR<\/strong>
      \nAs Siri translates “What is the meaning of life?” into letters.<\/div>\n
      SOTA<\/strong>
      \nEvery time someone improves the accuracy by 0.5%, it becomes the new SOTA.<\/div>\n
      FLOPs<\/strong>
      \nThe more FLOPs, the faster the model is trained or responds.<\/div>\n<\/div>\n

      7\ufe0f\u20e3 3 sentences to remember<\/h3>\n

      ChatGPT is like a microwave for words<\/strong>: fast and convenient, but you can’t cook everything in it.<\/p>\n

      AI is the whole kitchen<\/strong> – ovens, hobs, recipes, cooks.<\/p>\n

      Use it in moderation, check the results<\/strong> and never forget that behind every “smart” sentence there are only numbers and probabilities.<\/p>\n

      If you feel like going down from theory to practice, store.nit.bg<\/a><\/strong> is waiting for you with ready-made, practical online training on the subject – from scratch to daily use. There, you’ll learn how to implement ChatGPT, Microsoft Copilot and Google Gemini into your workday without being a programmer, and you’ll learn the basics of artificial intelligence and machine learning adapted to reality. <\/p>\n

      The content of this article is based on Dr. Milan Milanovic’s original publication “ChatGPT is not AI<\/a>“.<\/em><\/span><\/p>\n

      [\/vc_column_text][\/vc_column][\/vc_row]<\/p>\n<\/section>","protected":false},"excerpt":{"rendered":"

      [vc_row][vc_column][vc_column_text] Most people believe that ChatGPT is the artificial intelligence. They are wrong. Calling ChatGPT “artificial intelligence” is like calling the microwave “cooking” – it’s a single appliance, not the entire kitchen. ChatGPT really took the world by storm for a few years, which is why many now consider chatbots to be artificial intelligence itself. […]<\/p>\n","protected":false},"author":1,"featured_media":7102,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[164],"tags":[],"class_list":["post-7103","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-informaczionni-tehnologii"],"_links":{"self":[{"href":"https:\/\/store.nit.bg\/en\/wp-json\/wp\/v2\/posts\/7103","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/store.nit.bg\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/store.nit.bg\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/store.nit.bg\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/store.nit.bg\/en\/wp-json\/wp\/v2\/comments?post=7103"}],"version-history":[{"count":8,"href":"https:\/\/store.nit.bg\/en\/wp-json\/wp\/v2\/posts\/7103\/revisions"}],"predecessor-version":[{"id":7114,"href":"https:\/\/store.nit.bg\/en\/wp-json\/wp\/v2\/posts\/7103\/revisions\/7114"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/store.nit.bg\/en\/wp-json\/wp\/v2\/media\/7102"}],"wp:attachment":[{"href":"https:\/\/store.nit.bg\/en\/wp-json\/wp\/v2\/media?parent=7103"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/store.nit.bg\/en\/wp-json\/wp\/v2\/categories?post=7103"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/store.nit.bg\/en\/wp-json\/wp\/v2\/tags?post=7103"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}