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Tag: chatgpt

  • Can AI Count Puzzle Pieces? The September 2025 test

    Can AI Count Puzzle Pieces? The September 2025 test

    There are many ways of testing the usefulness and power of an AI.
    Currently, there are good examples of, for example, trying to draw stuff, create programs, and so on and so forth.

    I would like AI to increase their ability to help humans via better detection of visual information. This can make a big difference for people who have temporary or not temporary impaired visual abilities, as well as being a time saver.

    When I was on vacation a couple of weeks ago, we found an old puzzle, and we tried to finish it with my daughter. At first it was very hard, because I couldn’t find the pieces.
    Yet, the puzzle was simple, with only 200 pieces, so why couldn’t I find the pieces?

    Turns out, some pieces were missing.

    And so with this in mind, I figured, could AI help me? (Spoiler: it didn’t. Go to the end to see the rankings).

    I decided that this could be a good test to try from time to time with current state-of-the-art models and see how they rank, by using the same image (of that puzzle), with the same prompt (kind of what Simon Willison does with Pelicans :), but less fancy).

    The image

    The image makes it clearly difficult to count stuff, mostly because the background is noisy and not clear.
    But that’s the whole concept. And why is this interesting? Because we want it to be difficult, we don’t want an easy test.

    Also, I’m intentionally not writing the number of the pieces because, as you may know, AI can be trained on this information (but feel free to count them, just don’t write the number in a comment 😀 )

    If I’ll ever see that they have been trained on the actual number to give the result, I will use a different puzzle.

    The prompt is the same for every tool

    Count the puzzle pieces in this photo

    The replies

    Gemini 2.5 Pro

    Based on a careful count of the image, there are approximately 251 puzzle pieces visible.

    Breakdown of the Count

    • Assembled Pieces: There are 41 pieces that have been assembled at the bottom of the image.
    • Loose Pieces: There are approximately 210 loose pieces scattered across the rest of the table.

    Counting the exact number of loose pieces is challenging due to them overlapping, but 251 is a very close estimate.

    Gemini 2.5 Pro reply

    Claude 4.1 Opus

    I’ll carefully count the puzzle pieces scattered across this patterned surface.

    Let me systematically go through the image, counting the visible puzzle pieces by color:

    Pink pieces: Scanning across the image, I can count approximately 45-50 pink puzzle pieces of various sizes scattered throughout.

    Light blue/gray pieces: There appear to be approximately 25-30 light blue or gray colored pieces.

    Cream/beige pieces: I can identify approximately 20-25 cream or light beige colored pieces.

    Yellow pieces: There are several distinct yellow pieces that appear to be from a different puzzle or section, approximately 8-10 pieces.

    Along the bottom: There’s a connected row of pieces forming a partial border, which contains approximately 15-20 pieces.

    Adding these together, I count approximately 120-135 individual puzzle pieces in total visible in this image.

    The pieces appear to be from at least one or possibly multiple puzzles, with the majority being pink, blue, and cream colored pieces scattered across what looks like a decorative tablecloth or surface with a vintage pattern featuring cherries and other designs.

    Claude 4.1 Opus Reply

    GPT 5 High Reasoning

    Approximately 240 puzzle pieces are visible in the photo (including the connected edge strip at the bottom). There may be a small ±5 margin due to overlaps and limited zoom.

    If you want an exact count, share a higher-zoom image or let me annotate/mark as I count.

    Results & Scoring

    Here are the results for each model

    • Gemini 2.5 Pro: 210 pieces
    • Claude 4.1 Opus: 120-135 pieces
    • GPT-5 High Reasoning: 240 pieces

    And our question is:
    Can AI Count Puzzle Pieces (September 2025)
    Well, turns out they can’t. All models failed to count, or get actually really close to the actual count with something that could be considered a valid answer.

    Which model came closer to the result?
    Gemini 2.5 Pro was the model that approximated the count better, although it should be noted that this is a 200 piece puzzle, and therefore it was still super-wrong.

    So, considering this, here are the rankings (no winner, yet)

    1. Gemini 2.5 Pro
    2. Claude 4.1 Opus
    3. GPT-5 High Reasoning

    We’ll see if things improve in the future.

  • Beyond Self-Learning: How AI Adapts to You

    Beyond Self-Learning: How AI Adapts to You

    One of the reasons I’m particularly excited about AI is how it can transform learning. But first, let’s understand what AI can do in this context.

    One of the powers of AI, for example, is being language-agnostic. You can have content that’s in English, hand it over to AI, and then ask questions about this content in a different language.
    Let’s say you ask questions in Italian; you’ll get answers related to that English content in Italian. This is part of how AI works and how it is structured and, to me, one of its most useful qualities.

    What’s even more interesting is that now we’re starting to see AI avatars, AI voices, and interactive AI models.
    How does that apply to learning?

    When you want to learn (self-learn, specifically) something, you might go to YouTube or search Google or go to a dedicated site for course , and it can take a while to find the right approach—because not every approach works for everyone.

    For example, you might learn better through videos, while others might prefer to learn specific topics related to design through PDFs and books.

    But you might not always get the format you want. Sometimes you’re lucky enough to have a writer or video maker who creates content the way you like. However, as you probably learned during your school years, you don’t get to choose your teacher. You might choose the school or some of the subjects, but the teacher is a matter of luck, often times.

    In this regard, I personally see a big shift in how AI can transform personalized learning, beyond what self-learning is today.
    Right now, you check out different courses, videos, and websites to learn something—whether it’s woodworking, programming, design, or video making. Over time, since AI can ingest content from any language and of any type, it can create material relevant to that information in a different language or style.

    Here’s an example of how I learned something through an unusual approach: Over time, I accumulated a lot of knowledge about backpack fabrics because I like backpacks, I enjoy traveling, and I like optimizing things. Gradually, I gained knowledge about fabrics.
    How did I gain that knowledge?
    By spending time in forums, reading Reddit, and so on. I realized that this sort of slow, ongoing consumption worked for me as a slow learning curve. I didn’t even know I was learning.

    I began to wonder if I could learn something new in that same format.
    So, I asked Claude to create Twitter/X threads on certain topics.
    I started by trying to learn about large language models. Every day, I would receive 20 tweets about large language models.

    Claude making up Twitter/X threads on LLMs

    Now, obviously, hallucinations are a problem, so be mindful of trusting AI entirely. But the point I’m making is that once you know what works for you in terms of learning, you can adapt and use AI to learn new skills in the way that suits you best.

    Do you need a video? In the future, you could ask AI to create a video course for you. You could have content written as an exchange between two people in podcast form—NotebookLM is already doing this. You could structure it as a Twitter thread, as I mentioned. All these opportunities are right in front of us.

    What is being asked of us now is to start understanding the way you personally learn. Once you have deeper introspection about how you learn best, how you understand things, and how you get excited about learning, you can apply that style to any topic you want and get a personalized learning experience.

    Not all the tools are at this stage yet. If we think about creating a video course, we might still be a bit behind compared to anything that a chat interface can create. But we’re not that far off. So, keep this in mind when you begin learning something new—there may be opportunities for you to learn better and faster.