A poltextLAB nevében köszöntünk a mai képzésen! Néhány praktikus információ A képzés elvégzéséről, oklevelet és közösségi oldalakon megosztható badge-t állítunk ki. Az oklevél kiadásának feltétele a képzésen való részvétel, így kérjük, ne felejtsétek el aláírni a KÉPZÉS ELEJÉN és VÉGÉN kihelyezett JELENLÉTI ÍVET! Program 9:30 - 11:00: 1.
In this follow-up experiment, we tested how generative AI models handle handwritten text when provided as image input rather than PDF. Unlike in the earlier test, all models were able to process the images and return readable outputs. Performance Comparison 🏆 Champion as of October 2: Copilot, Gemini 2.5 Pro,
Optical character recognition (OCR) of handwritten text remains a demanding task, particularly once the focus shifts beyond English. In this experiment, we assessed a range of generative AI models on three handwritten text samples—one each in English, French, and Hungarian—to examine cross-linguistic performance. While accuracy was consistently high
In this experiment, we tested Manus AI’s ability to generate a full academic presentation from a research paper. The tool produced a slide deck that was functional and visually adequate, though not every slide was directly usable. The Manus AI interface supported adjustments before downloading—such as modifying layout,
We tested the new Sonnet 4.5 model on a demanding academic task: transforming a research paper into a structured 15-slide outline for a conference presentation. The prompt required not only conceptual rigour and narrative coherence but also attention to visual communication, with clear guidance on where charts, tables, and
In this post we present a comparative evaluation of several generative AI models, focusing on their ability to carry out a straightforward sentiment analysis task. The models were asked to classify customer reviews as either positive or negative, following a simple binary labelling instruction. While the task itself was unambiguous,
When using generative AI for structured data collection, the language of the prompt can make a real difference. In our test with Hungarian population statistics, both Claude Opus 4.1 and Sonnet 4 produced accurate outputs when prompted in Hungarian – but with an English prompt, Sonnet 4 generated rounded figures
In this post we examine how Claude Opus 4.1 can be applied to data collection tasks through a prompt-based approach. The model accessed and structured monthly U.S. unemployment rate data from the Federal Reserve Economic Data (FRED) platform for the period January 2000 to December 2024. The results
In this post we put Notebook LM’s new literature review feature to the test. The model was given six pre-selected sources on a single topic and asked to produce a structured review. The results were not bad at all: the output drew appropriately on the provided references and offered
In our previous experiment, we showed how DeepSeek-V3 could classify argumentative claims in a simple prompt-based setup. In this follow-up, we take the test one step further: running the same PRO/CON stance classification task programmatically via the DeepSeek API in a Google Colab environment. This shift from manual prompting
In this post we present a comparative test of several generative AI models, focusing on their ability to retrieve official sports statistics. The task involved collecting NBA game data through a single prompt, with the expectation that the models would accurately locate, extract, and structure the requested information. While the
In this post we explore how Manus AI can be used to collect official housing price index data from the U.S. Census Bureau. The task focused on the Single-Family Houses Sold series, requesting annual figures to be gathered and organised into a clean, structured dataset suitable for further research.
