Chmury punktów i dane z LiDAR-u (Point clouds and LiDAR data): Machine-learning methods for sets of 3D points from LiDAR or scanners. PointNet processes points in a permutation-invariant way, while other approaches group them into voxels or vertical pillars; these representations support classification, segmentation and 3D object detection.
Gaussian splatting: szybkie sceny 3D ze zdjęć (3D Gaussian splatting): A scene representation built from anisotropic 3D Gaussians optimised from images and rendered by splatting. It enables high-quality real-time novel-view synthesis and provides an alternative to radiance-field representations.
Generowanie obiektów 3D z tekstu lub zdjęcia (Text-to-3D and image-to-3D generation): Creating 3D objects or scenes — such as meshes, Gaussians or radiance fields — from text or images. Methods include distilling guidance from 2D generators, generating consistent multiple views, and models trained directly on 3D asset datasets; outputs often require further editing.
Jednoczesna lokalizacja i budowa mapy (SLAM) (Simultaneous localisation and mapping (SLAM)): Simultaneously estimating the pose of a camera or robot while building or updating a map of an unknown environment. Classical methods combine observations with geometric optimisation, while newer systems also use learned modules, recurrent networks and scene representations based on radiance fields or Gaussians.
Szacowanie głębi z jednego zdjęcia (Monocular depth estimation): Predicting a depth map — each pixel's distance from the camera — from a single image. The output may represent only relative depth ordering or metric distance; training on large, diverse datasets has improved generalisation to new scenes.
Generowanie obrazów z opisu tekstowego (Text-to-image generation): Creating an image from a text description. Many contemporary systems use latent diffusion or transformers trained with flow-based objectives. Evaluation includes prompt adherence and image quality; some tools expose settings such as guidance scale, negative prompts or a random seed.
Generowanie wideo z tekstu i obrazu (Text-to-video and image-to-video generation): Creating clips from text descriptions or animating an image while trying to preserve motion and appearance across frames. Many contemporary systems use latent diffusion models and transformers that process space and time; some also generate synchronised audio.
Odpowiadanie na pytania (QA) (Question answering (QA)): Answering natural-language questions. Extractive QA selects an answer span from a supplied text; open-domain QA first retrieves relevant sources and then derives an answer. This retriever-reader pattern is closely related to later RAG systems.
Streszczanie tekstu (Text summarisation): Shortening a text while preserving its key information. Extractive summarisation selects source passages, while abstractive summarisation creates new wording; modern language models are widely used for abstractive summaries, where quality evaluation and faithfulness to the source remain important challenges.
Tokenizacja tekstu (BPE, WordPiece, SentencePiece) (Text tokenisation (BPE, WordPiece, SentencePiece)): Turning text into a sequence of tokens — sub-word pieces from a fixed vocabulary — before the model sees it. Sub-word methods dominate: BPE (also byte-level, as in GPT), WordPiece (BERT) and Unigram via SentencePiece (T5). Tokenisation affects cost, context length and how well a model handles non-English languages.
Tłumaczenie maszynowe (Machine translation): Automatic translation of text between languages. It evolved from statistical systems to neural encoder–decoders with attention and Transformers, then to massively multilingual models and prompted LLMs.
Modele audio–język (rozumienie mowy, dźwięków i muzyki) (Audio-language models): Language models connected to audio representations that can jointly handle speech, environmental sounds and music: transcription, translation, questions about recordings, audio captioning or speaker-related analysis. The supported task range depends on each model's data and design.
Modele wizja–język (opisywanie obrazów i pytania o nie) (Vision-language models (VLMs)): Models that combine images or video with text to generate textual answers: they describe scenes, answer questions about visual content, read documents or identify objects. They vary in scale and design, from on-device models to large systems used for interface analysis and visual reasoning.
Rozumienie dokumentów, wykresów i zrzutów ekranu (Document, chart and screenshot understanding): Reading and interpreting images rich in text and spatial layout — scans, forms, invoices, charts, infographics and screenshots. Some models work without a separate OCR stage, answer questions or extract data directly from pixels; these capabilities are also used for interface analysis.
Łączenie enkodera obrazu z modelem językowym (Connecting an image encoder to a language model): A common way to build a VLM: a pretrained image encoder produces visual features, while a projector, adapter or cross-attention layers expose them to a language model. The system is then tuned on image-instruction data; the modules trained and number of stages depend on the architecture.
Agenci głosowi (Real-time voice agents): A system that conducts spoken conversations, understands utterances, makes decisions and completes tasks using models and tools.
Modele mowa–mowa (Speech-to-speech models): A model takes speech and generates speech without separate STT and TTS services in the pipeline. Its internal representation may still include text or transcription; tool use and context control depend on the model.
Dialog pełnodupleksowy (Full-duplex spoken dialogue): A system can receive speech and generate a response at the same time, handling overlap and listener backchannels. This can be achieved with an integrated model or by synchronizing modules.
Rozpoznawanie dźwięków i klasyfikacja nagrań (Audio and sound event classification): Assigning one or more labels that describe a recording, such as sound type, music genre, spoken command or language. Models often analyse a spectrogram or a learned audio representation; shared audio-text spaces also enable zero-shot classification.
Widzenie komputerowe (Computer vision): Recognising content and structure in images and video: what is in the picture, where it is, what shape it has and how it moves. Image generation, 3D and vision–language models have their own sibling nodes.
Detekcja obiektów (Object detection): Finding objects in an image: a bounding box plus a class for each object. Usually evaluated with IoU and mAP.
Klasyfikacja obrazów (Image classification): Assigning one or more labels to a whole image (e.g. “cat”, “defective product”). It is one of the foundational computer-vision tasks.
Vision Transformer (ViT): A transformer applied to an image split into patches treated as tokens; a major family of vision architectures alongside convolutional networks.