Sign in
Artificial Intelligence (AI) has undoubtedly become a transformative force in numerous sectors, demonstrating an unprecedented capacity to reshape traditional operational paradigms and create new efficiencies. The sheer spectrum of available AI tools and technologies, from Machine Learning algorithms to Natural Language Processing models, is staggering, each with unique capabilities and specific applicability. This technological diversity, while offering wide-ranging possibilities, also necessitates astute selection to ensure alignment with the task at hand.
As AI adoption accelerates across industries, the onus is on us to ensure that the chosen AI tool aligns with our task objectives. Misalignment can result in underutilization of resources, sub-optimal outcomes, and in some cases, counterproductive results.
Constructing a Crosswalk for Effective AI Tool Selection
The field of AI offers a broad range of tools capable of processing and analyzing different data types, including text, image, audio, and video. The selection of an AI tool is contingent upon a clear understanding of the task objective and the nature of the data at hand. This alignment ensures efficient utilization of AI capabilities and paves the way for successful outcomes.
- Understanding Task Objectives: Defining the task objective involves identifying the problem to solve, understanding the desired outcome, and outlining the key performance indicators.
- Recognizing Data Types: Different AI tools are designed to handle different data types. Text-based data is best handled by natural language processing (NLP) tools, images by computer vision algorithms, audio data by speech recognition and processing tools, and video data often requires a combination of computer vision and audio processing algorithms.
AI tools for generation tasks
Generation tasks in the context of AI refer to tasks where the AI system is required to create or generate output based on the given inputs. This output can be in various forms and is typically new content that the AI has synthesized based on the data it has been trained on.
| Text | Image | Audio | Visual | |
| Generation | Overview: Text generation is a subfield of Natural Language Processing (NLP), which involves the automated creation of text |
Overview: Image generation refers to the process of creating new, synthetic images that can resemble real-world photos, drawings, paintings, or other types of images. One of the most common methods used in generative AI for image generation is a type of model called a Generative Adversarial Network (GAN). GANs consist of two parts: a generator network, which creates new images, and a discriminator network, which tries to distinguish the generated images from real ones. |
Overview: Generative AI models for audio generation are designed to create new, synthetic audio content from given data or learned patterns. This can encompass a variety of applications, including music, speech, sound effects, and more. | Overview: Video generation is a field in generative artificial intelligence (AI) that focuses on creating new video content based on learning from a set of input videos. In a sense, video generation AI is tasked with understanding the semantics, structure, and patterns within a collection of videos, and then generating new videos that adhere to the same or similar principles. The creation of new videos can be conditioned on a variety of inputs such as a short description, a script, a rough sketch or storyboard, or even other videos. Synthesia, InVideo. Pictory |
|
Application: You can use text generation tools to generate blog posts, articles, or other written content quickly, thus significantly reducing the time spent on these tasks. This allows human creators to focus on strategy and creativity, where they excel. Example Tools: ChatGPT, Bard, Jasper. |
Application: AI can generate new pieces of art or design elements based on specific styles or themes, creating unique visuals for use in digital media. AI can generate textures, objects, characters, or entire landscapes, contributing to more immersive and visually appealing gaming experiences. AI can generate images of new clothing designs, predicting future trends or helping designers with new ideas. Generative models can create different designs for buildings, interior spaces, and urban layouts, providing architects with fresh perspectives and options. Example Tools: DALL-E, MidJourney, Stable Diffusion. |
Application Speech Synthesis: Generative models can also be used in Text-to-Speech (TTS) systems to generate human-like speech. They can take written text as input and generate an audio stream that sounds like a human reading the text. Advances in this field have resulted in incredibly realistic synthetic voices. Example Tools: Amazon Polly, Google Cloud Text-to-Speech, Microsoft Azure Text-to-Speech Sound Effects: These models can generate synthetic sound effects that mimic real-world sounds, like rain, traffic, or animal noises. This has applications in video games, film production, and virtual reality. Example Tools: AudioMicro, Zapsplat, Freesound Voice cloning: Some generative models can learn the characteristics of a specific person’s voice and then generate new audio that sounds like that person speaking. Example Tools: Respeecher, Coqui, ElevenLabs. |
Application Simulations: AI can generate hypothetical scenarios for training purposes or simulate events based on observed data, aiding in prediction and prevention efforts. AI can create simulations or virtual reality experiences for educational or training purposes, such as medical surgery simulations, virtual field trips, etc. Example Tools: Unity, Unreal Engine, Amazon Sumerian Other: AI can generate unique visual accompaniments for music tracks or abstract visual art. Example Tools: Magenta. |
AI tools for search and retrieval
AI productivity tools focused on search and retrieval tasks offer an effective way to harness the power of AI. Whether it’s text, image, audio, or video data, these tools leverage advanced machine learning algorithms to comprehend content at a deeper level and provide highly relevant results. They not only optimize the search process, but also empower users with the ability to extract structured insights from unstructured data, paving the way for smarter decisions and improved productivity.| Text | Image | Audio | Visual | |
| Search and Retrieval | Overview: Large language models enable semantic search, which involves understanding the meaning and context of search queries and documents, not just looking for exact keyword matches. This can greatly improve the relevance of search results. | Overview: AI uses deep learning techniques to recognize patterns in images better than traditional search algorithms. By analyzing large amounts of image data, deep learning models can identify specific objects, features, people, colors, styles, and much more within an image. Example Tools: Google Cloud Vision API, Microsoft Azure Cognitive Search, Amazon Rekognition. | Overview: These AI tools use techniques like speech recognition, speaker diarization, and audio fingerprinting, to transcribe, index, and retrieve relevant portions of audio data. | Overview: Large language models facilitate semantic search in video data, enabling a deeper understanding of context, objects, and actions within videos, beyond just keyword matching. This significantly enhances the precision and relevance of search and retrieval results, creating a more effective and efficient process of accessing video content. |
Application Question Answering: Large language models can provide direct answers to factual questions based on information it finds in a corpus of documents. Example Tools: Socratica, Google Search, PaLM. Conversation Agents: Large language models are increasingly being used to develop advanced chatbots and virtual assistants that can understand and respond to user queries in a natural, human-like way. Example Tools: ChatGPT, Bard, Bing Search. | Application Visual search capabilities: A user can search for images by using another image as a query instead of text. AI algorithms can compare the input image with a database of images to find similar ones, based on color, shape, texture, and other features. Example Tools: Google Lens, Microsoft Bing Visual Search, Pinterest Lens. AI algorithms can identify and distinguish individual faces with high accuracy. This can be used to search for specific people in image databases, social media platforms, and even surveillance systems. Example Tools: Microsoft Azure Face API, Amazon Rekognition, Google Cloud Vision API Optical Character Recognition (OCR): Allows systems to detect text within images, which can then be indexed and made searchable. This is useful for documents, signs, and any images containing text. Example Tools: Amazon Textract, Tesseract, Online OCR Complex search queries: AI can not only identify the objects in an image but also understand the relationship between them, providing a sort of “semantic understanding.” This allows for more complex search queries that include specific situations or scenes, rather than just individual objects. Example Tools: Clarifai, IBM Watson Visual Recognition. | Application Automatic Content Recognition (ACR): ACR technology, powered by AI and machine learning algorithms, can identify and tag audio content within clips. This is extremely helpful in identifying and categorizing songs, podcasts, radio shows, etc., thereby enhancing the search process. Example Tools: ACRCloud, IBM Watson Audio Content Recognition, Google Cloud Media Intelligence. Audio Fingerprinting: AI can generate unique fingerprints for individual audio clips, making them easily searchable. This can be useful in copyright infringement cases and for identifying duplicated content. Example Tools: ACRCloud, Mixixmatch, Acoustic ID AI-Enhanced Metadata Tagging: AI can auto-tag audio files with descriptive metadata like genre, mood, instruments used, etc., which can significantly enhance the search and retrieval process. Example Tools: ACRCloud. | Application
Transcription and Captioning: AI can automatically transcribe the audio of videos and generate closed captions, making it possible to search for specific words and phrases within a video. This is particularly useful in the context of educational videos, documentaries, and news broadcasts. Example Tools: Rev, Descript Visual Search: AI also enables visual search, where users can search for videos containing specific visual elements. For example, users could search for a video that includes a particular person, animal, or object. Example Tools: Google Lens, Cludo, Pixolution Visual Search Semantic Understanding: AI models like GPT-4 can be used to understand the semantic content of videos. This means the AI understands the context and meaning of a video, allowing it to retrieve videos based on complex queries that go beyond simple keyword matching. For example, you could ask the AI to find videos where “a dog plays with a ball in a park,” and it would understand this complex query. Example Tools: Vid.ai, Google Cloud Video Intelligence. |
AI tools for summarization
AI tools for summarization tasks are designed to condense lengthy, detailed information into a more manageable and succinct format without losing the essential points. From extracting the central ideas of complex research articles to presenting the main events of a long video, these tools employ advanced machine learning techniques to understand, interpret, and distill data across various formats and domains.
| Text | Image | Audio | Visual | |
| Summarization | Overview: Text summarization is a subfield of Natural Language Processing (NLP) that deals with the creation of shortened versions of text documents, while preserving their most important information. | Overview: Image summarization, also sometimes referred to as image compression, is the process of extracting the most important content or features from an image or a set of images. The goal is to provide a comprehensive and meaningful representation of the original image(s) that reduces redundancy and computational and storage requirements while retaining the crucial elements. Google Cloud Vision API, Summly. | Overview: Audio summarization is a process of creating a concise and coherent summary of longer audio content. The goal is to provide a shorter version that contains the most important and relevant information from the original audio. For audio clips that contain speech, the audio is transcribed into text before applying text summarization techniques. | Overview: Video summarization is a process used to shorten a video or extract the most important and relevant parts of it. The aim is to provide a brief version of the video content that still conveys the core information or story. |
Application Abstractive and Extractive Summarization: AI algorithms have facilitated both abstractive and extractive summarization. Extractive summarization involves identifying key phrases or sentences from the original text and combining them to form a summary. In contrast, abstractive summarization is about understanding the original text and creating new sentences to provide a condensed version, much like a human would do. AI has brought significant improvements in both types, especially abstractive summarization, which is a more complex task. Contextual Understanding: With techniques such as deep learning and transformers-based models like BERT, GPT, etc., AI can now generate summaries that better understand the context, semantics, and nuances of the original text. This results in more accurate and meaningful summaries. Customization: AI also allows customization of summaries based on specific needs. For example, it can generate shorter or longer summaries based on user requirements or even focus on specific aspects of the text, such as summarizing only the results in a research paper. Real-Time Summarization: AI can provide real-time summarization of data streams like news feeds, social media updates, financial reports, etc., thereby aiding quick decision-making and trend spotting. Example Tools: Genei, Jasper, Pepper Content. | Application Contextual Understanding: Advanced AI models can understand context in images. This means they can make connections between objects in the image and the environment. For example, they can determine if a person is indoors or outdoors based on the presence of certain objects in the image. Example Tools: Clarifai, Imagga Textual Summaries: AI has enabled the development of models that can generate textual summaries of images. These summaries can provide context and explanation about what is happening in the image, thereby making them more accessible to people with visual impairments or for use in search engine optimization. Example Tools: Picasion, DeepAI, Flickr Vision API. | Application The first step involves transcribing the audio data into text. This is achieved through Automatic Speech Recognition (ASR), an AI technology that converts spoken language into written words. Speaker Diarization: In multi-speaker audio files, it’s necessary to identify individual speakers and attribute speech to them correctly. This process is known as speaker diarization and helps in providing context to the summarized text. Example Tools: AudioRecap, Summarize.ai, TLDR.ai. | Application Dynamic (or skimming) summarization: This involves creating a shorter version of the original video, keeping the temporal aspect of the content. This form of summarization is more complex because it requires the selection and sequencing of specific scenes or segments to create a coherent and meaningful short video. Example Tools: Summly, TLDR, Vooks With the sheer volume of video content available today, sifting through hours of footage can be daunting. Summarization allows researchers, journalists, investigators, and others to quickly identify and focus on the most relevant content, saving a considerable amount of time. For video editors and content creators, summarization tools can streamline the process of identifying key moments or highlights in raw footage, speeding up the editing and production process. In security operations where hours of surveillance footage may need to be reviewed, video summarization can highlight unusual or notable activity, improving the efficiency of security personnel and possibly preventing or solving crimes more quickly. Summarization can help create condensed versions of lectures, webinars, or training materials, making it easier for students or trainees to review and retain the information. This leads to more efficient learning. Companies like Netflix or YouTube could use video summarization to provide users with brief previews or “trailers” of content, helping them decide what to watch more quickly and enhancing user experience. |
AI tools for enhancement
AI tools for enhancement tasks are designed to refine and improve the quality of data without altering its fundamental content or meaning. From improving the clarity of images to enhancing the readability of complex research articles, these tools employ advanced machine learning techniques to understand, interpret, and augment data across various formats and domains.
| Text | Image | Audio | Visual | |
| Enhancement | Overview: Text enhancement refers to the process of improving the quality, readability, structure, style, and clarity of text content. This process often involves a combination of various tasks such as proofreading, editing for grammar, punctuation and spelling, revising for style and tone, improving semantic coherence, rephrasing for clarity, and optimizing for specific objectives. | Overview: Image Enhancement refers to the process of adjusting digital images so that the results are more suitable for display or further image analysis. This involves amplifying certain image features for better visibility or suppressing others that may be irrelevant to the desired analysis. Various techniques can be employed such as brightness and contrast adjustments, noise reduction, sharpening, color correction, etc. Enhance.AI, Let’s Enhance, Adobe FireFly. | Overview: Audio enhancement refers to the range of methods and techniques used to improve the quality of sound or audio signals. This could involve reducing background noise, increasing clarity, removing unwanted sound, adjusting pitch or frequency levels, or otherwise improving the audibility and quality of sound. Descript, Audacity, Izotope RX. | Overview: Video enhancement refers to the process of improving the quality of a video signal using various methods such as increasing resolution, reducing noise, adjusting brightness/contrast, stabilizing shake, removing compression artifacts, color correction, and more. These enhancements can help bring out important details, improve the overall aesthetics of the footage, or make older, lower-quality videos more compatible with newer, high-definition displays. Runway ML, Pictory AI, Descript. |
Application Semantic Coherence: AI can help ensure that a piece of writing maintains semantic coherence, meaning that it remains consistent in its message and logic from beginning to end. It can suggest changes in phrasing or structure to ensure the text makes sense and flows well. Example Tools: ProWritingAid, QuilBot Personalized Writing Assistance: AI can learn a person’s writing style and provide personalized recommendations to enhance the text while keeping the individual’s style intact. For instance, AI could learn that a writer prefers shorter sentences and provide recommendations accordingly. Example Tools: Jasper, Writeful, Grammarly. | Application There’s a rise in AI-based photo editing software and platforms that simplify the editing process, making it accessible to non-professionals. Tools like Luminar AI and Adobe’s Sensei technology leverage AI to automate and improve many aspects of the editing workflow. | Application: Voice alteration: With AI, it’s possible to alter the characteristics of a voice, changing aspects like tone, pitch, accent, and even language, while still maintaining a natural-sounding voice. This can be extremely useful for dubbing, voice-over work, and other audio projects. Example Tools: Creatine, Lyrebird, Respeecher Edit recommendations: Intelligent editing: | Application: AI models can learn to distinguish between signal and noise, and therefore, can effectively reduce or remove visual noise or grain from videos, even in complex or poorly lit scenes. Example Tools: Denoise AI, Topaz Video Noise Reduction AI AI can be used to colorize black-and-white footage, applying realistic colors based on the training it has received on color video content. Example Tools: MyHeritage In Color, Colorize.ai, Deep Nostalgia AI can intelligently fill in gaps in video content by understanding the context of surrounding pixels, such as removing unwanted objects or people from scenes. Example Tools: Remove.bg, Inpainting.io, Adobe FireFly. |
AI Tools for Translation
AI tools for translation tasks are designed to convert information from one language to another without losing the original meaning or context. From interpreting the nuanced content of complex research articles to translating the spoken words in a lengthy video, these tools employ advanced machine learning techniques to understand, interpret, and transform data across various formats and domains. These AI-powered tools interpret and convert information between languages, thereby breaking down communication barriers and facilitating global interactions.
| Text | Image | Audio | Visual | |
| Translation | Overview: Text Translation is the process of converting the text of one language (source language) into the text of another language (target language) while preserving the original content and meaning as much as possible. This process is typically performed by a human translator who has a deep understanding of both the source and target languages, along with their cultural nuances. | Overview: The translation of text in images is another domain that AI has been greatly improving. Two major tasks associated with text in images are Optical Character Recognition (OCR) and Neural Machine Translation (NMT). | Overview: Convert spoken language into text through speech recognition, then translate the transcribed text into the target language using machine translation technologies. These tools can handle continuous, real-time translation, making them ideal for tasks such as live interpretation of speeches, translating audio content in foreign languages, and facilitating multilingual conversations. | Overview:AI translation tools for visual data use advanced techniques such as computer vision and image recognition to identify and understand the content within images or videos, then apply natural language processing to translate these visual elements into textual descriptions or other visual representations. This allows for tasks like automatically generating captions for images, converting visual signals into written or spoken language, and even translating between different visual elements (like sketches to digital images). |
Application: Example Tools: Google Translate, DeepL, DuoLingo, ChatGPT, Google Bard. | Application Neural Machine Translation (NMT): This technology is used to translate the text extracted from images into other languages. This is particularly useful in scenarios such as translating street signs or restaurant menus captured in images, which can be very helpful for travelers, for example. Example Tools: Google Translate, Yandex Translate, DeepL, Microsoft CoPilot. | Application Customization: AI-based systems can be trained to recognize and learn from the speaker’s style, accent, and vocabulary, leading to increasingly personalized translation experiences. This helps in overcoming the challenges of accents, dialects, and language variations. Pronounciation support: AI algorithms can analyze a student’s pronunciation and provide feedback on how to improve. This technology can identify specific sounds, accents, stress, and intonation issues, offering corrective suggestions. For example, Rosetta Stone uses TruAccent speech-recognition technology to provide feedback on pronunciation. Example Tools: Speechify, FluentU, Pimsleur. |
AI tools for classification
AI tools for classification tasks are designed to categorize diverse, detailed information into defined classes based on shared characteristics, without losing the nuanced differences. From identifying the primary topics in complex research articles to recognizing distinct events within a long video, these tools employ advanced machine learning techniques to understand, interpret, and sort data across various formats and domains.
| Text | Image | Audio | Visual | |
| Classification | Overview: Text classification is a subfield of Natural Language Processing (NLP) that involves categorizing text into predefined groups. | Overview: AI can now identify and classify images with a high degree of accuracy. This is being used in several ways: Object recognition, Facial recognition, etc. AI models can also interpret the emotional content or sentiment of an image. For instance, they might identify an image as sad or happy based on the expressions of people in the image or the overall color scheme. Google Teachable Machine, Microsoft Lobe, Apple Create ML. | Overview: Audio classification is a process in machine learning and signal processing that allows a computer to categorize and identify sounds or audio signals. It is a subfield of digital signal processing that includes algorithms that can identify and categorize sounds such as music, speech, environmental sounds, and other auditory signals. The objective is to extract meaningful features from the audio signal and use these features to classify the audio into predefined classes or categories. Sound ID, Amper Music, Amazon Transcribe. | Overview: Video classification is a task in the field of computer vision that involves categorizing video content into one or several classes. Video classification goes beyond image classification as it also involves analyzing temporal dimensions and understanding the sequence of frames. Google Cloud Video Intelligence API, Amazon Rekognition Video, Microsoft Azure Video Indexer, Vidooly. |
Application Sentiment Analysis: AI can be used to identify and categorize opinions expressed in a piece of text, especially to determine the writer’s attitude toward a particular topic or product as positive, negative, or neutral. Businesses use this to understand customer feedback on their products or services. Example Tools: MonkeyLearn, Google Cloud Natural Language API, ChatGPT Topic Labeling: Text classification can be used to assign topic labels to text documents automatically. This is particularly useful for organizing, searching, and summarizing large volumes of text data, for example in news articles or academic papers. Example Tools: Google Bard, Amazon Comprehend, TextRazor, ChatGPT Intent Detection: AI can classify user inputs based on the user’s intention, which is particularly useful in chatbots or virtual assistants. This allows the system to respond appropriately based. Example Tools: Dialogflow, Amazon Lex, Microsoft LUIS Language Detection: Text classification can be used to detect the language of a text, which can be useful for providing appropriate language-specific services or translations. on the identified intent. Plagiarism Detection: AI can detect instances of plagiarism, making it easier to ensure originality in writing. This feature is very useful in academic and professional settings where original content is highly valued. Example Tools: Turnitin, Grammarly, Plagiarism Checker. | Application Facial Recognition: Image classification is an integral part of facial recognition systems, which identify or verify a person’s identity using their face. This has applications in security, social media, and various other fields. Example Tools: Face++, Kairos. | Application Music classification: AI can classify music into different genres, moods, or even by instrument sounds. Example Tools: Amper Music Environmental Sound Classification: This can be used to identify sounds in an environment, such as industrial noises in a factory (to detect anomalies or malfunctions), urban sounds for smart city applications, or wildlife sounds for biodiversity monitoring. Example Tools: Sound ID Emotion Detection: By analyzing the prosodic features (pitch, volume, rate, etc.) of speech, AI systems can classify the emotional state of the speaker. Example Tools: Affectiva, Microsoft Azure Emotion API, Speech Analyzer, Speaker Identification: AI can be used to identify a speaker based on their unique vocal characteristics. This has applications in security (voice biometrics), personalized user interfaces, and forensics. Example Tools: SpeakerDiarization, SpeakerVerifier, Amazon Transcribe. | Application Activity Recognition: AI can identify specific activities or actions in a video. For example, it can recognize if a person is running, jumping, or sitting. This can be useful in various fields like sports analytics, healthcare (detecting falls in elderly people), or even video games. For example, the OpenPose algorithm can identify and track human body movement in video footage. Example Tools: Google Teachable Machine, Google Cloud Video Intelligence API, Amazon Rekognition Video, Microsoft Azure Video Indexer Object Tracking: AI can identify and follow specific objects through a sequence of video frames, which is especially important in applications like self-driving cars, where the AI needs to track other vehicles, pedestrians, and obstacles. Example Tools: Trackr AI, Object Tracker, DeepSort Scene Understanding: AI can be used to understand and classify the scene context in a video. For example, distinguishing between an indoor and outdoor scene, or classifying the type of location (a beach, forest, city, etc.). Example Tools: Clarifai, Microsoft Azure Video Indexer. |
AI tools for segmentation
AI tools for segmentation tasks are designed to divide extensive, detailed information into more manageable and distinct segments without losing the overarching context. From isolating specific sections in complex research articles to identifying distinct scenes within a long video, these tools employ advanced machine learning techniques to understand, interpret, and partition data across various formats and domains.
| Text | Image | Audio | Visual | |
| Segmentation | Overview: The most common form of text segmentation is sentence segmentation, also known as sentence boundary disambiguation, which is dividing a text into individual sentences. Other forms of text segmentation include word tokenization (dividing text into words), topical segmentation (dividing text into segments each of which is about a different topic), and named entity recognition (identifying and classifying named entities in a text, such as persons, organizations, locations, expressions of times, quantities, percentages, etc.). Example Tools: Textract, Google Cloud Natural Language API. | Overview: AI algorithms can not only detect and recognize objects but also understand where one object ends and another begins. This allows the algorithms to identify and separate different elements in the image, providing a more comprehensive summary. Example Tools: Clarifai, MonkeyLearn, AutoML Vision Edge. | Overview: AI can distinguish and separate different voices in an audio file, even when they overlap. This can be very useful in crowded or noisy environments where multiple voices can often blend together.. Example Tools: Audacity, Descript, Spoken Layer. | Overview: AI models have made possible not just static frame-by-frame segmentation but also understanding the temporal coherence between video frames. This means that the model doesn’t only understand individual frames but also the movement and transformation of objects from frame to frame (Video object segmentation VOS), making the segmentation more consistent and accurate over time. Example Tools: DeepLabCut, SegTrack++, MaskTrack. |
AI tools for prediction
AI tools for prediction tasks are designed to forecast future outcomes based on comprehensive, detailed information, and present those projections in a digestible format without losing the subtleties. From predicting the impact of trends discussed in complex research articles to anticipating the next events in a long video, these tools employ advanced machine learning techniques to understand, interpret, and extrapolate data across various formats and domains.
Numeric input, numeric output: Traditional predictive models like linear regression, decision trees, or even time series models often struggle to understand complex temporal dynamics in data. Transformers, introduced in the “Attention is All You Need” paper, effectively model long-term dependencies in sequential data, making them highly valuable for predictions in time series data.
| Text | Image | Audio | Visual | |
| Prediction | Overview: Text input, numeric output: AI methods like Transformers can be effectively used to predict numeric values using text as the input. For example, they can be used to predict house prices based on textual property descriptions found in real estate listings. | Overview: Image input, numeric output: AI can be used to predict a numeric value when given an image. For example, predicting someone’s age based on their photo. Example Tools: Google’s Teachable Machine, Azure Computer Vision, Amazon Rekognition. | Overview: Audio input, numeric output: In a manufacturing context, a model could be trained to predict the Remaining Useful Life (RUL) of machinery based on audio recordings of its operation. This AI model, trained on audio data labeled with the known remaining operational hours of corresponding machines, learns to associate specific sound patterns with the machinery’s lifespan. | Overview: Video input, numeric output: AI can be used to predict a numeric value based on a video. For example, predicting the speed of a baseball pitch. The model would be trained on numerous video clips of pitches, where each clip is labeled with the actual speed of the pitch as recorded by a radar gun. Example Tools: Google Cloud Video Intelligence API, Amazon Rekognition Video, Azure Video Indexer. |