Best AI tools for< Predict Distributions >

The Predict API is a powerful tool that allows you to forecast your data with simplicity and accuracy. It uses the latest advancements in stochastic modeling and machine learning to provide you with reliable projections. The API is easy to use and can be integrated with any application. It is also highly scalable, so you can use it to forecast large datasets. With the Predict API, you can gain valuable insights into your data and make better decisions.

AI Baby Generator is an AI application that predicts the face of your future child by generating ultra-realistic baby photos based on your photos and features. It offers customized baby photos, personality descriptions, and various packages to meet your needs. The application uses advanced AI technology to provide accurate results and ensures data privacy for its users.

Numerai is a data science tournament platform where users can compete to build models that predict the stock market. The platform provides users with clean and regularized hedge fund quality data, and users can build models using Python or R scripts. Numerai also has a cryptocurrency, NMR, which users can stake on their models to earn rewards.

Neurons is a platform that uses AI to predict consumer responses and behavior. It offers a variety of solutions for businesses, including marketing agencies, designers, and e-commerce companies. Neurons' AI-powered tools can help businesses optimize their marketing campaigns, improve their product design, and better understand their customers.

BforeAI is an AI-powered platform that specializes in fighting cyberthreats with intelligence. The platform offers predictive security solutions to prevent phishing, spoofing, impersonation, hijacking, ransomware, online fraud, and data exfiltration. BforeAI uses cutting-edge AI technology for behavioral analysis and predictive results, going beyond reactive blocklists to predict and prevent attacks before they occur. The platform caters to various industries such as financial, manufacturing, retail, and media & entertainment, providing tailored solutions to address unique security challenges.

Heatseeker is an AI-powered market experimentation tool that helps businesses predict customer preferences, conduct feature tests, and generate value propositions. It enables users to answer critical growth questions about market, audience, and product features through AI-powered experiments. Heatseeker provides insights into market trends, competitor analysis, and helps in making data-driven decisions. The platform offers curated recommendations, competitive intelligence, and continuous testing for refining strategies. It automates ad campaign generation, data collection, and provides recommendations for launching new products. Heatseeker is designed to help businesses optimize their marketing efforts and improve their product offerings.

ClosedLoop is a healthcare data science platform that helps organizations improve outcomes and reduce costs by providing accurate, explainable, and actionable predictions of individual-level health risks. The platform offers predictive analytics for various healthcare sectors, data science automation, and a healthcare content library to accelerate time to value. ClosedLoop's AI/ML platform is designed exclusively for the data science needs of modern healthcare organizations, enabling proactive interventions, improved clinical outcomes, and innovative healthcare offerings.

AutoPredict is an AI application that predicts how long a car will last by analyzing over 100 million data points. It offers accurate estimates of a car's life span, providing users with valuable insights into their vehicle's longevity. In addition to the prediction feature, AutoPredict also offers an API for businesses to integrate the predictions and statistics into their operations. The AutoPredict Blog shares insights and statistics discovered during the development of the AI model.

MonkeeMath is an AI tool designed to scrape comments from Reddit and Stocktwits that mention stock tickers. It utilizes ChatGPT to analyze the sentiment of these comments, determining whether they are bullish or bearish on the outlook of the ticker. The data collected is then used to generate charts and tables displayed on the website. Users can create an account to view predictions and participate in a prediction mini-game to earn a spot on the MonkeeMath user leaderboard.

Lotto Chart is a highly accurate AI-powered chart for predicting lottery numbers. It harnesses the power of artificial intelligence, statistical analysis, and probability to generate winning combinations for various lotteries. The application processes billions of data points, utilizes 7 powerful prediction models, and provides advanced data-driven predictions to help users increase their chances of winning. Lotto Chart also offers support for seeded predictions, daily updated insights and reports, and tools to easily identify patterns and trends in lottery numbers.

Simpleem is an Artificial Emotional Intelligence (AEI) tool that helps users uncover intentions, predict success, and leverage behavior for successful interactions. By measuring all interactions and correlating them with concrete outcomes, Simpleem provides insights into verbal, para-verbal, and non-verbal cues to enhance customer relationships, track customer rapport, and assess team performance. The tool aims to identify win/lose patterns in behavior, guide users on boosting performance, and prevent burnout by promptly identifying red flags. Simpleem uses proprietary AI models to analyze real-world data and translate behavioral insights into concrete business metrics, achieving a high accuracy rate of 94% in success prediction.

Onoco is the first super-app for parents that aims to track, predict, and share care for babies. It provides a smart, simple, safe, and easy-to-share platform where parents can monitor their baby's development milestones, daily routines, and growth progress. Onoco empowers parents with expert knowledge on essential topics such as baby sleep, nutrition, and postpartum health, bridging the gap between scientific research and everyday parenting. The app allows users to create personalized routines, monitor developmental milestones from birth to 5 years, share updates with caregivers, and receive brain-building tips tailored to the child's age and learning areas.

nventr is an AI platform for predictive automation, offering a suite of products and services powered by predictive analytics. The company focuses on applying new approaches to uncover patterns, extract valuable intelligence, and predict outcomes within vast datasets. nventr solutions support enterprise-grade AI acceleration, intelligent data processing, and digital transformation. The platform, nventr.ai, enables rapid building of AI models and software applications through collaborative tools and cloud-based infrastructure.

Tomorrow.io is a Weather Intelligence & Resilience Platform that provides hyper-accurate weather data and insights for organizations and consumers. It offers a range of products and solutions for various industries, leveraging proprietary space data and AI/ML technology to help users predict, make informed decisions, and address weather-related challenges. The platform enables proactive measures to protect infrastructure, optimize operations, and enhance safety in the face of extreme weather events.

CreatorML is an AI-powered platform designed to help YouTube creators optimize their content and grow their channels. Using machine learning, CreatorML's tools can predict how well a video will perform before it's even published, suggest title and thumbnail ideas, and provide insights into what's trending on YouTube. CreatorML is designed for YouTube creators of all levels, from beginners to experienced professionals. It offers a variety of subscription plans to fit every budget and need.

Keepme is an AI-powered platform designed for gyms to boost sales, predict and prevent attrition, and enhance member retention. It offers features such as Keepme Score™ for predicting attrition, smart lead scoring, gym tours & trials scheduler, NPS surveys, smart campaigns & automations, smart content production, and WhatsApp integration. The platform provides personalized training and world-class support through Keepme Academy and customer success team. Keepme is trusted by over 450 fitness clubs globally and offers valuable AI resources to empower users with knowledge.

The website is an AI sports betting prediction platform called COPA. It offers high-quality sports predictions using Artificial Intelligence (AI) for various football events. Users can access match predictions, statistics, and betting insights for top global leagues. The platform aims to provide informed betting choices and predictive tools for European football leagues, with plans to expand to other sports in the future. COPA is designed to empower sports fans with accurate forecasts at an affordable cost.

CaseYak is an AI tool that utilizes artificial intelligence to predict the value of personal injury claims. It offers a lead generation solution for law firms by providing case value predictions based on historical data and using large language models to create an empathetic agent to engage with potential clients. The tool aims to help law firms convert website visitors into signed clients by offering data-driven appraisals of their cases.

Focia is an AI-powered engagement optimization tool that helps users predict, analyze, and enhance their content performance across various digital platforms. It offers features such as ranking and comparing content ideas, content analysis, feedback generation, engagement predictions, workspace customization, and real-time model training. Focia's AI models, including Blaze, Neon, Phantom, and Omni, specialize in analyzing different types of content on platforms like YouTube, Instagram, TikTok, and e-commerce sites. By leveraging Focia, users can boost their engagement, conduct A/B testing, measure performance, and conceptualize content ideas effectively.

NovaResp cMAP™ is an AI-powered platform software designed to improve adherence to Continuous Positive Airway Pressure (CPAP) therapy for sleep apnea patients. It utilizes artificial intelligence and machine learning to predict and prevent apnea episodes during therapy, delivering personalized treatment at more comfortable air pressure levels. The application aims to enhance patient quality of life, increase sales for device manufacturers, and reduce labor costs for DMEs. NovaResp cMAP™ is compatible with all major PAP machines and is a revolutionary solution in the treatment of Obstructive Sleep Apnea (OSA).

skpro is a library for supervised probabilistic prediction in python. It provides `scikit-learn`-like, `scikit-base` compatible interfaces to: * tabular **supervised regressors for probabilistic prediction** \- interval, quantile and distribution predictions * tabular **probabilistic time-to-event and survival prediction** \- instance-individual survival distributions * **metrics to evaluate probabilistic predictions** , e.g., pinball loss, empirical coverage, CRPS, survival losses * **reductions** to turn `scikit-learn` regressors into probabilistic `skpro` regressors, such as bootstrap or conformal * building **pipelines and composite models** , including tuning via probabilistic performance metrics * symbolic **probability distributions** with value domain of `pandas.DataFrame`-s and `pandas`-like interface

imodelsX is a Scikit-learn friendly library that provides tools for explaining, predicting, and steering text models/data. It also includes a collection of utilities for getting started with text data. **Explainable modeling/steering** | Model | Reference | Output | Description | |---|---|---|---| | Tree-Prompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/tree_prompt) | Explanation + Steering | Generates a tree of prompts to steer an LLM (_Official_) | | iPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/iprompt) | Explanation + Steering | Generates a prompt that explains patterns in data (_Official_) | | AutoPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/autoprompt) | Explanation + Steering | Find a natural-language prompt using input-gradients (⌛ In progress)| | D3 | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/d3) | Explanation | Explain the difference between two distributions | | SASC | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/sasc) | Explanation | Explain a black-box text module using an LLM (_Official_) | | Aug-Linear | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_linear) | Linear model | Fit better linear model using an LLM to extract embeddings (_Official_) | | Aug-Tree | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_tree) | Decision tree | Fit better decision tree using an LLM to expand features (_Official_) | **General utilities** | Model | Reference | |---|---| | LLM wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/llm) | Easily call different LLMs | | | Dataset wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/data) | Download minimially processed huggingface datasets | | | Bag of Ngrams | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/bag_of_ngrams) | Learn a linear model of ngrams | | | Linear Finetune | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/linear_finetune) | Finetune a single linear layer on top of LLM embeddings | | **Related work** * [imodels package](https://github.com/microsoft/interpretml/tree/main/imodels) (JOSS 2021) - interpretable ML package for concise, transparent, and accurate predictive modeling (sklearn-compatible). * [Adaptive wavelet distillation](https://arxiv.org/abs/2111.06185) (NeurIPS 2021) - distilling a neural network into a concise wavelet model * [Transformation importance](https://arxiv.org/abs/1912.04938) (ICLR 2020 workshop) - using simple reparameterizations, allows for calculating disentangled importances to transformations of the input (e.g. assigning importances to different frequencies) * [Hierarchical interpretations](https://arxiv.org/abs/1807.03343) (ICLR 2019) - extends CD to CNNs / arbitrary DNNs, and aggregates explanations into a hierarchy * [Interpretation regularization](https://arxiv.org/abs/2006.14340) (ICML 2020) - penalizes CD / ACD scores during training to make models generalize better * [PDR interpretability framework](https://www.pnas.org/doi/10.1073/pnas.1814225116) (PNAS 2019) - an overarching framewwork for guiding and framing interpretable machine learning

Nixtla is a production-ready generative pretrained transformer for time series forecasting and anomaly detection. It can accurately predict various domains such as retail, electricity, finance, and IoT with just a few lines of code. TimeGPT introduces a paradigm shift with its standout performance, efficiency, and simplicity, making it accessible even to users with minimal coding experience. The model is based on self-attention and is independently trained on a vast time series dataset to minimize forecasting error. It offers features like zero-shot inference, fine-tuning, API access, adding exogenous variables, multiple series forecasting, custom loss function, cross-validation, prediction intervals, and handling irregular timestamps.

The 'awesome-AI4MolConformation-MD' repository focuses on protein conformations and molecular dynamics using generative artificial intelligence and deep learning. It provides resources, reviews, datasets, packages, and tools related to AI-driven molecular dynamics simulations. The repository covers a wide range of topics such as neural networks potentials, force fields, AI engines/frameworks, trajectory analysis, visualization tools, and various AI-based models for protein conformational sampling. It serves as a comprehensive guide for researchers and practitioners interested in leveraging AI for studying molecular structures and dynamics.

sktime is a Python library for time series analysis that provides a unified interface for various time series learning tasks such as classification, regression, clustering, annotation, and forecasting. It offers time series algorithms and tools compatible with scikit-learn for building, tuning, and validating time series models. sktime aims to enhance the interoperability and usability of the time series analysis ecosystem by empowering users to apply algorithms across different tasks and providing interfaces to related libraries like scikit-learn, statsmodels, tsfresh, PyOD, and fbprophet.

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment interfaces and libraries for understanding AI systems. It empowers developers and stakeholders to develop and monitor AI responsibly, enabling better data-driven actions. The toolbox includes visualization widgets for model assessment, error analysis, interpretability, fairness assessment, and mitigations library. It also offers a JupyterLab extension for managing machine learning experiments and a library for measuring gender bias in NLP datasets.

AI4Animation is a comprehensive framework for data-driven character animation, including data processing, neural network training, and runtime control, developed in Unity3D/PyTorch. It explores deep learning opportunities for character animation, covering biped and quadruped locomotion, character-scene interactions, sports and fighting games, and embodied avatar motions in AR/VR. The research focuses on generative frameworks, codebook matching, periodic autoencoders, animation layering, local motion phases, and neural state machines for character control and animation.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

Awesome-Attention-Heads is a platform providing the latest research on Attention Heads, focusing on enhancing understanding of Transformer structure for model interpretability. It explores attention mechanisms for behavior, inference, and analysis, alongside feed-forward networks for knowledge storage. The repository aims to support researchers studying LLM interpretability and hallucination by offering cutting-edge information on Attention Head Mining.

AlphaFold3 is an implementation of the Alpha Fold 3 model in PyTorch for accurate structure prediction of biomolecular interactions. It includes modules for genetic diffusion and full model examples for forward pass computations. The tool allows users to generate random pair and single representations, operate on atomic coordinates, and perform structure predictions based on input tensors. The implementation also provides functionalities for training and evaluating the model.

Scientific Large Language Models (Sci-LLMs) is a repository that collects papers on scientific large language models, focusing on biology and chemistry domains. It includes textual, molecular, protein, and genomic languages, as well as multimodal language. The repository covers various large language models for tasks such as molecule property prediction, interaction prediction, protein sequence representation, protein sequence generation/design, DNA-protein interaction prediction, and RNA prediction. It also provides datasets and benchmarks for evaluating these models. The repository aims to facilitate research and development in the field of scientific language modeling.

Chronos is a family of pretrained time series forecasting models based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes.

I predict DAU based on growth and retention.

Predict the future, opine on politics and controversial topics, and have GPT assess what is "true"

I predict future stock market prices. AI analyst. Your trading analysis assistant. Press H to bring up prompt hot key menu. Not financial advice.

Analyze Financial Statements step by step to Predict Earnings Direction

Predict fashion trends

AI expert in analyzing historical patterns to predict future events

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A moot competition companion. & Trial Prep companion . Test and improve arguments- predict your opponent's reaction.

Personalized, analytical sports prediction AI.

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Predicts gender for first names using data

Our college entrance exam prediction app uses advanced algorithms and data analysis to provide accurate predictions for students preparing to take their college entrance exams.

Prédictif des tendances crypto à partir de la presse et des réseaux sociaux

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Analyzes and predicts the SSE 500 Index.

1620年代の主要な歴史的出来事を分析し未来の予測に役立てる。

A question ? Ask me for a prediction ... ( /help for help )

MMA Expert & Trainer

Advanced AI for real-time predictive analysis using machine learning.

Advanced NFL and parlay predictor with comprehensive analysis