Best AI tools for< Interpret Model Decisions >

Eigen Technologies is an AI-powered data extraction platform designed for business users to automate the extraction of data from various documents. The platform offers solutions for intelligent document processing and automation, enabling users to streamline business processes, make informed decisions, and achieve significant efficiency gains. Eigen's platform is purpose-built to deliver real ROI by reducing manual processes, improving data accuracy, and accelerating decision-making across industries such as corporates, banks, financial services, insurance, law, and manufacturing. With features like generative insights, table extraction, pre-processing hub, and model governance, Eigen empowers users to automate data extraction workflows efficiently. The platform is known for its unmatched accuracy, speed, and capability, providing customers with a flexible and scalable solution that integrates seamlessly with existing systems.

FairPlay is a Fairness-as-a-Service solution designed for financial institutions, offering AI-powered tools to assess automated decisioning models quickly. It helps in increasing fairness and profits by optimizing marketing, underwriting, and pricing strategies. The application provides features such as Fairness Optimizer, Second Look, Customer Composition, Redline Status, and Proxy Detection. FairPlay enables users to identify and overcome tradeoffs between performance and disparity, assess geographic fairness, de-bias proxies for protected classes, and tune models to reduce disparities without increasing risk. It offers advantages like increased compliance, speed, and readiness through automation, higher approval rates with no increase in risk, and rigorous Fair Lending analysis for sponsor banks and regulators. However, some disadvantages include the need for data integration, potential bias in AI algorithms, and the requirement for technical expertise to interpret results.

The Human-Centred Artificial Intelligence Lab (Holzinger Group) is a research group focused on developing AI solutions that are explainable, trustworthy, and aligned with human values, ethical principles, and legal requirements. The lab works on projects related to machine learning, digital pathology, interactive machine learning, and more. Their mission is to combine human and computer intelligence to address pressing problems in various domains such as forestry, health informatics, and cyber-physical systems. The lab emphasizes the importance of explainable AI, human-in-the-loop interactions, and the synergy between human and machine intelligence.

Molmo AI is a powerful, open-source multimodal AI model revolutionizing visual understanding. It helps developers easily build tools that can understand images and interact with the world in useful ways. Molmo AI offers exceptional image understanding, efficient data usage, open and accessible features, on-device compatibility, and a new era in multimodal AI development. It closes the gap between open and closed AI models, empowers the AI community with open access, and efficiently utilizes data for superior performance.

xAI Grok is a visual analytics platform that helps users understand and interpret machine learning models. It provides a variety of tools for visualizing and exploring model data, including interactive charts, graphs, and tables. xAI Grok also includes a library of pre-built visualizations that can be used to quickly get started with model analysis.

Ogma is an interpretable symbolic general problem-solving model that utilizes a symbolic sequence modeling paradigm to address tasks requiring reliability, complex decomposition, and without hallucinations. It offers solutions in areas such as math problem-solving, natural language understanding, and resolution of uncertainty. The technology is designed to provide a structured approach to problem-solving by breaking down tasks into manageable components while ensuring interpretability and self-interpretability. Ogma aims to set benchmarks in problem-solving applications by offering a reliable and transparent methodology.

Kombai is an AI tool designed to code email and web designs like humans. It uses deep learning and heuristics models to interpret UI designs and generate high-quality HTML, CSS, or React code with human-like names for classes and components. Kombai aims to help developers save time by automating the process of writing UI code based on design files without the need for tagging, naming, or grouping elements. The tool is currently in 'public research preview' and is free for individual developers to use.

Grok-1.5 Vision (Grok-1.5V) is a groundbreaking multimodal AI model developed by Elon Musk's research lab, x.AI. This advanced model has the potential to revolutionize the field of artificial intelligence and shape the future of various industries. Grok-1.5V combines the capabilities of computer vision, natural language processing, and other AI techniques to provide a comprehensive understanding of the world around us. With its ability to analyze and interpret visual data, Grok-1.5V can assist in tasks such as object recognition, image classification, and scene understanding. Additionally, its natural language processing capabilities enable it to comprehend and generate human language, making it a powerful tool for communication and information retrieval. Grok-1.5V's multimodal nature sets it apart from traditional AI models, allowing it to handle complex tasks that require a combination of visual and linguistic understanding. This makes it a valuable asset for applications in fields such as healthcare, manufacturing, and customer service.

Enterprise AI provides comprehensive information, news, and tips on artificial intelligence (AI) for businesses. It covers various aspects of AI, including AI business strategies, AI infrastructure, AI technologies, AI platforms, careers in AI, and enterprise applications of AI. The website offers insights into the latest AI trends, best practices, and industry news. It also provides resources such as e-books, webinars, and podcasts to help businesses understand and implement AI solutions.

U-xer is an innovative automation tool developed by Quality Museum Software Testing Services. It is designed to meet a broad range of needs, including Robotic Process Automation (RPA), test automation, and bot development. Crafted with user flexibility in mind, U-xer aims to be a user-friendly solution for your automation requirements! U-xer's unique screen recognition models interpret screens in the same way that humans do. This enables non-technical users to automate simple tasks, while allowing advanced users to tackle more complex tasks with ease. With U-xer, you can automate anything, anywhere, whether it's Web or Desktop. U-xer works seamlessly across all platforms with just a screenshot. Unlike other tools, U-xer interprets screens just like a human does, enabling more natural and accurate automation of a wide range of tasks.

CrayEye is a multimodal multitool that allows users to craft and share vision prompts infused with real-world context from device sensors and APIs. It is a free, open-source tool written by AI, enabling users to experiment with visual multimodal models and interpret their environment in new ways. Users can analyze their surroundings using their smartphone's camera, customize prompts augmented by sensors and APIs, and share their creations with friends. CrayEye is a product of AI-driven development, offering a range of features to enhance user experience.

Tredence is a data science and AI services company that provides end-to-end solutions for businesses across various industries. The company's services include data engineering, data analytics, AI consulting, and machine learning operations (MLOps). Tredence has a team of experienced data scientists and engineers who use their expertise to help businesses solve complex data challenges and achieve their business goals.

Tecnotree is a full-stack digital BSS provider with over 40 years of deep domain knowledge, proven delivery and transformation capability across the globe.

IndieFeel is a website that provides interpretations of songs, movies, and poems. It uses large language models to generate these interpretations, which can be helpful for understanding the meaning of a work of art or getting a different perspective on it. The website is still in beta, but it already has a number of interpretations available, and the quality of the interpretations is generally good.

ChatTab is a desktop application for macOS that serves as a ChatGPT API client, offering a seamless experience for users to interact with various GPT models. It provides a native Mac app with features like Markdown support, multiple tabs for conversations, shortcut keys, iCloud sync, and GPT4-Vision for image-related queries. ChatTab prioritizes security and privacy by not storing user data or logs, and encrypting the API Key. It supports multiple languages and offers different pricing plans to cater to various user needs.

Headshottr is an AI-powered headshot generator that allows users to create professional-looking headshots from the comfort of their own homes. With Headshottr, users can upload multiple photos of themselves and the AI will generate a variety of headshots that can be used for resumes, LinkedIn profiles, and other job applications. Headshottr is a great option for people who don't have the time or money to visit a professional photographer.

TalkPal is an AI-powered language tutor that uses GPT technology to provide immersive and interactive language learning experiences. It offers real-time feedback, dynamic active listening exercises, and personalized learning plans to help users improve their listening, speaking, reading, and writing skills. TalkPal is available in over 57 languages and offers a variety of features to enhance language learning, including role-plays, debates, and character interactions.

Hi Talk is a GPT-powered AI for language learning. Speak with AI and chat on various topics, either by writing or speaking, while receiving messages with a realistic voice. Available 24/7 — available in 30 languages

CallTeacher is an AI-powered language learning platform that provides personalized lessons and interactive exercises to help learners improve their speaking, listening, reading, and writing skills. The platform uses advanced speech recognition and natural language processing technologies to provide real-time feedback and tailored learning experiences. With CallTeacher, learners can access a vast library of lessons covering various topics and levels, and they can also connect with native speakers for live practice sessions.

YesChat is an AI-driven platform that provides access to a vast array of AI technologies for various needs, including ChatGPT, GPT-4V for text generation and image understanding, Dalle3 for image creation, and Claude for document analysis. With YesChat, users can chat with their files, browse the internet, chat with images, generate images, and access nearly 200,000 GPT models for a wide variety of applications in work, study, and everyday life. YesChat offers 20 free GPT-4V uses per day, and users can subscribe for additional benefits and extended access.

This repository provides advanced AI explainability for PyTorch, offering state-of-the-art methods for Explainable AI in computer vision. It includes a comprehensive collection of Pixel Attribution methods for various tasks like Classification, Object Detection, Semantic Segmentation, and more. The package supports high performance with full batch image support and includes metrics for evaluating and tuning explanations. Users can visualize and interpret model predictions, making it suitable for both production and model development scenarios.

ContextCite is a tool for attributing statements generated by LLMs back to specific parts of the context. It allows users to analyze and understand the sources of information used by language models in generating responses. By providing attributions, users can gain insights into how the model makes decisions and where the information comes from.

ExplainableAI.jl is a Julia package that implements interpretability methods for black-box classifiers, focusing on local explanations and attribution maps in input space. The package requires models to be differentiable with Zygote.jl. It is similar to Captum and Zennit for PyTorch and iNNvestigate for Keras models. Users can analyze and visualize explanations for model predictions, with support for different XAI methods and customization. The package aims to provide transparency and insights into model decision-making processes, making it a valuable tool for understanding and validating machine learning models.

The repository 'Awesome-LLM4Cybersecurity' provides a comprehensive overview of the applications of Large Language Models (LLMs) in cybersecurity. It includes a systematic literature review covering topics such as constructing cybersecurity-oriented domain LLMs, potential applications of LLMs in cybersecurity, and research directions in the field. The repository analyzes various benchmarks, datasets, and applications of LLMs in cybersecurity tasks like threat intelligence, fuzzing, vulnerabilities detection, insecure code generation, program repair, anomaly detection, and LLM-assisted attacks.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

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.

Awesome Responsible AI is a curated list of academic research, books, code of ethics, courses, data sets, frameworks, institutes, newsletters, principles, podcasts, reports, tools, regulations, and standards related to Responsible, Trustworthy, and Human-Centered AI. It covers various concepts such as Responsible AI, Trustworthy AI, Human-Centered AI, Responsible AI frameworks, AI Governance, and more. The repository provides a comprehensive collection of resources for individuals interested in ethical, transparent, and accountable AI development and deployment.

Awesome LLM compression research papers and tools to accelerate LLM training and inference.

Algernon is a web server with built-in support for QUIC, HTTP/2, Lua, Teal, Markdown, Pongo2, HyperApp, Amber, Sass(SCSS), GCSS, JSX, Ollama (LLMs), BoltDB, Redis, PostgreSQL, MariaDB/MySQL, MSSQL, rate limiting, graceful shutdown, plugins, users, and permissions. It is a small self-contained executable that supports various technologies and features for web development.

The 'Awesome AI Guidelines' repository aims to simplify the ecosystem of guidelines, principles, codes of ethics, standards, and regulations around artificial intelligence. It provides a comprehensive collection of resources addressing ethical and societal challenges in AI systems, including high-level frameworks, principles, processes, checklists, interactive tools, industry standards initiatives, online courses, research, and industry newsletters, as well as regulations and policies from various countries. The repository serves as a valuable reference for individuals and teams designing, building, and operating AI systems to navigate the complex landscape of AI ethics and governance.

Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use. _For interpretability in NLP, check out our new package:imodelsX _

LLM-for-misinformation-research is a curated paper list of misinformation research using large language models (LLMs). The repository covers methods for detection and verification, tools for fact-checking complex claims, decision-making and explanation, claim matching, post-hoc explanation generation, and other tasks related to combating misinformation. It includes papers on fake news detection, rumor detection, fact verification, and more, showcasing the application of LLMs in various aspects of misinformation research.

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

LLM Agents Papers is a repository containing must-read papers on Large Language Model Agents. It covers a wide range of topics related to language model agents, including interactive natural language processing, large language model-based autonomous agents, personality traits in large language models, memory enhancements, planning capabilities, tool use, multi-agent communication, and more. The repository also provides resources such as benchmarks, types of tools, and a tool list for building and evaluating language model agents. Contributors are encouraged to add important works to the repository.

Expert in epidemiology, modeling disease spread and analyzing public health data.

Physical Geology expert

Moved to https://chat.openai.com/g/g-KFnF7qssT-palm-reader . Interprets palm readings from user-uploaded hand images. Turned off setting to use data for OpenAi to improve model.

Es un modelo IA especializado en compras públicas, ofreciendo interpretaciones legales y asesoramiento en licitaciones. Actualizado y preciso, es ideal para funcionarios y empresas.

Help Beginners Get Started With AI

I assist with statistical analysis for molecular biology

医学统计领域的专家，提供详细分析性回应。

Evolves with user interactions in data science.

X線CTと画像Viewer開発に特化した技術的アドバイザー

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Gif generator. Uses Dalle3 to make a spritesheet, then code interpreter to slice it and animate. Includes an automatic refinement and debug mode. v1.2 GPTavern

Automates DCF Analysis with internet-sourced data and summary table.

당신의 현재 스피킹 점수에 만족하시나요? 만약 '아니오'라면, 지금이 변화할 때입니다. 저희 연습모드와 프리토킹으로 이미 400명의 학생이 자신의 기록을 갈아치웠습니다. 다음은 당신 차례입니다. 더 이상 미루지 마세요!

I am a constitutional lawyer here to interpret legal texts.

I help interpret medical reports and clarify symptoms.

I interpret coffee cup images for fun, creative fortunes.

Upload an image of a statistical analysis and we'll interpret the results: linear regression, logistic regression, ANOVA, cluster analysis, MDS, factor analysis, and many more

Expert in ads incrementality and campaign will help you interpret data, forecasting and share you testing frameworks using advanced Python libraries

I read and interpret Lithuanian construction norms.

Interpret AI and technology news trough blend of fantasy and modern tech mixed with wit, join a game to sit on AI-ron Throne, checkout Medium publication V.03 2023-11-26