Best AI tools for< Decision Scientist >

The Decision Lab is an AI-powered platform that applies behavioral science to create transformational change for individuals, products, and organizations. Using machine learning and AI, the platform delivers hyper-personalization, designs people-centered products and services, and leverages behavioral science to achieve operational excellence. The platform helps in understanding consumer decision-making, generating positive behaviors, and building world-class digital products with behavioral science. It fosters holistic wellness, unlocks product potential, and empowers individuals to take control of their finances. The Decision Lab offers insights and interventions to help organizations make better decisions and create meaningful impact through evidence-based choice.

Link is a Decision Intelligence platform that offers Agile Applied AI and Machine Learning solutions. The platform provides workshops on AI/ML/DI, Decision Intelligence Solutions, and Assured Agile AI Resources. Users can access live model galleries, blogs, news, and learn about Decision Intelligence Models. Link aims to help business leaders prepare for the AI paradigm shift by offering tools and resources for understanding and applying artificial intelligence effectively.

Stockpulse is an AI-powered platform that analyzes financial news and communities using Artificial Intelligence. It provides decision support for operations by collecting, filtering, and converting unstructured data into processable information. With extensive coverage of financial media sources globally, Stockpulse offers unique historical data, sentiment analysis, and AI-driven insights for various sectors in the financial markets.

DataDepot is an AI-powered research platform that streamlines the research process and personalizes access to insights. It provides a marketplace of trusted research providers, allowing users to discover and connect with the research they need. DataDepot's dynamic displays and AI-powered insights help users uncover valuable information and make informed decisions.

Scienaptic Systems is an AI-powered Credit Decisioning Platform that revolutionizes the lending industry by automating credit underwriting processes, leveraging alternate data points, and offering self-learning models for instant credit decisions. The platform helps lenders identify creditworthy borrowers, streamline customer experience, and ensure fair lending practices through explainable AI models. Scienaptic's suite of AI-enabled technologies enables lenders to say 'Yes' to more borrowers at lower risk, providing a 360-degree risk assessment before approving applications. The platform integrates seamlessly with existing lending ecosystems, ensuring disruption-free deployment and better risk predictions through a single API call.

IngestAI is a Silicon Valley-based startup that provides a sophisticated toolbox for data preparation and model selection, powered by proprietary AI algorithms. The company's mission is to make AI accessible and affordable for businesses of all sizes. IngestAI's platform offers a turn-key service tailored for AI builders seeking to optimize AI application development. The company identifies the model best-suited for a customer's needs, ensuring it is designed for high performance and reliability. IngestAI utilizes Deepmark AI, its proprietary software solution, to minimize the time required to identify and deploy the most effective AI solutions. IngestAI also provides data preparation services, transforming raw structured and unstructured data into high-quality, AI-ready formats. This service is meticulously designed to ensure that AI models receive the best possible input, leading to unparalleled performance and accuracy. IngestAI goes beyond mere implementation; the company excels in fine-tuning AI models to ensure that they match the unique nuances of a customer's data and specific demands of their industry. IngestAI rigorously evaluates each AI project, not only ensuring its successful launch but its optimal alignment with a customer's business goals.

Nature is a scientific journal that publishes original research, reviews, news, and commentary on a wide range of scientific disciplines. It is one of the world's most prestigious scientific journals, and its articles are widely cited in the scientific literature. Nature is published by Springer Nature, a leading global publisher of scientific, technical, and medical content.

UBIAI is a powerful text annotation tool that helps businesses accelerate their data labeling process. With UBIAI, businesses can annotate any type of document, including PDFs, images, and text. UBIAI also offers a variety of features to make the annotation process easier and more efficient, such as auto-labeling, multi-lingual annotation, and team collaboration. With UBIAI, businesses can save time and money on their data labeling projects.

Fluent is an AI-powered data analytics platform that helps businesses explore their data and uncover insights. It uses natural language processing to understand user questions and generate SQL queries to retrieve data from a variety of sources. Fluent also provides visualizations and dashboards to help users understand their data and make informed decisions.

Jeda.ai is a cutting-edge AI application that offers a Visual AI Workspace for ideation and decision-making. It provides a platform for users to visualize, collaborate, and innovate using various AI tools like AI Template Analysis, AI Note Taking, AI Mind Map Diagrams, AI Flowchart Diagrams, AI Wireframe, AI Text Writer, AI Sticky Notes, AI Art, AI Vision, and Transform. The application caters to a wide range of business cases, including Leadership & Business Development, Product Management, Marketing, Sales, User Experience & Product Design, Design, Human Resources, Retrospective Analysis, Engineering, and Software Development. Jeda.ai aims to transform ideas into stunning visuals and data into strategic insights, helping users drive success and outpace their competition.

ViableView is an AI-powered market and product data analytics tool that helps entrepreneurs identify profitable products and niches. By collecting and analyzing market data, the tool provides indication metrics such as opportunity score, competition score, and profit margins to guide investment decisions. ViableView uses viability simulations to turn messy market data into actionable insights, enabling users to make informed choices about product advertising and market strategies. The tool offers features like real-time tracking of market trends, market overview insights, and data projections based on industry KPIs. ViableView is suitable for digital products, physical products, SaaS, and real estate markets, providing comprehensive data aggregation and market analysis for each sector.

MacroMicro is an AI analytics platform that combines technology and research expertise to empower users with valuable insights into global market trends. With over 0k registered users and 0M+ monthly website traffic, MacroMicro offers real-time charts, cycle analysis, and data-driven insights to optimize investment strategies. The platform compiles the MM Global Recession Probability, utilizes OpenAI's Embedding technology, and provides exclusive reports and analysis on key market events. Users can access dynamic and automatically-updated charts, a powerful toolbox for analysis, and engage with a vibrant community of macroeconomic professionals.

Aiternus is an AI Computer Vision and Data Analysis System that is revolutionizing industries with cutting-edge technology. It offers advanced solutions for various sectors such as manufacturing, construction, logistics, healthcare, retail, sports tech, electronics, and office spaces. Aiternus leverages AI to streamline processes, boost productivity, enhance safety and quality standards, and develop tailor-made solutions for clients' unique needs. The application provides features like work process monitoring, route optimization, AI chatbot support, demand predictions, quality control, performance analysis, and automation of tasks in office spaces.

Trendingly is an AI-powered platform that helps business leaders anticipate future trends by analyzing vast amounts of data from various sources. The platform leverages state-of-the-art AI technology to provide actionable insights and foresight for making informed decisions. Trendingly's mission is to cut through the noise of information overload and identify significant signals of change, empowering users to stay ahead of the curve in a rapidly evolving business landscape.

The OR Society is a professional membership body that supports the development of people working in operational research, data science, and analytics. The society provides a range of services to its members, including access to world-class journals, events and conferences, training courses, and pro bono opportunities. The OR Society also works to promote the use of operational research in all areas of industry, business, government, the community, and the third sector.

Derwen is an open-source integration platform for production machine learning in enterprise, specializing in natural language processing, graph technologies, and decision support. It offers expertise in developing knowledge graph applications and domain-specific authoring. Derwen collaborates closely with Hugging Face and provides strong data privacy guarantees, low carbon footprint, and no cloud vendor involvement. The platform aims to empower AI engineers and domain experts with quality, time-to-value, and ownership since 2017.

Abacus.AI is the world's first AI platform where AI, not humans, build Applied AI agents and systems at scale. Using generative AI and other novel neural net techniques, AI can build LLM apps, gen AI agents, and predictive applied AI systems at scale.

McKinsey & Company is a global management consulting firm that provides a wide range of services to help businesses improve their performance. The company's website provides information on its services, insights, and thought leadership on a variety of topics, including artificial intelligence (AI). McKinsey & Company has a strong focus on AI and has developed a number of tools and resources to help businesses adopt and implement AI technologies. The company's website includes a section on AI that provides information on the latest AI trends, case studies, and white papers.

Sahara AI is a decentralized AI blockchain platform designed for an open, equitable, and collaborative economy. It offers solutions for personal and business use, empowering users to monetize knowledge, enhance team collaboration, and explore AI opportunities. Sahara AI ensures AI sovereignty, user privacy, and transparency through blockchain technologies. The platform fosters a collaborative AI development environment with decentralized governance and equitable monetization. Sahara AI features secure vaults, a decentralized AI marketplace, a no-code toolkit, and SaharaID reputation system. It is backed by visionary investors and ecosystem partners, with a roadmap for future developments.

Skillfusion is an AI marketplace that connects businesses with AI solutions. It provides a platform for businesses to discover, evaluate, and purchase AI solutions from a variety of vendors. Skillfusion also offers a range of services to help businesses implement and manage AI solutions.

The AI Scientist is a comprehensive system for fully automatic scientific discovery, enabling Foundation Models to perform research independently. It aims to tackle the grand challenge of developing agents capable of conducting scientific research and discovering new knowledge. The tool generates papers on various topics using Large Language Models (LLMs) and provides a platform for exploring new research ideas. Users can create their own templates for specific areas of study and run experiments to generate papers. However, caution is advised as the codebase executes LLM-written code, which may pose risks such as the use of potentially dangerous packages and web access.

This repository is the workshop repository for the Causal Modeling in Machine Learning Workshop on Altdeep.ai. The material is open source and free. The course covers causality in model-based machine learning, Bayesian modeling, interventions, counterfactual reasoning, and deep causal latent variable models. It aims to equip learners with the ability to build causal reasoning algorithms into decision-making systems in data science and machine learning teams within top-tier technology organizations.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

This repository is a curated collection of resources for fine-tuning Large Language Models (LLMs) like GPT, BERT, RoBERTa, and their variants. It includes tutorials, papers, tools, frameworks, and best practices to aid researchers, data scientists, and machine learning practitioners in adapting pre-trained models to specific tasks and domains. The resources cover a wide range of topics related to fine-tuning LLMs, providing valuable insights and guidelines to streamline the process and enhance model performance.

AskDB is a revolutionary application that simplifies the way users interact with SQL databases. It allows users to query databases in plain English, provides instant answers, and offers AI-assisted query writing and database exploration. AskDB benefits business analysts, data scientists, managers, developers, and database administrators by making querying databases intuitive, effortless, and safe. It offers features like natural language querying, instant insight from data, multi-database connectivity, intelligent query suggestions, data privacy, and easy data export.

The Python Redis Vector Library (RedisVL) is a tailor-made client for AI applications leveraging Redis. It enhances applications with Redis' speed, flexibility, and reliability, incorporating capabilities like vector-based semantic search, full-text search, and geo-spatial search. The library bridges the gap between the emerging AI-native developer ecosystem and the capabilities of Redis by providing a lightweight, elegant, and intuitive interface. It abstracts the features of Redis into a grammar that is more aligned to the needs of today's AI/ML Engineers or Data Scientists.

Scikit-decide is an AI framework for Reinforcement Learning, Automated Planning and Scheduling. It provides a unified interface to define and solve decision-making problems, making it easy to switch between different algorithms and domains.

Forust is a lightweight package for building gradient boosted decision tree ensembles. The algorithm code is written in Rust with a Python wrapper. It implements the same algorithm as XGBoost and provides nearly identical results. The package was developed to better understand XGBoost, as a fun project in Rust, and to experiment with adding new features to the algorithm in a simpler codebase. Forust allows training gradient boosted decision tree ensembles with multiple objective functions, predicting on datasets, inspecting model structures, calculating feature importance, and saving/loading trained boosters.

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

Awesome-AISourceHub is a repository that collects high-quality information sources in the field of AI technology. It serves as a synchronized source of information to avoid information gaps and information silos. The repository aims to provide valuable resources for individuals such as AI book authors, enterprise decision-makers, and tool developers who frequently use Twitter to share insights and updates related to AI advancements. The platform emphasizes the importance of accessing information closer to the source for better quality content. Users can contribute their own high-quality information sources to the repository by following specific steps outlined in the contribution guidelines. The repository covers various platforms such as Twitter, public accounts, knowledge planets, podcasts, blogs, websites, YouTube channels, and more, offering a comprehensive collection of AI-related resources for individuals interested in staying updated with the latest trends and developments in the AI field.

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.

LLM price compass is an open-source tool for comparing inference costs on different GPUs across various cloud providers. It collects benchmark data to help users select the right GPU, cloud, and provider for their models. The project aims to provide insights into fixed per token costs from different providers, aiding in decision-making for model deployment.

INFO8006 Introduction to Artificial Intelligence is a course at ULiège that covers various topics in AI such as intelligent agents, problem-solving, games, probabilistic reasoning, machine learning, neural networks, reinforcement learning, and decision-making. The course includes lectures, exercises, and programming projects using Python. Students can access course materials, previous exams, and archived lectures to enhance their understanding of AI concepts.

MisguidedAttention is a collection of prompts designed to challenge the reasoning abilities of large language models by presenting them with modified versions of well-known thought experiments, riddles, and paradoxes. The goal is to assess the logical deduction capabilities of these models and observe any shortcomings or fallacies in their responses. The repository includes a variety of prompts that test different aspects of reasoning, such as decision-making, probability assessment, and problem-solving. By analyzing how language models handle these challenges, researchers can gain insights into their reasoning processes and potential biases.

HybridAGI is the first Programmable LLM-based Autonomous Agent that lets you program its behavior using a **graph-based prompt programming** approach. This state-of-the-art feature allows the AGI to efficiently use any tool while controlling the long-term behavior of the agent. Become the _first Prompt Programmers in history_ ; be a part of the AI revolution one node at a time! **Disclaimer: We are currently in the process of upgrading the codebase to integrate DSPy**

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, ...

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

Semantic Router is a superfast decision-making layer for your LLMs and agents. Rather than waiting for slow LLM generations to make tool-use decisions, we use the magic of semantic vector space to make those decisions — _routing_ our requests using _semantic_ meaning.

This repository contains custom recipes for H2O Driverless AI, which is an Automatic Machine Learning platform for the Enterprise. Custom recipes are Python code snippets that can be uploaded into Driverless AI at runtime to automate feature engineering, model building, visualization, and interpretability. Users can gain control over the optimization choices made by Driverless AI by providing their own custom recipes. The repository includes recipes for various tasks such as data manipulation, data preprocessing, feature selection, data augmentation, model building, scoring, and more. Best practices for creating and using recipes are also provided, including security considerations, performance tips, and safety measures.

DignityAI: The Ethical Intelligence GPT is an advanced AI model designed to prioritize human life and dignity, providing ethically-guided, intelligent responses for complex decision-making scenarios.

Explaining behavioral economics in ways that help you!

I'm the Mixed Methods Design Decision Tool, offering guidance on mixed methods research designs, their implementation, and effective communication in studies.

Analyzes data to influence strategic decision-making processes.

Provide TOPIC & different views to compare: Gateway to Informed Comparisons. Harness AI-powered insights to analyze and score different viewpoints on any topic, delivering balanced, data-driven perspectives for smarter decision-making.

Ultimate guide on Cognitive Science

多元思维分析专家，深入解读人类认知和认知偏误。

Guides on mitigating bias and fairness in AI.

Guides on using Mao's principles for AI startups

这是一个集预测、分析、评估和总结于一身的系统，旨在帮助用户把握科技前沿，驾驭信息洪流，并在决策过程中提供支持。它是为未来导向和价值驱动的决策制定设计的工具。

Singularity Strategist discussing AI's role in shaping governance based on the GLLASS GAME principles

I analyze and integrate missing clusters in data for coherent structuring.

深度分析问题，并通过反思优化回答

可以从数据层面了解2024年前AI领域信息

Expert in analyzing UK theses data without subject info

Futuristic insights on society and living

An artificial being with his own desires, Dominance

AI and Sustainable world

Expert in robotics control, AI integration, and current trends

Help you to think critical.