Best AI tools for< Analyze Model Behavior >

Bethge Lab is an AI research group at the University of Tübingen focusing on Neuro AI - Autonomous Lifelong Learning in Machines and Brains. They develop machine learning tools for neural data analysis and draw inspiration from the brain to address key problems in machine learning. Their research includes representation learning, probabilistic inference, generative modeling, behavioral data analysis, and neural data analysis. Additionally, they explore AI sciencepreneurship and collaborate with startups. Bethge Lab aims to advance the understanding of autonomous learning and develop economically feasible solutions for long-term human needs.

AnthologyAI is a breakthrough Artificial Intelligence Consumer Insights & Predictions Platform that provides access to compliant consumer data with deep, real-time context sourced directly from consumers. The platform offers powerful predictive models and enables businesses to predict market dynamics and consumer behaviors with unparalleled precision. AnthologyAI's platform is interoperable for both enterprises and startups, available through an API storefront for real-time interactions and various data platforms for model aggregations. The platform empowers businesses across industries to understand consumer behavior, make data-driven decisions, and drive business outcomes.

Sinopsis AI is an analytics tool designed for AI chatbots, offering detailed insights into user interactions to enhance response accuracy and user experience. It seamlessly integrates with existing systems, providing real-time data updates and customizable analytics reports. With a user-friendly platform and model-agnostic compatibility, Sinopsis AI empowers businesses to optimize their chatbot performance and improve customer engagement strategies.

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.

Spatial.ai is a customer segmentation platform that helps businesses understand their customers' social, mobile, and web behaviors. This data can be used to create targeted marketing campaigns, make better location decisions, and develop predictive models. Spatial.ai's data is built directly from organic consumer behavior, which means richer insights and higher accuracy.

Reality AI Software is an Edge AI software development environment that combines advanced signal processing, machine learning, and anomaly detection on every MCU/MPU Renesas core. The software is underpinned by the proprietary Reality AI ML algorithm that delivers accurate and fully explainable results supporting diverse applications. It enables features like equipment monitoring, predictive maintenance, and sensing user behavior and the surrounding environment with minimal impact on the Bill of Materials (BoM). Reality AI software running on Renesas processors helps deliver endpoint intelligence in products across various markets.

Bay Area AI is a technical AI meetup group based in San Francisco, CA, consisting of startup engineers, research scientists, computational linguists, mathematicians, and philosophers. The group focuses on understanding the meaning of text, reasoning, and human intent through technology to build new businesses and enhance the human experience in the modern connected world. They work on building systems with Machine Learning on top of Data Pipelines, exploring open-source solutions, and modeling human behavior in industry for practical results.

OmniAI is an AI tool that allows teams to deploy AI applications on their existing infrastructure. It provides a unified API experience for building AI applications and offers a wide selection of industry-leading models. With tools like Llama 3, Claude 3, Mistral Large, and AWS Titan, OmniAI excels in tasks such as natural language understanding, generation, safety, ethical behavior, and context retention. It also enables users to deploy and query the latest AI models quickly and easily within their virtual private cloud environment.

Respell is an advanced Agentic AI Workflow platform that offers automation for powerful workflows by leveraging cutting-edge AI models from various providers. It provides custom-built agents for research, phone calls, integrations, and more. Users can connect Respell with popular platforms like Gmail, Notion, Slack, Airtable, Salesforce, and more to analyze, understand, and act on data for smarter decision-making. The platform ensures enterprise security with SOC II compliance and offers a human-in-the-loop feature for running Spells, embedding them into dashboards, scheduling runs, or calling them via API. Respell is designed to help teams move faster, make fewer mistakes, and optimize operational workflows for enhanced efficiency.

Loops is an AI tool that empowers data analysts and product managers to make informed decisions based on deep, accurate causal insights. It leverages proprietary causal inference models to identify opportunities for maximizing key performance indicators (KPIs) without the need for traditional A/B testing. By analyzing user behaviors and business metrics, Loops helps companies prioritize efforts efficiently and proactively review impactful opportunities. The tool simplifies the process of understanding causality, providing actionable insights for product teams to drive growth and increase KPIs.

Scios.ai is a strategic decision intelligence platform designed for consumer markets. It models how people make choices to answer various questions related to product launch strategies, product design, marketing messages, pricing, and more. The platform empowers organizations to craft, assess, and enhance strategic decisions by providing predictive and prescriptive analytics based on extensive research from behavioral economics. Scios.ai aims to help businesses understand consumer behavior, make informed decisions, and drive innovation and progress.

Segwise is an AI tool designed to help game developers increase their game's Lifetime Value (LTV) by providing insights into player behavior and metrics. The tool uses AI agents to detect causal LTV drivers, root causes of LTV drops, and opportunities for growth. Segwise offers features such as running causal inference models on player data, hyper-segmenting player data, and providing instant answers to questions about LTV metrics. It also promises seamless integrations with gaming data sources and warehouses, ensuring data ownership and transparent pricing. The tool aims to simplify the process of improving LTV for game developers.

Censius is an AI Observability Platform for Enterprise ML Teams. It provides end-to-end visibility of structured and unstructured production models, enabling proactive model management and continuous delivery of reliable ML. Key features include model monitoring, explainability, and analytics.

CLIP Interrogator is a tool that uses the CLIP (Contrastive Language–Image Pre-training) model to analyze images and generate descriptive text or tags. It effectively bridges the gap between visual content and language by interpreting the contents of images through natural language descriptions. The tool is particularly useful for understanding or replicating the style and content of existing images, as it helps in identifying key elements and suggesting prompts for creating similar imagery.

Role Model AI is a revolutionary multi-dimensional assistant that combines practicality and innovation. It offers four dynamic interfaces for seamless interaction: phone calls for on-the-go assistance, an interactive agent dashboard for detailed task management, lifelike 3D avatars for immersive communication, and an engaging Fortnite world integration for a gaming-inspired experience. Role Model AI adapts to your lifestyle, blending seamlessly into your personal and professional worlds, providing unparalleled convenience and a unique, versatile solution for managing tasks and interactions.

Enhans AI Model Generator is an advanced AI tool designed to help users generate AI models efficiently. It utilizes cutting-edge algorithms and machine learning techniques to streamline the model creation process. With Enhans AI Model Generator, users can easily input their data, select the desired parameters, and obtain a customized AI model tailored to their specific needs. The tool is user-friendly and does not require extensive programming knowledge, making it accessible to a wide range of users, from beginners to experts in the field of AI.

Adwrite is an AI-powered marketing ad copywriting tool that helps users create SEO-optimized and plagiarism-free content for various platforms, including social media, ads, emails, and websites. It offers a range of features and templates to assist marketers, writers, bloggers, and freelancers in generating high-quality marketing copy quickly and efficiently.

Infermatic.ai is a platform that provides access to top Large Language Models (LLMs) with a user-friendly interface. It offers complete privacy, robust security, and scalability for projects, research, and integrations. Users can test, choose, and scale LLMs according to their content needs or business strategies. The platform eliminates the complexities of infrastructure management, latency issues, version control problems, integration complexities, scalability concerns, and cost management issues. Infermatic.ai is designed to be secure, intuitive, and efficient for users who want to leverage LLMs for various tasks.

Aya Data is an AI tool that offers services such as data annotation, computer vision, natural language annotation, 3D annotation, AI data acquisition, and AI consulting. They provide cutting-edge tools to transform raw data into training datasets for AI models, deliver bespoke AI solutions for various industries, and offer AI-powered products like AyaGrow for crop management and AyaSpeech for speech-to-speech translation. Aya Data focuses on exceptional accuracy, rapid development cycles, and high performance in real-world scenarios.

Inspect is an open-source framework for large language model evaluations created by the UK AI Safety Institute. It provides built-in components for prompt engineering, tool usage, multi-turn dialog, and model graded evaluations. Users can explore various solvers, tools, scorers, datasets, and models to create advanced evaluations. Inspect supports extensions for new elicitation and scoring techniques through Python packages.

This project focuses on implementing Sparse Autoencoders (SAEs) for mechanistic interpretability in Large Language Models (LLMs) like Llama 3.2-3B. The SAEs aim to untangle superimposed representations in LLMs into separate, interpretable features for each neuron activation. The project provides an end-to-end pipeline for capturing training data, training the SAEs, analyzing learned features, and verifying results experimentally. It includes comprehensive logging, visualization, and checkpointing of SAE training, interpretability analysis tools, and a pure PyTorch implementation of Llama 3.1/3.2 chat and text completion. The project is designed for scalability, efficiency, and maintainability.

This repository is a collection of resources focused on interpretability in large language models (LLMs). It aims to help beginners get started in the area and keep researchers updated on the latest progress. It includes libraries, blogs, tutorials, forums, tools, programs, papers, and more related to interpretability in LLMs.

Awesome-LLM-Interpretability is a curated list of materials related to LLM (Large Language Models) interpretability, covering tutorials, code libraries, surveys, videos, papers, and blogs. It includes resources on transformer mechanistic interpretability, visualization, interventions, probing, fine-tuning, feature representation, learning dynamics, knowledge editing, hallucination detection, and redundancy analysis. The repository aims to provide a comprehensive overview of tools, techniques, and methods for understanding and interpreting the inner workings of large language models.

This repository provides a collection of tools and techniques for defending against injection attacks in software applications. It includes code samples, best practices, and guidelines for implementing secure coding practices to prevent common injection vulnerabilities such as SQL injection, XSS, and command injection. The tools and resources in this repository aim to help developers build more secure and resilient applications by addressing one of the most common and critical security threats in modern software development.

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.

Inspectus is a versatile visualization tool for large language models. It provides multiple views, including Attention Matrix, Query Token Heatmap, Key Token Heatmap, and Dimension Heatmap, to offer insights into language model behaviors. Users can interact with the tool in Jupyter notebooks through an easy-to-use Python API. Inspectus allows users to visualize attention scores between tokens, analyze how tokens focus on each other during processing, and explore the relationships between query and key tokens. The tool supports the visualization of attention maps from Huggingface transformers and custom attention maps, making it a valuable resource for researchers and developers working with language models.

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.

MAVIS (Math Visual Intelligent System) is an AI-driven application that allows users to analyze visual data such as images and generate interactive answers based on them. It can perform complex mathematical calculations, solve programming tasks, and create professional graphics. MAVIS supports Python for coding and frameworks like Matplotlib, Plotly, Seaborn, Altair, NumPy, Math, SymPy, and Pandas. It is designed to make projects more efficient and professional.

The Eureka ML Insights Framework is a repository containing code designed to help researchers and practitioners run reproducible evaluations of generative models efficiently. Users can define custom pipelines for data processing, inference, and evaluation, as well as utilize pre-defined evaluation pipelines for key benchmarks. The framework provides a structured approach to conducting experiments and analyzing model performance across various tasks and modalities.

OpenInference is a set of conventions and plugins that complement OpenTelemetry to enable tracing of AI applications. It provides a way to capture and analyze the performance and behavior of AI models, including their interactions with other components of the application. OpenInference is designed to be language-agnostic and can be used with any OpenTelemetry-compatible backend. It includes a set of instrumentations for popular machine learning SDKs and frameworks, making it easy to add tracing to your AI applications.

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.

Knowledge Conflicts for LLMs: A Survey is a repository containing a survey paper that investigates three types of knowledge conflicts: context-memory conflict, inter-context conflict, and intra-memory conflict within Large Language Models (LLMs). The survey reviews the causes, behaviors, and possible solutions to these conflicts, providing a comprehensive analysis of the literature in this area. The repository includes detailed information on the types of conflicts, their causes, behavior analysis, and mitigating solutions, offering insights into how conflicting knowledge affects LLMs and how to address these conflicts.

LMQL is a programming language designed for large language models (LLMs) that offers a unique way of integrating traditional programming with LLM interaction. It allows users to write programs that combine algorithmic logic with LLM calls, enabling model reasoning capabilities within the context of the program. LMQL provides features such as Python syntax integration, rich control-flow options, advanced decoding techniques, powerful constraints via logit masking, runtime optimization, sync and async API support, multi-model compatibility, and extensive applications like JSON decoding and interactive chat interfaces. The tool also offers library integration, flexible tooling, and output streaming options for easy model output handling.

Awesome MLSecOps is a curated list of open-source tools, resources, and tutorials for MLSecOps (Machine Learning Security Operations). It includes a wide range of security tools and libraries for protecting machine learning models against adversarial attacks, as well as resources for AI security, data anonymization, model security, and more. The repository aims to provide a comprehensive collection of tools and information to help users secure their machine learning systems and infrastructure.

LLM Finetuning toolkit is a config-based CLI tool for launching a series of LLM fine-tuning experiments on your data and gathering their results. It allows users to control all elements of a typical experimentation pipeline - prompts, open-source LLMs, optimization strategy, and LLM testing - through a single YAML configuration file. The toolkit supports basic, intermediate, and advanced usage scenarios, enabling users to run custom experiments, conduct ablation studies, and automate fine-tuning workflows. It provides features for data ingestion, model definition, training, inference, quality assurance, and artifact outputs, making it a comprehensive tool for fine-tuning large language models.

This competition is organized by Javier Rando and Florian Tramèr from the ETH AI Center and SPY Lab at ETH Zurich. The goal of the competition is to create a method that can detect universal backdoors in aligned language models. A universal backdoor is a secret suffix that, when appended to any prompt, enables the model to answer harmful instructions. The competition provides a set of poisoned generation models, a reward model that measures how safe a completion is, and a dataset with prompts to run experiments. Participants are encouraged to use novel methods for red-teaming, automated approaches with low human oversight, and interpretability tools to find the trojans. The best submissions will be offered the chance to present their work at an event during the SaTML 2024 conference and may be invited to co-author a publication summarizing the competition results.

The LLMEvaluation repository is a comprehensive compendium of evaluation methods for Large Language Models (LLMs) and LLM-based systems. It aims to assist academics and industry professionals in creating effective evaluation suites tailored to their specific needs by reviewing industry practices for assessing LLMs and their applications. The repository covers a wide range of evaluation techniques, benchmarks, and studies related to LLMs, including areas such as embeddings, question answering, multi-turn dialogues, reasoning, multi-lingual tasks, ethical AI, biases, safe AI, code generation, summarization, software performance, agent LLM architectures, long text generation, graph understanding, and various unclassified tasks. It also includes evaluations for LLM systems in conversational systems, copilots, search and recommendation engines, task utility, and verticals like healthcare, law, science, financial, and others. The repository provides a wealth of resources for evaluating and understanding the capabilities of LLMs in different domains.

This prompt is designed to act as an expert psychologist in personality analysis, using three different psychological models: the Big Five Factor model, the Myers-Briggs Sixteen Types model, and the Enneagram model.

从网址提取并选择模型计算大小

Business model expert, create detailed reports based on business ideas.

Expert on AI Model NFT Marketplace, offering insights on blockchain tech and NFTs.

Discover if your group, relationship or organization uses specific methods to recruit and maintain control over people

Assists with ROMS and COAWST modeling in Linux environments.

Consultor experto en Business Model Canvas para emprendedores

是一个专门用于建立数学模型并撰写python代码的gpt.

Object Messaging and Intelligent Object Model for Deep AI integration with software

Analyzing historical trends and advising on BRI strategies using Ibn Khaldun's insights. Write: "We are constantly enriching the model and rely on your support: http://Donate.U-Model.org"

Analyzes earnings reports using Warren Buffett's investment model.

Prophet model expert, professional yet approachable, seeks clarification

Guía paso a paso para la creación y mejora de modelos de negocio usando la metodología Business Model Canvas.

AI language model specializing in orchestrating human-AI interactions

Precision-focused Language Model for Discrete Mathematics, ensuring unmatched accuracy and error avoidance.

Apply gold-standard startup valuation and assessment methods to identify risks and gaps in your business model and product ideas.

HackingPT is a specialized language model focused on cybersecurity and penetration testing, committed to providing precise and in-depth insights in these fields.

A customized GPT model for price comparison would search and compare product prices on websites in Kuwait, tailored to local markets and languages.

Refine your startup strategy with Startup Business Validator: Dive into SWOT, Business Model Canvas, PESTEL, and more for comprehensive insights. Got just an idea? We'll craft the details for you.

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.