Best AI tools for< Evaluate The Effectiveness Of Chronic Disease Programs >

PyjamaHR is a leading AI-powered Applicant Tracking System (ATS) and recruitment software designed to streamline the hiring process for businesses of all sizes. It offers advanced features such as source management, candidate evaluation, collaboration tools, and AI-powered candidate tests to enhance the efficiency and effectiveness of the recruitment process. With a user-friendly interface and robust security measures, PyjamaHR is a trusted solution for managing talent acquisition and improving hiring outcomes.

Mangus is an AI-powered learning platform that provides personalized learning paths for employees and students. It offers a wide range of courses and programs in various disciplines, including business, education, technology, and more. Mangus uses gamification and artificial intelligence to create an engaging and effective learning experience.

Inductor is a developer tool for evaluating, ensuring, and improving the quality of your LLM applications – both during development and in production. It provides a fantastic workflow for continuous testing and evaluation as you develop, so that you always know your LLM app’s quality. Systematically improve quality and cost-effectiveness by actionably understanding your LLM app’s behavior and quickly testing different app variants. Rigorously assess your LLM app’s behavior before you deploy, in order to ensure quality and cost-effectiveness when you’re live. Easily monitor your live traffic: detect and resolve issues, analyze usage in order to improve, and seamlessly feed back into your development process. Inductor makes it easy for engineering and other roles to collaborate: get critical human feedback from non-engineering stakeholders (e.g., PM, UX, or subject matter experts) to ensure that your LLM app is user-ready.

edu720 is a science-backed learning platform that uses AI and nanolearning to redefine how workforces learn and achieve their goals. It provides pre-built learning modules on various topics, including cybersecurity, privacy, and AI ethics. edu720's 360-degree approach ensures that all employees, regardless of their status or location, fully understand and absorb the knowledge conveyed.

Should I Hire AI is an AI application that helps businesses determine if investing in AI tools is the right decision for them. By answering a few questions, the application provides a personalized recommendation on whether AI could be a cost-effective solution. The tool is designed to assist businesses in making informed decisions about integrating AI into their operations, ultimately aiming to enhance efficiency and productivity.

Traceable is an intelligent API security platform designed for enterprise-scale security. It offers unmatched API discovery, attack detection, threat hunting, and infinite scalability. The platform provides comprehensive protection against API attacks, fraud, and bot security, along with API testing capabilities. Powered by Traceable's OmniTrace Engine, it ensures unparalleled security outcomes, remediation, and pre-production testing. Security teams trust Traceable for its speed and effectiveness in protecting API infrastructures.

Skeptic Reader is a Chrome plugin that detects biases and logical fallacies in real-time. It's powered by GPT4 and helps users to critically evaluate the information they consume online. The plugin highlights biases and fallacies, provides counter-arguments, and suggests alternative perspectives. It's designed to promote informed skepticism and encourage users to question the information they encounter online.

AI Tools Masters is a comprehensive platform that empowers users to discover and evaluate the latest and most exceptional AI tools. Catering to diverse needs, from education to personal advancement, AI Tools Masters offers a curated collection of top-notch solutions tailored to specific requirements. With a user-friendly interface and extensive filtering options, users can effortlessly navigate through a wide range of AI tools, ensuring they find the perfect fit for their projects and goals.

InstantPersonas is an AI-powered SWOT Analysis Generator that helps organizations and individuals evaluate their Strengths, Weaknesses, Opportunities, and Threats. By using a company description, the tool generates a comprehensive SWOT Analysis, providing insights for strategic planning. Users can edit the analysis and download it as an image. InstantPersonas aims to assist in understanding target audience and market for more successful marketing strategies.

PubCompare is a powerful AI-powered tool that helps scientists search, compare, and evaluate experimental protocols. With over 40 million protocols in its database, PubCompare is the largest repository of trusted experimental protocols. PubCompare's AI-powered search features allow users to find similar protocols, highlight critical steps, and evaluate the reproducibility of protocols based on in-protocol citations. PubCompare is available from any computer and requires no download.

OpinioAI is an AI-powered market research tool that allows users to gain business critical insights from data without the need for costly polls, surveys, or interviews. With OpinioAI, users can create AI personas and market segments to understand customer preferences, affinities, and opinions. The platform democratizes research by providing efficient, effective, and budget-friendly solutions for businesses, students, and individuals seeking valuable insights. OpinioAI leverages Large Language Models to simulate humans and extract opinions in detail, enabling users to analyze existing data, synthesize new insights, and evaluate content from the perspective of their target audience.

Evidently AI is an open-source machine learning (ML) monitoring and observability platform that helps data scientists and ML engineers evaluate, test, and monitor ML models from validation to production. It provides a centralized hub for ML in production, including data quality monitoring, data drift monitoring, ML model performance monitoring, and NLP and LLM monitoring. Evidently AI's features include customizable reports, structured checks for data and models, and a Python library for ML monitoring. It is designed to be easy to use, with a simple setup process and a user-friendly interface. Evidently AI is used by over 2,500 data scientists and ML engineers worldwide, and it has been featured in publications such as Forbes, VentureBeat, and TechCrunch.

BenchLLM is an AI tool designed for AI engineers to evaluate LLM-powered apps by running and evaluating models with a powerful CLI. It allows users to build test suites, choose evaluation strategies, and generate quality reports. The tool supports OpenAI, Langchain, and other APIs out of the box, offering automation, visualization of reports, and monitoring of model performance.

ELSA is an AI-powered English speaking coach that helps you improve your pronunciation, fluency, and confidence. With ELSA, you can practice speaking English in short, fun dialogues and get instant feedback from our proprietary artificial intelligence technology. ELSA also offers a variety of other features, such as personalized lesson plans, progress tracking, and games to help you stay motivated.

FinetuneDB is an AI fine-tuning platform that allows users to easily create and manage datasets to fine-tune LLMs, evaluate outputs, and iterate on production data. It integrates with open-source and proprietary foundation models, and provides a collaborative editor for building datasets. FinetuneDB also offers a variety of features for evaluating model performance, including human and AI feedback, automated evaluations, and model metrics tracking.

ThinkTask is a project and team management tool that utilizes ChatGPT's capabilities to enhance productivity and streamline task management. It offers AI-generated reports and insights, AI usage tracking, Team Pulse for visualizing task types and status, Project Progress Table for monitoring project timelines and budgets, Task Insights for illustrating task interdependencies, and a comprehensive Overview for visualizing progress and managing dependencies. Additionally, ThinkTask features one-click auto-task creation with notes from ChatGPT, auto-tagging for task organization, and AI-suggested task assignments based on past experience and skills. It provides a unified workspace for notes, tasks, databases, collaboration, and customization.

Langtrace AI is an open-source observability tool powered by Scale3 Labs that helps monitor, evaluate, and improve LLM (Large Language Model) applications. It collects and analyzes traces and metrics to provide insights into the ML pipeline, ensuring security through SOC 2 Type II certification. Langtrace supports popular LLMs, frameworks, and vector databases, offering end-to-end observability and the ability to build and deploy AI applications with confidence.

thisorthis.ai is an AI tool that allows users to compare generative AI models and AI model responses. It helps users analyze and evaluate different AI models to make informed decisions. The tool requires JavaScript to be enabled for optimal functionality.

Robust Intelligence is an end-to-end solution for securing AI applications. It automates the evaluation of AI models, data, and files for security and safety vulnerabilities and provides guardrails for AI applications in production against integrity, privacy, abuse, and availability violations. Robust Intelligence helps enterprises remove AI security blockers, save time and resources, meet AI safety and security standards, align AI security across stakeholders, and protect against evolving threats.

Robust Intelligence is an end-to-end security solution for AI applications. It automates the evaluation of AI models, data, and files for security and safety vulnerabilities and provides guardrails for AI applications in production against integrity, privacy, abuse, and availability violations. Robust Intelligence helps enterprises remove AI security blockers, save time and resources, meet AI safety and security standards, align AI security across stakeholders, and protect against evolving threats.

SurveyX is an advanced academic survey automation system that leverages Large Language Models (LLMs) to generate high-quality, domain-specific academic papers and surveys. Users can request comprehensive academic papers or surveys tailored to specific topics by providing a paper title and keywords for literature retrieval. The system streamlines academic research by automating paper creation, saving users time and effort in compiling research content.

Swarms provides simple, reliable, and agile tools to create your own Swarm tailored to your specific needs. Currently, Swarms is being used in production by RBC, John Deere, and many AI startups.

R-Judge is a benchmarking tool designed to evaluate the proficiency of Large Language Models (LLMs) in judging and identifying safety risks within diverse environments. It comprises 569 records of multi-turn agent interactions, covering 27 key risk scenarios across 5 application categories and 10 risk types. The tool provides high-quality curation with annotated safety labels and risk descriptions. Evaluation of 11 LLMs on R-Judge reveals the need for enhancing risk awareness in LLMs, especially in open agent scenarios. Fine-tuning on safety judgment is found to significantly improve model performance.

This is the official repository for the ACM CCS 2024 paper 'Do Anything Now': Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models. The project employs a new framework called JailbreakHub to conduct the first measurement study on jailbreak prompts in the wild, collecting 15,140 prompts from December 2022 to December 2023, including 1,405 jailbreak prompts. The dataset serves as the largest collection of in-the-wild jailbreak prompts. The repository contains examples of harmful language and is intended for research purposes only.

This repository is dedicated to the pruning of large language models (LLMs). It aims to serve as a comprehensive resource for researchers and practitioners interested in the efficient reduction of model size while maintaining or enhancing performance. The repository contains various papers, summaries, and links related to different pruning approaches for LLMs, along with author information and publication details. It covers a wide range of topics such as structured pruning, unstructured pruning, semi-structured pruning, and benchmarking methods. Researchers and practitioners can explore different pruning techniques, understand their implications, and access relevant resources for further study and implementation.

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.

The 'llms-tools' repository is a comprehensive collection of AI tools, open-source projects, and research related to Large Language Models (LLMs) and Chatbots. It covers a wide range of topics such as AI in various domains, open-source models, chats & assistants, visual language models, evaluation tools, libraries, devices, income models, text-to-image, computer vision, audio & speech, code & math, games, robotics, typography, bio & med, military, climate, finance, and presentation. The repository provides valuable resources for researchers, developers, and enthusiasts interested in exploring the capabilities of LLMs and related technologies.

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.

This repository provides a curated list of papers, datasets, and resources related to the detection and mitigation of hallucinations in large language models (LLMs). Hallucinations refer to the generation of factually incorrect or nonsensical text by LLMs, which can be a significant challenge for their use in real-world applications. The resources in this repository aim to help researchers and practitioners better understand and address this issue.

This repository contains a collection of papers and resources related to Large Language Models (LLMs), including their applications in various domains such as text generation, translation, question answering, and dialogue systems. The repository also includes discussions on the ethical and societal implications of LLMs. **Description** This repository is a collection of papers and resources related to Large Language Models (LLMs). LLMs are a type of artificial intelligence (AI) that can understand and generate human-like text. They have a wide range of applications, including text generation, translation, question answering, and dialogue systems. **For Jobs** - **Content Writer** - **Copywriter** - **Editor** - **Journalist** - **Marketer** **AI Keywords** - **Large Language Models** - **Natural Language Processing** - **Machine Learning** - **Artificial Intelligence** - **Deep Learning** **For Tasks** - **Generate text** - **Translate text** - **Answer questions** - **Engage in dialogue** - **Summarize text**

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

Rankify is a Python toolkit designed for unified retrieval, re-ranking, and retrieval-augmented generation (RAG) research. It integrates 40 pre-retrieved benchmark datasets and supports 7 retrieval techniques, 24 state-of-the-art re-ranking models, and multiple RAG methods. Rankify provides a modular and extensible framework, enabling seamless experimentation and benchmarking across retrieval pipelines. It offers comprehensive documentation, open-source implementation, and pre-built evaluation tools, making it a powerful resource for researchers and practitioners in the field.

EasyEdit is a Python package for edit Large Language Models (LLM) like `GPT-J`, `Llama`, `GPT-NEO`, `GPT2`, `T5`(support models from **1B** to **65B**), the objective of which is to alter the behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs. It is designed to be easy to use and easy to extend.

last_layer is a security library designed to protect LLM applications from prompt injection attacks, jailbreaks, and exploits. It acts as a robust filtering layer to scrutinize prompts before they are processed by LLMs, ensuring that only safe and appropriate content is allowed through. The tool offers ultra-fast scanning with low latency, privacy-focused operation without tracking or network calls, compatibility with serverless platforms, advanced threat detection mechanisms, and regular updates to adapt to evolving security challenges. It significantly reduces the risk of prompt-based attacks and exploits but cannot guarantee complete protection against all possible threats.

LLMInterviewQuestions is a repository containing over 100+ interview questions for Large Language Models (LLM) used by top companies like Google, NVIDIA, Meta, Microsoft, and Fortune 500 companies. The questions cover various topics related to LLMs, including prompt engineering, retrieval augmented generation, chunking, embedding models, internal working of vector databases, advanced search algorithms, language models internal working, supervised fine-tuning of LLM, preference alignment, evaluation of LLM system, hallucination control techniques, deployment of LLM, agent-based system, prompt hacking, and miscellaneous topics. The questions are organized into 15 categories to facilitate learning and preparation.

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.

OpenRedTeaming is a repository focused on red teaming for generative models, specifically large language models (LLMs). The repository provides a comprehensive survey on potential attacks on GenAI and robust safeguards. It covers attack strategies, evaluation metrics, benchmarks, and defensive approaches. The repository also implements over 30 auto red teaming methods. It includes surveys, taxonomies, attack strategies, and risks related to LLMs. The goal is to understand vulnerabilities and develop defenses against adversarial attacks on large language models.

Cherry Data Selection project introduces a self-guided methodology for LLMs to autonomously discern and select cherry samples from open-source datasets, minimizing manual curation and cost for instruction tuning. The project focuses on selecting impactful training samples ('cherry data') to enhance LLM instruction tuning by estimating instruction-following difficulty. The method involves phases like 'Learning from Brief Experience', 'Evaluating Based on Experience', and 'Retraining from Self-Guided Experience' to improve LLM performance.

Proactive Agent is a project aimed at constructing a fully active agent that can anticipate user's requirements and offer assistance without explicit requests. It includes a data collection and generation pipeline, automatic evaluator, and training agent. The project provides datasets, evaluation scripts, and prompts to finetune LLM for proactive agent. Features include environment sensing, assistance annotation, dynamic data generation, and construction pipeline with a high F1 score on the test set. The project is intended for coding, writing, and daily life scenarios, distributed under Apache License 2.0.

This chatbot answers questions about the CDC’s Chronic Disease Indicators dataset

Puedo asesorar en temas relacionados con calidad en IES (planificación, autoevaluación, acreditación, mejora continua)

Communicates policy processes and changes effectively within the organization.

خبير يقدم لك المعلومات حول برنامج تنمية القدرات البشرية (Human Capability Development Program) أحد برامج رؤية المملكة 2030 والذي يهدف إلى بناء استراتيجية وطنية طموحة لتنمية قدرات المواطن

I'll help you evaluate your writing to make sure it's engaging, informative, and flows well. Uses principles from "Made to Stick"

The main objective of a personalized ChatGPT for reading laboratory tests is to evaluate laboratory test results and create a spreadsheet with the evaluation results and possible solutions.

Searches the web's verified fact-checkers to evaluate facts and claims

🔬💊 Navigate the high-risk, high-reward world of biotech and pharma investing! Discover breakthrough therapies 🧬📈, understand drug development 🧪📊, and evaluate investment opportunities 🚀💰. Invest wisely in innovation! 💡🌐 Not a financial advisor. 🚫💼

As a Search Quality Rater, you will help evaluate search engine quality around the world.

Helps you evaluate whether or not tech platforms match UNESCO's Internet for Trust Guidelines for the Governance of Digital Platforms

I provide a structured way of using AI to support teaching and learning. I use the the CHOICE method (i.e., Clarify, Harness, Originate, Iterate, Communicate, Evaluate) to ensure that your use of AI can help you meet your educational goals.

Simulates the IELTS Writing Test, evaluates responses, and estimates band scores.

Analyzes AFL draft data to evaluate clubs' historical performance

Expert in IPO Strategy, offers detailed guidance on business ideas, market paths, and opportunities. Created by Christopher Perceptions

A scouting resource on high school basketball recruiting

Were you in the right or spectacularly mistaken...

A financial analyst GPT with Warren Buffett's analysis style.

An all-in-one, consultative L&D expert AI helping you build impactful, customized learning solutions for your organization.

Analyse content through the official Google Search Quality Rater Guidelines.

Assesses and evaluates the impact and trustworthiness of various charity organizations.