Best AI tools for< Evaluate Decisions >

Harver is an AI-powered platform that specializes in predictive assessments and automated solutions for talent acquisition. The platform helps companies make better hiring decisions by prioritizing potential over past experiences, leading to impactful results. Harver offers a range of assessments, including personality questionnaires, cognitive ability testing, situational judgment tests, and more. By leveraging data-driven insights and intelligent automation, Harver streamlines the hiring process, reduces bias, and improves the quality of hires. The platform is designed to enhance the candidate experience, optimize sourcing investments, and provide actionable business insights for informed decision-making.

ZestyAI is an artificial intelligence tool that helps users make brilliant climate and property risk decisions. The tool uses AI to provide insights on property values and risk exposure to natural disasters. It offers products such as Property Insights, Digital Roof, Roof Age, Location Insights, and Climate Risk Models to evaluate and understand property risks. ZestyAI is trusted by top insurers in North America and aims to bring a ten times return on investment to its customers.

Joinery is an AI-powered recruitment platform that combines AI hiring with human decisions to streamline the hiring process. It offers comprehensive hiring tools, including candidate summary cards, Culture & Fit Scores, and an AI Hiring Assistant, to help companies make informed hiring decisions. Joinery aims to remove bias, increase efficiency, and promote diversity and inclusion in recruitment. The platform automates resume screening, engages candidates with personalized dialogues, and provides objective assessments of culture and skill fit. Joinery enhances transparency, engagement, and communication throughout the hiring process, ensuring a positive experience for both candidates and hiring teams.

JudgeAI is an AI tool designed to assist users in making judgments or decisions. It utilizes artificial intelligence algorithms to analyze data and provide insights. The tool helps users in evaluating information and reaching conclusions based on the input data. JudgeAI aims to streamline decision-making processes and enhance accuracy by leveraging AI technology.

Monexa is a professional-grade financial analysis platform that offers institutional-grade market insights, news, and data analysis in one powerful platform. It provides comprehensive market analysis, AI-powered insights, rich data visualizations, research and analysis tools, advanced screener, rich financial history, automated SWOT analysis, comprehensive reports, interactive performance analytics, institutional investment tracking, company intelligence, dividend analysis, earnings call analysis, portfolio analytics, strategy explorer, real-time market intelligence, and more. Monexa is designed to help users make data-driven investment decisions with a comprehensive suite of analytical capabilities.

Ergodic is an AI tool called Kepler that enables data-driven decisions for businesses. Kepler acts as an AI action engine, bridging the knowledge gap between business context and data to help optimize processes, identify opportunities, and mitigate risks. It goes beyond number crunching to build a digital version of the business, allowing users to create scenarios and evaluate outcomes. Kepler focuses on taking action directly, without the need for complex dashboards, providing insights on what needs to be done, why, and the potential outcomes. Ergodic aims to empower businesses with AI-driven solutions for strategic decision-making.

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.

AnalyStock.ai is a financial application leveraging AI to provide users with a next-generation investment toolbox. It helps users better understand businesses, risks, and make informed investment decisions. The platform offers direct access to the stock market, powerful data-driven tools to build top-ranking portfolios, and insights into company valuations and growth prospects. AnalyStock.ai aims to optimize the investment process, offering a reliable strategy with factors like A-Score, factor investing scores for value, growth, quality, volatility, momentum, and yield. Users can discover hidden gems, fine-tune filters, access company scorecards, perform activity analysis, understand industry dynamics, evaluate capital structure, profitability, and peers' valuation. The application also provides adjustable DCF valuation, portfolio management tools, net asset value computation, monthly commentary, and an AI assistant for personalized insights and assistance.

Borea AI is an AI application that provides stock price predictions and stock ratings based on past market behavior and historical stock performance. It empowers users to unlock intelligent financial mastery by offering insights on popular stocks, market leaders, index ETFs, top movers, most tweeted stocks, and best-performing predictions. Borea AI serves as a personal financial assistant, but it is important to note that past performance is not an indicator of future results, and professional investment advice should not be substituted.

Betterworks is an intelligent performance management platform that simplifies performance management, fosters greater manager effectiveness, higher employee engagement, and intelligent decision-making for HR leaders and organizations. It offers features such as AI for HR analytics & insights, integrations, accessibility, security, and manager effectiveness. Betterworks is designed to help organizations align their workforce around strategic priorities, drive collaboration, and continuously improve performance.

EarningsCall.ai is an AI-powered platform that provides stock earnings call summaries and insights to help users turn earnings calls into a competitive advantage. The platform allows users to compare competitors, analyze trends, and make smarter business and investment decisions without the need to read full transcripts. EarningsCall.ai offers instant competitive benchmarking, market trend analysis, and use cases for business leaders, financial professionals, consultants, and advisors. The platform is designed to save time and provide valuable insights for corporate strategy, investment decisions, and financial research.

RevSure is an AI-powered platform designed for high-growth marketing teams to optimize marketing ROI and attribution. It offers full-funnel attribution, deep funnel optimization, predictive insights, and campaign performance tracking. The platform integrates with various data sources to provide unified funnel reporting and personalized recommendations for improving pipeline health and conversion rates. RevSure's AI engine powers features like campaign spend reallocation, next-best touch analysis, and journey timeline construction, enabling users to make data-driven decisions and accelerate revenue growth.

RoundOneAI is an AI-driven platform revolutionizing tech recruitment by offering unbiased and efficient candidate assessments, ensuring skill-based evaluations free from demographic biases. The platform streamlines the hiring process with tailored job descriptions, AI-powered interviews, and insightful analytics. RoundOneAI helps companies evaluate candidates simultaneously, make informed hiring decisions, and identify top talent efficiently.

Kerplunk is an AI-powered video interviewing tool designed to streamline the recruitment process. It leverages artificial intelligence to analyze candidate responses, body language, and facial expressions, providing valuable insights to recruiters. With Kerplunk, organizations can conduct remote interviews efficiently and make data-driven hiring decisions. The platform offers a user-friendly interface and customizable features to meet the unique needs of each organization.

Skyline AI is an AI tool that specializes in the analysis of commercial real estate properties. It offers a platform for faster and more comprehensive evaluation of real estate investments. The tool leverages artificial intelligence to provide state-of-the-art updates on real estate and technology, enabling users to make informed decisions in the real estate sector.

Skeptic Reader is a Chrome plugin that helps users detect bias and logical fallacies in real-time while browsing the internet. It uses GPT-4 technology to identify potential biases and fallacies in news articles, social media posts, and other online content. The plugin provides users with counter-arguments and suggestions for further research, helping them to make more informed decisions about the information they consume. Skeptic Reader is designed to promote critical thinking and media literacy, and it is a valuable tool for anyone who wants to navigate the online world with a more discerning eye.

WellTrade.ai is an AI-powered financial advisor tool that leverages artificial intelligence to provide clear, actionable, and data-driven investment recommendations for stocks and ETFs. It simplifies the investment process by analyzing comprehensive financial data and offering insights to help users make informed decisions. The tool aims to assist investors in navigating the complexities of stock and ETF investments by providing valuable AI-driven insights.

Sourcer AI is an AI-powered fact-checking tool that provides real-time assessments of source credibility and bias in online information. It revolutionizes the evaluation process by using cutting-edge artificial intelligence to uncover reputability ratings and political biases of online sources, helping users combat misinformation and make informed decisions.

SQOR is a plug-n-play AI tool designed for C-Level Executives to make stress-free decision-making in business intelligence. It provides a zero-code BI solution, offering KPIs at your fingertips without the need for expert knowledge. The platform enables users to access and share business intelligence data from various SaaS tools, facilitating collaboration and informed decision-making across the organization. SQOR's unique Execution Score Algorithm evaluates execution health at different levels, ensuring stakeholders are empowered with actionable insights.

AI Interviewer Pro is an AI-driven interviewing platform that helps businesses streamline their hiring process. With AI Interviewer Pro, businesses can automate the interview process, reduce bias, and make more informed hiring decisions. AI Interviewer Pro uses AI to assess candidates' skills, personality, and fit for a given role. The platform also provides businesses with insights into candidates' strengths and weaknesses, which can help them make better hiring decisions. AI Interviewer Pro is easy to use and integrates with most job application pages. Businesses can get started with AI Interviewer Pro for free and there are no long-term contracts.

OpenAdapt is an open-source software adapter between Large Multimodal Models (LMMs) and traditional desktop and web Graphical User Interfaces (GUIs). It aims to automate repetitive GUI workflows by leveraging the power of LMMs. OpenAdapt records user input and screenshots, converts them into tokenized format, and generates synthetic input via transformer model completions. It also analyzes recordings to generate task trees and replay synthetic input to complete tasks. OpenAdapt is model agnostic and generates prompts automatically by learning from human demonstration, ensuring that agents are grounded in existing processes and mitigating hallucinations. It works with all types of desktop GUIs, including virtualized and web, and is open source under the MIT license.

llm-colosseum is a tool designed to evaluate Language Model Models (LLMs) in real-time by making them fight each other in Street Fighter III. The tool assesses LLMs based on speed, strategic thinking, adaptability, out-of-the-box thinking, and resilience. It provides a benchmark for LLMs to understand their environment and take context-based actions. Users can analyze the performance of different LLMs through ELO rankings and win rate matrices. The tool allows users to run experiments, test different LLM models, and customize prompts for LLM interactions. It offers installation instructions, test mode options, logging configurations, and the ability to run the tool with local models. Users can also contribute their own LLM models for evaluation and ranking.

Seismometer is a suite of tools designed to evaluate AI model performance in healthcare settings. It helps healthcare organizations assess the accuracy of AI models and ensure equitable care for diverse patient populations. The tool allows users to validate model performance using standardized evaluation criteria based on local data and workflows. It includes templates for analyzing statistical performance, fairness across different cohorts, and the impact of interventions on outcomes. Seismometer is continuously evolving to incorporate new validation and analysis techniques.

This repository compiles a list of academic papers that evaluate, align, simulate, and provide surveys or perspectives on the use of Large Language Models (LLMs) in the field of Social Science. The papers cover various aspects of LLM research, including assessing their alignment with human values, evaluating their capabilities in tasks such as opinion formation and moral reasoning, and exploring their potential for simulating social interactions and addressing issues in diverse fields of Social Science. The repository aims to provide a comprehensive resource for researchers and practitioners interested in the intersection of LLMs and Social Science.

pytest-evals is a minimalistic pytest plugin designed to help evaluate the performance of Language Model (LLM) outputs against test cases. It allows users to test and evaluate LLM prompts against multiple cases, track metrics, and integrate easily with pytest, Jupyter notebooks, and CI/CD pipelines. Users can scale up by running tests in parallel with pytest-xdist and asynchronously with pytest-asyncio. The tool focuses on simplifying evaluation processes without the need for complex frameworks, keeping tests and evaluations together, and emphasizing logic over infrastructure.

Large Language Models Play StarCraft II is a project that explores the capabilities of large language models (LLMs) in playing the game StarCraft II. The project introduces TextStarCraft II, a textual environment for the game, and a Chain of Summarization method for analyzing game information and making strategic decisions. Through experiments, the project demonstrates that LLM agents can defeat the built-in AI at a challenging difficulty level. The project provides benchmarks and a summarization approach to enhance strategic planning and interpretability in StarCraft II gameplay.

The 'awesome-deliberative-prompting' repository focuses on how to ask Large Language Models (LLMs) to produce reliable reasoning and make reason-responsive decisions through deliberative prompting. It includes success stories, prompting patterns and strategies, multi-agent deliberation, reflection and meta-cognition, text generation techniques, self-correction methods, reasoning analytics, limitations, failures, puzzles, datasets, tools, and other resources related to deliberative prompting. The repository provides a comprehensive overview of research, techniques, and tools for enhancing reasoning capabilities of LLMs.

AgentKit is a framework for constructing complex human thought processes from simple natural language prompts. It offers a unified way to represent and execute these processes as graphs, making it easy to design and tune agents without any programming experience. AgentKit can be used for a variety of tasks, including generating text, answering questions, and making decisions.

This practical free hands-on course focuses on Large Language models and their applications, providing a hands-on experience using models from OpenAI and the Hugging Face library. The course is divided into three major sections: Techniques and Libraries, Projects, and Enterprise Solutions. It covers topics such as Chatbots, Code Generation, Vector databases, LangChain, Fine Tuning, PEFT Fine Tuning, Soft Prompt tuning, LoRA, QLoRA, Evaluate Models, Knowledge Distillation, and more. Each section contains chapters with lessons supported by notebooks and articles. The course aims to help users build projects and explore enterprise solutions using Large Language Models.

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

The Intelligence Toolkit is a suite of interactive workflows designed to help domain experts make sense of real-world data by identifying patterns, themes, relationships, and risks within complex datasets. It utilizes generative AI (GPT models) to create reports on findings of interest. The toolkit supports analysis of case, entity, and text data, providing various interactive workflows for different intelligence tasks. Users are expected to evaluate the quality of data insights and AI interpretations before taking action. The system is designed for moderate-sized datasets and responsible use of personal case data. It uses the GPT-4 model from OpenAI or Azure OpenAI APIs for generating reports and insights.

hackingBuddyGPT is a framework for testing LLM-based agents for security testing. It aims to create common ground truth by creating common security testbeds and benchmarks, evaluating multiple LLMs and techniques against those, and publishing prototypes and findings as open-source/open-access reports. The initial focus is on evaluating the efficiency of LLMs for Linux privilege escalation attacks, but the framework is being expanded to evaluate the use of LLMs for web penetration-testing and web API testing. hackingBuddyGPT is released as open-source to level the playing field for blue teams against APTs that have access to more sophisticated resources.

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.

Qlib is an open-source, AI-oriented quantitative investment platform that supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning. It covers the entire chain of quantitative investment, from alpha seeking to order execution. The platform empowers researchers to explore ideas and implement productions using AI technologies in quantitative investment. Qlib collaboratively solves key challenges in quantitative investment by releasing state-of-the-art research works in various paradigms. It provides a full ML pipeline for data processing, model training, and back-testing, enabling users to perform tasks such as forecasting market patterns, adapting to market dynamics, and modeling continuous investment decisions.

The AI Enablement Stack is a curated collection of venture-backed companies, tools, and technologies that enable developers to build, deploy, and manage AI applications. It provides a structured view of the AI development ecosystem across five key layers: Agent Consumer Layer, Observability and Governance Layer, Engineering Layer, Intelligence Layer, and Infrastructure Layer. Each layer focuses on specific aspects of AI development, from end-user interaction to model training and deployment. The stack aims to help developers find the right tools for building AI applications faster and more efficiently, assist engineering leaders in making informed decisions about AI infrastructure and tooling, and help organizations understand the AI development landscape to plan technology adoption.

GuideLLM is a powerful tool for evaluating and optimizing the deployment of large language models (LLMs). By simulating real-world inference workloads, GuideLLM helps users gauge the performance, resource needs, and cost implications of deploying LLMs on various hardware configurations. This approach ensures efficient, scalable, and cost-effective LLM inference serving while maintaining high service quality. Key features include performance evaluation, resource optimization, cost estimation, and scalability testing.

TinyTroupe is an experimental Python library that leverages Large Language Models (LLMs) to simulate artificial agents called TinyPersons with specific personalities, interests, and goals in simulated environments. The focus is on understanding human behavior through convincing interactions and customizable personas for various applications like advertisement evaluation, software testing, data generation, project management, and brainstorming. The tool aims to enhance human imagination and provide insights for better decision-making in business and productivity scenarios.

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.

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.

LLMOps with Prompt flow is a template and guidance for building LLM-infused apps using Prompt flow. It provides centralized code hosting, lifecycle management, variant and hyperparameter experimentation, A/B deployment, many-to-many dataset/flow relationships, multiple deployment targets, comprehensive reporting, BYOF capabilities, configuration-based development, local prompt experimentation and evaluation, endpoint testing, and optional Human-in-loop validation. The tool is customizable to suit various application needs.

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Expert in applying game theory to real-world strategic decisions

Analyzes crowdfunding ventures for market potential and business viability, aiding investment decisions. by neuralvault

Guides B2B startups in a structured customer segment evaluation process. Stop guessing! Ideate, Evaluate & Make data-driven decision.

I simulate various businesses, guiding users through realistic scenarios. Make decisions, see their impact, and learn about business dynamics. Engaging and educational for aspiring entrepreneurs and business enthusiasts.

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

A decisive entity that makes choices and explains reasoning.

A professional assistant for technical item evaluations in banking

「痛みを経験する前に考える」または「痛みを経験してから考える」を分析する

A discerning VC evaluating web3 pitches

Scientific expert in evaluating articles using ROBINS-I and Cochrane tools

Guides users through understanding cognitive biases and fallacies

Help you to think critical.

Multilingual analytical critic.

A scouting resource on high school basketball recruiting

Helps with valuation and evaluation of your patent

A gpt assistant that helps with analyzing a startup/market. The answers you get back is already structured to give you the core elements you would want to see in an investment memo/ market analysis

Innovation guide with a customer focus.

Faced with an opportunity in life? Let's work out the cost of not taking it.

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