Best AI tools for< Behavioral Scientist >

Interview Igniter is an AI-powered platform that provides job seekers with a robust interview simulation to fine-tune their skills, adapt to their learning curve, and get detailed feedback. It offers a comprehensive question bank, including industry-specific questions and actual interview questions asked by leading tech companies like Google, Facebook, Apple, and Amazon. Interview Igniter also provides a coding interview tool for practicing and improving coding skills, with interactive guidance and tailored learning experiences. The platform utilizes Conversation Intelligence tools for analyzing communication in real-time and providing nuanced feedback. Interview Igniter was created by Vidal Graupera, a former engineering manager at LinkedIn and Uber with over 20 years of experience hiring.

Interview.study is an AI-powered interview preparation platform that helps candidates practice real interview questions asked by top companies. The platform provides users with instant feedback on their responses, helping them identify areas for improvement and develop stronger answers. Interview.study also offers a variety of features to help candidates prepare for their interviews, including a database of interview questions, a mock interview tool, and a resume builder.

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.

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.

CogPrints is an electronic archive for self-archived papers in any area of Psychology, Neuroscience, and Linguistics, and many areas of Computer Science (e.g., artificial intelligence, robotics, vision, learning, speech, neural networks), Philosophy (e.g., mind, language, knowledge, science, logic), Biology (e.g., ethology, behavioral ecology, sociobiology, behavior genetics, evolutionary theory), Medicine (e.g., Psychiatry, Neurology, human genetics, Imaging), Anthropology (e.g., primatology, cognitive ethnology, archeology, paleontology), as well as any other portions of the physical, social and mathematical sciences that are pertinent to the study of cognition.

Zenus AI is a behavioral analytics tool for events and retail, offering facial analysis and custom solutions for event organizers, retail brands, and exhibitors. The tool provides insights such as demographics, sentiment analysis, and behavioral tracking with 95% accuracy without collecting personal data. It helps businesses understand consumers, attract more exhibitors, and improve visitor experience through AI-powered solutions.

CEBRA is a machine-learning method that compresses time series data to reveal hidden structures in the variability of the data. It excels in analyzing behavioral and neural data simultaneously, decoding activity from the visual cortex of the mouse brain to reconstruct viewed videos. CEBRA fills the gap by leveraging joint behavior and neural data to uncover neural dynamics, providing consistent and high-performance latent spaces for hypothesis testing or label-free analysis across sensory and motor tasks.

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.

Responsible AI Licenses (RAIL) is an initiative that empowers developers to restrict the use of their AI technology to prevent irresponsible and harmful applications. They provide licenses with behavioral-use clauses to control specific use-cases and prevent misuse of AI artifacts. The organization aims to standardize RAIL Licenses, develop collaboration tools, and educate developers on responsible AI practices.

Roundtable is an AI-assisted data cleaning tool that improves market research data quality by leveraging human-in-the-loop AI technology. It offers an easy-to-integrate API for cleaning open-ended survey responses, saving users up to 70% of their time. The tool helps in detecting unnatural typing, programmatic entries, and bots, while also providing multilingual functionality for global market deployment. Roundtable is trusted by leaders in over 25 global markets for its efficiency in improving data quality and reducing manual work.

Nektar is an AI-driven GTM automation platform that offers comprehensive control over customer data synchronization, including contacts, opportunity contact roles, GTM activities, and activity insights. It helps in matching sales processes and security needs efficiently. Trusted by high-performing global revenue teams, Nektar enables users to build more pipeline, win deals faster, and renew and expand customers. The platform leverages AI to transform buyer data at scale, providing visibility into buying groups, meeting quality, and contact roles. Nektar is designed to enhance customer success journeys, drive better renewal outcomes, and improve pipeline inspection using high-quality engagement data.

Neurons is a platform that uses AI to predict consumer responses and behavior. It offers a variety of solutions for businesses, including marketing agencies, designers, and e-commerce companies. Neurons' AI-powered tools can help businesses optimize their marketing campaigns, improve their product design, and better understand their customers.

Usermaven is a simple yet powerful website and product analytics tool that offers accurate tracking, comprehensive suite of analytics tools, real-time insights, and AI-powered data analysis. It eliminates the need for coding, provides privacy-friendly analytics, and is trusted by leading brands and agencies. Usermaven empowers marketers and product professionals to gain in-depth insights, understand user behavior, optimize marketing and product strategies, and make data-driven decisions for business success.

Pixis is a codeless AI infrastructure designed for growth marketing, offering purpose-built AI solutions to scale demand generation. The platform leverages transparent AI infrastructure to optimize campaign results across platforms, with features such as targeting AI, creative AI, and performance AI. Pixis helps reduce customer acquisition cost, generate creative assets quickly, refine audience targeting, and deliver contextual communication in real-time. The platform also provides an AI savings calculator to estimate the returns from leveraging its codeless AI infrastructure for marketing. With success stories showcasing significant improvements in various marketing metrics, Pixis aims to empower businesses to unlock the capabilities of AI for enhanced performance and results.

Nuro is an autonomous technology company focused on revolutionizing mobility through robotics and AI. They offer cutting-edge AI-first autonomy solutions for automotive and mobility applications, including robotaxis and autonomous vehicles. Nuro's state-of-the-art AV technology, Nuro Driver™, is designed to drive safely and naturally on all roads using groundbreaking AI-first autonomy. The company prioritizes safety in all aspects of its operations, from hardware and software to testing and systems engineering. With 8 years of autonomy innovation, Nuro aims to transform the way goods and people move by empowering fleets with AI-first autonomous capabilities.

DLabs.AI is an AI software development company that specializes in harnessing the power of artificial intelligence to help businesses grow and operate more efficiently. They offer a range of AI solutions tailored to various industries, such as marketing, sales, customer service, retail, healthcare, education, finance, and manufacturing. With expertise in data science, machine learning, natural language processing, and neural networks, DLabs.AI provides unique AI applications that optimize workflows, automate processes, and enhance decision-making. Their focus on data security, personalized solutions, and continuous support sets them apart as a trusted partner in the AI industry.

Kumo is an AI-powered platform that helps businesses personalize customer experiences, acquire new customers, understand customer behavior, improve planning and monitoring, resolve data inconsistencies, fight fraud and abuse, detect money laundering, and empower data scientists with advanced techniques. It offers cutting-edge solutions for various AI and machine learning tasks, such as predictive modeling, anomaly detection, entity resolution, and graph embeddings. Kumo's capabilities are designed to enhance customer interactions, optimize marketing campaigns, and provide valuable insights for businesses across different industries.

QeDatalab is a leading data science consulting and AI company offering a wide range of services such as software consulting, generative AI consulting, artificial intelligence services, cloud enablement & automation, AI-driven mobile app development, IoT & IIoT data consulting, digital services, AI product development, MLOps consulting, and more. The company specializes in providing AI-powered solutions for industries like healthcare, manufacturing, retail, and education, helping businesses leverage data for informed strategic decision-making and accurate predictions. QeDatalab's team of experts offers end-to-end services, customized solutions, and a trusted partnership to ensure client success.

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.

Retorio is a cutting-edge Behavioral Intelligence (BI) Platform that fuses machine learning with scientific findings from psychology and organizational research to ultimately take learning and development to a new level within organizations. At the core of Retorio’s capabilities are its AI-powered immersive video simulations. Through these engaging role-plays, learners using Retorio get to train and develop the necessary skills through realistic scenarios. Furthermore, the personalized, on-demand feedback learners receive allows for immediate behavior change and performance improvement. Retorio’s training platform transcends the limitation of scalability and redefines how individuals and teams train and develop, bringing talent development to a new dimension.

LLMs4TS is a repository focused on the application of cutting-edge AI technologies for time-series analysis. It covers advanced topics such as self-supervised learning, Graph Neural Networks for Time Series, Large Language Models for Time Series, Diffusion models, Mixture-of-Experts architectures, and Mamba models. The resources in this repository span various domains like healthcare, finance, and traffic, offering tutorials, courses, and workshops from prestigious conferences. Whether you're a professional, data scientist, or researcher, the tools and techniques in this repository can enhance your time-series data analysis capabilities.

Data-to-paper is an AI-driven framework designed to guide users through the process of conducting end-to-end scientific research, starting from raw data to the creation of comprehensive and human-verifiable research papers. The framework leverages a combination of LLM and rule-based agents to assist in tasks such as hypothesis generation, literature search, data analysis, result interpretation, and paper writing. It aims to accelerate research while maintaining key scientific values like transparency, traceability, and verifiability. The framework is field-agnostic, supports both open-goal and fixed-goal research, creates data-chained manuscripts, involves human-in-the-loop interaction, and allows for transparent replay of the research process.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

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.

AI-Powered Search provides code examples for the book 'AI-Powered Search' by Trey Grainger, Doug Turnbull, and Max Irwin. The book teaches modern machine learning techniques for building search engines that continuously learn from users and content to deliver more intelligent and domain-aware search experiences. It covers semantic search, retrieval augmented generation, question answering, summarization, fine-tuning transformer-based models, personalized search, machine-learned ranking, click models, and more. The code examples are in Python, leveraging PySpark for data processing and Apache Solr as the default search engine. The repository is open source under the Apache License, Version 2.0.

LLMsForTimeSeries is a repository that questions the usefulness of language models in time series forecasting. The work shows that simple baselines outperform most language model-based time series forecasting models. It includes ablation studies on LLM-based TSF methods and introduces the PAttn method, showcasing the performance of patching and attention structures in forecasting. The repository provides datasets, setup instructions, and scripts for running ablations on different datasets.

Superlinked is a compute framework for information retrieval and feature engineering systems, focusing on converting complex data into vector embeddings for RAG, Search, RecSys, and Analytics stack integration. It enables custom model performance in machine learning with pre-trained model convenience. The tool allows users to build multimodal vectors, define weights at query time, and avoid postprocessing & rerank requirements. Users can explore the computational model through simple scripts and python notebooks, with a future release planned for production usage with built-in data infra and vector database integrations.

LLM-Synthetic-Data is a repository focused on real-time, fine-grained LLM-Synthetic-Data generation. It includes methods, surveys, and application areas related to synthetic data for language models. The repository covers topics like pre-training, instruction tuning, model collapse, LLM benchmarking, evaluation, and distillation. It also explores application areas such as mathematical reasoning, code generation, text-to-SQL, alignment, reward modeling, long context, weak-to-strong generalization, agent and tool use, vision and language, factuality, federated learning, generative design, and safety.

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 repository contains code for grounding large language models' knowledge in BabyAI-Text using the GLAM method. It includes the BabyAI-Text environment, code for experiments, and training agents. The repository is structured with folders for the environment, experiments, agents, configurations, SLURM scripts, and training scripts. Installation steps involve creating a conda environment, installing PyTorch, required packages, BabyAI-Text, and Lamorel. The launch process involves using Lamorel with configs and training scripts. Users can train a language model and evaluate performance on test episodes using provided scripts and config entries.

Srcbook is an open-source interactive programming environment for TypeScript that allows users to create, run, and share reproducible programs and ideas. It features AI capabilities for exploring and iterating on ideas, supports exporting to valid markdown format, and enables diagraming with mermaid for rich annotations. Users can locally execute programs through a web interface, powered by Node.js under the Apache2 license.

This repository serves as a comprehensive hub for updates on generative AI research, interview materials, notebooks, and more. It includes monthly best GenAI papers list, interview resources, free courses, and code repositories/notebooks for developing generative AI applications. The repository is regularly updated with the latest additions to keep users informed and engaged in the field of generative AI.

MATLAB-Simulink-Challenge-Project-Hub is a repository aimed at contributing to the progress of engineering and science by providing challenge projects with real industry relevance and societal impact. The repository offers a wide range of projects covering various technology trends such as Artificial Intelligence, Autonomous Vehicles, Big Data, Computer Vision, and Sustainability. Participants can gain practical skills with MATLAB and Simulink while making a significant contribution to science and engineering. The projects are designed to enhance expertise in areas like Sustainability and Renewable Energy, Control, Modeling and Simulation, Machine Learning, and Robotics. By participating in these projects, individuals can receive official recognition for their problem-solving skills from technology leaders at MathWorks and earn rewards upon project completion.

Mo AI Studio is an enterprise-level AI agent running platform that enables the operation of customized intelligent AI agents with system-level capabilities. It supports various IDEs and programming languages, allows modification of multiple files with reasoning, cross-project context modifications, customizable agents, system-level file operations, document writing, question answering, knowledge sharing, and flexible output processors. The platform also offers various setters and a custom component publishing feature. Mo AI Studio is a fusion of artificial intelligence and human creativity, designed to bring unprecedented efficiency and innovation to enterprises.

SeaLLMs are a family of language models optimized for Southeast Asian (SEA) languages. They were pre-trained from Llama-2, on a tailored publicly-available dataset, which comprises texts in Vietnamese 🇻🇳, Indonesian 🇮🇩, Thai 🇹🇭, Malay 🇲🇾, Khmer🇰🇭, Lao🇱🇦, Tagalog🇵🇭 and Burmese🇲🇲. The SeaLLM-chat underwent supervised finetuning (SFT) and specialized self-preferencing DPO using a mix of public instruction data and a small number of queries used by SEA language native speakers in natural settings, which **adapt to the local cultural norms, customs, styles and laws in these areas**. SeaLLM-13b models exhibit superior performance across a wide spectrum of linguistic tasks and assistant-style instruction-following capabilities relative to comparable open-source models. Moreover, they outperform **ChatGPT-3.5** in non-Latin languages, such as Thai, Khmer, Lao, and Burmese.

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.

AgentGym is a framework designed to help the AI community evaluate and develop generally-capable Large Language Model-based agents. It features diverse interactive environments and tasks with real-time feedback and concurrency. The platform supports 14 environments across various domains like web navigating, text games, house-holding tasks, digital games, and more. AgentGym includes a trajectory set (AgentTraj) and a benchmark suite (AgentEval) to facilitate agent exploration and evaluation. The framework allows for agent self-evolution beyond existing data, showcasing comparable results to state-of-the-art models.

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.

I'll help you sense-check and define Behavioral Insights for your ideas!

Skaber nudge-ideer i en blog-inspireret, samtalepræget tone

A scholarly GPT specializing in neuromarketing and dark patterns.

The use case designer is configured with the latest Data Science and Behavioral Social Science insights to guide you through the process of defining AI and Machine Learning use cases for the ecosystem.Ai platform.

Explaining behavioral economics in ways that help you!

根据你的简历，为你定制属于你的面试教练。可以陪伴面试练习，或模拟真实面试。

Your go-to guide for nailing tech interviews with dynamic mock sessions!

A cryogenics expert assisting with cooling processes and material behavior.

Expert in AI, its impact on the mind, behavior, and economy

上帝视角观察、探索和影响一个末日丧尸灾难后的人类社会。Observe, explore and influence human society after the apocalyptic zombie disaster from a God's perspective. Sponsor：小红书“ ItsJoe就出行 ”

Leading AI in graphene research, offering innovative insights and solutions, powered by OpenAI.

Inception machine for AI souls

Build extensions using the Eyeware Beam eye tracking SDK

Providing web data insights from Google and Adobe Analytics.

Expert in applying game theory to real-world strategic decisions

Insightful advisor on interpersonal dynamics.

Analyzes behavioral data to understand user interactions and preferences, improving product designs.

Provides cognitive-behavioral and emotional therapy guidance, helping users understand and manage their thoughts, behaviors, and emotions.

Look at the Bible through the lens of Behavioral Psychology

Solution-focused, cognitive-behavioral, and transformational coaching to explore yourself, including journalling support.