Best AI tools for< Provide Training >

Grow with Google is an AI tool designed to provide training and resources to help individuals boost their productivity and skills in various fields such as cybersecurity, data analytics, digital marketing, IT support, project management, UX design, and AI essentials. The platform offers online courses, tools, and professional certificates to help users develop ideas, make informed decisions, and enhance their daily work tasks using generative AI tools. With a focus on career growth and business development, Grow with Google aims to empower individuals with essential AI skills to succeed in today's competitive job market.

Mursion is an immersive learning platform that utilizes human-powered AI to provide training simulations for developing interpersonal skills in various workplace scenarios. The platform offers 1:1 immersive training sessions with virtual avatars, designed to enhance communication and people skills. Mursion's simulations are supported by a team of Simulation Specialists who deliver realistic interactions to help learners practice and improve their skills effectively. The platform is backed by over a decade of research and psychology, focusing on providing a safe and impactful learning environment for individuals across different industries.

Emocional is a platform that helps businesses evaluate, plan, and act to develop their employees' soft skills and promote well-being. It offers a unique personality and soft skills assessment, a personalized action plan, and access to expert training, coaching, therapy, and digital tools like EVA AI.

The Future of Privacy Forum (FPF) is an AI tool that serves as a catalyst for privacy leadership and scholarship, advancing principled data practices in support of emerging technologies. It provides resources, training sessions, and guidance on AI-related topics, online advertising, youth privacy legislation, and more. FPF brings together industry, academics, civil society, policymakers, and other stakeholders to explore challenges posed by emerging technologies and develop privacy protections, ethical norms, and best practices.

The Media Copilot is an AI tool that offers content, courses, and consulting services on how newsrooms, agencies, and other content-based organizations can integrate generative AI into their work. They provide training on using AI for content creation, offer courses for individuals and teams, and help build organizations' AI roadmap. The tool also provides public speaking services and sponsorships for reaching a large audience of media executives, journalists, PR professionals, and creatives.

BRACAI AI Consulting Services is a platform that offers AI consulting services to businesses looking to leverage artificial intelligence to improve productivity, reduce costs, and boost efficiency. The platform helps companies identify AI use cases, develop AI solutions, and provide training to ensure successful AI transformation. With a focus on simplifying AI for businesses, BRACAI aims to help organizations navigate the path to AI adoption and implementation.

Accessibility Desk is an accredited research and training agency specializing in digital accessibility and the WCAG. They offer services such as accessibility audits for websites and apps, self-assessment tools, and assistance in creating accessible WordPress websites. The organization aims to make digital products more accessible to all individuals, including those with disabilities, by providing knowledge sharing, specific advice, and official accessibility tests based on the WCAG-EM standard.

RespectablyAI is an AI-driven platform revolutionizing workplace communication by combining gamification and AI insights to provide interactive and impactful learning experiences on respect, inclusivity, and diversity. The platform offers contextual learning, adaptive feedback, seamless integration with corporate systems, engaging gamified training, and insightful analytics for targeted learning. It aims to help organizations enhance workplace culture and create respectful, inclusive, and diverse environments.

HEROZ is a Japanese company that specializes in AI technology. They offer a variety of AI-related services, including AI/DX support, AI consulting, and AI development. HEROZ's mission is to use AI to solve various problems in different industries and create a better future.

ParallelDots is a next-generation retail execution software powered by image recognition technology. The software offers solutions like ShelfWatch, Saarthi, and SmartGaze to enhance the efficiency of sales reps and merchandisers, provide faster training of image recognition models, and offer automated gaze-coding solutions for mobile and retail eye-tracking research. ParallelDots' computer vision technology helps CPG and retail brands track in-store compliance, address gaps in retail execution, and gain real-time insights into brand performance. The platform enables users to generate real-time KPI insights, evaluate compliance levels, convert insights into actionable strategies, and integrate computer vision with existing retail solutions seamlessly.

Simplilearn is an online bootcamp and certification platform that offers courses in various fields, including AI and machine learning, project management, cyber security, cloud computing, and data science. The platform partners with leading universities and companies to provide industry-relevant training and certification programs. Simplilearn's courses are designed to help learners develop job-ready skills and advance their careers.

JobInterview.guru is an AI-powered platform designed to provide personalized interview training for job seekers. Leveraging advanced AI technology, the platform offers realistic job interview simulations, detailed insights into interview questions, and personalized feedback to help users prepare effectively. With a focus on efficiency and cost-effectiveness, JobInterview.guru aims to empower users to confidently navigate their job interviews and land their dream jobs.

AIBrain is a tech start-up in Palo Alto, California with its focus on Education and Entertainment. AIBrain was recognized as a top 5 entertainment AI company in 2023 by Datamation. This includes bestseller AI courses, Autonomous Game AI, Humanoid AI, and Soccer AI/VR Assistant. AIBrain has also been actively involved in the Stanford Computer Forum as a member company since 2013. AIBrain has been leading the technology development on the areas of entertainment and education. AIBrain provides the Game Changer Football AI x VR solutions, called SAIVA (Sports AI Virtual Assistant) and SAICA (Sports AI Coach Assistant). As a world-class football / soccer solution, it was ranked at top 3 contender in the Camera Calibration Challenge, Soccer Net Challenges 2023. AIBrain Asia has been developing robotic AI such as Tyche, Talking Robot AI and Gretchen, Humanoid AI. In addition, we provide bestseller AI training program for non-AI professionals including Udemy Online: Automated Machine Learning for Beginners (Google & Apple), Bestseller, Udemy, 60,829 students, Dec 2023 Gretchen: Open Humanoid AI Platform. Beta Launch: January.

Macgence is an AI training data services platform that offers high-quality off-the-shelf structured training data for organizations to build effective AI systems at scale. They provide services such as custom data sourcing, data annotation, data validation, content moderation, and localization. Macgence combines global linguistic, cultural, and technological expertise to create high-quality datasets for AI models, enabling faster time-to-market across the entire model value chain. With more than 5 years of experience, they support and scale AI initiatives of leading global innovators by designing custom data collection programs. Macgence specializes in handling AI training data for text, speech, image, and video data, offering cognitive annotation services to unlock the potential of unstructured textual data.

BotGPT is a 24/7 custom AI chatbot assistant for websites. It offers a data-driven ChatGPT that allows users to create virtual assistants from their own data. Users can easily upload files or crawl their website to start asking questions and deploy a custom chatbot on their website within minutes. The platform provides a simple and efficient way to enhance customer engagement through AI-powered chatbots.

WTRI is an AI application that offers FutureView™, a suite of tools designed to help individuals and businesses rehearse their future scenarios in a virtual environment. By leveraging cognitive agility assessment, event generation, modeling, and virtual world platforms, WTRI aims to assist users in making agile decisions and adapting to rapidly changing business environments. With a focus on risk management and outcome optimization, WTRI provides a unique approach to strategic planning and preparedness.

BuildAi is an AI tool designed to provide the lowest cost GPU cloud for AI training on the market. The platform is powered with renewable energy, enabling companies to train AI models at a significantly reduced cost. BuildAi offers interruptible pricing, short term reserved capacity, and high uptime pricing options. The application focuses on optimizing infrastructure for training and fine-tuning machine learning models, not inference, and aims to decrease the impact of computing on the planet. With features like data transfer support, SSH access, and monitoring tools, BuildAi offers a comprehensive solution for ML teams.

PocketCoach is an online AI-powered learning platform designed for athletes worldwide who aim to enhance their sports skills. The platform utilizes cutting-edge AI and computer vision technology to provide gamified drills, comprehensive analytics, and real-time feedback to help athletes break through plateaus and achieve faster results. PocketCoach also offers an innovative computer vision app called Playi, which transforms practice sessions by offering real-time analysis and personalized feedback on technique using a smartphone's camera.

Cornerstone is an AI-powered workforce agility platform that helps organizations close the workforce readiness gap by providing personalized experiences and deeper insights. It offers modern learning experiences and skills-forward, AI-powered HR technology. The platform ensures regulatory compliance, focuses on skills and talent-driven human capital management, and aims to build a diverse, equitable, and inclusive workplace. Cornerstone caters to various industries such as the public sector, financial services, healthcare, life sciences, manufacturing, higher education, and nonprofit organizations. It offers a range of resources, tools, and tips to empower people at work and prepare them for future trends in the workforce.

Impakt is an AI fitness coaching application that offers personalized workout plans, real-time feedback, community support, and tailored workouts to help users reach their fitness goals. The app allows users to track workouts, analyze performance, share progress, and receive insights to improve their fitness journey. With a focus on community engagement and athlete endorsements, Impakt aims to provide a comprehensive fitness experience for users of all levels.

AI Audio Datasets List (AI-ADL) is a comprehensive collection of datasets consisting of speech, music, and sound effects, used for Generative AI, AIGC, AI model training, and audio applications. It includes datasets for speech recognition, speech synthesis, music information retrieval, music generation, audio processing, sound synthesis, and more. The repository provides a curated list of diverse datasets suitable for various AI audio tasks.

The STM32 AI model zoo is a collection of reference machine learning models optimized to run on STM32 microcontrollers. It provides a large collection of application-oriented models ready for re-training, scripts for easy retraining from user datasets, pre-trained models on reference datasets, and application code examples generated from user AI models. The project offers training scripts for transfer learning or training custom models from scratch. It includes performances on reference STM32 MCU and MPU for float and quantized models. The project is organized by application, providing step-by-step guides for training and deploying models.

AnyGPT is a unified multimodal language model that utilizes discrete representations for processing various modalities like speech, text, images, and music. It aligns the modalities for intermodal conversions and text processing. AnyInstruct dataset is constructed for generative models. The model proposes a generative training scheme using Next Token Prediction task for training on a Large Language Model (LLM). It aims to compress vast multimodal data on the internet into a single model for emerging capabilities. The tool supports tasks like text-to-image, image captioning, ASR, TTS, text-to-music, and music captioning.

FedLLM-Bench is a realistic benchmark for the Federated Learning of Large Language Models community. It includes datasets for federated instruction tuning and preference alignment tasks, exhibiting diversities in language, quality, quantity, instruction, sequence length, embedding, and preference. The repository provides training scripts and code for open-ended evaluation, aiming to facilitate research and development in federated learning of large language models.

LL3DA is a Large Language 3D Assistant that responds to both visual and textual interactions within complex 3D environments. It aims to help Large Multimodal Models (LMM) comprehend, reason, and plan in diverse 3D scenes by directly taking point cloud input and responding to textual instructions and visual prompts. LL3DA achieves remarkable results in 3D Dense Captioning and 3D Question Answering, surpassing various 3D vision-language models. The code is fully released, allowing users to train customized models and work with pre-trained weights. The tool supports training with different LLM backends and provides scripts for tuning and evaluating models on various tasks.

Promptwright is a Python library designed for generating large synthetic datasets using a local LLM and various LLM service providers. It offers flexible interfaces for generating prompt-led synthetic datasets. The library supports multiple providers, configurable instructions and prompts, YAML configuration for tasks, command line interface for running tasks, push to Hugging Face Hub for dataset upload, and system message control. Users can define generation tasks using YAML configuration or Python code. Promptwright integrates with LiteLLM to interface with LLM providers and supports automatic dataset upload to Hugging Face Hub.

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.

CIFAR-10 Airbench is a project offering fast and stable training baselines for CIFAR-10 dataset, facilitating machine learning research. It provides easily runnable PyTorch scripts for training neural networks with high accuracy levels. The methods used in this project aim to accelerate research on fundamental properties of deep learning. The project includes GPU-accelerated dataloader for custom experiments and trainings, and can be used for data selection and active learning experiments. The training methods provided are faster than standard ResNet training, offering improved performance for research projects.

LLM4Decompile is an open-source large language model dedicated to decompilation of Linux x86_64 binaries, supporting GCC's O0 to O3 optimization levels. It focuses on assessing re-executability of decompiled code through HumanEval-Decompile benchmark. The tool includes models with sizes ranging from 1.3 billion to 33 billion parameters, available on Hugging Face. Users can preprocess C code into binary and assembly instructions, then decompile assembly instructions into C using LLM4Decompile. Ongoing efforts aim to expand capabilities to support more architectures and configurations, integrate with decompilation tools like Ghidra and Rizin, and enhance performance with larger training datasets.

FarmVibes.AI is a repository focused on developing multi-modal geospatial machine learning models for agriculture and sustainability. It enables users to fuse various geospatial and spatiotemporal datasets, such as satellite imagery, drone imagery, and weather data, to generate robust insights for agriculture-related problems. The repository provides fusion workflows, data preparation tools, model training notebooks, and an inference engine to facilitate the creation of geospatial models tailored for agriculture and farming. Users can interact with the tools via a local cluster, REST API, or a Python client, and the repository includes documentation and notebook examples to guide users in utilizing FarmVibes.AI for tasks like harvest date detection, climate impact estimation, micro climate prediction, and crop identification.

Cambrian-1 is a fully open project focused on exploring multimodal Large Language Models (LLMs) with a vision-centric approach. It offers competitive performance across various benchmarks with models at different parameter levels. The project includes training configurations, model weights, instruction tuning data, and evaluation details. Users can interact with Cambrian-1 through a Gradio web interface for inference. The project is inspired by LLaVA and incorporates contributions from Vicuna, LLaMA, and Yi. Cambrian-1 is licensed under Apache 2.0 and utilizes datasets and checkpoints subject to their respective original licenses.

LLMGA (Multimodal Large Language Model-based Generation Assistant) is a tool that leverages Large Language Models (LLMs) to assist users in image generation and editing. It provides detailed language generation prompts for precise control over Stable Diffusion (SD), resulting in more intricate and precise content in generated images. The tool curates a dataset for prompt refinement, similar image generation, inpainting & outpainting, and visual question answering. It offers a two-stage training scheme to optimize SD alignment and a reference-based restoration network to alleviate texture, brightness, and contrast disparities in image editing. LLMGA shows promising generative capabilities and enables wider applications in an interactive manner.

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

Embedded inference of Deep Learning models is quite challenging due to high compute requirements. TI’s Edge AI software product helps optimize and accelerate inference on TI’s embedded devices. It supports heterogeneous execution of DNNs across cortex-A based MPUs, TI’s latest generation C7x DSP, and DNN accelerator (MMA). The solution simplifies the product life cycle of DNN development and deployment by providing a rich set of tools and optimized libraries.

A public repository collecting links to state-of-the-art training sets, QA, benchmarks and other evaluations for various ML and LLM applications in materials science and chemistry. It includes datasets related to chemistry, materials, multimodal data, and knowledge graphs in the field. The repository aims to provide resources for training and evaluating machine learning models in the materials science and chemistry domains.

The 'fms-fsdp' repository is a companion to the Foundation Model Stack, providing a (pre)training example to efficiently train FMS models, specifically Llama2, using native PyTorch features like FSDP for training and SDPA implementation of Flash attention v2. It focuses on leveraging FSDP for training efficiently, not as an end-to-end framework. The repo benchmarks training throughput on different GPUs, shares strategies, and provides installation and training instructions. It trained a model on IBM curated data achieving high efficiency and performance metrics.

Liger Kernel is a collection of Triton kernels designed for LLM training, increasing training throughput by 20% and reducing memory usage by 60%. It includes Hugging Face Compatible modules like RMSNorm, RoPE, SwiGLU, CrossEntropy, and FusedLinearCrossEntropy. The tool works with Flash Attention, PyTorch FSDP, and Microsoft DeepSpeed, aiming to enhance model efficiency and performance for researchers, ML practitioners, and curious novices.

Co-LLM (Collaborative Language Models) is a tool for learning to decode collaboratively with multiple language models. It provides a method for data processing, training, and inference using a collaborative approach. The tool involves steps such as formatting/tokenization, scoring logits, initializing Z vector, deferral training, and generating results using multiple models. Co-LLM supports training with different collaboration pairs and provides baseline training scripts for various models. In inference, it uses 'vllm' services to orchestrate models and generate results through API-like services. The tool is inspired by allenai/open-instruct and aims to improve decoding performance through collaborative learning.

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.

AICIty-reID 2020 is a repository containing the 1st Place submission to AICity Challenge 2020 re-id track by Baidu-UTS. It includes models trained on Paddlepaddle and Pytorch, with performance metrics and trained models provided. Users can extract features, perform camera and direction prediction, and access related repositories for drone-based building re-id, vehicle re-ID, person re-ID baseline, and person/vehicle generation. Citations are also provided for research purposes.

I assist network marketers in managing and developing their business by providing training, advice, and strategies to grow their team.

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