AI tools for Google Research bike aerodynamics AI simulations
Related Tools:

Google Research
Google Research is a team of scientists and engineers working on a wide range of topics in computer science, including artificial intelligence, machine learning, and quantum computing. Our mission is to advance the state of the art in these fields and to develop new technologies that can benefit society. We publish hundreds of research papers each year and collaborate with researchers from around the world. Our work has led to the development of many new products and services, including Google Search, Google Translate, and Google Maps.

Google Research
Google Research is a leading research organization focusing on advancing science and artificial intelligence. They conduct research in various domains such as AI/ML foundations, responsible human-centric technology, science & societal impact, computing paradigms, and algorithms & optimization. Google Research aims to create an environment for diverse research across different time scales and levels of risk, driving advancements in computer science through fundamental and applied research. They publish hundreds of research papers annually, collaborate with the academic community, and work on projects that impact technology used by billions of people worldwide.

Imagen
Imagen is an AI application that leverages text-to-image diffusion models to create photorealistic images based on input text. The application utilizes large transformer language models for text understanding and diffusion models for high-fidelity image generation. Imagen has achieved state-of-the-art results in terms of image fidelity and alignment with text. The application is part of Google Research's text-to-image work and focuses on encoding text for image synthesis effectively.

Google Research Blog
The Google Research Blog is a platform for researchers at Google to share their latest work in artificial intelligence, machine learning, and other related fields. The blog covers a wide range of topics, from theoretical research to practical applications. The goal of the blog is to provide a forum for researchers to share their ideas and findings, and to foster collaboration between researchers at Google and around the world.

Enric Corona
Enric Corona is a Research Scientist at Google Research, working on 3D Humans and Generative AI. His research is in areas of computer vision and machine learning, including modelling and reconstruction of 3D human bodies and hands.

Google Colab
Google Colab is a free Jupyter notebook environment that runs in the cloud. It allows you to write and execute Python code without having to install any software or set up a local environment. Colab notebooks are shareable, so you can easily collaborate with others on projects.

Google AI
Google AI is a research and development laboratory focused on advancing the state-of-the-art in artificial intelligence. The company's mission is to develop AI that is beneficial to humanity, and its research focuses on a wide range of topics, including machine learning, computer vision, natural language processing, and robotics. Google AI has developed a number of products and services that use AI, including the Google Assistant, Google Translate, and Gmail's spam filter. The company is also working on developing new AI applications for healthcare, transportation, and other industries.

Olympia
Olympia is an AI-powered consultancy platform that offers smart and affordable AI consultants to help businesses with various tasks such as business strategy, online marketing, content generation, legal advice, software development, and sales. The platform features continuous learning capabilities, real-time research, email integration, vision capabilities, and more. Olympia aims to streamline operations, reduce expenses, and boost productivity for startups, small businesses, and solopreneurs by providing expert AI teams powered by advanced language models like GPT4 and Claude 3. The platform ensures secure communication, no rate limits, long-term memory, and outbound email capabilities.

Google DeepMind
Google DeepMind is a British artificial intelligence research laboratory owned by Google. The company was founded in 2010 by Demis Hassabis, Shane Legg, and Mustafa Suleyman. DeepMind's mission is to develop safe and beneficial artificial intelligence. The company's research focuses on a variety of topics, including machine learning, reinforcement learning, and computer vision. DeepMind has made significant contributions to the field of artificial intelligence, including the development of AlphaGo, the first computer program to defeat a professional human Go player.

NotebookLM
NotebookLM is an AI-powered note-taking and research assistant that leverages Google's Gemini 1.5 Pro model. It helps users organize and analyze information from uploaded documents, providing personalized insights and in-line citations. NotebookLM prioritizes user privacy by not using personal data to train its AI, ensuring the security of sensitive information. The application is designed to assist users in transforming information into actionable insights efficiently and collaboratively.

Google Quantum AI
Google Quantum AI is a leading platform dedicated to advancing the field of quantum computing. The platform offers educational resources, research publications, open-source tools like Cirq, and career opportunities. Google Quantum AI's mission is to build best-in-class quantum computing systems with error correction capabilities, enabling the solution of complex problems that are currently intractable for classical computers. The platform also hosts the XPRIZE Quantum Applications competition, collaborates with industry and academic partners to explore future applications of quantum computing, and shares research and blog posts on quantum algorithms and advancements.

Google Gemma
Google Gemma is a lightweight, state-of-the-art open language model (LLM) developed by Google. It is part of the same research used in the creation of Google's Gemini models. Gemma models come in two sizes, the 2B and 7B parameter versions, where each has a base (pre-trained) and instruction-tuned modifications. Gemma models are designed to be cross-device compatible and optimized for Google Cloud and NVIDIA GPUs. They are also accessible through Kaggle, Hugging Face, Google Cloud with Vertex AI or GKE. Gemma models can be used for a variety of applications, including text generation, summarization, RAG, and both commercial and research use.

Google Patents
Google Patents is a search engine that allows users to search through the full text of patents that have been granted by the United States Patent and Trademark Office (USPTO). The database includes patents from 1790 to the present day, and users can search by keyword, inventor, assignee, or patent number. Google Patents also provides access to images of the original patent documents, as well as links to related patents and articles.

PromptLoop
PromptLoop is an AI-powered tool that integrates with Excel and Google Sheets to enhance market research and data analysis. It offers custom AI models tailored to specific needs, enabling users to extract insights from complex information. With PromptLoop, users can leverage advanced AI capabilities for tasks such as web research, content analysis, and data labeling, streamlining workflows and improving efficiency.

BlogSEO AI
BlogSEO AI is an AI-powered content creation and SEO optimization tool that helps businesses create high-quality, SEO-friendly blog posts and articles. It offers a range of features including keyword research, content generation, auto-blogging, and analytics. BlogSEO AI is designed to help businesses improve their organic traffic and website performance.

KeywordSearch
KeywordSearch is an AI-powered platform that supercharges ad audiences for Google and YouTube by providing advanced keyword research, audience building, and ad spying capabilities. It helps marketers, businesses, and agencies elevate their content, fuel business growth, and unlock superior targeting for clients. With features like AI Audience Builder, Keyword Research, Keyword Topic Auto Expansion, YouTube Ad Spy, and more, KeywordSearch simplifies audience discovery and targeting best practices. Trusted by top leaders, it offers automated audience creation and expansion to boost conversions and ROI effortlessly.

Ad-Free AI Chat
Ad-Free AI Chat is an Android app available in the Play Store. It offers ad-free GPT voice chat, games, language learning, and research assistance. Users can create their own commands to automate tasks and use the app with Android Auto. The app supports ChatGPT 4, ChatGPT 3.5, and Google Gemini Pro.

Looppanel
Looppanel is a user research analysis and repository tool that uses AI to help researchers save time and improve the quality of their work. It offers a range of features, including automated transcription, AI note-taking, video snipping, and advanced search capabilities. Looppanel is designed to make it easy for researchers to capture, organize, and analyze their research data, so they can focus on what matters most: uncovering insights and making better decisions.

Is it a ranking factor?
Explore the 14,000 ranking factors, signals, and features revealed in the latest leaked Google Search docs. Updated May 2024.

BREEBS - Chat with Knowledge
Power your chats with capsules of pure Knowledge, built from specialized PDF document collections on Google Drive. Chat with Breebs, create and share a Breeb ! 𝗪𝗪𝗪.𝗕𝗥𝗘𝗘𝗕𝗦.𝗖𝗢𝗠 🚀

Touché par 1 MAJ GG ?
Découvrez si votre site a été impacté par une mise à jour de GG et laquelle

GSC Keyword Ranking Changes Scatter Plot
Export comparison data from GSC to get a scatter plot of keyword rankings before and after an update.

Tech Stock Analyst
Analyzes tech stocks with in-depth, qualitative and quantitative analysis

Earnings Explorer
Analyzes and summarizes company earnings transcripts, asking and answering questions.

Lead Scout
I compile and enrich precise company and professional profiles. Simply provide any name, email address, or company and I'll generate a complete profile.

Simplexity (TM)
Simple rich summaries ---keyword it with movies, celebs, sports figures, technologies, cities, patents, company name

Search Helper with Henk van Ess and Translation
Refines search queries with specific terms and includes Google links

SGEPatentReader
This GPT explains the US11769017B1 patent and how it relates to Google's SGE (Search Generative Experience).

Awesome-Efficient-LLM
Awesome-Efficient-LLM is a curated list focusing on efficient large language models. It includes topics such as knowledge distillation, network pruning, quantization, inference acceleration, efficient MOE, efficient architecture of LLM, KV cache compression, text compression, low-rank decomposition, hardware/system, tuning, and survey. The repository provides a collection of papers and projects related to improving the efficiency of large language models through various techniques like sparsity, quantization, and compression.

google-research
This repository contains code released by Google Research. All datasets in this repository are released under the CC BY 4.0 International license, which can be found here: https://creativecommons.org/licenses/by/4.0/legalcode. All source files in this repository are released under the Apache 2.0 license, the text of which can be found in the LICENSE file.

AI-PhD-S24
AI-PhD-S24 is a mono-repo for the PhD course 'AI for Business Research' at CUHK Business School in Spring 2024. The course aims to provide a basic understanding of machine learning and artificial intelligence concepts/methods used in business research, showcase how ML/AI is utilized in business research, and introduce state-of-the-art AI/ML technologies. The course includes scribed lecture notes, class recordings, and covers topics like AI/ML fundamentals, DL, NLP, CV, unsupervised learning, and diffusion models.

SEED-Bench
SEED-Bench is a comprehensive benchmark for evaluating the performance of multimodal large language models (LLMs) on a wide range of tasks that require both text and image understanding. It consists of two versions: SEED-Bench-1 and SEED-Bench-2. SEED-Bench-1 focuses on evaluating the spatial and temporal understanding of LLMs, while SEED-Bench-2 extends the evaluation to include text and image generation tasks. Both versions of SEED-Bench provide a diverse set of tasks that cover different aspects of multimodal understanding, making it a valuable tool for researchers and practitioners working on LLMs.

evalverse
Evalverse is an open-source project designed to support Large Language Model (LLM) evaluation needs. It provides a standardized and user-friendly solution for processing and managing LLM evaluations, catering to AI research engineers and scientists. Evalverse supports various evaluation methods, insightful reports, and no-code evaluation processes. Users can access unified evaluation with submodules, request evaluations without code via Slack bot, and obtain comprehensive reports with scores, rankings, and visuals. The tool allows for easy comparison of scores across different models and swift addition of new evaluation tools.

llms-tools
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.

openrl
OpenRL is an open-source general reinforcement learning research framework that supports training for various tasks such as single-agent, multi-agent, offline RL, self-play, and natural language. Developed based on PyTorch, the goal of OpenRL is to provide a simple-to-use, flexible, efficient and sustainable platform for the reinforcement learning research community. It supports a universal interface for all tasks/environments, single-agent and multi-agent tasks, offline RL training with expert dataset, self-play training, reinforcement learning training for natural language tasks, DeepSpeed, Arena for evaluation, importing models and datasets from Hugging Face, user-defined environments, models, and datasets, gymnasium environments, callbacks, visualization tools, unit testing, and code coverage testing. It also supports various algorithms like PPO, DQN, SAC, and environments like Gymnasium, MuJoCo, Atari, and more.

AI-PhD-S25
AI-PhD-S25 is a mono-repo for the DOTE 6635 course on AI for Business Research at CUHK Business School. The course aims to provide a fundamental understanding of ML/AI concepts and methods relevant to business research, explore applications of ML/AI in business research, and discover cutting-edge AI/ML technologies. The course resources include Google CoLab for code distribution, Jupyter Notebooks, Google Sheets for group tasks, Overleaf template for lecture notes, replication projects, and access to HPC Server compute resource. The course covers topics like AI/ML in business research, deep learning basics, attention mechanisms, transformer models, LLM pretraining, posttraining, causal inference fundamentals, and more.

Awesome-LLM-Inference
Awesome-LLM-Inference: A curated list of 📙Awesome LLM Inference Papers with Codes, check 📖Contents for more details. This repo is still updated frequently ~ 👨💻 Welcome to star ⭐️ or submit a PR to this repo!

Awesome-LLM-Safety
Welcome to our Awesome-llm-safety repository! We've curated a collection of the latest, most comprehensive, and most valuable resources on large language model safety (llm-safety). But we don't stop there; included are also relevant talks, tutorials, conferences, news, and articles. Our repository is constantly updated to ensure you have the most current information at your fingertips.

Awesome-LLM-Long-Context-Modeling
This repository includes papers and blogs about Efficient Transformers, Length Extrapolation, Long Term Memory, Retrieval Augmented Generation(RAG), and Evaluation for Long Context Modeling.

Awesome-AGI
Awesome-AGI is a curated list of resources related to Artificial General Intelligence (AGI), including models, pipelines, applications, and concepts. It provides a comprehensive overview of the current state of AGI research and development, covering various aspects such as model training, fine-tuning, deployment, and applications in different domains. The repository also includes resources on prompt engineering, RLHF, LLM vocabulary expansion, long text generation, hallucination mitigation, controllability and safety, and text detection. It serves as a valuable resource for researchers, practitioners, and anyone interested in the field of AGI.

Awesome-Graph-LLM
Awesome-Graph-LLM is a curated collection of research papers exploring the intersection of graph-based techniques with Large Language Models (LLMs). The repository aims to bridge the gap between LLMs and graph structures prevalent in real-world applications by providing a comprehensive list of papers covering various aspects of graph reasoning, node classification, graph classification/regression, knowledge graphs, multimodal models, applications, and tools. It serves as a valuable resource for researchers and practitioners interested in leveraging LLMs for graph-related tasks.

AudioLLM
AudioLLMs is a curated collection of research papers focusing on developing, implementing, and evaluating language models for audio data. The repository aims to provide researchers and practitioners with a comprehensive resource to explore the latest advancements in AudioLLMs. It includes models for speech interaction, speech recognition, speech translation, audio generation, and more. Additionally, it covers methodologies like multitask audioLLMs and segment-level Q-Former, as well as evaluation benchmarks like AudioBench and AIR-Bench. Adversarial attacks such as VoiceJailbreak are also discussed.

AwesomeResponsibleAI
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.

Efficient-LLMs-Survey
This repository provides a systematic and comprehensive review of efficient LLMs research. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient LLMs topics from **model-centric** , **data-centric** , and **framework-centric** perspective, respectively. We hope our survey and this GitHub repository can serve as valuable resources to help researchers and practitioners gain a systematic understanding of the research developments in efficient LLMs and inspire them to contribute to this important and exciting field.

FlashRank
FlashRank is an ultra-lite and super-fast Python library designed to add re-ranking capabilities to existing search and retrieval pipelines. It is based on state-of-the-art Language Models (LLMs) and cross-encoders, offering support for pairwise/pointwise rerankers and listwise LLM-based rerankers. The library boasts the tiniest reranking model in the world (~4MB) and runs on CPU without the need for Torch or Transformers. FlashRank is cost-conscious, with a focus on low cost per invocation and smaller package size for efficient serverless deployments. It supports various models like ms-marco-TinyBERT, ms-marco-MiniLM, rank-T5-flan, ms-marco-MultiBERT, and more, with plans for future model additions. The tool is ideal for enhancing search precision and speed in scenarios where lightweight models with competitive performance are preferred.