Best AI tools for< Oral Surgeon >

KELLS is an AI-powered personal dental companion platform that revolutionizes dental care by providing convenient, affordable, and transparent services. It offers virtual checkups, second opinions, personalized dental reports, and real-time responses to dental questions. The platform combines AI technology with expert knowledge to empower users to make informed decisions about their oral health. KELLS aims to address the shortcomings of traditional dental care by offering 24/7 access, cost-effective solutions, and unbiased treatment evaluations.

SMILE Dx is a revolutionary dental AI application that aims to transform the dental field by providing advanced technology to detect cavities, gum disease, and root canals at a pixel level. The application offers a unique opportunity for early investment in the dental x-ray AI market, with the potential to significantly impact patient acceptance of treatment. With a dedicated team and strategic exit options, SMILE Dx is poised to make a mark in the dental industry.

VideaHealth is a dental AI platform trusted by dentists and DSOs. It enhances diagnostics and streamlines workflows using clinical AI to identify and convert treatments across major oral conditions. The platform combines practice management system data with AI insights to elevate patient care and empower dental practices. VideaHealth offers advanced FDA-cleared detection algorithms to detect suspect diseases, provides AI-powered insights for data-driven decisions, and delivers real-time chairside assistance to dentists.

StrAIberry is an AI solution for the Patient, Insurance, Dentist triangle that can organize and solve the issues of personal oral hygiene, appointment setting, second eye opinion with the highest precision for dentists, insurance fraud, and risk management for insurance while saving cost, time and paper waste.

SmallTalk2Me is an AI-powered simulator designed to help users improve their spoken English. It offers a range of features, including mock job interviews, IELTS speaking test simulations, and daily stories and courses. The platform uses AI to provide users with instant feedback on their performance, helping them to identify areas for improvement and track their progress over time.

Linda is an AI-powered platform that helps users transform guided conversations into shareable audio memories. It offers a secure and private environment for users to create personalized podcast episodes based on their life stories. Linda uses advanced voice technology to conduct interviews, create podcasts, and provide a seamless user experience. With expertly crafted interviews and memory-driven conversations, Linda aims to help users explore and articulate their memories in a meaningful way. The platform also offers privacy controls, customization options, and themed interviews to capture special moments throughout the year.

Talkio AI is a language training app that uses AI technology to help users improve their oral language skills. It offers a variety of features, including voice conversations with AI tutors, pronunciation assessment, feedback on language skills, and a wide range of topics to discuss. Talkio AI is suitable for learners of all levels, from beginners to advanced speakers.

Overjet is the #1 Dental AI Platform for providers and payers, offering artificial intelligence solutions to enhance clinical care and administrative efficiency in the dental industry. The platform leverages AI technology to improve oral health by providing clinically precise, efficient, and patient-centric services. Overjet is recognized by Forbes as one of the top 50 AI companies shaping the future, trusted by leading payers and providers in the dental field. It offers features such as Clinical Intelligence Platform for providers and Claim Intelligence Platform for payers, empowering teams to achieve better patient outcomes and streamline claims processes.

Since the emergence of chatGPT in 2022, the acceleration of Large Language Model has become increasingly important. Here is a list of papers on LLMs inference and serving.

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

BabelDuck is a highly customizable AI oral conversation practice application for language learners at all levels, with a focus on being more beginner-friendly. It aims to minimize the threshold and mental burden of oral expression practice. The tool supports various AI conversation features such as managing multiple dialogues, customizing system prompts, and providing suggestions for grammar, translation, or expression refinement without interrupting the current conversation. Users can seek further discussion through sub-dialogues when in doubt about AI suggestions, seamlessly returning to the original conversation afterward. BabelDuck also offers voice input and output, integrates browser-built text-to-speech, and Azure TTS, and supports different dialogue preferences, data stored locally for user privacy, multilingual interface, and built-in tutorials.

AIDE @ unipi is a repository containing students' material for the course in Artificial Intelligence and Data Engineering at University of Pisa. It includes slides, students' notes, information about exams methods, oral questions, past exams, and links to past students' projects. The material is unofficial and created by students for students, checked only by students. Contributions are welcome through pull requests, issues, or contacting maintainers. The repository aims to provide non-profit resources for the course, with the opportunity for contributors to be acknowledged and credited. It also offers links to Telegram and WhatsApp groups for further interaction and a Google Drive folder with additional resources for AIDE published by past students.

Aligner is a model-agnostic alignment tool designed to efficiently correct responses from large language models. It redistributes initial answers to align with human intentions, improving performance across various LLMs. The tool can be applied with minimal training, enhancing upstream models and reducing hallucination. Aligner's 'copy and correct' method preserves the base structure while enhancing responses. It achieves significant performance improvements in helpfulness, harmlessness, and honesty dimensions, with notable success in boosting Win Rates on evaluation leaderboards.

This repository contains code for the paper 'Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models'. It focuses on fine-tuning CLIP in an unsupervised manner to enhance its robustness against visual adversarial attacks. By replacing the vision encoder of large vision-language models with the fine-tuned CLIP models, it achieves state-of-the-art adversarial robustness on various vision-language tasks. The repository provides adversarially fine-tuned ViT-L/14 CLIP models and offers insights into zero-shot classification settings and clean accuracy improvements.

The 'llm-past-tense' repository contains code related to the research paper 'Does Refusal Training in LLMs Generalize to the Past Tense?' by Maksym Andriushchenko and Nicolas Flammarion. It explores the generalization of refusal training in large language models (LLMs) to the past tense. The code includes experiments and examples for running different models and requests related to the study. Users can cite the work if found useful in their research, and the codebase is released under the MIT License.

Awesome LLM Security is a curated collection of tools, documents, and projects related to Large Language Model (LLM) security. It covers various aspects of LLM security including white-box, black-box, and backdoor attacks, defense mechanisms, platform security, and surveys. The repository provides resources for researchers and practitioners interested in understanding and safeguarding LLMs against adversarial attacks. It also includes a list of tools specifically designed for testing and enhancing LLM security.

ReaLHF is a distributed system designed for efficient RLHF training with Large Language Models (LLMs). It introduces a novel approach called parameter reallocation to dynamically redistribute LLM parameters across the cluster, optimizing allocations and parallelism for each computation workload. ReaL minimizes redundant communication while maximizing GPU utilization, achieving significantly higher Proximal Policy Optimization (PPO) training throughput compared to other systems. It supports large-scale training with various parallelism strategies and enables memory-efficient training with parameter and optimizer offloading. The system seamlessly integrates with HuggingFace checkpoints and inference frameworks, allowing for easy launching of local or distributed experiments. ReaLHF offers flexibility through versatile configuration customization and supports various RLHF algorithms, including DPO, PPO, RAFT, and more, while allowing the addition of custom algorithms for high efficiency.

The 'awesome-ai-llm4education' repository is a curated list of papers related to artificial intelligence (AI) and large language models (LLM) for education. It collects papers from top conferences, journals, and specialized domain-specific conferences, categorizing them based on specific tasks for better organization. The repository covers a wide range of topics including tutoring, personalized learning, assessment, material preparation, specific scenarios like computer science, language, math, and medicine, aided teaching, as well as datasets and benchmarks for educational research.

MetaGPT is a multi-agent framework that enables GPT to work in a software company, collaborating to tackle more complex tasks. It assigns different roles to GPTs to form a collaborative entity for complex tasks. MetaGPT takes a one-line requirement as input and outputs user stories, competitive analysis, requirements, data structures, APIs, documents, etc. Internally, MetaGPT includes product managers, architects, project managers, and engineers. It provides the entire process of a software company along with carefully orchestrated SOPs. MetaGPT's core philosophy is "Code = SOP(Team)", materializing SOP and applying it to teams composed of LLMs.

KG-LLM-Papers is a repository that collects papers integrating knowledge graphs (KGs) and large language models (LLMs). It serves as a comprehensive resource for research on the role of KGs in the era of LLMs, covering surveys, methods, and resources related to this integration.

GPTSwarm is a graph-based framework for LLM-based agents that enables the creation of LLM-based agents from graphs and facilitates the customized and automatic self-organization of agent swarms with self-improvement capabilities. The library includes components for domain-specific operations, graph-related functions, LLM backend selection, memory management, and optimization algorithms to enhance agent performance and swarm efficiency. Users can quickly run predefined swarms or utilize tools like the file analyzer. GPTSwarm supports local LM inference via LM Studio, allowing users to run with a local LLM model. The framework has been accepted by ICML2024 and offers advanced features for experimentation and customization.

Awesome-LLM-Interpretability is a curated list of materials related to LLM (Large Language Models) interpretability, covering tutorials, code libraries, surveys, videos, papers, and blogs. It includes resources on transformer mechanistic interpretability, visualization, interventions, probing, fine-tuning, feature representation, learning dynamics, knowledge editing, hallucination detection, and redundancy analysis. The repository aims to provide a comprehensive overview of tools, techniques, and methods for understanding and interpreting the inner workings of large language models.

Awesome Mamba is a curated collection of groundbreaking research papers and articles on Mamba Architecture, a pioneering framework in deep learning known for its selective state spaces and efficiency in processing complex data structures. The repository offers a comprehensive exploration of Mamba architecture through categorized research papers covering various domains like visual recognition, speech processing, remote sensing, video processing, activity recognition, image enhancement, medical imaging, reinforcement learning, natural language processing, 3D recognition, multi-modal understanding, time series analysis, graph neural networks, point cloud analysis, and tabular data handling.

This repository, named Awesome-Embodied-Agent-with-LLMs, is a curated list of research related to Embodied AI or agents with Large Language Models. It includes various papers, surveys, and projects focusing on topics such as self-evolving agents, advanced agent applications, LLMs with RL or world models, planning and manipulation, multi-agent learning and coordination, vision and language navigation, detection, 3D grounding, interactive embodied learning, rearrangement, benchmarks, simulators, and more. The repository provides a comprehensive collection of resources for individuals interested in exploring the intersection of embodied agents and large language models.

LongLoRA is a tool for efficient fine-tuning of long-context large language models. It includes LongAlpaca data with long QA data collected and short QA sampled, models from 7B to 70B with context length from 8k to 100k, and support for GPTNeoX models. The tool supports supervised fine-tuning, context extension, and improved LoRA fine-tuning. It provides pre-trained weights, fine-tuning instructions, evaluation methods, local and online demos, streaming inference, and data generation via Pdf2text. LongLoRA is licensed under Apache License 2.0, while data and weights are under CC-BY-NC 4.0 License for research use only.

Awesome-LLM-Quantization is a curated list of resources related to quantization techniques for Large Language Models (LLMs). Quantization is a crucial step in deploying LLMs on resource-constrained devices, such as mobile phones or edge devices, by reducing the model's size and computational requirements.

Awesome-Papers-Autonomous-Agent is a curated collection of recent papers focusing on autonomous agents, specifically interested in RL-based agents and LLM-based agents. The repository aims to provide a comprehensive resource for researchers and practitioners interested in intelligent agents that can achieve goals, acquire knowledge, and continually improve. The collection includes papers on various topics such as instruction following, building agents based on world models, using language as knowledge, leveraging LLMs as a tool, generalization across tasks, continual learning, combining RL and LLM, transformer-based policies, trajectory to language, trajectory prediction, multimodal agents, training LLMs for generalization and adaptation, task-specific designing, multi-agent systems, experimental analysis, benchmarking, applications, algorithm design, and combining with RL.

This repository is a collection of papers on Generative AI Machine Unlearning, categorized based on modality and applications. It includes datasets, benchmarks, and surveys related to unlearning scenarios in generative AI. The repository aims to provide a comprehensive overview of research in the field of machine unlearning for generative models.

A medical advisor for oral healthcare professionals, providing expert dental advice.

Explains oral surgery in simple terms and suggests diagnoses.

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Focuses on pioneers of the cable TV industry.

Conseiller pour formuler des problématiques de grand oral en STAV

Experienced IELTS oral examiner for realistic test simulations and precise grading. 微信:DigitalNomadRyan；小红书：Ryan(小红书号：49443039026)，欢迎关注交流！雅思口语练习见GPTs：满分雅思口语老师Kelly（https://chat.openai.com/g/g-X6HcBQwrV-man-fen-ya-si-kou-yu-lao-shi-kelly）

Powered by Rams Dental World and Kids Dental World, Nellore. We are offering advice on Children and Adolescent's Oral Hygiene especially at home. For customised solutions, visit us at www.ramsdentalworld.com.home.

A dental care expert offering oral health tips and general dental information.

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A moot competition companion. & Trial Prep companion . Test and improve arguments- predict your opponent's reaction.

トンデモ歯゛スターズの記事から質問に答えます。

Orthodontic expert offering advice on braces and dental alignment.

Dental implant patient education guru