Best AI tools for< systematic reviewer >

Rayyan is a web-based systematic review tool that helps researchers organize, manage, and accelerate their collaborative systematic literature reviews. It is trusted by more than 350,000 researchers and is used by students, librarians, and researchers from 180 countries and across industry sectors and disciplines. Rayyan's features include the ability to work remotely and collaborate with a distributed research team, work on-the-go or off-line using the Rayyan mobile app, and access to additional advanced features through membership packages.

Crowdbotics is an AI-powered platform that leverages systematic code reuse to help users build applications faster and with reduced risk. The platform uses AI to improve the process of defining app requirements, link reusable code modules with app requirements, assemble code modules into a nearly complete app, and develop differentiating features. Crowdbotics aims to transform the software development lifecycle by enabling successful code reuse through its CodeOps approach.

Syllaby is an AI-powered tool that streamlines the process of creating viral social media videos for businesses by assisting with ideation, content scheduling, outline and script generation, and even avatar-based video creation. It offers a systematic workflow tailored to various industries, along with features like an organizational content calendar and in-tool tutorials, making video marketing more accessible and efficient.

Empy AI is a platform designed to detect and resolve team conflicts before they escalate and negatively impact the workplace. It looks after communication between employees in real-time, alerting to conflicts or exhaustion before they affect the team's emotional well-being. Powered by LLM and Bert, Empy AI offers intuitive management approaches to mitigate risks for growing teams and managers, such as missing conflicts, not enough support, misjudging emotions, burnout danger, and limited diversity. It provides systematic improvements in team emotional well-being by spotting conflicts early, analyzing data, measuring progress, making data-informed decisions, proactive problem-solving, and offering unbiased feedback.

Hotball.ai is an AI application designed to help entrepreneurs turn their startup ideas into actionable plans. The platform offers a user-friendly interface for business planning, leveraging AI to automate routine tasks and provide insights for strategic decision-making. Hotball.ai aims to support visionaries, systematic thinkers, and innovative builders in transforming their ideas into successful ventures by fostering a community of like-minded individuals.

The OECD Observatory of Public Sector Innovation (OPSI) is a website that provides resources and tools to help governments and public servants explore new possibilities for innovation. OPSI's work areas include European Commission Collaboration, Anticipatory Innovation, Cross-Border Government Innovation, Behavioural Insights, Innovative Capacity, Innovation Trends, Innovation Portfolios, Mission-Oriented Innovation, Innovation Management, and Systems Approaches. OPSI also has a number of resources available, including a Toolkit Navigator, Case Study Library, Portfolio Exploration Tool, and Anticipatory Innovation Resource (AIR).

ModelOp is the leading AI Governance software for enterprises, providing a single source of truth for all AI systems, automated process workflows, real-time insights, and integrations to extend the value of existing technology investments. It helps organizations safeguard AI initiatives without stifling innovation, ensuring compliance, accelerating innovation, and improving key performance indicators. ModelOp supports generative AI, Large Language Models (LLMs), in-house, third-party vendor, and embedded systems. The software enables visibility, accountability, risk tiering, systemic tracking, enforceable controls, workflow automation, reporting, and rapid establishment of AI governance.

The ASReview project implements active learning for systematic reviews, utilizing AI-aided pipelines to assist in finding relevant texts for search tasks. It accelerates the screening of textual data with minimal human input, saving time and increasing output quality. The software offers three modes: Oracle for interactive screening, Exploration for teaching purposes, and Simulation for evaluating active learning models. ASReview LAB is designed to support decision-making in any discipline or industry by improving efficiency and transparency in screening large amounts of textual data.

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)

This repository provides a curated list of open-source projects that implement instruction-tuned large language models (LLMs) with reinforcement learning from human feedback (RLHF). The projects are evaluated in terms of their openness across a predefined set of criteria in the areas of Availability, Documentation, and Access. The goal of this repository is to promote transparency and accountability in the development and deployment of LLMs.

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.

Data-Juicer is a one-stop data processing system to make data higher-quality, juicier, and more digestible for LLMs. It is a systematic & reusable library of 80+ core OPs, 20+ reusable config recipes, and 20+ feature-rich dedicated toolkits, designed to function independently of specific LLM datasets and processing pipelines. Data-Juicer allows detailed data analyses with an automated report generation feature for a deeper understanding of your dataset. Coupled with multi-dimension automatic evaluation capabilities, it supports a timely feedback loop at multiple stages in the LLM development process. Data-Juicer offers tens of pre-built data processing recipes for pre-training, fine-tuning, en, zh, and more scenarios. It provides a speedy data processing pipeline requiring less memory and CPU usage, optimized for maximum productivity. Data-Juicer is flexible & extensible, accommodating most types of data formats and allowing flexible combinations of OPs. It is designed for simplicity, with comprehensive documentation, easy start guides and demo configs, and intuitive configuration with simple adding/removing OPs from existing configs.

cl-waffe2 is an experimental deep learning framework in Common Lisp, providing fast, systematic, and customizable matrix operations, reverse mode tape-based Automatic Differentiation, and neural network model building and training features accelerated by a JIT Compiler. It offers abstraction layers, extensibility, inlining, graph-level optimization, visualization, debugging, systematic nodes, and symbolic differentiation. Users can easily write extensions and optimize their networks without overheads. The framework is designed to eliminate barriers between users and developers, allowing for easy customization and extension.

Awesome-Lists is a curated index of selected resources spanning various fields including programming languages and theories, web and frontend development, server-side development and infrastructure, cloud computing and big data, data science and artificial intelligence, product design, etc. It includes articles, books, courses, examples, open-source projects, and more. The repository categorizes resources according to the knowledge system of different domains, aiming to provide valuable and concise material indexes for readers. Users can explore and learn from a wide range of high-quality resources in a systematic way.

Awesome CS Books is a repository that archives excellent books related to computer science and technology, named in the format of {year}-{author}-{title}-{version}. It includes reading notes for each book, with PDF links provided at the beginning of the notes. The repository focuses on IT CS-related books, valuable open courses, and aims to provide a systematic way of learning to alleviate fragmented skills and one-sidedness. It respects the original authors by linking to official/copyright websites and emphasizes non-commercial use of the documents.

Autonomous agents are designed to achieve specific objectives through self-guided instructions. With the emergence and growth of large language models (LLMs), there is a growing trend in utilizing LLMs as fundamental controllers for these autonomous agents. This repository conducts a comprehensive survey study on the construction, application, and evaluation of LLM-based autonomous agents. It explores essential components of AI agents, application domains in natural sciences, social sciences, and engineering, and evaluation strategies. The survey aims to be a resource for researchers and practitioners in this rapidly evolving field.

Academic_LLM_Sec_Papers is a curated collection of academic papers related to LLM Security Application. The repository includes papers sorted by conference name and published year, covering topics such as large language models for blockchain security, software engineering, machine learning, and more. Developers and researchers are welcome to contribute additional published papers to the list. The repository also provides information on listed conferences and journals related to security, networking, software engineering, and cryptography. The papers cover a wide range of topics including privacy risks, ethical concerns, vulnerabilities, threat modeling, code analysis, fuzzing, and more.

Agents 2.0 is a framework for training language agents using symbolic learning, inspired by connectionist learning for neural nets. It implements main components of connectionist learning like back-propagation and gradient-based weight update in the context of agent training using language-based loss, gradients, and weights. The framework supports optimizing multi-agent systems and allows multiple agents to take actions in one node.

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.

db-ally is a library for creating natural language interfaces to data sources. It allows developers to outline specific use cases for a large language model (LLM) to handle, detailing the desired data format and the possible operations to fetch this data. db-ally effectively shields the complexity of the underlying data source from the model, presenting only the essential information needed for solving the specific use cases. Instead of generating arbitrary SQL, the model is asked to generate responses in a simplified query language.

Paper-Reading-ConvAI is a repository that contains a list of papers, datasets, and resources related to Conversational AI, mainly encompassing dialogue systems and natural language generation. This repository is constantly updating.

The IBM Analog Hardware Acceleration Kit is an open-source Python toolkit for exploring and using the capabilities of in-memory computing devices in the context of artificial intelligence. It consists of two main components: Pytorch integration and Analog devices simulator. The Pytorch integration provides a series of primitives and features that allow using the toolkit within PyTorch, including analog neural network modules, analog training using torch training workflow, and analog inference using torch inference workflow. The Analog devices simulator is a high-performant (CUDA-capable) C++ simulator that allows for simulating a wide range of analog devices and crossbar configurations by using abstract functional models of material characteristics with adjustable parameters. Along with the two main components, the toolkit includes other functionalities such as a library of device presets, a module for executing high-level use cases, a utility to automatically convert a downloaded model to its equivalent Analog model, and integration with the AIHW Composer platform. The toolkit is currently in beta and under active development, and users are advised to be mindful of potential issues and keep an eye for improvements, new features, and bug fixes in upcoming versions.

Comflowy is a community dedicated to providing comprehensive tutorials, fostering discussions, and building a database of workflows and models for ComfyUI and Stable Diffusion. Our mission is to lower the entry barrier for ComfyUI users, promote its mainstream adoption, and contribute to the growth of the AI generative graphics community.

If you crave a systematic approach to learning, I'm your Knowledge Architect. I'll navigate you through comprehensive knowledge hierarchies, step by step, in any subject you choose. Share this systematic learning method with your friends to elevate their learning experiences.

Ensures product quality through systematic process monitoring and evaluation.

Dedicated to systematic legal understanding by Prof. Kiskinov

Ensures product quality through rigorous, systematic testing processes.

Fun and scholarly insights into systems theory

Counter-Racist Scholar & Editor of Dismantled Newsletter: Malcolm ai X

Aiding in systemic thinking and analysis