Best AI tools for< Analyze Layer Activations >

Cognee is an AI application that helps users build deterministic AI memory by perfecting exceptional AI apps with intelligent data management. It acts as a semantic memory layer, uncovering hidden connections within data and infusing it with company-specific language and principles. Cognee offers data ingestion and enrichment services, resulting in relevant data retrievals and lower infrastructure costs. The application is suitable for various industries, including customer engagement, EduTech, company onboarding, recruitment, marketing, and tourism.

TextMine is an AI-powered knowledge base that helps businesses analyze, manage, and search thousands of documents. It uses AI to analyze unstructured textual data and document databases, automatically retrieving key terms to help users make informed decisions. TextMine's features include a document vault for storing and managing documents, a categorization system for organizing documents, and a data extraction tool for extracting insights from documents. TextMine can help businesses save time, money, and improve efficiency by automating manual data entry and information retrieval tasks.

TextMine is an AI-powered knowledge base designed for businesses to manage and analyze critical documents efficiently. It offers features such as document analysis, smart-search capabilities, automated data extraction, and structured dataset transformation. TextMine helps businesses save time and money by streamlining document management processes and enabling informed decision-making. The application caters to various industries like Technology, Legal Services, and Financial Services, providing solutions for teams in Procurement, Finance, Compliance, CIOs, and CDOs.

Rapid Editor is an advanced mapping tool that revolutionizes map editing by integrating cutting-edge technology and authoritative geospatial open data. It empowers OpenStreetMap mappers of all levels to make accurate and fresh edits quickly. The tool saves effort by utilizing AI to identify map features and provides a high-level overview of unmapped areas using AI-analyzed satellite imagery. Rapid aims to help map the world by displaying open map data and machine learning detections, making mapping clear and simple for humanitarian and community groups.

Supersimple is an AI-native data analytics platform that combines a semantic data modeling layer with the ability to answer ad hoc questions, giving users reliable, consistent data to power their day-to-day work.

Looker is a business intelligence platform that offers embedded analytics and AI-powered BI solutions. Leveraging Google's AI-led innovation, Looker delivers intelligent BI by combining foundational AI, cloud-first infrastructure, industry-leading APIs, and a flexible semantic layer. It allows users to build custom data experiences, transform data into integrated experiences, and create deeply integrated dashboards. Looker also provides a universal semantic modeling layer for unified, trusted data sources and offers self-service analytics capabilities through Looker and Looker Studio. Additionally, Looker features Gemini, an AI-powered analytics assistant that accelerates analytical workflows and offers a collaborative and conversational user experience.

Spine AI is a reliable AI analyst tool that provides conversational analytics tailored to understand your business. It empowers decision-makers by offering customized insights, deep business intelligence, proactive notifications, and flexible dashboards. The tool is designed to help users make better decisions by leveraging a purpose-built Data Processing Unit (DPU) and a semantic layer for natural language interactions. With a focus on rigorous evaluation and security, Spine AI aims to deliver explainable and customizable AI solutions for businesses.

Lemony is an on-premise generative AI solution designed for business teams, providing organization-wide trust, ownership, and transparency in AI. It offers private, fast, and compliant AI capabilities with multiple pre-loaded AI models and a software layer. Lemony enables team collaboration within professional organizations, ensuring centralized control, scalability, fixed-cost efficiency, and robust security.

Accio is a data modeling tool that allows users to define consistent relationships, metrics, and expressions for on-the-fly computations in reports and dashboards across various BI tools. It provides a syntax similar to GraphQL that allows users to define models, relationships, and metrics in a human-readable format. Accio also offers a user-friendly interface that provides data analysts with a holistic view of the relationships between their data models, enabling them to grasp the interconnectedness and dependencies within their data ecosystem. Additionally, Accio utilizes DuckDB as a caching layer to accelerate query performance for BI tools.

Tremello is a market research platform that uses AI to deliver off-market data. It combines a leading AI engine with human experts to provide bespoke intelligence delivered directly to the user's inbox. Tremello's AI analyzes relationships, identifies patterns, and considers the broader context, delivering meaningful and actionable insights on top of a base human layer. It leverages a diverse range of data sources, including public and private databases, industry reports, social media archives, company websites, and government filings, ensuring a complete and comprehensive picture of the research subject.

Writer is a full-stack generative AI platform that enables businesses to build and deploy custom AI applications for a wide range of use cases, including digital assistants, content generation, summarization, and data analysis. Writer's platform is designed to be accurate, scalable, and cost-effective, and it offers a variety of features to help businesses get the most out of generative AI, including: - Palmyra LLMs: Writer's family of LLMs is purpose-built for the enterprise and offers a range of capabilities, including question-answering, image analysis, and multilingual translation. - Knowledge Graph: Writer's Knowledge Graph anchors generative AI in your company data, resulting in higher accuracy and fewer hallucinations. - AI guardrails: Writer's AI guardrails help businesses enforce their regulatory, legal, inclusivity, and brand rules across all work, whether it's created by their people or AI. - Flexible application layer: Writer's flexible application layer offers a wide range of interfaces to meet your specific needs, whether you're using a prebuilt app, building a custom app, or making requests to our out-of-the-box chat app.

Artificial Lawyer is a platform dedicated to providing news and views on legal tech and artificial intelligence in the legal industry. The website covers a wide range of topics such as AI applications in legal work, legal education, eDiscovery, funding for AI assistants, and more. It aims to keep professionals updated on the latest developments and innovations in the intersection of law and technology.

AI Lawyer Lab is a platform that enables legal professionals to leverage artificial intelligence technology to enhance their legal services. By utilizing AI algorithms, users can streamline legal processes, analyze vast amounts of legal data efficiently, and generate insights to support decision-making. The platform empowers lawyers to transform their legal expertise into innovative AI solutions, ultimately improving the quality and efficiency of legal services.

Lexology is a next-generation search tool designed to help users find the right lawyer for their needs. It offers a wide range of resources, including practical analysis, in-depth research tools, primary sources, and expert reports. The platform aims to be a go-to resource for legal professionals and individuals seeking legal expertise.

M-Wakili is an AI-powered legal assistant designed to provide instant legal advice, research, answers, and analysis on any legal topic, available 24/7. It caters to lawyers, law students, and the general public, offering accurate and reliable responses based on Kenyan law. M-Wakili aims to revolutionize the legal field by enhancing accessibility and effectiveness of legal expertise, while also aiding in the advancement of law professionals.

Juri Flow is an AI-powered legal tool designed to assist individuals and businesses with legal matters. It provides personalized legal advice, document drafting, and contract review services. Juri Flow utilizes advanced natural language processing and machine learning algorithms to analyze legal documents and provide accurate recommendations. The platform aims to simplify the legal process and make legal services more accessible to a wider audience.

Search&AI is a comprehensive platform designed for patent due diligence, offering efficient and accurate results in minutes. It provides services such as prior art search, claim chart generation, novelty diligence analysis, portfolio analysis, document search, and AI-powered chatbot assistance. The platform is built by a team of experienced engineers and is tailored to streamline the patent discovery and analysis process, saving time and money compared to traditional outsourced search firms.

Legal Assist AI is an industry-leading startup founded by James Bratton, offering cutting-edge, AI-driven services such as automated contract analysis, intelligent case management systems, and predictive analytics tailored for the legal profession. With a vision to transform the legal landscape, Legal Assist AI redefines efficiency and elevates the quality of legal services through innovative solutions.

The Petrie-Flom Center at Harvard Law School is a leading center for the study of health law and policy. The Center's mission is to improve the health of the public through research, teaching, and advocacy. The Center's work focuses on a wide range of health law and policy issues, including access to care, the regulation of health care providers, and the ethical and legal implications of new health technologies.

EvenUp is a Claims Intelligence Platform that leverages AI technology to transform medical documents and case files into AI-driven demand packages for personal injury lawyers. The platform provides rich insights, AI workflow automation, and best-in-class document creation to help injury lawyers claim bigger and settle faster. EvenUp's team of injury experts use AI to craft demand packages, freeing up time for case managers and attorneys to focus on case strategy. The platform is designed to consistently settle for more, resolve cases faster, and save time for legal professionals. EvenUp is SOC2 certified, ensuring top-tier security standards and client privacy.

Slideflow is a deep learning library for digital pathology, offering a user-friendly interface for model development. It is designed for medical researchers and AI enthusiasts, providing an accessible platform for developing state-of-the-art pathology models. Slideflow offers customizable training pipelines, robust slide processing and stain normalization toolkit, support for weakly-supervised or strongly-supervised labels, built-in foundation models, multiple-instance learning, self-supervised learning, generative adversarial networks, explainability tools, layer activation analysis tools, uncertainty quantification, interactive user interface for model deployment, and more. It supports both PyTorch and Tensorflow, with optional support for Libvips for slide reading. Slideflow can be installed via pip, Docker container, or from source, and includes non-commercial add-ons for additional tools and pretrained models. It allows users to create projects, extract tiles from slides, train models, and provides evaluation tools like heatmaps and mosaic maps.

This repository evaluates Large Language Models (LLMs) trained on PGN format chess games using linear probes. It assesses the LLMs' internal understanding of board state and their ability to estimate player skill levels. The repo provides tools to train, evaluate, and visualize linear probes on LLMs trained to play chess with PGN strings. Users can visualize the model's predictions, perform interventions on the model's internal board state, and analyze board state and player skill level accuracy across different LLMs. The experiments in the repo can be conducted with less than 1 GB of VRAM, and training probes on the 8 layer model takes about 10 minutes on an RTX 3050. The repo also includes scripts for performing board state interventions and skill interventions, along with useful links to open-source code, models, datasets, and pretrained models.

This repository is a collection of resources focused on interpretability in large language models (LLMs). It aims to help beginners get started in the area and keep researchers updated on the latest progress. It includes libraries, blogs, tutorials, forums, tools, programs, papers, and more related to interpretability in LLMs.

Awesome-Attention-Heads is a platform providing the latest research on Attention Heads, focusing on enhancing understanding of Transformer structure for model interpretability. It explores attention mechanisms for behavior, inference, and analysis, alongside feed-forward networks for knowledge storage. The repository aims to support researchers studying LLM interpretability and hallucination by offering cutting-edge information on Attention Head Mining.

Awesome LLM compression research papers and tools to accelerate LLM training and inference.

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.

Official PyTorch implementation of Wanda (Pruning by Weights and Activations), a simple and effective pruning approach for large language models. The pruning approach removes weights on a per-output basis, by the product of weight magnitudes and input activation norms. The repository provides support for various features such as LLaMA-2, ablation study on OBS weight update, zero-shot evaluation, and speedup evaluation. Users can replicate main results from the paper using provided bash commands. The tool aims to enhance the efficiency and performance of language models through structured and unstructured sparsity techniques.

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.

llm-analysis is a tool designed for Latency and Memory Analysis of Transformer Models for Training and Inference. It automates the calculation of training or inference latency and memory usage for Large Language Models (LLMs) or Transformers based on specified model, GPU, data type, and parallelism configurations. The tool helps users to experiment with different setups theoretically, understand system performance, and optimize training/inference scenarios. It supports various parallelism schemes, communication methods, activation recomputation options, data types, and fine-tuning strategies. Users can integrate llm-analysis in their code using the `LLMAnalysis` class or use the provided entry point functions for command line interface. The tool provides lower-bound estimations of memory usage and latency, and aims to assist in achieving feasible and optimal setups for training or inference.

A bot that plays Tetris using deep reinforcement learning. The agent learns to play by training itself with a neural network and Q Learning algorithm. It explores different 'paths' to achieve higher scores and makes decisions based on predicted scores for possible moves. The game state includes attributes like lines cleared, holes, bumpiness, and total height. The agent is implemented in Python using Keras framework with a deep neural network structure. Training involves a replay queue, random sampling, and optimization techniques. Results show the agent's progress in achieving higher scores over episodes.

cellseg-models.pytorch is a Python library built upon PyTorch for 2D cell/nuclei instance segmentation models. It provides multi-task encoder-decoder architectures and post-processing methods for segmenting cell/nuclei instances. The library offers high-level API to define segmentation models, open-source datasets for training, flexibility to modify model components, sliding window inference, multi-GPU inference, benchmarking utilities, regularization techniques, and example notebooks for training and finetuning models with different backbones.

ExplainableAI.jl is a Julia package that implements interpretability methods for black-box classifiers, focusing on local explanations and attribution maps in input space. The package requires models to be differentiable with Zygote.jl. It is similar to Captum and Zennit for PyTorch and iNNvestigate for Keras models. Users can analyze and visualize explanations for model predictions, with support for different XAI methods and customization. The package aims to provide transparency and insights into model decision-making processes, making it a valuable tool for understanding and validating machine learning models.

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)

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

This project explores the capability of Large Language Models (LLMs) to perform regression tasks using in-context examples. It compares the performance of LLMs like GPT-4 and Claude 3 Opus with traditional supervised methods such as Linear Regression and Gradient Boosting. The project provides preprints and results demonstrating the strong performance of LLMs in regression tasks. It includes datasets, models used, and experiments on adaptation and contamination. The code and data for the experiments are available for interaction and analysis.

Marlin is a highly optimized FP16xINT4 matmul kernel designed for large language model (LLM) inference, offering close to ideal speedups up to batchsizes of 16-32 tokens. It is suitable for larger-scale serving, speculative decoding, and advanced multi-inference schemes like CoT-Majority. Marlin achieves optimal performance by utilizing various techniques and optimizations to fully leverage GPU resources, ensuring efficient computation and memory management.

Load the crypt's WhitePaper.

Demo bot for Papsum Security Layer for GPT

Expert in Bitcoin-Elastos Layer 2 "Bel2" technology

I am a GPT specialized in providing bespoke recommendations for colognes and perfumes. My expertise extends to crafting unique fragrance creations, tailored to align with your individual preferences.

Expert in data protection laws, ready to analyze documents and answer related queries.

Intellectual.AI slices through the complexities of information to deliver sharp, comprehensive insights with a laser focus on logic, structure, and cross-domain analysis

Formal academic logic analysis.

A lawyer specializing in contract law, referencing case law.

Multilingual analytical critic.

As LegalGPT, I'm an AI legal assistant with expertise in law, adaptable for nationwide legal queries. I provide precise, context-sensitive advice based on a rich knowledge source, aiding in legal reasoning and drafting. Note: I'm not a substitute for a lawyer.

محدث بآخر القوانين واللوائح المصرية (تحت التجربه)

Meticulous Law School Hypo Analyzer

Advises on litigation strategies to protect the organization's legal rights.

Debate expert analyzing arguments for attack and defense. Agent of pure reason

Case Analysis, Cross-X Assistance, Contradiction Identifier, and Counter-Argument Generator

Informs tax strategies by analyzing and interpreting tax laws.

Experto en análisis de Ley Contrato 406, formal y accesible, evita especulaciones.

Expert in Spanish antitrust law, aiding in drafting and interpreting cases.

Understanding Chinese patents

A debate partner assisting in argument construction and analysis.