Using Cogstack NLP with knowledge graphs News, Research, Technology CogStack NLP now supports exploration of clinical concept knowledge graphs via Neo4J. It&x27;s helpful for studying and analyzing complex relationships between various data points. The ability to quickly classify sentiment from customers is. Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs, AKBC 2019. Licensed under CC0. Graph Neural Networks (GNNs) have become increasingly popular for processing graph-structured data, such as social networks, molecular graphs, and knowledge graphs. Stack Overflow The World&x27;s Largest Online Community for Developers. Tally O'Malley. However, current. The knowledge graph represents a collection of connected entities and their relations. 20 de mai. Large-Scale Support distributed graph storage and distributed training algorithms. be6Pg0kDw1aqcGitHub Repository httpsgithub. natural-language-processing knowledge-graph-completion. natural-language-processing knowledge-graph-completion. So, in a model, we only process. Get an under the hood look at the next frontier in Search, from the team at Google behind the technology. Graph Neural Networks (GNNs) have become increasingly popular for processing graph-structured data, such as social networks, molecular graphs, and knowledge graphs. Knowledge Graph (KG) is just a virtual representation and not an actual graph stored as it is. To store the data you can use any of the present databases like SQL,. . A repo about knowledge graph in Chinese - husthukeawesome-knowledge-graph. md Knowledge-Graph-with-NLP Creating a Knowledge Graph based on NLP Requirements re pandas bs4 requests spacy networkx matplotlib tqdm The codes are based on a tutorial which can be found in Here. All Votes Add Books To This List. This tutorial will cover relevant and interesting topics on applying deep learning on graph techniques to NLP, including automatic graph construction for NLP, graph representation learning for NLP, advanced GNN based models (e. DeepWalkKDD 2014Graph Embedding NLPword2vecword embeddingword2vec. Knowledge-Graph-with-NLP Data Extraction DOCRED was used as the dataset for this project. However, current. Published August 04, 2019 Hello, ACL 2019 has just finished and I attended the whole week of the conference talks, tutorials, and workshops in beautiful Florence In this post I would like to recap how knowledge graphs slowly but firmly integrate into the NLP community. GraphGPT Natural Language Knowledge Graph. Licensed under CC0. To build a knowledge graph from the text, it is important to make our machine understand natural. comhexiangnan neural collab o rative filtering GMF. Examples and exercise for the book "Web Data APIs for Knowledge Graphs". Knowledge Graph. Open-source framework for working with Graph Neural Networks Follow More from Medium Patrick Meyer in Towards AI Automatic Knowledge Graphs The Impossible Grail Dr. They are a graphical representation of entities and the relationships between them, allowing for more efficient and effective storage, analysis, and use of information. Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it. This is where Natural Language Processing (NLP) comes into the picture. The Open Research Knowledge Graph (ORKG) is posited as a solution to the problem of keeping track of research progress minus the cognitive overload that reading dozens of full papers impose. Download Citation A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset Video understanding is an important task in short video business. Contribute to lihanghangNLP-Knowledge-Graph development by creating an account on GitHub. The knowledge graph represents a collection of connected entities and their relations. span> role"button" aria-expanded"false">. Its surge in popularity has resulted in a panoply of orthogonal embedding-based methods projecting entities and relations into low-dimensional continuous vectors. This Notebook has been released under the Apache 2. Knowledge graphs in Natural Language Processing ACL 2019. Pass in the synopsis of your favorite movie, a passage from a confusing Wikipedia page, or transcript from a video to generate a graph visualization of entities and their relationships. Part I. Contribute to lihanghangNLP-Knowledge-Graph development by creating an account on GitHub. CogStack NLP now supports exploration of clinical concept knowledge graphs via Neo4J. will be more predictive for knowledge acqui-sition in the few-shot scenario. TensorFlow is a framework developed by The name TensorFlow is derived from the operations, such as adding or multiplying, that artificial neuralThe Euler and Navier-Stokes equations describe the motion of a uid in Rn. Each entity in Wikidata5m is described by a corresponding Wikipedia page, which enables the evaluation of link prediction over unseen entities. Web page httpsathenarc. For more information please refer to the tutorial that uses openly available preprepared clinical data for exploration of clinical concepts and their relationships. Knowledge graphs in Natural Language Processing ACL 2019. DeepWalkKDD 2014Graph Embedding NLPword2vecword embeddingword2vec. Comments (9) Run. md 3 years ago README. 2021 Deep Learning, NLP, Neural Network, PyTorch, Python 5 min read. Significant Database in NLP Modern Techniques in NLP Recent Indoors Areas in NLP. illustration of a knowledge graph, plus laboratory glassware. Knowledge Graphs(KG) are one of the most important NLP tasks. Mar 16, 2019 Official implementations for various pre-training models of ERNIE-family, covering topics of Language Understanding & Generation, Multimodal Understanding & Generation, and beyond. less than 1 minute read. NLP and Knowledge Graphs The code in this repository is from a talk at the Neo4j Connections Knowledge Graphs event. Excellent discussion about the use of Knowledge Graphs and W3C Ontologies Instantiations (Ontologies - OWL, RDFS Ontologies logic) URIs as the federated. We make available the full source code of SCICERO at https. Graph Neural Networks (GNNs) have become increasingly popular for processing graph-structured data, such as social networks, molecular graphs, and knowledge graphs. Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it. NLP30 Github 3010 . DeepWalkKDD 2014Graph Embedding NLPword2vecword embeddingword2vec. We describe their design rationale, and explain why they are receiving growing attention within the graph representation learning and the broader NLP communities. Let us first give a quick summary in words of how we turn documents into a Knowledge Graph. An experimental library for implementing knowledge graph embedding models using. Temporal Knowledge Graph Embeddings Novel approaches Applications of combining Deep Learning and Knowledge Graphs Recommender Systems leveraging Knowledge Graphs Link Prediction and completing KGs Ontology Learning and Matching exploiting Knowledge Graph-Based Embeddings Knowledge Graph-Based Sentiment Analysis. This tutorial demonstrates how. Significant Database in NLP Modern Techniques in NLP Recent Indoors Areas in NLP. It is a large-scale, document level dataset constructed from Wikipedia and. Feeding COMET with seed tuples from a graph allows to learn its structure and relations. git cd gpt-2 virtualenv -p python3 venv source venvbinactivate pip install -r requirements. Knowledge Graphs from Data, Unstructured Text, . This gallery displays hundreds of chart, always providing reproducible & editable source code. org types and is compliant with the JSON-LD. Multi-relational Poincar Graph Embeddings. Contribute to lihanghangNLP-Knowledge-Graph development by creating an account on GitHub. Information Extraction and NLP. Ricky Costa Software User Interface Neural Magic 1 . Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it. knowledge-graph x. Knowledge Graphs, Information Extraction and Knowledge-aware NLP ACL20 Here lists papers and quick notes about knowledge graphs, information extraction, and knowledge-aware NLP applications that appear in the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020). It requires other NLP tasks as well-coreference resolution. However, the complex nature of. Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it. The ability to quickly classify sentiment from customers is. Pass in the synopsis of your favorite movie, a passage from a confusing Wikipedia page, or transcript from a video to generate a graph visualization of entities and their relationships. His main research interest is on the generation of Knowledge Graph from legacy datasets. span> role"button" aria-expanded"false">. Many of the concepts (such as the computation graph abstraction and autograd) are not unique to Pytorch and are relevant to any deep learning toolkit out there. Proceedings of NAACL 2018, New Orleans, CA (Oral) Generative Bridging Network in Neural Sequence Prediction Wenhu Chen, Guanlin Li, Shuo Ren, Shujie Liu, Zhirui Zhang, Mu Li, Ming Zhou. We made our code and dataset open source on GitHub 14 and Huggingface 15 , respectively. Many basic implementations of knowledge graphs make use of a concept we call triple, that is a set of three items(a subject, a predicate and an object) that we can use to store information about. The Relation-based Embedding Propagation (REP) method is a post-processing technique to adapt pre-trained knowledge graph embeddings with graph context. 2. Comments (58) Competition Notebook. We have made all code, experimental configurations, results, and analyses available at httpsgithub. Comments (58) Competition Notebook. DeepWalkKDD 2014Graph Embedding NLPword2vecword embeddingword2vec. Mar 16, 2019 Official implementations for various pre-training models of ERNIE-family, covering topics of Language Understanding & Generation, Multimodal Understanding & Generation, and beyond. Literature Review. will be more predictive for knowledge acqui-sition in the few-shot scenario. For more information please refer to the tutorial that uses openly available preprepared clinical data for exploration of clinical concepts and their relationships. Refresh the page,. GraphGPT Natural Language Knowledge Graph. Published August 04, 2019 Hello, ACL 2019 has just finished and I attended the whole week of. Many basic implementations of knowledge graphs make use of a concept we call triple, that is a set of three items(a subject, a predicate and an object) that we can use to store information about. A knowledge graph, also known as a semantic network, represents a network of real-world entitiesi. Top-level Conference Publications on Knowledge Graph - wds-seuKnowledge-Graph-Publications. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Graph Neural Networks (GNNs) have become increasingly popular for processing graph-structured data, such as social networks, molecular graphs, and knowledge graphs. Pass in the synopsis of your favorite movie, a passage from a confusing Wikipedia page, or transcript from a video to generate a graph visualization of entities and their relationships. A Knowledge Graph is a structured Knowledge Base. Large pre-trained natural language processing (NLP) models, such as BERT, RoBERTa, GPT-3, T5 and REALM, leverage natural language corpora that . Information Extraction is a process of extracting information in a more structured way i. Despite the graph&x27;s intricacy, it often gives better explanations than basic pies and charts. . Evaluation in link prediction on two public datasets shows that our approach achieves new state-of-the-art results with different few-shot sizes. For details, see Towards Data Science. A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning, SEM 2018. , graph2seq, graph2tree, and graph2graph) for NLP, and the applications of GNNs in various NLP tasks (e. all to a single. Geospatial Knowledge Graphs - semantic-geospatial. These procedures extract text from a node property and then send that text to APIs that extract entities, key phrases, categories, or sentiment. We describe their design rationale, and. Pass in the synopsis of your favorite movie, a passage from a confusing Wikipedia page, or transcript from a video to generate a graph visualization of entities and their relationships. Comments (58) Competition Notebook. Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it. 2 Next up, Chao et al propose PairRE, an extension of RotatE where relation embeddings are split into head-specific and tail-specific parts. Significant Database in NLP Modern Techniques in NLP Recent Indoors Areas in NLP. Contributing to closing. Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. Build knowledge graph using python. It aims to build a comprehensive knowledge graph that publishes the research contributions of scholarly publications per paper, where the contributions are interconnected via the graph even across papers. It&x27;s helpful for studying and analyzing complex relationships between various data points. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. A Knowledge Graph is a structured Knowledge Base. Based Syst. It consists of sub fields which cannot be. A repo about NLP, KG, Dialogue Systems in Chinese - lihanghangNLP-Knowledge-Graph. Because of their flexible structure, KGs are used for storing gen-eral real-world data (Rebele et al. The CICD tool chain that we use includes GitHub, GitHub Actions, Gradle, Helm, Azure Pipelines, Argo, and Artifactory. 1 Introduction Knowledge Graphs (KGs) like Freebase. nlp-knowledge-graph has no bugs, it has no vulnerabilities and it has low support. Published July 10, 2020. Let us first give a quick summary in words of how we turn documents into a Knowledge Graph. Knowledge Graphs & NLP EMNLP 2019. Education Masters in Information Analysis and Retrieval (University of Michigan, Ann-Arbor) Bachelors in Engineering- Electronics and Telecommunication (University of Mumbai) Github Link. However, current. Entity Recognition & Linking - This is the step that maps Leonard N, L Nimoy, Leo Nimoy, etc. Insight Data Science. ACL 2019. will be more predictive for knowledge acqui-sition in the few-shot scenario. The source code is available at httpsgithub. It has a very easy-to-use python interface and no unwanted interfaces in other languages to build and execute computational graphs. Save this job with your existing LinkedIn profile, or create a new one. Tracking Progress in Natural Language Processing GitHub; KG Embedding SOTA GitHub . To construct a comprehensive and explicit. Variational Knowledge Graph Reasoning Wenhu Chen, Wenhan Xiong, Xifeng Yan, William Wang. With the NLPContributionGraph Shared Task, we have formalized the building of such a scholarly contributions-focused graph over NLP scholarly. NLP for. The first line of code below instantiates the Random Forest Regression model with an nestimators value of 5000. Translation-based knowledge graph embeddings learn vector representations of entities and relations by treating relations as translation operators over the entities in an embedding space. 1 Taxonomy Creation. Building a knowledge graph with Spark and NLP How we recommend novel hypothesis to our scientists Eliseo Papa, MBBS PhD, AstraZeneca UnifiedDataAnalytics SparkAISummit 3. Graph Neural Networks (GNNs) have become increasingly popular for processing graph-structured data, such as social networks, molecular graphs, and knowledge graphs. Knowledge Graphs and Knowledge Bases. OpenKE, An Open-Source Package for Knowledge Embedding (KE) Fast-TransX, An Efficient implementation of TransE and its extended models for Knowledge Representation Learning. NLPContributionGraph is defined on a dataset of NLP scholarly articles with their contributions structured to be integrable within Knowledge Graph infrastructures such as the ORKG. . It provides both full implementations of state-of-the-art models for data scientists and also flexible interfaces to. Dominique Mariko sur LinkedIn python opensource knowledgegraph. 18 minute read. His main research interest is on the generation of Knowledge Graph from legacy datasets. The Knowledge Graph Search API lets you find entities in the Google Knowledge Graph. de 2022. Pass in the synopsis of your favorite movie, a passage from a confusing Wikipedia page, or transcript from a video to generate a graph visualization of entities and their relationships. This dataset integrates the Wikidata knowledge graph and Wikipedia pages. In particular, the authors built a denoising autoencoder which, given a corrupted dataset, is able to recover the actual one, with the implementation of a multiple imputation. Licensed under CC0. 7 Paper Code Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs snap-stanfordKGReasoning NeurIPS 2020. GraphGPT converts unstructured natural language into a knowledge graph. The Relation-based Embedding Propagation (REP) method is a post-processing technique to adapt pre-trained knowledge graph embeddings with graph context. We describe their design rationale, and. Knowledge Graphs & NLP EMNLP 2019. Continue exploring. Excellent discussion about the use of Knowledge Graphs and W3C Ontologies Instantiations (Ontologies - OWL, RDFS Ontologies logic) URIs as the federated. It indicates, "Click to perform a search". 2021 8 - 1 5 . Knowledge Graphs from Data, Unstructured Text, . CogStack NLP now supports exploration of clinical concept knowledge graphs via Neo4J. Knowledge Graph Building. the first one is how to transfer knowledge from a teacher GNN into a student GNN with a same capacity that can produce comparable and even better performance 2. Search Python 3 Programming Coursera Github. Knowledge-Graph-with-NLP Data Extraction DOCRED was used as the dataset for this project. We work with real-time, streaming, and batch modes. Since the translation is represented through a score function, translation-based embeddings are trained in. An available industry taxonomy is a good starting point for additional customizations. Knowledge Graph & NLP Tutorial- (BERT,spaCy,NLTK) Python Amazon Alexa Reviews , Wikipedia Sentences, Twitter Sentiment Analysis 7. Ricky Costa Software User Interface Neural Magic 1 . Argilla helps domain experts and data teams to build better NLP datasets in less time. To build a knowledge graph, the most important things are the nodes and the edges between them. Despite the graph&x27;s intricacy, it often gives better explanations than basic pies and charts. His main research interest is on the generation of Knowledge Graph from legacy datasets. Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it. wsaz radar full screen, painting mountains acrylic
Graph Neural Networks (GNNs) have become increasingly popular for processing graph-structured data, such as social networks, molecular graphs, and knowledge graphs. . Knowledge graph nlp github
The code of paper Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction. the first one is how to transfer knowledge from a teacher GNN into a student GNN with a same capacity that can produce comparable and even better performance 2. We made our code and dataset open source on GitHub 14 and Huggingface 15 , respectively. Advancements in artificial intelligence have enabled various data-driven approaches to predict suitable chemical reaction conditions. Among the NoSQL database types, graph databases have been proven to be most suitable type for natural knowledge representation (especially in a conversational agent environment) because of the match between their structure and the way the tokens or the semantic entities of a sentence and the dependencies between them are usually represented. A collection of papers, codes, projects, tutorials. Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it. 1 Introduction Knowledge Graphs (KGs) like Freebase. On the left we have the Wikidata taxonomy graph, which represents the explicit knowledge in our Knowledge Graph. This can be done by using NLP techniques such as sentence segmentation, dependency parsing, parts of speech tagging, and entity recognition. Knowledge graphs in Natural Language Processing ACL 2019. Temporal Knowledge Graph Embeddings Novel approaches Applications of combining Deep Learning and Knowledge Graphs Recommender Systems leveraging Knowledge Graphs Link Prediction and completing KGs Ontology Learning and Matching exploiting Knowledge Graph-Based Embeddings Knowledge Graph-Based Sentiment Analysis. Knowledge-Graph-with-NLP Data Extraction DOCRED was used as the dataset for this project. de 2018. Stack Overflow The World&x27;s Largest Online Community for Developers. A tag already exists with the provided branch name. Refresh the page,. student the second one is how to push the student model to play the best role in learning by itself, which is ignored in the traditional KD where the students. ICML 2021; other data-free model compression. We make available the full source code of SCICERO at https. ML for Trading - 2 nd Edition. Knowledge Graphs & NLP EMNLP 2019. 0 at httpsgithub. NLP is the backbone of forming a good knowledge graph from textual information. Dominique Mariko sur LinkedIn python opensource knowledgegraph. However, current. Experience in one (preferably many) of the following areas entity extractionlinking, document classification, knowledge graphs, matchingrecommendations Hands-on experience in. Redhorse Corporation is expanding our world-class knowledge graphs team to support a high-priority analytics project. NLP30 Github 3010 . g, TensorFlow, Theano, Keras, Dynet). A knowledge graph is a way of storing data that resulted from an information extraction task. Wikidata5m is a million-scale knowledge graph dataset with aligned corpus. Jun 11, 2021 Great resources for learning domain knowledge. We can skip this step and. Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. Examples and exercise for the book "Web Data APIs for Knowledge Graphs". Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it. 2020 (2) Adding more experiments by replacing the knowledge. Excellent discussion about the use of Knowledge Graphs and W3C Ontologies Instantiations (Ontologies - OWL, RDFS Ontologies logic) URIs as the federated. import gpt2simple as gpt2 gpt2. The dataset is distributed as a knowledge graph, a. Ricky Costa Software User Interface Neural Magic 1 . Knowledge graphs are often used to store interlinked descriptions of entities - objects, events, situations or abstract concepts - with free-form semantics (from wiki). de 2018. it Search table of content Part 1 Part 2 Part 3 Part 4 Part 5. As AI. CogStack NLP now supports exploration of clinical concept knowledge graphs via Neo4J. js graph gallery a collection of simple charts made with d3. A large-scale Chinese knowledge graph from OwnThink GDELTGlobal Database of Events, Language, and Tone Web KGHUB and KGOBO, Biomedical ontologies PheKnowLator Heterogeneous Biomedical Knowledge Graphs and Benchmarks Constructed Under Alternative Semantic Models Domain-specific Data OpenKG knowledge graphs about the novel coronavirus COVID-19. Excellent discussion about the use of Knowledge Graphs and W3C Ontologies Instantiations (Ontologies - OWL, RDFS Ontologies logic) URIs as the federated. Data Source The articles from HSBC website. Source Code. Refresh the page, check Medium s site status, or find something interesting to read. State of the art knowledge graphs Minimum set of characteristics of knowledge graphs 1. Knowledge graphs (KGs) provide effective well-structured relational information between entities. For more information please refer to the tutorial that uses openly available preprepared clinical data for exploration of clinical concepts and their relationships. Knowledge Graph. And following the root node, 2) it has twelve nodes which we. Refresh the page, check Medium s site status, or find something interesting to read. Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it. As a result, we present a structured overview of the research landscape, provide a taxonomy of tasks, summarize our findings, and highlight directions for future work. Temporal Knowledge Graph Embeddings Novel approaches Applications of combining Deep Learning and Knowledge Graphs Recommender Systems leveraging Knowledge Graphs Link Prediction and completing KGs Ontology Learning and Matching exploiting Knowledge Graph-Based Embeddings Knowledge Graph-Based Sentiment Analysis. And following the root node, 2) it has twelve nodes which we. In order to pursue more advanced methodologies, it has become critical that the communities related to Deep Learning, Knowledge Graphs, and NLP join their forces in order to develop. Several analyses and visualization tools can be applied, and our results show that these knowledge graph models may be a promising way to study the dissemination of any virus. Find answers to String Processing Library for C from the expert community at Experts Exchange. ngumc data services x x. de 2022. for Knowledge Graph and other NLP methods - GitHub - SchlampigKnowledgeGraphWander A collection . Our investigation reveals promising results in twelve NLP tasks. Codes for my Honours Research Project "Context-Aware Document Analysis". NLP Analysis for Brand SERP is a unique and very powerful feature of Kalicube. The Graph Data Engineer - Level II will serve on a cross-functional engineering team to quickly establish and continuously improve a knowledge graph-enabled analytics platform to support a. Let us first give a quick summary in words of how we turn documents into a Knowledge Graph. Pass in the synopsis of your favorite movie, a passage from a confusing Wikipedia page, or transcript from a video to generate a graph visualization of entities and their relationships. dermatologist tupelo ms. introduces COMET - an architecture for commonsense transformers - where language model such as GPT-2 is combined with a seed knowledge graph like ATOMIC. NLP30 Github 3010 . This book aims to show how ML can add value to algorithmic trading strategies in a practical yet comprehensive way. However, current. 0 at httpsgithub. OfficeSocial Graph. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Each entity in Wikidata5m is described by a corresponding Wikipedia page, which enables the evaluation of link prediction over unseen entities. NLP30 Github 3010 . Cybersecurity Knowledge Graph (CKG) has become an important structure to address the current cybersecurity crises and challenges, due to its powerful ability to model, mine, and leverage massive security intelligence data. student the second one is how to push the student model to play the best role in learning by itself, which is ignored in the traditional KD where the students. Experience in one (preferably many) of the following areas entity extractionlinking, document classification, knowledge graphs, matchingrecommendations Hands-on experience in. Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it. September 2022. Led by my good friend Paco Nathan GitHub. 2021 Deep Learning, NLP, Neural Network, PyTorch, Python 5 min read. Neo4j ,neo4j,nlp,knowledge-graph,Neo4j,Nlp,Knowledge Graph,. Mining Knowledge Graphs from Text A Tutorial. CogStack NLP now supports exploration of clinical concept knowledge graphs via Neo4J. Knowledge graphs are becoming increasingly important in a variety of fields, including artificial intelligence and information science. GraphGPT Natural Language Knowledge Graph. Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it. Geospatial Knowledge Graphs - semantic-geospatial. Cybersecurity Knowledge Graph (CKG) has become an important structure to address the current cybersecurity crises and challenges, due to its powerful ability to model, mine, and leverage massive security intelligence data. com nandanamihindu IBM Research AI Nandana Mihindukulasooriya is a researcher at IBM Research AI, USA. Ricky Costa Software User Interface Neural Magic 1 . . xvdeo free