#KnowledgeDiscovery

[shravya96]

Microsoft GraphRAG Meets Neo4j: A Game-Changer!#TechRevolution #Scientif...

Microsoft GraphRAG Meets Neo4j: A Game-Changer!" highlights the revolutionary integration of Microsoft GraphRAG with Neo4j, transforming data analytics and relationship mapping. Microsoft GraphRAG, known for its robust graph-based AI capabilities, now seamlessly collaborates with Neo4j, the leading graph database technology. This powerful synergy enhances data connectivity, enabling organizations to uncover complex relationships and insights with unprecedented efficiency. By combining Microsoft’s advanced AI with Neo4j's scalable graph database, users can achieve superior data modeling, real-time analytics, and predictive intelligence. This integration paves the way for innovative applications across various industries, setting a new standard in data-driven decision-making and knowledge discovery 

 #MicrosoftGraphRAG #Neo4j #GraphDatabase #DataAnalytics #AI #BigData #DataScience #MachineLearning #PredictiveAnalytics #DataVisualization #TechInnovation #KnowledgeDiscovery #ScienceFather #Innovation #Research #STEM #ScienceConference #TechConference #Researcher #ScientificResearch #AcademicResearch #Science #STEM #Innovation #ResearchCommunity #PhDLife #LabLife #ResearchAndDevelopment #R&D #ScienceMatters #ResearchImpact #DataScience #TechResearch #ScientificDiscovery #ResearchLab #ResearcherLife #FieldResearch #ScienceCommunication #Scholar #PostDoc #ResearchProjects #ScienceInnovation #FutureOfResearch #AcademicLife #ResearchFunding #ResearchCollaboration #ScienceIsAwesome #ScientificInnovation

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[wedaniel1989-blog]

[throughmachineanddarkness]

Dimensionality of data

‘These types of dimensionality techniques [PCA and ICA methods for finding parameter combinations within a dataset], can be useful for identifying classes of objects, for detecting rare or outlying objects, and for constructing compact representations of the distribution of observed objects. One potential weakness of dimensionality reduction algorithms is that the components are defined statistically, and as such have no guarantee of reflecting true physical aspects of the systems being observed. Because of this, one must be careful when making physical inferences from such results.’

—Introduction to astroML: Machine Learning forAstrophysics, Jacob T. VanderPlas, Andrew J. Connolly, Zeljko Ivezic, Alex Gray, 2014

[blockgeni]

IIITM-K develops search engine for Covid researchers

State-run Indian Institute of Information Technology and Management – Kerala (IIITM-K) has developed an AI Semantic search engine to enable researchers to get deeper insights into scientific studies, especially when the need to find an early solution to the COVID-19 crisis has resulted in a large set of complex scientific information. Named ‘www.vilokana.in’, which in Sanskrit means ‘finding out’, the search engine is developed by a team led by A.P. James, Professor at Centre for Artificial General Intelligence and Neuromorphic Systems (neuro GI), IIITM-K, as part of the open science initiative of the research centre. The search engine will guide the researchers to the right information very fast. This makes easy the contextual search of information from the vast domain of scientific literature, which is a difficult problem faced by scientists and common people to get deeper insights into scientific studies. “A good initiative in the fight against Covid times, as such AI tools can help extract contextual insights from research articles,” said Saji Gopinath, CEO, Kerala Startup Mission and Director of IIITM-K. The user can supply key-words based queries, the meaning of which the search engine will be able to understand, and find the relevant text from the scientific papers. The user also has the option to upload any scientific text for analysis, which can be used for sentiment analysis and knowledge discovery. This article has been published from a wire agency feed without modifications to the text. Only the headline has been changed. #AISemanticsearchengine#Kerala#COVID19#ArtificialGeneralIntelligence#NeuromorphicSystems#searchengine#sentimentanalysis#knowledgediscovery#news#blockgeni Source link Read the full article

[billloguidice]

Personal and Ubiquitous Computing - Using gamification to discover cultural heritage locations from geo-tagged photos

Personal and Ubiquitous Computing - Using gamification to discover cultural heritage locations from geo-tagged photos #Gamification #geolocalization #knowledgediscovery #culturalheritage

From the journal, Personal and Ubiquitous Computing, comes a paper on Using gamification to discover cultural heritage locations from geo-tagged photos. This paper is free to read (link) through September 2019.

Abstract

Many enchanting cultural heritage locations are hidden from tourists, especially when considering countries full of historic attractions. Tourists tend to consider only…

[taliemsite-blog]

Data mining [Chen et al. 1996] is the process of extracting interesting (non-trivial implicit, previously unknown and potentially useful) information or patterns from large information repositories such as: relational database, data warehouses, XMLrepository, etc. Also data mining is known as one of the core processes of KnowledgeDiscovery in Database (KDD)

[halifaxconventioncentre]

KDD 2017 coming to Halifax!

We have BIG news to share with you. We won the bid to host the 2017 Conference on Knowledge Discovery and Data Mining, and we can`t wait to welcome over 1,000 of the world's leading edge researchers and practitioners in big data to our city!

This is an exciting opportunity, and we couldn’t be more proud to have partnered with the Institute for Big Data Analytics to make this all possible. Get all the details here!

[throughmachineanddarkness]

‘Matteo Pasquinelli and Vladan Joler describe ML as ‘an instrument of knowledge magnification’ and diffraction, comparing it to optical instruments of perception such as the telescope or the microscope, which have the power to extend human insight but always come with inbuilt aberrations that distort how we ‘see’ through them.’ –– A Different Research Agenda: Julia Kaganskiy Introduces the Creative AI Lab