Systems & Languages



Building--and scaling--a reliable distributed architecture

#artificialintelligence

I recently asked Joseph Breuer and Robert Reta, both Senior Software Engineers at Netflix, to discuss what they have learned through implementing a service at scale at Netflix. Joseph and Robert will be presenting a session on Event Sourcing at Global Scale at Netflix at O'Reilly Velocity Conference, taking place October 1-4 in New York. The primary challenge when operating a service in a distributed architecture at scale is managing for the behavior of your downstream dependencies. Continue reading Building--and scaling--a reliable distributed architecture.


To Code or Not to Code with KNIME

@machinelearnbot

Many modern data analysis environments allow for code-free creation of advanced analytics workflows. The advantages are obvious: more casual users, who cannot possibly stay on top of the complexity of working in a programming environment, are empowered to use existing workflows as templates and modify them to fit their needs, thus creating complex analytics protocols that they would never have been able to create in a programming environment. In some areas this may not be as dramatic, as the need for new ways of solving (parts of) problems isn't as critical anymore and a carefully designed visual environment may capture everything needed. The screenshot below shows how expert code written in those two languages can be integrated in a KNIME analytical workflow.


A Distributed AI Lab – AI Grant

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Together, we're excited to announce AI Grant 2.0! AI Grant 2.0 Fellows will receive some new treats, including: We've learned from the previous cohort that $2,500 will satisfy the needs of most projects. Our aspiration with AI Grant is to build a distributed AI lab. Stop reading, and click here to start the application.


A Simple Introduction To Data Structures: Part One – Linked Lists

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When many developers first realize how important data structures are (after trying to write a system that processes millions of records in seconds) they are often presented with books or articles that were written for people with computer science degrees from Stanford. The second field (the Pointer field) is storing the location in memory to the next node (memory location 2000). Hopefully, this was a quick and simple introduction to why data structures are important to learn and shed some light on when and why Linked List are an important starting point for data structures. If you can think of any better ways of explaining Linked Lists or why data structures are important to understand, leave them in the comments!


Cisco: Distributed AI Development Using Blockchain

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Jack Clark of OpenAI believes that this situation seems to benefit large-scale cloud providers like Amazon, Microsoft, and Google. This is also why our data center people are working with NVIDIA to add GPUs to our Unified Computing System (UCS) line (Dec 2016). The addition of GPUs makes it likely that each cloud/appliance will specialize around one or more particular frameworks to add value as well as services that play to each provider's strengths. And Google: TensorFlow integrated with ecosystem ML services.


A Knowledge Graph-based Semantic Database for Biomedical Sciences

@machinelearnbot

In short: BioGrakn is a graph-based semantic database that takes advantage of the power of knowledge graphs and machine reasoning to solve problems in the domain of biomedical science. We address the major issue of semantic integrity, that is, interpreting the real meaning of data derived from multiple sources or manipulated by various tools. We've discussed how BioGrakn takes advantage of the power of knowledge graphs and machine reasoning to solve problems in the domain of biomedical science. We address the major issue of semantic integrity, that is, interpreting the real meaning of data derived from multiple sources or manipulated by various tools.




Building and Maintaining the KBpedia Knowledge Graph

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The Cognonto demo is powered by an extensive knowledge graph called the KBpedia Knowledge Graph, as organized according to the KBpedia Knowledge Ontology (KKO). The KBpedia Knowledge Graph is a structure of more than 39,000 reference concepts linked to 6 major knowledge bases and 20 popular ontologies in use across the Web. It is for these reasons that we developed an extensive knowledge graph building process that includes a series of tests that are run every time that the knowledge graph get modified. The process of checking if external concepts linked to the KBpedia Knowledge Graph satisfies the structure is the same.