Python vs C++ Difference You Must Know

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This post will show the top 6 differences between Python and C++ web development technology. Check them out now to learn more.

Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs. 1. Unifying Large Language Models and Knowledge Graphs: A Roadmap. https://arxiv.org/abs/2306.08302 2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs: https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/

Securing your Kubernetes cluster_ a step-by-step guide to success !

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Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster. However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks. In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.

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Do you want to learn how to model and simulate an electrical network from scratch in under an hour? Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)! During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook. PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides: - A fully editable and extendable library for grid component modelling; - Visualization tools to display your network; - Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses; The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well. What you will learn during the webinar: - For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills; - For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.

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Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”. All of this illustrated with link prediction over knowledge graphs, but the argument is general.

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As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other? Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.

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Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024 https://alandix.com/academic/papers/synergy2024-epistemic/ As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.

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