Machine learning
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Current machine learning systems are mostly trained with large datasets rather than truly learning, and consist of arbitrarily designed networks without proof of optimal structure. Silicon Brains aims to develop a true machine learning system that learns continuously through self-optimization and self-building based on principles of biological evolution, gradually improving itself without predefined layers or functions.
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Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Mind map of terminologies used in context of Generative AI
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Building RAG with self-deployed Milvus vector database and Snowpark Container...
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UiPath Test Automation using UiPath Test Suite series, part 5
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Topics covered:
CI/CD with in UiPath
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Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.Communications Mining Series - Zero to Hero - Session 1
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Securing your Kubernetes cluster_ a step-by-step guide to success !
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.
Microsoft - Power Platform_G.Aspiotis.pdf
Revolutionizing Application Development
with AI-powered low-code, presentation by George Aspiotis, Sr. Partner Development Manager, Microsoft
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Machine learning
- 1. MACHINE LEARNING 機械学習 ON THE RIGHT PATH? 正しい道に？ Silicon Brains www.si-brains.com
- 2. Silicon Brains www.si-brains.com Machine learning: field of AI that gives computer systems the ability to "learn" (progressively improve performance on a specific task) with data, without being explicitly programmed. DEFINITION WHAT IS OUT THERE? ANN (Artificial Neural Networks): Fixed structure of an interconnected group of functions with a number of unknown parameters to be found in order to model complex, multi-variable functions, find patterns in data or capture the statistical structure of an unknown probability function. NOT MUCH DIFFERENT FROM REGRESSION METHODS
- 3. Silicon Brains www.si-brains.com WHAT IS THE PROBLEM? • Current systems do not learn, are “trained” (not much different than fitting regression coefficients) • Current systems are static, do not change. Only the parameters (data) contain the learning • Current systems are made of arbitrary layers, without any justification or proof of being optimal • Current systems use mostly functions that allow an easy cost function • Current systems need huge amounts of training data and time.
- 4. Silicon Brains www.si-brains.com HOW SHOULD MACHINE LEARNING BE? • Systems should evolve and change incrementally and only if they improve with the change • Learning resides in parameters, functions, nodes and connections • Systems can be hierarchically more complex than a number of layers • Systems self-optimize continuously • Learning happens during normal usage • Learning uses no much more data and effort than during normal usage
- 5. Silicon Brains www.si-brains.com WHAT DO SILICON BRAINS PURSUE? A true Machine Learning system that: • Learns on the go • Never stop optimizing itself • Builds itself based on global optimization • Contains the minimum or no a priori structure • Focus is on system self-building, not on problem solving
- 6. Silicon Brains www.si-brains.com AND WE BASE OURSELVES ON: LIFE, the force that has made living beings from plants to humans along millions of years of evolution • We learn as we try things, not before • Life continuously improves (*) • Life and performance determines success and failure • Starts from scratch (*) • Focus is on system self-building, not on problem solving (*) Living beings inherit evolution, systems are copied