A Guide to #MachineLearning Engineering for Real-Time Data — Building Intelligent Systems: http://
amzn.to/2C1Ai7r
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#BigData #DataScience #DeepLearning #IoT #IIoT #EdgeAI #StreamAnalytics #EdgeComputing #IoTCommunity #Industry40 #AI #Edge #TimeSeries #IntelligentEdge
SYSTEMS
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Machine Learning Engineering Guide for Real-Time Data Systems
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Meaning as Universe Model Function and Representation
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Meaning is the function that you use to model the entirety or your universe. Understanding the meaning of a representation is the establishment of relationships between that representation and the universe model.
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Brain USB-C Interface: Direct Neural Connection Technology
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Yeah, via USB via dongle. My brain only has USB-C.
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Technological Singularity: Understanding AI’s Phase Transition
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had a techno singularity a few years back and everyone is confused during takeoff, but it’s really just another traumatic phase transition akin to industrialization, infinitely scalable agro states, grammatical language before; each transition is orders of magnitude more rapid
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System Control and Latency Cost Optimization in AI
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Not always — often, it's more of a way to control the system more and cut latency/cost.
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Finding a Better Term for Speed of Light Analysis in System Engineering
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I often use “speed of light analysis” when talking about system engineering for a task, but I should probably find another term. I use it around questions like “what is the minimum latency for pass through video and synthetic frames in this architecture”, but “speed of light”
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Singapore’s Smart Nation: AI and Digital Twins Implementation
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Harnessing the Power of AI and Digital Twins: Lessons from Singapore's Smart Nation Initiative https://
linkedin.com/pulse/harnessi
ng-power-ai-digital-twins-lessons-from-singapores-babin-0bi3e
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#ArtificialIntelligence #DigitalTwins #singapore #innovation #technology @PawlowskiMario @JolaBurnett @CurieuxExplorer @Shi4Tech @Fabriziobustama @enilev @AkwyZ -
IT and Worker Views Diverge on AI Implementation Strategy
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Different "thought worlds" are a problem in AI. The IT teams in charge of AI think of "implementation" as scale: worrying tokens cost a lot, inference is slow, customization needed. The workers using AI often view "implementation" as "let me use ChatGPT how I want 20 times/day."
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Rethinking AI Chip Architecture: Complexity in System Integration
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@karlfreund of @Forbes writes, "Why cut up a wafer of chips, package each with HBM, put the package on a board, connect to CPUs with a fabric, then tie them all back together with networking chips and cables? That's a lot of complexity that leads to a lot of programming to
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Data Compression and Loading Challenges in AI Systems
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Most people compress so well that it's not hard to be saved. The true challenge lies in being loaded again