By "not any time soon", I mean "clearly not in the next 5 years", contrary to a number of folks in the AI industry.
Yes, I'm skeptical of quantum computing, particularly when it comes to its application to AI.
COMPUTING
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Quantum Computing AI Applications: Skepticism on Five-Year Timeline
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Computing Power Buildup and Alternative Simulation Applications
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We are in a midst of perhaps the greatest computing power buildup ever. Sure, the vast majority of it is going towards the AI products, but at some point, between two LLM trainings, someone will decide to use at least a fraction of it for other purposes. Hey, let’s simulate
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CUDA GPT: Specialized Tool for GPU Programming
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Use CUDA GPT: https://
chat.openai.com/g/g-foiuswpIJ-
cuda-gpt
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ConvNets computational efficiency compared to 2007 object recognition models
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Not true. ConvNets were actually relatively cheap compared to the dominant models of object recognition circa 2007.
They were much smaller than current architectures, of course. -
AI Vision Limitations with Dynamic Movement and Scenes
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I agree. I also wonder how it will deal with movements like sitting down, lying down, running – I imagine it only works well with static scenery.
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AI Power Consumption and Deployment Cost Analysis
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Power Hungry Processing: Watts Driving the Cost of AI Deployment? Luccioni et al.: https://
arxiv.org/abs/2311.16863 #ArtificialIntelligence #ClimateAI #MachineLearning -
NASA Lunar VIPER Mission AI Planning System Success
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During undergrad and after graduation, I spent 3.5 years at @NASA working on the Lunar VIPER mission helping build a system health aware real time planning advisor using the @JuliaLanguage
. It’s so cool to see this coming together for the mission: -
Cloud Compute Consumption Shift Driven by AI Growth
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The other big shift we're seeing is a change in consumption in cloud compute. Snowflake's earnings signaled that there would be a rebound in consumption based pricing, and you hear about it more these days among startups. AI isn't the whole story, but it seems like a big part.
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RAG system reduces token input from 128k to 2k
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Exactly!
To complement on the end: thanks to the RAG system, the model was fed around 2k tokens each time, down from the 128k tokens of the original document. -
ART-V: Auto-Regressive Text-to-Video Generation Diffusion Model
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ART⋅V: Auto-Regressive Text-to-Video Generation with Diffusion Models Weng et al.: https://
arxiv.org/abs/2311.18834 #ArtificialIntelligence #DeepLearning #MachineLearning