A Good Overview of The Future of #AI & Data in Magazine Format Including a Fascinating Chart of AI Across the Business https://
res.cloudinary.com/yumyoshojin/im
age/upload/v1679656732/pdf/FDA_AW_CM.pdf
… #MachineLearning #DeepLearning #Fintech @pierrepinna @sallyeaves @PawlowskiMario @Xbond49 @psb_dc @SpirosMargaris @HaroldSinnott
MACHINE LEARNING
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Future of AI and Data: Comprehensive Business Applications Overview
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Domain-Adapted Automatic Speech Recognition Models Deployment
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TECHNICAL BLOG: Domain Adapted Automatic Speech Recognition SambaNova Suite customers can quickly experiment and deploy state-of-the-art models, customized for specific languages and domains, behind the customer firewall with full data & model governance
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Building Machines That Learn and Think Like People
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If you're interested in this, I highly recommend reading/comparing this (2016) paper Building Machines That Learn and Think Like People https://
arxiv.org/abs/1604.00289
https://arxiv.org/abs/1604.00289
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Broad AI Era: GPT-4 Multimodal Transformers and Path to AGI
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GPT-4 and other Multitasking Multimodal Transformer based models demonstrated that we are in the era of Broad AI (Artificial Broad Intelligence, ABI) not Narrow AI and not AGI either. It will take solving for causal reasoning before we really approach AGI & ideally compressing
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GPT-4 Demonstrates Advanced Reasoning and Common Sense Capabilities
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I was reacting to the title but the paper itself is interesting. It looks at stuff that GPT-4 can do that seems to have been missing from AI systems (more common sense reasoning, grasping the elements that make an animal recognizable, and so on). It's well worth a look.
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Semantic Networks: Understanding AI Knowledge Representation
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Unravel the mysteries of AI with semantic networks! These networks are like a web of meaning, connecting words and ideas to help machines understand the world. Dive into the depths of AI and unlock its potential here: https://
bit.ly/3K41Lt7 @jeevprabnivash @_DigitalIndia -
Transformer-Base Training Efficiency on TPU V2 Hardware
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As I said above, use of Transformer-Base as proxy task *is* in So et al: "Specifically, to train a Transformer
to peak performance on WMT’14 En-De requires ∼300K
training steps, or 10 hours, in the base size when using a
single Google TPU V.2 chip, as we do in our search" -
SVM Dual Solver Implementation and Convergence Proof
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That time I wrote a solver for an SVM in the dual, proved it’s convergence and felt pretty swole 😀
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Expanding AI Use Cases Across Industries
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Encouraging to see the expansive use cases of AI @SpirosMargaris
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NVIDIA Spring GTC 2023 Closing: Ray Tracing, AI, Jetson Orin
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NVIDIA Spring GTC 2023 has come to an end. In the closing article of this series, we discuss Ray Tracing and AI in Unreal Engine, Jetson Orin & Nsight, new features in CUDA C++, and Automated Pipeline Parallelism for PyTorch. https://
learnopencv.com/nvidia-spring-
gtc-2023-day-4/
… https://
learnopencv.com/win-a-free-nvi
dia-4080-gpu/
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