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Cory Doctorow: How to Do Everything (Lifehacking Considered Harmful) – Locus Online
File under Innovation Requires Waste.

Cory is finding that optimizing his tasks with GTD is leaving little room to do anything other than what he’s prioritized. “Optimization is calcification.”

But today, thanks to a vicious Darwinian winnowing process, the only activities left in my day serve double- and triple-duty. There is virtually no moment in my working day that can cleanly be billed to only one ledger.

The corollary of this is that it gets much, much harder to winnow out activities over time. Anything I remove from the Jenga stack of my day disturbs the whole tower.

And that means that undertaking new things, speculative things that have no proven value to any of the domains where I work (let alone all of them) has gotten progressively harder, even as I’ve grown more productive. Optimization is a form of calcification.
productivity  efficiency  innovation  technology  culture  gtd 
6 days ago by jefframnani
The Boring Company
"increasing tunneling speed and dropping costs by a factor of 10 or more – this is the goal"
tunnelling  tunnels  boring  engineering  transportation  underground  building  construction  business  innovative  interesting  tunneling  speed  costs  efficiency  safety  urban  congestion  traffic  Elon-Musk  ElonMusk  geeky 
16 days ago by Mykl
[1703.09039] Efficient Processing of Deep Neural Networks: A Tutorial and Survey
"Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI systems.
This article aims to provide a comprehensive tutorial and survey about the recent advances towards the goal of enabling efficient processing of DNNs. Specifically, it will provide an overview of DNNs, discuss various hardware platforms and architectures that support DNNs, and highlight key trends in reducing the computation cost of DNNs either solely via hardware design changes or via joint hardware design and DNN algorithm changes. It will also summarize various development resources that enable researchers and practitioners to quickly get started in this field, and highlight important benchmarking metrics and design considerations that should be used for evaluating the rapidly growing number of DNN hardware designs, optionally including algorithmic co-designs, being proposed in academia and industry.
The reader will take away the following concepts from this article: understand the key design considerations for DNNs; be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics; understand the trade-offs between various hardware architectures and platforms; be able to evaluate the utility of various DNN design techniques for efficient processing; and understand recent implementation trends and opportunities."
papers  survey  neural-net  efficiency 
18 days ago by arsyed

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