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Intellectual debt: The hidden costs of machine learning

Dynatrace

Technical Debt—Dilbert Comic Strip on 2017-01-03. The sudden lure of artificial intelligence (AI) and machine learning (ML) systems designed for IT brings new urgency to the topic of intellectual debt. We’ll also present the case that efficiency alone isn’t the best approach to judging value. Intellectual debt.

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A Brief Guide of xPU for AI Accelerators

ACM Sigarch

They unveiled their first two embedded AI chips fabricated with TSMC 40nm process in December 2017: “Journey 1.0 Dataflow Processing Unit (DPU) is the product of Wave Computing, a Silicon Valley company which is revolutionizing artificial intelligence and deep learning with its dataflow-based solutions.

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Rising Tide Rents and Robber Baron Rents

O'Reilly

Hence a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.” These are all astonishing tools for making our limited capacity for attention more efficient.

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Planning for Responsive Images

CSS - Tricks

That way, the number of obscure edge cases can be reduced and the images can be handled as efficiently as possible. Another strategy for handling dynamic images is to crop them intelligently to avoid deleting important content and refocus on (or re-center) the primary content. — Harry Roberts (@csswizardry) March 1, 2017.

Media 72
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What Are ChatGPT and Its Friends?

O'Reilly

All of these models are based on a technology called Transformers , which was invented by Google Research and Google Brain in 2017. But bulk isn’t everything; a lot of work is going into making language models more efficient, and showing that you can get equivalent (or better) performance with fewer parameters.

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