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I finally have the chance to read @emilymbender, @timnitGebru, @mcmillan_majora and Shmargaret Shmitchell #AI paper “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?”

I love this reflection even more than the title of the paper. https://t.co/0EgIQWDFOm

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You can weaponize or radicalize an #AI with the right training dataset. Then, you just have to unleash it.

From @emilymbender, @timnitGebru, @mcmillan_majora and Shmargaret Shmitchell paper “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” https://t.co/avVti8u8zS

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I finally have the chance to read @emilymbender, @timnitGebru, @mcmillan_majora and Shmargaret Shmitchell #AI paper “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?”

A must-read to understand the invisible way people can be influenced and AI manipulated. https://t.co/hyZ6xZrq3o

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Cost of entry for #AI startups?

“The amount of compute used to train the largest deep learning models…has increased 300,000x in 6y, increasing at a far higher pace than Moore’s Law.”

From “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?”

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I wish you success for a really *open* #AI > “EleutherAI concedes that because of OpenAI’s decision not to release some key details of GPT-3’s architecture, GPT-Neo will deviate from it”

Meet the people trying to replicate and open-source OpenAI’s GPT-3 https://t.co/AUEGkx77yF

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On the risk of misuse of #AI language models:

“When Peter put his contact information online, it had an intended context of use. Unfortunately, applications built on top of GPT-2 are unaware of this context…”

Does GPT-2 Know Your Phone Number? https://t.co/4S1y9Xk9yh https://t.co/IAOl2EpcLl

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Back to work, and my first paper for 2021 is @Gartner_inc “Implementing an Enterprise Open-Source Machine Learning Stack”.

I’m glad to see that research on #AI is expanding beyond whatever top cloud providers offer. There’s a universe of solutions that IT orgs should know about