A popular technique to make AI more efficient has drawbacks

Quick Summary:

In the context of AI, quantization refers to lowering the number of bits — the smallest units a computer can process — needed to represent information.

One of the most widely used techniques to make AI models more efficient, quantization, has limits — and the industry could be fast approaching them.

Bear with us for a moment as we dive in a bit.“Precision” here refers to the number of digits a numerical data type can represent accurately.

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Article Details

Author / Journalist: Kyle Wiggers

Category: Technology

Markets: United States

Topics: Deep learning Research Quantization Study AI

Source Website Secure: Yes (HTTPS)

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Fact Checked: Legitimate

Article Type: News Report

Published On: 2024-11-17 @ 15:00:00 (13 hours ago)

News Timezone: GMT -5:00

News Source URL: techcrunch.com

Language: English

Article Length: 1101 words

Reading Time: 7 minutes read

Sentences: 41 lines

Sentence Length: 27 words per sentence (average)

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Copyright Owner: © TechCrunch

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