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Explaining Tokens — the Language and Currency of AI

NVIDIA AI Blog

Under the hood of every AI application are algorithms that churn through data in their own language, one based on a vocabulary of tokens. AI factories a new class of data centers designed to accelerate AI workloads efficiently crunch through tokens, converting them from the language of AI to the currency of AI, which is intelligence.

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How test-time scaling unlocks hidden reasoning abilities in small language models (and allows them to outperform LLMs)

VentureBeat

A 1B small language model can beat a 405B large language model in reasoning tasks if provided with the right test-time scaling strategy. Read More

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Why job postings may be attracting narcissistic candidates—without companies realizing it

Fast Company Tech

We decided to study job postings after noticing that the language used to describe an ideal candidate often included traits linked to narcissism. We call the two sets rule-follower and rule-bender language. Our current findings shed light on the importance of carefully crafting job posting language.

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Over half of LLM-written news summaries have “significant issues”—BBC analysis

Ars Technica

In an extensive report published this week , the BBC analyzed how four popular large language models used or abused information from BBC articles when answering questions about the news.

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Larger language models do in-context learning differently

Google Research AI blog

In “ Larger language models do in-context learning differently ”, we aim to learn about how these two factors (semantic priors and input-label mappings) interact with each other in ICL settings, especially with respect to the scale of the language model that’s used. targets) instead of natural language labels.

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Universal Speech Model (USM): State-of-the-art speech AI for 100+ languages

Google Research AI blog

Posted by Yu Zhang, Research Scientist, and James Qin, Software Engineer, Google Research Last November, we announced the 1,000 Languages Initiative , an ambitious commitment to build a machine learning (ML) model that would support the world’s one thousand most-spoken languages, bringing greater inclusion to billions of people around the globe.

Language 140
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Google Research, 2022 & Beyond: Language, Vision and Generative Models

Google Research AI blog

Transform modalities, or translate the world’s information into any language. I will begin with a discussion of language, computer vision, multi-modal models, and generative machine learning models. We want to solve complex mathematical or scientific problems. Diagnose complex diseases, or understand the physical world.

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