Meta just released its next large language model, Llama 2, and also published various supporting docs about research behind the open-source LLM, what it can do, and steps it's taking to develop AI in what it believes is a responsible way.
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Preventing Revenue Loss Through Digital Risk Infrastructure Solutions
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Can we anticipate and prevent the loss of revenue, time, and most importantly, life? Hear how @lloydsregister AllAssets makes that possible on @SAP
's all-new #BetterTogetherStories podcast: https://
bit.ly/3M9Gb5P #risk #infrastructure #DigitalTransformation -
Measuring Faithfulness in Chain-of-Thought Reasoning
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Measuring Faithfulness in Chain-of-Thought Reasoning paper: https://
www-files.anthropic.com/production/fil
es/measuring-faithfulness-in-chain-of-thought-reasoning.pdf
… Large language models (LLMs) perform better when they produce step-by-step, “Chain-ofThought” (CoT) reasoning before answering a question, but it is unclear if the stated reasoning is a faithful -
AI’s Growing Role in Manufacturing Software Systems
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In recent years, there has been a growing interest in the role of #AI in #manufacturing software systems. Francisco Lobo, the CEO of @CriticalMfg
, shares his insights and vision on how AI is shaping the future of manufacturing. http://
ow.ly/hlv450Oic5O #cm_iiot #industry40 #iiot -
Healthcare Innovator Shiv Gaglani Shares His Story Podcast
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🎧 We have a special treat for all healthcare enthusiasts! Learn from a trailblazer, Shiv Gaglani, listen to this. 👉https://t.co/CyQL0zB0RD#Podcast #Health #Clinicians #WEAREELSEVIER pic.twitter.com/SxQ6GDFqJn
— Catherine Adenle (@CatherineAdenle) 18 juillet 2023We have a special treat for all healthcare enthusiasts! Learn from a trailblazer, Shiv Gaglani, listen to this. https://
weareelsevier.podbean.com/e/weareelsevie
r-shiv-gaglani-s-story/
…
#Podcast #Health #Clinicians #WEAREELSEVIER -
Meta releases LLaMa-2 open-weight language model free
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LLaMa-2 from @MetaAI is here!
Open weights, free for research and commercial use. Pre-trained on 2T tokens.
Fine-tuned too (unlike v1). Lets gooo…. https://
ai.meta.com/llama/
The paper lists the amazing authors who worked to make this happen night and day. Be sure to thank -
Anthropic improves language model reasoning through faithful explanations
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We’re excited about ways to make language models generate more faithful explanations that help them reason better! We encourage you to check out our papers for more results and details: https://
www-files.anthropic.com/production/fil
es/measuring-faithfulness-in-chain-of-thought-reasoning.pdf
… https://
www-files.anthropic.com/production/fil
es/question-decomposition-improves-the-faithfulness-of-model-generated-reasoning.pdf
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Anthropic Hiring Research Engineers and Scientists
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If you’re excited about this kind of research, the team that led this work is actively hiring research engineers and scientists, and we’d encourage you to apply: https://
jobs.lever.co/Anthropic/436c
a148-6440-460f-b2a2-3334d9b142a5
… https://
jobs.lever.co/Anthropic/eb9e
6d83-626c-4f59-8a0e-fa7c413b2014
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Question Decomposition Methods: Chain-of-Thought and Factored Approaches
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These question-decomposition methods help mark points on a spectrum, with chain-of-thought prompting occupying one end, factored decomposition occupying the other, and chain-of-thought decomposition bridging the middle.
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Decomposition techniques reduce model reasoning bias
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Decomposition could mitigate issues with models ignoring their reasoning by clearly specifying the relationship between reasoning steps. Answering subquestions in isolated contexts could also reduce the model’s ability to generate biased reasoning.