Vihan Jain
@vihaniaj
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Member of Technical Staff @ Anthropic (prev Google DeepMind, Waymo)
San Francisco, CA
Joined July 2018
Excited to continue advancing the natural language reasoning system which builds on my team’s previous work released as Gemini Math Specialized 1.5 Pro
Intense few weeks on Math-Specialized 1.5Pro, thrilled with the end results!! Can't wait to keep pushing the boundaries in math reasoning and beyond! Full report:
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RT @bneyshabur: Silver medal in International Math Olympiad! And we were so close to getting a gold medal! Congrats to the AlphaProof, Alph…
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RT @OriolVinyalsML: AI is beating me at the things I love, one step at a time (coding, StarCraft, mathematics, ...). AlphaProof solved the…
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RT @sundarpichai: Exciting news! 🇮🇳 Today, we're launching the Gemini mobile app in India, available in English and 9 Indian languages. We’…
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RT @sundarpichai: Updated Gemini 1.5 Pro report: MATH benchmark for specialized version now at 91.1%, SOTA 3 years ago was 6.9%, overall a…
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RT @HarshMeh1a: Checkout our work on pushing the boundaries of reasoning capabilities of Gemini 1.5 Pro! We've been working hard at this.…
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RT @jacobaustin132: The Blueshift team has done awesome work pushing Hendryck's MATH above 90%. MATH isn't the hardest dataset in the worl…
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RT @vedantmisra: Bridging the gap between state-of-the-art systems and superintelligence hinges on quantitative and technical reasoning. Th…
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Intense few weeks on Math-Specialized 1.5Pro, thrilled with the end results!! Can't wait to keep pushing the boundaries in math reasoning and beyond! Full report:
Today we have published our updated Gemini 1.5 Model Technical Report. As @JeffDean highlights, we have made significant progress in Gemini 1.5 Pro across all key benchmarks; TL;DR: 1.5 Pro > 1.0 Ultra, 1.5 Flash (our fastest model) ~= 1.0 Ultra. As a math undergrad, our drastic results in mathematics are particularly exciting to me! In section 7 of the tech report, we present new results on a math-specialised variant of Gemini 1.5 Pro which performs strongly on competition-level math problems, including a breakthrough performance of 91.1% on Hendryck’s MATH benchmark without tool-use (examples below 🧵). Gemini 1.5 is widely available, try it out for free here & read the full tech report here:
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RT @bwzhang_usc: Bringing Denotation Graph to improve the Image and Text Retrieval. Our EMNLP paper is on arxiv now:
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RT @AkshaySubraman9: Had a great learning experience attending the lectures and discussions at the @GoogleAI Summer school's NLU track over…
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RT @GoogleAI: As a leader in #NaturalLanguageProcessing research, we are excited to be a Diamond Level sponsor of @ACL2019_Italy. If you're…
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4.20, Hall 5!
If your at @ACL2019_Italy and multimodal learning and natural language grounding interests you, come check out our presentation of
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