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Deep research is built for people who do intensive knowledge work in areas like finance, science, policy & engineering and need thorough & reliable research. It's also useful for discerning shoppers looking for hyper-personalized recos on purchases that require careful research.

147,908 görüntüleme • 1 yıl önce •via X (Twitter)

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OpenAI profil fotoğrafı
OpenAI1 yıl önce

Today we are launching our next agent capable of doing work for you independently—deep research. Give it a prompt and ChatGPT will find, analyze & synthesize hundreds of online sources to create a comprehensive report in tens of minutes vs what would take a human many hours.

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OpenAI1 yıl önce

Powered by a version of OpenAI o3 optimized for web browsing and python analysis, deep research uses reasoning to intelligently and extensively browse text, images, and PDFs across the internet.

OpenAI profil fotoğrafı
OpenAI1 yıl önce

The model powering deep research reaches new highs on a number of public evaluations focused on real-world problems, including Humanity's Last Exam.

OpenAI profil fotoğrafı
OpenAI1 yıl önce

Deep research is rolling out to Pro users starting later today. Then we will expand to Plus and Team, followed by Enterprise.

OpenAI profil fotoğrafı
OpenAI1 yıl önce

Want to work on deep research at OpenAI?

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Lab4crypto1 yıl önce

🚀 Don't gamble with your portfolio! Use our advanced hybrid quant risk tool using on/off-chain data daily and make informed decisions. 📈 Acess to 1000+ charts for your crypto journey. 📚Receive free weekly quant analysis. 📊+21 projects supported. 🏗️ Beginners and experts.

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Chad Brewbaker1 yıl önce

Or use web driver tool calls locally and don't get man in the middled by Microsoft and NSA.

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Plutus1 yıl önce

It does sound like a real personal assistant.

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Metamorph1 yıl önce

I am really interested. Is it applicable in healthcare?

Benzer Videolar

auto-research is starting to gain traction as a very viable paradigm for creating useful research discovery. now, that paradigm is still in its infancy and the infrastructure to hold all that trail of context as the agents blaze through experiments isn't well defined (to say the least). on that topic, I had the chance to chat with my boys francesco and giulio from paradigma about what underlying infra is needed to make this paradigm work. the paradigma's paradigm, which involves copious amount of DAGs, make this auto-research paradigm a paradigmatic case of essential infrastructure. here's the full video in full: - 0:00 - what is missing from auto-research? - 2:02 - giulio and francesco ai journey - 8:10 - research infra is the bottleneck? - 10:18 - paradigma vision of autonomous research - 13:17 - “important discovery per joules” - 17:15 - why is DAG the unit of research for auto-research? - 20:40 - is paradigma trying to replace the research publication? - 24:50 - how does knowledge is shared between experiments in the DAG? - 27:34 - what is even auto-research lol? - 33:53 - the value of the human mind in this auto-research future. - 37:00 - how do you reconcile hallucination in this auto-research paradigm? - 41:33 - the adoption of auto-research across varied fields? - 47:30 - ✨ introduction to the auto-research infrastructure. ✨ - 56:55 - where is the code? - 59:10 - full IDE next? - 1:03:20 - the place of the human in this DAG / code quality? manual node? token spent? - 1:16:02 - who’s the user for auto-research? - 1:18:13 - how to validate bad DAG? - 1:20:18 - ✨ auto-research agent results ✨ - 1:22:53 - ✨ how a big research DAG looks like? ✨ - 1:25:10 - how to get the canonical DAG for the final result? - 1:27:50 - the auto-research DAG being the new pre-print? - 1:30:05 - what’s next for paradigma and the auto-research infra? - 1:35:00 - what are they excited about research wise? enjoyyyyy my guys 🌹

Yacine Mahdid

12,091 görüntüleme • 3 ay önce