President & Co-Founder @OpenAI
Joined July 2010
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Thing that stands out about current AI buzz is an unfamiliar realistic optimism, about the field specifically and tech generally.
Still very early days (and plenty of concerns!) for AI, but seems like people are sensing tremendous opportunities personally and for society.
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In AI, too often debates are “which is more important, ideas or execution.” Wrong question since both are critical, though seems clear that execution tends to be more undervalued.
Many believe that great AI advances must contain a new “idea”. But it is not so: many of AI’s greatest advances had the form “huh, turns out this familiar unimportant idea, when done right, is downright incredible”
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One thing I admire about @BillGates—issuing challenges to change his mind:
“If you show me [neural net that passes AP bio], then I will say that it has the ability to represent things in a deeply abstract form, that’s more than just statistical things”
forbes.com/sites/alexkonrad/…
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Everyone talking about the future of search, but I'm particularly excited about the future of the browser — Edge will now include an AI assistant that can help you anywhere on the web.
Really starting to point at the future of UI:
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Copilot for the web:
Bing and Edge + AI: a new way to search starts today blogs.microsoft.com/blog/202…
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Was reflecting today on how far AI alignment has come, from being entirely a fringe concern to a critical product feature.
Other aspects of AI safety out of limelight (e.g. capability prediction, monitoring for power-seeking, supervising self-improving agents); may change too!
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Manual inspection of data has probably the highest value-to-prestige ratio of any activity in machine learning.
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Programming is a way to convert an abstract description of a process into value. Machine learning is a way to convert inputs and outputs of a process you can’t describe into value.
Much harder to get an ML system off the ground, but the upside is correspondingly much higher too.
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GPT-3 API is still the hardest single project I've worked on. Most common startup mistake is to start with a technology, rather than a user problem to solve — but working on one problem would sacrifice AGI focus. So felt like it probably wouldn't work, but had to push through.
Around this time 3 years ago, early 2020, we started testing the OpenAI GPT-3 API with the first external users. It was really hard to get anyone to try it, let alone build on it. So much has changed since then, but still feels like we're in the early days.
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When starting OpenAI, we thought hard about what job titles to use—didn't want to bucket people into researchers & engineers. Alan Kay advised that they used "Member of Technical Staff" at Xerox Parc, we loved & adopted it. Recently have seen many companies doing same—very cool!
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We're piloting ChatGPT Plus, a $20/mo subscription for faster response times and reliability during peak hours: openai.com/blog/chatgpt-plus…
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We are piloting the initial version of a tool to distinguish AI-written & human-written text. Works for AI text from a variety of providers.
Looking for feedback on the tool itself, and broad input on the topic of identifying AI-written text:
We’re developing a new tool to help distinguish between AI-written and human-written text. We’re releasing an initial version to collect feedback and hope to share improved methods in the future. openai.com/blog/new-ai-class…
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Seeking software engineers across frontend/backend/infra to help us scale & improve ChatGPT who can join ASAP.
Looking for track record of building great products with a team.
Describe an exceptional accomplishment, area(s) of expertise, & ideal start date to gdb@openai.com.
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