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Joanne Jang
Product professional in technology
Joanne Jang is a Product Manager at Google, specializing in Google Assistant. She has a strong background in technology, having previously worked as a Product Manager for Dropbox and as a Software Engineer at Rimeto. Jang's expertise includes natural language understanding and conversation planning, which she applied during her time at Google Assistant from March 2020 to December 2021.34
Education and Early Career
Jang holds both a Master of Science in Computer Science and a Bachelor of Science in Mathematical and Computational Science from Stanford University. Her educational journey also included various internships, such as at Apple, The Walt Disney Company, and NASA's Jet Propulsion Laboratory. She was actively involved in teaching as a Teaching Assistant for computer science courses at Stanford and participated in initiatives like "Girls Teaching Girls to Code" as an Executive Lead.13
Current Role and Impact
As a Product Manager, Jang focuses on enhancing user experience and developing innovative solutions. Her work at Google involved significant contributions to improving the capabilities of Google Assistant, particularly in natural language processing.24 After her tenure at Google, she transitioned to OpenAI, where she currently leads projects related to DALL·E, an AI system that generates images from textual descriptions, further showcasing her commitment to advancing AI technologies.45
Overall, Jang's career reflects a blend of technical proficiency and a passion for creating user-centric products in the tech industry.
Highlights
📖 we just shared the model spec, i.e. a “spec” for openai’s models. it’s a work in progress that we’re sharing for early feedback.
it also features profanity & cats, flat earth theory, and why the model says “sorry, i can’t help with that”.
from a product perspective, i’m personally excited about this concept of a model spec for three reasons:
- there’ll be more clarity on whether something is a policy or rlhf bug
if a model says or does something that you disagree with, was that intended by openai or an rlhf bug? should you yell at sama or researchers?? jk please don’t yell at researchers
- principles are easier to debate and get feedback on, vs. hyper-specific screenshots or abstract feel-good statements
it’s easy for most people to agree on “models should be [adjective]”, but the more important questions lie deeper in thorny scenarios: how should the model engage with someone who claims that the earth is flat?
the model spec introduces language we can use to debate model behavior questions: objectives, rules, defaults, with difficult cases we’ve encountered through real-world use. i’m hoping that these new concepts will enable more nuanced and important discussions.
- model spec feedback will help us steer our efforts in steerability
an explicit non-goal for the model spec is to reach consensus on a one-size-fits-all model. that will never happen. we ultimately want to give users and developers as much control as possible while staying within hard boundaries that people understand.
hearing feedback on where & how everyone wants to steer the model is helpful in (a) designing a more rigorous survey process and (b) informing the research and product roadmap.
a lot of people worked on this (both the model spec and all the thinking that led up to it), and they’re all genuinely excited to hear from you on the hardest questions — so please don’t be shy and tell us your thoughts!
