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BQ AI Open Source

BQ AI Open Source is an initiative to develop a platform that consolidates and links all readily available public sources of government statistics from U.S. federal, state, and local agencies, as well as international organizations such as the United Nations and World Bank. The information gathered will cover all topics of interest to humanity and the environment, including employment, demographics, climate change, manufacturing, healthcare, crime, and education, among others. The platform will be powered by a novel AI architecture that enables retrieval of facts, as well as advanced analysis of the data being gathered. Interactions with users will occur in natural language and feature real-time visuals and statistical calculations. 

BQ AI Open Source has partnered with leading enterprises, experts, and universities to bring this vision to life. Below are some of our existing members. If you are interested in participating, please contact open-source@brightquery.com or open-source@brightquery.ai. 
Click here to read the BQ AI Open Source Mission Statement.

BQ AI Open Source Corporate Partners

Google Data Commons

Stanford Artificial Institute Laboratory

MIT Center for Collective Intelligence

University of Chicago Data Science Institute

Contextual.ai

Gradient.ai

BQ AI Open Source Individual Partners

Andrew Ng, Ph.D.

Andrew Ng, Ph.D.

Dr. Andrew Ng is a globally recognized leader in AI (Artificial Intelligence). He is Founder of DeepLearning.AI, Founder & CEO of Landing AI, General Partner at AI Fund, Chairman and Co-Founder of Coursera and an Adjunct Professor at Stanford University’s Computer Science Department. As a pioneer in machine learning and online education, Dr. Ng has changed countless lives through his work in AI, and has authored or co-authored over 200 research papers in machine learning, robotics and related fields. In 2023, he was named to the Time100 AI list of the most influential AI persons in the world.
John Santerre, Ph.D.

John Santerre, Ph.D.

Currently, his primary job involves working directly with Andrew Ng on special (stealth) projects largely related to large language models at DeepLearning.AI. As a part of this, he works on a number of early-stage investigations into how agentive workflows can provide benefits. Additionally, for 6 years, John has been serving as a professor for Master’s students where he teaches the intersection of data science, deep learning, and now large language models. He is currently teaching courses at the University of California, Berkeley, Southern Methodist University, and Syracuse University. John also supports a research team at NASA Goddard pursuing research into how large language models can support scientific endeavors on a planetary scale.
Chris Potts, Ph.D.

Chris Potts, Ph.D.

Chris Potts is Professor and Chair of Linguistics and Professor (by courtesy) of Computer Science at Stanford, a faculty member in the Stanford NLP Group and the Stanford AI Lab, and an Amazon Scholar. His group at Stanford uses computational methods to explore topics in context-dependent language use, systematicity and compositionality, model interpretability, and neural information retrieval.
Douwe Kiela, Ph.D.

Douwe Kiela, Ph.D.

Douwe works as CEO at Contextual AI. He is also adjunct Professor in Symbolic Systems at Stanford University. Previously, Dr. Douwe was the Head of Research at Hugging Face and before that a Research Scientist at Facebook AI Research. He received his PhD and MPhil from the University of Cambridge. He is an expert in Machine Learning and Natural Language Processing.
Kira Radinsky, Ph.D.

Kira Radinsky, Ph.D.

Kira Radinsky, Ph.D., is an inventor and entrepreneur, specializing in predictive data mining. She currently works as the CEO and CTO of Diagnostic Robotics. She is also a visiting professor at Technion teaching the applications of predictive data mining in medicine. She has co-authored over 10 patents and more than 50 peer-reviewed articles.
Amy Crew Cutts, Ph.D.

Amy Crew Cutts, Ph.D.

Dr Amy Cutts is a world famous economist who has worked as the chief economist of multiple large companies including Equifax and FreddieMac. Often quoted in national print and broadcast media, Cutts has also published numerous studies on such topics as the economics of subprime lending, the impact of technology on foreclosure prevention, and drivers of strategic mortgage default. Cutts holds a Master’s and PhD in Economics from the University of Virginia and a Bachelor’s degree in Applied Mathematics from Trinity University, San Antonio, TX.
Prem Ramaswami

Prem Ramaswami

Prem is a product management leader focused on the intersection of technology and wide-scale social impact at Google. He led efforts in Google Search’s Social Impact projects in the domains of Health, Civics, Education, Arts & Culture, Crisis Response, and Social Good for many years including leading datacommons.org. Prem also teaches product management courses at Harvard and Stanford. Prem holds an MBA from Harvard.
Laurence Moroney

Laurence Moroney

Laurence Moroney leads AI Advocacy at Google, working as part of the Google Research into Machine Intelligence (RMI) team. He's the author of more programming books than he can count, including 'AI and Machine Learning for Coders' with OReilly, to be published in October 2020. He's also the instructor and creator of the TensorFlow In Practice, and TensorFlow Data and Deployment specializations on Coursera. He runs the YouTube channel for tensorflow, and the TensorFlow certificate program for developers at tensorflow.org/certificate. When not working on AI, he's a published Sci-Fi author, comic book creator and IMDB-listed screenwriter.
Ramanathan V Guha, Ph.D.

Ramanathan V Guha, Ph.D.

Ramanathan V. Guha is a Google Fellow and one of co-founders of Data Commons. He was a principal scientist at Apple, and a principal engineer at Netscape, where he created the first version of RSS. He co-founded Epinions, and has been a researcher at IBM Almaden Research Center. Guha joined Google in May of 2005. There, he started Custom Search, Search based keyword tool, SMS Channels and Schema.org. Guha graduated with a B.Tech (Mechanical Engineering) from Indian Institute of Technology Madras, MS in Mechanical Engineering from University of California, Berkeley and Ph.D in Computer Science from Stanford University under John McCarthy.
Gianni Giacomelli

Gianni Giacomelli

Gianni is a world leader in technology-driven innovation through human-centered design methods. Gianni is the Head of Design Innovation, Collective Intelligence Design Lab at MIT. In addition to his role at MIT, Gianni is a member of the leadership team at GE-spinoff Genpact, a professional services firm, where he oversees the company’s digital innovation efforts. His previous career spans more than two decades across innovation strategy, marketing, and transformation consulting with global and emerging leaders in professional services (Boston Consulting Group, Everest, Datamonitor) and software (SAP).
Mark Huang

Mark Huang

Mark is a Co-Founder and Chief Architect at Gradient, a platform that helps companies build custom AI applications by making it extremely easy to fine tune foundational models and deploy them into production. Previously, he has been a tech lead in machine learning teams at Splunk and Box developing and deploying production systems for streaming analytics, personalization and forecasting. In another life he was also an algorithmic trader at quantitative hedge funds.
Anthony Chan, Ph.D.

Anthony Chan, Ph.D.

Anthony worked as Chief Economist at Chase for 25 years in addition to being a public speaker and world renowned economist. He holds a Ph.D. in Economics and has made over 700 live TV appearances on CNBC, Bloomberg TV, CNN International, Fox Business News, CGTN, and other TV outlets. He taught Economics at many universities in the US and is an expert in Banking.

BQ AI Open Source Mission Statement

We write to advocate for widening access to verifiable, open, and truthful data. In today’s era, marked by the prevalence of ‘alternative facts,’ the need for reliable, easily accessible data has never been more critical. It is a fundamental component in preserving our cultural heritage and upholding the very fabric of our civilization.

In our current digital age, data is not just a resource but the cornerstone of informed decision-making, innovation, and transparency. By making data openly available and user-friendly via a structure like data commons, we would go beyond providing information towards empowering individuals, communities, and developers to engage with this information meaningfully. Open data fosters an environment where truth is sought, easily found, and shared, giving you the power to shape the narrative.

The importance of this initiative cannot be overstated. Across the globe, we witness the consequences of misinformation and the speed at which it can spread. 

By committing to open, well-structured data, we provide a reliable reference point for educators, researchers, and the general public, safeguarding our historical context, promoting an informed citizenry, and supporting democratic governance. The time to act is now, and your role in this is crucial.

Moreover, open, structured data would act as a catalyst for technological and social innovation. By providing developers with access to comprehensive, accurate datasets, we enable the creation of applications and tools that can disseminate factual information widely and effectively, enhancing access to information and supporting public discourse. 

Therefore, we urge policymakers and government officials to prioritize the development of policies and technologies that support the availability of open data. Such initiatives should focus on releasing data and ensuring its accessibility and usability. Doing so can cultivate a more informed, engaged, and connected world.

Thank you for considering this vital issue. We greatly appreciate your support in this endeavor and look forward to the positive changes our combined efforts can bring about.