Black in AI

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🎉 Congrats to Fireflies.ai on their incredible scaling and growth - 4th most popular AI platform! Krish Ramineni, Sam Udotong, & team have amassed 16M users and a massive global customer base - see the blurb below and watch the podcast interview for more. 🙌🏽 👉🏽 According to Ramp's Quarterly Spend report, Fireflies.ai was the 4th most popular AI platform by customer count (behind only OpenAI, Midjourney and Anthropic). With over 300,000 customers worldwide and 16M users, Fireflies.ai is operating at a scale few AI companies have reached.

A year ago, I became the first Nigerian, 4th Black woman, and 6th Black student to defend a PhD in Computer Science from the Cornell Ann S. Bowers College of Computing and Information Science at Cornell University! Since then, I’ve started my career at the world’s foremost think tank, attended numerous prominent convenings, had 50+ speaking engagements, reached 200+ citations of my research, and engaged in collaborations that have produced nearly a dozen research papers, policy commentaries, white papers, and technical reports. Data from the Computing Research Association (CRA) Taulbee Survey, the principal source of information on the enrollment, production, and employment of PhDs in computer science, information science, and computer engineering in North America (United States and Canada), also indicates that I was 1/18 Black students and 1/387 women to graduate with a PhD in Computer Science during the 2022-2023 academic year. While the numbers for women CS PhD graduates have gradually increased over the past decade, the production of Black CS PhD graduates has remained dismal, never exceeding more than 2% of all awarded computer science doctoral degrees in North America. As the world continues to contemplate the harms caused by technical systems, particularly those enabled by AI, how do we reconcile that those most likely to experience disparate impacts continue to be underrepresented in researching, developing, and critically evaluating these systems? This past year has taught me much about myself, both personally and professionally. It has also helped me define the research I want to pursue and the impact I want to make within the fields of AI, global development, and technology governance. Over the next year, I look forward to pursuing new opportunities to scale my impact and create opportunities for marginalized voices to contribute to AI research and shape public policy. Stay tuned!

The moment you've been all waiting for is finally here ⌛️💥 📢 We have officially opened up applications for 2024 Fellows!! 📢 Get the chance to be mentored by world-class CS researchers 💫 Apply today: https://lnkd.in/eTq6Ymuk

Distributed AI Research Institute is on Mastodon @DAIRInstitute· Jul 8 Thank you for joining our Data Workers' Inquiry launch Register for the next event on July 22 @ 8am PDT/5pm CET @ https://lnkd.in/gMhxWeeZ Repository @ data-workers.org http://data-workers.org Event schedule @ http://data-workers.org Donate to the workers @ https://lnkd.in/gycS6iQB https://lnkd.in/gfH8wK53 Data Workers Inquiry Talks Series Pt. 2: What does precarity look like for data workers? July 22nd, 2024

One week to go until our hashtag #FreedomFlyers Summit kicks off! From July 15-19, we’ll be hosting a series of events to highlight the importance of opting out of TSA’s facial recognition technology. Participate in daily activities to submit TSA scorecards, and make your voice heard. Don’t miss our culminating livestream event on July 19th with a panel featuring @jovialjoy and others with exciting prizes. We will host special guests including @wewatchwatchers, author of Dark Matters, @kingavriel, @CornellUniversity Civic Science Postdoctoral Fellow, and @evan_greer, Executive Director of @fightfortheftr — see you there! RSVP http://bit.ly/optoutclub Freedom Flyers Summit: Resisting Airport Face Scanseventbrite.com

Learn to optimize RAG for cost and performance in our new short course, Prompt Compression and Query Optimization, created with MongoDB and taught by Richmond Alake. This course teaches you to combine traditional database capabilities with vector search using MongoDB for RAG. You'll learn these techniques: - Vector search: For semantic matching of user queries - Filtering using metadata: Pre- and post-filtering to narrow search results - Projections: Selecting only necessary fields to minimize data returned - Boosting: Reranking results to improve relevance - Prompt compression: Using a small LLM to compress context, significantly reducing token count and processing costs These methods address scaling, performance, and security challenges in large-scale RAG applications. You can sign up here: https://lnkd.in/gMVN3hzM

Are you interested in pursuing a Masters or PhD in an AI-related field but unsure where to start? Apply to Black In AI's Emerging Leaders In AI Grad Prep Program. APPLY HERE: https://bit.ly/3xQG0d2 Founded in 2018, Black In AI's Emerging Leaders in AI program aims to break down financial and social barriers, increasing the representation of black and brown researchers, professionals, and academics in the field of AI through graduate admissions.

Excited to share our preprint on "Vibrational Entropy and Free Energy of Solid Lithium using Covariance of Atomic Displacements (CAD) Enabled by Machine Learning". As materials discovery and materials databases such as Materials Project get better, it's clear that one of the next issues to address is the inclusion of entropy and free energies at arbitrary pressure and temperature. One of the most difficult to compute is typically vibrational entropy, especially for anharmonic systems. We will be looking to extend this workflow to multi-element systems and all code will be open-source. Thanks to my co-authors Yang Huang, Michael Widom and Venkat Viswanathan. Also grateful for computing credits from Oracle Cloud and Google that made this possible. Abstract: Vibrational properties of solids are key to determining stability, response and functionality. However, they are challenging to computationally predict at Ab-Initio accuracy, even for elemental systems. Ab-Initio methods for modeling atomic interactions are limited in the system sizes and simulation times that can be achieved. Due to these limitations, Machine Learning Interatomic Potentials (MLIPs) are gaining popularity and success as a faster, more scalable approach for modeling atomic interactions, potentially at Ab-Initio accuracy. Even with faster potentials, methodologies for predicting entropy, free energy and vibrational properties vary in accuracy, cost and difficulty to implement. Using the Covariance of Atomic Displacements (CAD) to predict entropy, free energy and finite-temperature phonon dispersions is a promising approach but thorough benchmarking has been hampered by the cost of Ab-Initio methods for sampling. In this work, we use a MLIP and the CAD to characterize the convergence of the predicted properties and determine optimal sampling strategies. We focus on solid lithium at zero pressure, showing that the MLIP-CAD approach reproduces experimental entropy, phonon dispersions and the martensitic transition while also comparing to more established methods. Preprint: https://lnkd.in/dT36JNSF

Joining us for Observe at SHACK15 – Jeremy Nixon, Co-Founder and CEO at Omniscience and agihouse.ai creator. At Arize:Observe, attendees will gain pragmatic experience evaluating and measuring the performance and reliability of AI systems using the latest open-source tools available. Join us on July 11 in San Francisco: https://lnkd.in/gpEitQb5 👋 Attn Builders: Apply to demo your app onsite at Observe! If you’re working on an OSS app and want the opportunity to demo your work to a broad audience of builders, researchers, and innovators. The Builders Lounge, powered by Microsoft, is dedicated to showcasing innovative and exciting LLM-powered applications built using open-source tools and emerging platforms. Apply for the builders lounge: https://lnkd.in/gW-6fJp9