Computer Vision

Datacast Episode 117: Vector Databases, The Embeddings Revolution, and Working in China with Frank Liu

Datacast Episode 117: Vector Databases, The Embeddings Revolution, and Working in China with Frank Liu

Frank Liu is the Director of Operations at Zilliz with nearly a decade of industry experience in machine learning and hardware engineering. Prior to joining Zilliz, Frank co-founded an IoT startup based in Shanghai and worked as an ML Software Engineer at Yahoo in San Francisco. He presents at major industry events such as Open Source Summit and writes tech content for leading publications such as Towards Data Science and DZone. Frank holds MS and BS degrees in Electrical Engineering from Stanford University.

Datacast Episode 108: Computer Vision, Product Management, and Enterprise Investing with Tom Rikert

Datacast Episode 108: Computer Vision, Product Management, and Enterprise Investing with Tom Rikert

Tom Rikert is the co-founder and CEO of Masterful AI, the training platform for computer vision that helps developers build models faster and with much less labeling. He is a former VC and invested in enterprise software and AI/ML at Andreessen Horowitz. Before VC, he held product management roles at Wildfire (acquired by Google), YouTube, and Autodesk. He began his AI/ML journey at MIT and started his career as an engineer at Silicon Graphics. Tom lives in the Bay Area with his wife and daughter and loves going fast, whether on a mountain bike or flying racing drones.

Datacast Episode 100: Data-Centric Computer Vision, Productizing AI, and Scaling a Global Startup with Hyun Kim

Datacast Episode 100: Data-Centric Computer Vision, Productizing AI, and Scaling a Global Startup with Hyun Kim

Hyun Kim is the co-founder and CEO of Superb AI, an ML DataOps platform that helps computer vision teams automate and manage the entire data pipeline: from ingestion and labeling to data quality assessment and delivery. He initially studied Biomedical Engineering and Electrical Engineering at Duke but shifted from genetic engineering to robotics and deep learning. He then pursued a Ph.D. in computer science at Duke with a focus on Robotics and Deep Learning but ended up taking leave to further immerse himself in the world of AI R&D at a corporate research lab. During this time, he started to experience the bottlenecks and obstacles that many companies still face to this day: data labeling and management were very manual, and the available solutions were nowhere near sufficient.

Datacast Episode 82: Enabling AI-Powered AR Navigation For Driving with Chen-Ping Yu

Datacast Episode 82: Enabling AI-Powered AR Navigation For Driving with Chen-Ping Yu

Dr. Chen-Ping Yu is the co-founder and CEO of Phiar, a company that is bringing human-like perception to every vehicle with its advanced lightweight spatial AI. Prior to founding Phiar, he was a postdoctoral fellow at Harvard University, researching neuro-inspired deep learning.

Chen-Ping received his Ph.D. from Stony Brook University in Computer Vision and Machine Learning and his MS from Penn State University. He was the recipient of numerous honors and awards, including from the NSF, and has published more than 15 scientific publications at top computer vision, AI, and cognitive science conferences and journals.

Datacast Episode 80: Creating The Sense of Sight with Alberto Rizzoli

Datacast Episode 80: Creating The Sense of Sight with Alberto Rizzoli

Alberto Rizzoli is co-Founder of V7, a platform for deep learning teams to manage training data workflows and create image recognition AI. V7 is used by AI-first companies and enterprises, including Honeywell, Merck, General Electrics, and MIT.

Alberto founded his first startup at age 19 and made the MakerFaire’s 20 under 20 list. In 2015, he began working on AI with Simon Edwardson while studying under Ray Kurzweil, leading to the creation of the first engine capable of running large deep neural networks on smartphone CPUs. Later, this project became Aipoly, a startup that helped the blind identify over 3 billion objects to date using their phones.

Alberto's work on AI granted him an award and personal audience by Italian President Sergio Mattarella and Italy’s Premio Gentile for Science and Innovation. V7's underlying technology won the CES Best of Innovation in 2017 and 2018.

Datacast Episode 73: Datasets for Software 2.0 with Taivo Pungas

Datacast Episode 73: Datasets for Software 2.0 with Taivo Pungas

Taivo Pungas is a tech entrepreneur working on a stealth-mode startup. Previously, he built the AI team at Veriff from scratch to 20+ people and contributed to various ML/data roles at Starship and other Estonian startups. On the side, he advises startups and writes a blog at taivo.ai.

Datacast Episode 70: Machine Learning Testing with Mohamed Elgendy

Datacast Episode 70: Machine Learning Testing with Mohamed Elgendy

Mohamed Elgendy is a seasoned AI expert, who has previously built and managed AI organizations at Amazon, Rakuten, Twilio, and Synapse. In particular, he founded and managed Amazon's computer vision think tank. He is the author of the "Deep Learning for Vision Systems" book published by Manning in November 2020. Mohamed regularly speaks at many AI conferences like Amazon's DevCon, O'Reilly's AI, and Google's I/O.

Datacast Episode 67: Model Observability, AI Ethics, and ML Infrastructure Ecosystem with Aparna Dhinakaran

Datacast Episode 67: Model Observability, AI Ethics, and ML Infrastructure Ecosystem with Aparna Dhinakaran

Aparna Dhinakaran is the Chief Product Officer at Arize AI, a startup focused on ML Observability. She was previously an ML engineer at Uber, Apple, and TubeMogul (acquired by Adobe). During her time at Uber, she built several core ML Infrastructure platforms, including Michelangelo. She has a bachelor’s from Berkeley's Electrical Engineering and Computer Science program, where she published research with Berkeley's AI Research group. She is on a leave of absence from the Computer Vision Ph.D. program at Cornell University.

What I Learned From Attending REWORK AI Applications Summit 2021

What I Learned From Attending REWORK AI Applications Summit 2021

Last month, I attended REWORK’s AI Applications Virtual Summit, which discovers machine learning tools and techniques to improve the financial, retail, and insurance experience. As a previous attendee of REWORK’s in-person summit, I have always enjoyed the unique mix of academia and industry, enabling attendees to meet with AI pioneers at the forefront of research and explore real-world case studies to discover the business value of AI.

In this long-form blog recap, I will dissect content from the talks that I found most useful from attending the summit. The post consists of 13 talks that are divided into 3 sections: (1) AI in Finance and RegTech, (2) AI in Retail and Marketing, and (3) AI in Insurance.

Datacast Episode 63: Real-World Transfer Learning with Azin Asgarian

Datacast Episode 63: Real-World Transfer Learning with Azin Asgarian

Azin Asgarian is currently an applied research scientist on Georgian’s R&D team, where she works with companies to help adopt applied research techniques to overcome business challenges. Azin holds a Master of Science in Computer Science from the University of Toronto and a Bachelor of Computer Science from the University of Tehran. Before joining Georgian, Azin was a research assistant at the University of Toronto and part of the Computer Vision Group, where she worked on the intersection of Machine Learning, Transfer Learning, and Computer Vision. In addition, due to her interest in HealthCare, she has worked on various healthcare projects as a research assistant at University Health Network.