Gradient Flow #38: Large Language Models, Infinite Laptop, Overhyping AI

Subscribe • Previous Issues This edition has 455 words which will take you about 3 minutes to read. “Yesterday I was clever, so I wanted to change the world. Today I am wise, so I am changing myself.”- Rumi Data Exchange podcast Training and Sharing Large Language Models    Connor Leahy, AI Researcher at Aleph Alpha GmbH,Continue reading “Gradient Flow #38: Large Language Models, Infinite Laptop, Overhyping AI”

Gradient Flow #38: Large Language Models, Infinite Laptop, Overhyping AI

Subscribe • Previous Issues This edition has 455 words which will take you about 3 minutes to read. “Yesterday I was clever, so I wanted to change the world. Today I am wise, so I am changing myself.”- Rumi Data Exchange podcast Training and Sharing Large Language Models    Connor Leahy, AI Researcher at Aleph Alpha GmbH,Continue reading “Gradient Flow #38: Large Language Models, Infinite Laptop, Overhyping AI”

Combine the development experience of a laptop with the scale of the cloud

Highlights from opening keynotes at the 2021 Ray Summit. A newly released report from McKinsey forecasts an upcoming explosion in AI applications across all industries and domains. With this surge in demand to incorporate machine learning (ML) and AI into software, developers now have an array of open source and commercial software tools and componentsContinue reading “Combine the development experience of a laptop with the scale of the cloud”

Gradient Flow #37: Automation in DataOps, Neural RecSys, Self-Supervision

Subscribe • Previous Issues This edition has 485 words which will take you about 3 minutes to read. “Moses was technically the first person to download files to his tablet from the cloud.” – @ADDiane Data Exchange podcast Automation in Data Management and Data Labeling  Hyun Kim is co-founder and CEO of Superb AI, a startup buildingContinue reading “Gradient Flow #37: Automation in DataOps, Neural RecSys, Self-Supervision”

Gradient Flow #36: Model Monitoring, Hydrofoils, Data Portability

Subscribe • Previous Issues This edition has 428 words which will take you about 2 minutes to read. “Preferences are optional and subject to constraints, whereas constraints are neither optional nor subject to preferences.” – Marko Papic Data Exchange podcast Making Boats Fly with Reinforcement Learning and Ray   Nic Hohn (Chief Data Scientist, McKinsey/QuantumBlack Australia) describesContinue reading “Gradient Flow #36: Model Monitoring, Hydrofoils, Data Portability”

Model Monitoring Enables Robust Machine Learning Applications

Key features of ML monitoring solutions, why companies need a holistic MLOps platform that includes model monitoring, and challenges companies face in making that happen. By Ben Lorica and Paco Nathan. According to the 2020 Gartner Hype Cycle for Artificial Intelligence, machine learning (ML) is entering the Trough of Disillusionment phase. This is the phaseContinue reading “Model Monitoring Enables Robust Machine Learning Applications”

Gradient Flow #35: Optimizing Inference, Workflow Tools, RL in Large Enterprises

Subscribe • Previous Issues This edition has 510 words which will take you about 3 minutes to read. “The thing about machine learning scientists is that they never admit defeat because all of their problems can be solved with more data.” – William Tunstall-Pedoe Data Exchange podcast Why You Should Optimize Your Deep Learning Inference Platform  Continue reading “Gradient Flow #35: Optimizing Inference, Workflow Tools, RL in Large Enterprises”

Applications of Reinforcement Learning: Recent examples from large US companies

When I wrote about enterprise applications of reinforcement learning (RL) a little over a year ago, I cited a few examples of applications for recommenders and personalization systems. At the time, the examples I listed came from large technology companies, specifically Netflix, JD, Facebook, and YouTube, as only large companies tended to have the resourcesContinue reading “Applications of Reinforcement Learning: Recent examples from large US companies”