Andrew Fairless – Page 28 – Andrew Fairless, Ph.D.

What I Read: Realtime User Actions in Recommendation

By Andrew Fairless on February 27, 2023January 9, 2023

https://medium.com/pinterest-engineering/how-pinterest-leverages-realtime-user-actions-in-recommendation-to-boost-homefeed-engagement-volume-165ae2e8cde8 How Pinterest Leverages Realtime User Actions in Recommendation to Boost Homefeed Engagement VolumeXue Xia, Software Engineer, Homefeed Ranking; Neng Gu, Software Engineer, Content & User Understanding; Dhruvil Deven Badani,Continue readingWhat I Read: Realtime User Actions in Recommendation

What I Read: Building “Copilot for X”

By Andrew Fairless on February 23, 2023January 9, 2023

https://lspace.swyx.io/p/what-building-copilot-for-x-really What Building “Copilot for X” Really TakesA “Copilot for X” guide from the team that built the first real Copilot competitor!swyx and Anshul RamachandranDec 19 ““Copilot for X” refersContinue readingWhat I Read: Building “Copilot for X”

What I Read: AI, Human Values

By Andrew Fairless on February 22, 2023December 16, 2022

https://www.quantamagazine.org/what-does-it-mean-to-align-ai-with-human-values-20221213/ What Does It Mean to Align AI With Human Values?Melanie MitchellDecember 13, 2022 “Making sure our machines understand the intent behind our instructions is an important problem that requiresContinue readingWhat I Read: AI, Human Values

What I Read: Offline RL, Large Language Models

By Andrew Fairless on February 21, 2023December 16, 2022

https://sergeylevine.substack.com/p/offline-rl-and-large-language-models Offline RL and Large Language ModelsSergey LevineDec 4 “What if the purpose of a language model should not be to generate text at all, at least not directly? …Continue readingWhat I Read: Offline RL, Large Language Models

What I Read: Data Engineering 2023 Predictions

By Andrew Fairless on February 20, 2023December 16, 2022

https://www.montecarlodata.com/blog-13-data-engineering-trends-for-2023/ What’s Next for Data Engineering in 2023? 13 PredictionsBarr MosesCEO and Co-founder, Monte Carlo. Proponent of data reliability and action movies.Updated November 29, 2022 “…let’s dive into Tomasz’s predictionsContinue readingWhat I Read: Data Engineering 2023 Predictions

What I Read: Realtime ML

By Andrew Fairless on February 16, 2023December 16, 2022

https://fennel.ai/blog/seven-reasons-why-realtime-ml-is/ Seven Reasons Why Realtime ML Is Here To StayNikhil GargJune 17, 2022 “…more and more top tech companies are making a larger part of their machine learning as realtimeContinue readingWhat I Read: Realtime ML

What I Read: ELT Schedules, Root Cause Analysis

By Andrew Fairless on February 15, 2023December 16, 2022

https://www.montecarlodata.com/how-elt-schedules-can-improve-root-cause-analysis-for-data-engineers/ How ELT Schedules Can Improve Root Cause Analysis For Data EngineersRyan KearnsUpdated December 9, 2022 “In this article, Ryan Kearns… discusses the limitations of segmentation analysis when it comesContinue readingWhat I Read: ELT Schedules, Root Cause Analysis

What I Read: Convolutions, Probability

By Andrew Fairless on February 13, 2023December 16, 2022

https://www.countbayesie.com/blog/2022/11/30/understanding-convolutions-in-probability-a-mad-science-perspective Understanding Convolutions in Probability: A Mad-Science PerspectiveWill Kurt, Count BayesieDecember 01, 2022 “If you’ve ever done much mad science you’ll know that a major problem is selectively determining theContinue readingWhat I Read: Convolutions, Probability

What I Read: Biases, Saliency

By Andrew Fairless on February 9, 2023December 16, 2022

https://pair.withgoogle.com/explorables/saliency/ Searching for Unintended Biases With SaliencyAstrid Bertrand, Adam Pearce and Nithum ThainDecember 2022 “If there are spurious correlations in the training data, the model might develop unintended biases thatContinue readingWhat I Read: Biases, Saliency

What I Read: ML Observability

By Andrew Fairless on February 7, 2023December 4, 2022

https://medium.com/at-the-front-line/ml-observability-hype-or-here-to-stay-acef064ff843 ML Observability — Hype or Here to Stay?RuthOct 17 “Machine Learning Operations (MLOps) infrastructure is evolving at a phenomenal pace, and ML Observability is becoming a critical component ofContinue readingWhat I Read: ML Observability

Author: Andrew Fairless