The Pathways System – Google’s NextGen ML Platform for LLMs
(T) Jeff Dean announced the vision for Google’s next generation model Pathways in a blog post in the fall of 2021. Another blog article and a paper was published about […]
From Heart Transplant Rejection to Lung Cancer Detection – The Power of Computer Vision
(T) Not a new news, but it is amazing the see the applications of computer vision for medical applications from heart transplant rejection to lung cancer detection. Probably, many more […]
Google Research in 2021 and Beyond
(T) As he did since 2017, Jeff Dean, the tech lead of the Google Research and Health Teams, and probably one of the most famous Silicon Valley engineers, shared some of […]
Machine Learning in Non-Euclidian Spaces
(T) The fundamental representation of any feature for any machine learning model is the vector and its multidimensional generalization which is the tensor. This has led to developing machine learning […]
Distributed and Declarative Deep Learning Systems
(T) Developing deep learning models is hard. Debugging deep learning models is even harder. And, few companies have data pipelines that provide the training and inference of large scale deep […]
Covid-19 – Day 503: A Summary of Machine Learning Contributions to Stop the Pandemic
(T) An excellent article from Maxime Nauwynck on “how has AI contributed to dealing with the COVID-19 pandemic?” published in Stanford Gradient’s blog. The article looks at the contribution in […]
Statistical Modeling on a Machine Learning Platform
(T) Statistical modeling and machine learning are sometime considered as the “same side of the same coin” since both fields leverages statistical methods to learn about data. But there are […]
Covid-19 – Day 421: Mapping T Cell Receptors to Covid-19 Antigens
(T) A biotech company Adaptive Biotechnologies has developed a new test, called T-Detect Covid, that looks at signals of past Covid infections in the body T cells immune system. The […]
Google Brain Research in 2020
(T) As he did since 2017, Jeff Dean, the tech lead of the Google Brain Team, and probably one of the most famous Silicon Valley engineers, shared some of the key […]
Developing Autonomous Decision Making Systems with Deep RL
(T) I really enjoyed watching this fun video from Professor Sergey Levine on autonomous decision making systems using deep reinforcement learning. Most of the content is somewhat “high level” but require some […]
OpenAI’s GPT-3 Insane Number of Parameters!
(T) I finally had a chance to start studying OpenAI’s GPT-3. GPT-3, like GPT-2 provides many natural language processing (NLP) tasks, and in particular reading comprehension, summarization, translation and question […]
How to Learn Faster? Multi-Task and Meta-Learning to the Rescue
(T) One of the key goals of many data scientist teams is to develop models faster for new use cases and new applications. To that end, “transfer learning” which aims […]
How to be a Successful Machine Learning Product Manager?
(B) Someone from LinkedIn emailed me to attend a virtual meet-up “AI/ML: in Product Management: Applications and best practices for leveraging AI/ML in products”, organized by the “LinkedIn Women in […]
A Gentle Introduction to Machine Learning for Product Managers
(B) I taught a machine learning class to Product Managers two years ago. I have slightly updated the content of that class. A lot of Product Managers working on machine […]
Second Scaled Machine Learning Conference @ the Computer History Museum
(T) This week, I had the opportunity to attend for the fourth time, the fifth Scaled Machine Learning conference that was held for the second time at the Computer History Museum in […]
Google Brain Research in 2019
(T) As he did since 2017, Jeff Dean, the tech lead of the Google Brain Team and probably one of the most famous Silicon Valley engineers, shared some of the […]
NeurIPS 2019 @ Vancouver
(T) The 2019 Conference on Neural Information Processing Systems (NeurIPS) was held this year from December 8th through December 14th, 2019 at the Vancouver Convention Center. Over 13,000 attendees! And […]
Mitigating Adversarial Examples by Re-Constructing the Input Images
(T) So far the best-known technique to mitigate adversarial examples is adversarial training. Adversarial examples are images that have been modified to fool a generative adversarial network (GAN). And in […]
ACM-IMS Interdisciplinary Summit on the Foundations of Data Science
(T) I was kindly invited to attend the first “ACM-IMS Interdisciplinary Summit on the Foundations of Data Science” yesterday at the Palace Hotel in San Francisco. The summit organized by […]
Quantum-inspired Classical Linear Algebra Algorithms
(T) Erwin Tang, last year at the age of 18 rocked the world of quantum-based algorithms, by has given recently a very interesting talk titled “Quantum-inspired classical linear algebra algorithms: […]
Scaled Machine Learning Conference @ the Computer History Museum
(T) This week, I had the opportunity to attend for the third time, the fourth Scaled Machine Learning conference that was not held at Stanford University but the Computer History Museum […]
Google Brain Research in 2018
(T) As he did last year, Jeff Dean, the tech lead of the Google Brain Team and probably one of the most famous Silicon Valley engineers, shared some of the […]
Fooling Humans with Adversarial Examples
(T) Adversarial examples introduce small changes to an image that leads the model to misclassify the input image. Instead of recognizing a cat, the model will recognize a dog even […]
Everyone can Dance Now…
(T) Four students of UC Berkeley, Caroline Chan, Shiry Ginosar, Tinghui Zhou, and Alexei A. Efros have developed a not-so-simple but efficient way based on GAN (Generative Adversarial Models) to […]
Erwin Tang – Changing the World When You are Only 18 Years Old!
(T) Qantamagazine has a fantastic article about the story of Erwin Tang “Major Quantum Computing Advance Made Obsolete by Teenager.” Long story short, in her paper posted online earlier this […]
A Silicon Valley Insider 