Waymo (ChauffeurNet) versus Telsa (HydraNet)
(T) Following are some easy to understand summaries of how Waymo and Telsa are approaching the design, architectures, and modeling of their self-driving cars. Note how much they differ. Waymo […]
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 […]
How Reinforcement Learning Enables a new Generation of Robots
(T) I had the pleasure to listen to several great talks about reinforcement learning from Professor Pieter Abbeel from UC Berkeley at the ODSC West Conference last Fall. I am […]
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 […]
Waymo ChauffeurNet
(T) I had the opportunity to attend at the ODSC West conference, a talk from Waymo’s engineering manager’s Chen Wu. Following is a summary of what I learned about the […]
The Power and Limits of Deep Learning
(T) I recently listened to a lecture from Yann LeCun on “The Power and Limits of Deep Learning” organized by ACM. Following are a summary in plain English based on […]
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 […]
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 […]
Todays’ Turing Awards to the Three Musketeers of Deep Learning
(T) Earlier this year, I tried to summarize in one slide, the top 20 scientists who contributed the most to the field of artificial intelligence over three generations. Obviously, this […]
OpenAI’s GPT-2 Language Model
(T) OpenAI released this week its second-generation language model GPT-2. That model provides reading comprehension, summarization, translation, and question answering. It is so good so OpenAI fears that it could […]
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 […]
The Beauty of Generative Models
(T) Google, Facebook, Apple, and Amazon have a key asset: a huge amount of data from their customers. And with data, you can do so much! But what if you […]
Machine Learning Platforms and Data Pipelines at Google, Facebook and Uber
(T) When you are a small start-up of fewer than 20 employees, you obviously have only one machine learning platform for all your analytics and services. That is not obviously […]
Google TensorFlow Developer Summit
(T) Google had yesterday its second TensorFlow (TF) developer summit at the Computer Museum in Mountain View. This second summit was quite different from the first one. While the first […]
Stanford Scaled Machine Learning Conference
(T) I had the opportunity to attend last Saturday, the third Scaled Machine Learning conference @ Stanford University organized by Professor Reza Zadeh, and his venture Madroid. The speakers were […]
The Expansion of the TensorFlow Platform
(T) I had the opportunity to attend this week IBM Index Developer conference at the Moscone Center. In particular, I attended on Tuesday the Open Community Meetings on TensorFlow (and […]
Google Brain Research in 2017
(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 […]
Learning with Limited Supervision
(T) The Silicon Valley IEEE AI group had an interesting lecture last month, that I attended, from Stanford’s Professor Stefano Ermon. Many of the recent successes of machine learning have […]
Scaled Machine Learning Conference @ Stanford
(T) I had the opportunity to attend last Saturday, the second Scaled Machine Learning conference @ Stanford University organized by Professor Reza Zadeh, and his venture Madroid. The speakers were […]
AI for Dummies: History and Techniques
(E) An excellent and fun article to read, if you want to understand the recent developments and most popular applications of AI, Roger Parloff’s in Forbes, ”Why deep learning is […]
So Many AI and ML Product Announcements from the Big Players…
(T) There are so many AI and ML product announcements from the largest tech companies, that it is hard to keep up – competitive technology advantages are going to erode quickly: […]
From Mobile-First to AI-First?
(T) If you did not have the chance to attend Google IO this week at the Shoreline Amphitheater in Mountain View, most of the videos of the talks are now […]
Playing the Go Game with Deep Neural Networks and Tree Search
(T) Deep Blue, an IBM supercomputer programmed with brute force analysis, defeated Chess grandmaster Garry Kasparov in 1997 over six games. IBM’s Watson won Jeopardy! in 2011. This week the […]
A Silicon Valley Insider 