Let be Clear About the Capabilities of Generative Models and Dialogue Agents
(T) “chatGPT has taken the world by storm”. That is what everyone of us has been reading everywhere and everyday for over two months now about chatGPT. And, everyone of […]
Google Research in 2022 and Beyond
(T) As he did since 2017, Jeff Dean, the tech lead of the Google Research and Health Teams shared some of the key achievements of his team for 2022 in a […]
Theoretical Deep Learning
(T) Research in deep learning is moving rapidly in every direction: reinforcement and imitation earning for robots, computer vision for self driving cars, graph neural networks for biology, and language […]
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 […]
OpenAI’s DALL.E 2
(T) OpenAi has released its second generation of model DALL.E 2 which generates images from text inputs. DALL.E showcases OpenAI’s research in multimodal and generative models. DALL.E 2 is capable […]
Toward the Magic Recipe to Make AI Systems “Intelligent”
(T) In 1956, a small group of scientists led by John McCarthy gathered for a Summer Research Project at Dartmouth University and established Artificial Intelligence (AI) as a scientific discipline […]
Generalization to New Tasks with Imitation Learning
(T) Babies learn by exploring their environments, manipulating any objects that can fall in their hands, observing the behaviors of adults, and interacting with them. Animals are good observers too. […]
State of the Art for NLP Models
(T) NLP models are still the “big thing” in machine learning even if they cannot understand the meaning of the text or the speech that they are supposed to decipher […]
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 […]
Graph Neural Networks (GNNs)
(T) Graphs have recently been “the big thing” for deep learning. There are perfect examples of machine learning applications in the Non-Euclidian space. Graphs have been used to predict friends […]
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 […]
Tesla AI Day 2021
(T) Tesla had this week a recruiting event for data scientists and software engineers: “Tesla AI Day”. In my opinion, Andrej Karpathy and his team during that event have shown […]
Multi-Task Prediction of Organ Dysfunctions in ICUs
(T) An interesting research from Google Health on predicting organ dysfunctions in intensive care units (ICUs). The system uses multi-task models, which take into account a variety of competing risks along […]
Designing the Next Version of TPUs with Reinforcement Learning and Graph CNNs
(T) A team of Google researchers developed a reinforcement learning system to design the billions of transistors in the next version of Google’s Tensor Processing Unit (TPU) chips optimized for computing TensorFlow […]
Could Machine Learning Become a New Instrument for Musicians?
(T) When I am listening to Bach’s Brandenburg Concertos, I have always been fascinated by how much I have the wrong certitude of listening to a concerto for violins from […]
Generating Perfect Humanoids through Reinforcement Learning
(T) This is probably the state of the art (or close to it) of character physics-based animation for generating humanoids through reinforcement learning (RL) from a team of researchers at […]
Understanding Deep Learning in the Infinite Width Limit
(T) Deep learning systems have achieved impressive results from language understanding, to protein sequencing, and autonomous vehicles. While it has long been known that artificial neural networks can approximate any […]
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 […]
The Transformer Architecture Replacing CNNs?
(T) Most of the innovations in deep learning have come over the last few years from new natural language processing (NLP) techniques. Google’s Transformer with self-attention mechanisms has certainly changed […]
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 […]
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 […]
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