Teaching Common Sense and Maths to LLMs
(T) The most common dissatisfaction of users with large language models (LLMs) is hallucination. In addition, LLMs are not good at maths. Here are a few resources to explore the […]
Deep Dive into Google Generative AI Announcements at Google IO
(T) Following is my quick summary of this week Google Generative AI and other major ML announcements at Google IO 2023. The breath and depth of those announcements is quite […]
Generating Generative AI
(T) There are now five ways to develop and launch a generative model: Here is a fun video from Yannic introducing “en grande pompe” OpenAssistant: References: Note: The picture above […]
LLMs and Generative AI Start-Ups and Investments
(B) According to a recent report, VCs firms have increased their investments in generative AI from $408M in 2018 to $4.5B in 2022. Most start-ups that have started the development […]
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 […]
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 […]
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 […]
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 […]
Predicting SARS-CoV-2, HIV, and Influenza Viral Mutations with Language Models
(T) In this third blog post about the applications of deep learning techniques to structural biology research, I will summarize a recent research study on virus mutation.The recent Covid-19 pandemic […]
Generative Modeling for Engineering Protein Sequences
(T) In this second post about the applications of deep learning techniques to structural biology research, I will try to describe the state of the art of protein sequencing. Protein […]
Deep Mind’s AlphaFold 2 Prediction of Protein Folding
(T) In the next three blog posts, I will try to summarize the present state of the art regarding the applications of deep learning techniques to structural biology research, and […]
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 […]
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 […]
Machine Learning Models for Autonomous Vehicles
(T) Waymo’s principal scientist Drago Anguelov recently highlighted the latest innovations of Waymo’s machine learning models for its autonomous vehicles at CVPR 2020. In a previous blog article, Waymo (ChauffeurNet) […]
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