AI Economist is a project which aims to research the dynamics of economics by using Reinforcement learning (RL).

After quick introduction of AI Economist, I will show my some experiments which evaluate some scenarios such as Communism and etc.

What’s the AI Economist?

What’s the AI Economist?, it’s well described on their blog post. So, I would like to focus on explaining it, from the perspective of training RL agents.

The authors have published Gym-style API. Based on the API, we can train RL agents as well as CartPole and other RL environments. …

As we know, BERT has made remarkable results ever in NLP. Recently, some researches are reported which apply BERT to solve tasks for Protein.

In this article, I introduce how the protein is related with BERT. Furthermore, I will show my own experiment to solve the protein structure, which is one of the most difficult, but important tasks in biochemistry.

Protein is a sequence

Protein is a sequence of amino acids. There are 20 standard amino acids such as Alanine, Arginine and etc. They are chained from N-term to C-term by peptide bond.


The actual structure of the protein is not like a straight…

From Exploiting chemistry and molecular systems for quantum information science

Self pre-training becomes to be commonly used method in machine learning, especially in NLP (Natural Language Processing). The most famous ones are MLM (Masked Language Modeling) and NSP (Next Sentence Prediction), which are introduced with BERT.

Some recent researches actively try to apply this idea to other domains such as Computer Vision. You can find SimCLR, MoCo and etc, which are based on Contrastive Learning.

GNN (Graph Neural Network) is another type of DNN (Deep Neural Network), which is used for various domains. …

Recently, I have challenged a competition in Kaggle. Though it was my 1st challenge, I could get fairly good result. I would like to share my approaches and what I’ve learned from it.

Statoil/C-CORE Iceberg Classifier Challenge is the competition I chose. In this competition, competitors classifies ship and iceberg by using the radar images which are taken from satellite. As additional information, inc_angle is given. More details are available at the page of the competition.

Here is the summary of my approaches and more.

Successful Approaches Taken

  • Stacking, Blending
  • Bagging
  • Handmade feature extraction
  • Experiment with base model
  • Diversity


There is a new movement around mobile applications and deep learning.

  • Apr 2017: Google released MobileNets which is a light weight neural network intended to be used in computationally limited environment.
  • Jun 2017: Apple released Core ML which enables machine learning model to run in mobile device.

Further more, recent high-end mobile devices have GPU tip inside it. Actually, they work faster than my Mac Book Pro for computation of machine learning.

Table 1. Comparison of Processing unit of mobile device

Deep learning on the edge is coming to everywhere.

In this article, I would like to introduce a real world usage of them and report how fast it…

Akira Sosa

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