First Tokyo WiMLDS Meeting!

We are thrilled to announce the first event of Tokyo’s chapter of Women in Machine Learning and Data Science hosted and sponsored by Amazon Web Sevices Japan. All genders welcomed.

■　Venue　　Meguro Central Square ：3-1-1 Kamiosaki, Shinagawa-ku, Tokyo
■　Reception Meguro Central Square 17F

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Schedule:

10:00-10:15: Welcome and presentation of Tokyo’s chapter.

10:15-11:00: Wonders and Limits of Predictive Coding. Lana Sinapayen, Artificial Life researcher at Sony CSL and research scientist at ELSI of Tokyo Institute of Technology.

11:00-11:45: Effective MLOps on AWS Cloud. Shoko Utsunomiya. Machine Learning Solutions Architect at AWS Japan.

11:45-12:30: Machine learning for Science. Jing Li, Machine Learning Research Engineer at Yokozuna Data.

12:30-13:00: Open discussion. What do we want WiMLDS Tokyo to be?

After the event, there will be time for informal chats and connecting, and our kind host will be offering us snacks.

Technical talks:

Wonders and Limits of Predictive Coding. Lana Sinapayen, Artificial Life researcher at Sony CSL and research scientist at Earth-Life Science Institute of Tokyo Institute of Technology

Predictive coding, the idea that intelligence is the ability to predict one’s environment, has been picking up steam for several years in Cognitive Sciences, Artificial Intelligence, and Artificial Life.
Predictive coding has led to better theories about how the brain works; it has also led to artificial agents with better performance in simulated environments and real world robots with better manipulation abilities.

I am myself a staunch supporter of predictive coding. I use it to measure the complexity in time series and to analyze data from agricultural applications to planetary science. Yet I have recently started to focus on the limits of predictive models. The best way to understand a system is to study its failures, and thankfully for me predictive systems are not perfect. In this talk, I will present my work on false memories, discontinuous prediction, and visual perception.

Effective MLOps on AWS Cloud. Shoko Utsunomiya. Machine Learning Solutions Architect at Amazon Web Services Japan.

In the operation of machine learning, there are various issues such as acquisition and annotation of high quality data, quick construction of training environment, preparation of elastic compute resources such as GPUs against demand fluctuation of calculation resource, and reduction of operation load of machine learning workflow.

Amazon Web Services (AWS) offers Amazon SageMaker, a fully managed machine learning platform service for solving these issues, and is used by customers of various sizes and stages. By removing Undifferentiated Heavy Lifting in a machine learning environment efficiently using managed services, you can focus on the more important differentiation issue, the data science tasks.

In this session, I will present the principles and best practices for machine learning in the cloud based on the past customer experiences. I will also introduce issues common to various use cases and their solutions, such as handling of large-scale data, transitioning research issues to actual operation, and launching of quick services.

Machine learning for Science. Jing Li, Machine Learning Research Engineer at Yokozuna Data.

I will give a brief introduction about recent progress in machine learning for chemical science, material science, and some other science fields, based on my experience as Assistant Professor at the Aerospace Engineering department in Tohoku University. From these examples, you can see how Machine learning is accelerating Science research.