AI News, CHI 2019 artificial intelligence

2019 O’Reilly AI Conference (Beijing) Review

I previously had had an image of Intel as an old hulking behemoth that was falling behind in terms of hardware for AI and didn’t have many software contributions to the world, but Intel seemed more relevant for AI in my eyes after the conference.

They described their pains around data scientists needing to worry about and doing a poor job of setting up infrastructure, model deployment to production, etc.

So, they developed an end-to-end framework (running on top of Kubernetes and leveraging common tools like Airflow, Jupyter, Slack, Jenkins, etc.) to make it quick and easy for data scientists to scale and deploy machine learning models.

They plan to open source this “Data Science Platform” later, potentially this year (yay!) Typically, recommendation engines use collaborative filtering or content-based filtering, but it’s also possible to treat the items (e.g.

products in an e-commerce store) viewed/clicked by the user as a sequence, and then process that using NLP algorithms to predict the next item to show to the user, since many NLP algorithms are designed to predict the next word given a sequence of words.

The speaker developed software that can take an audio clip of a poor singer and an audio clip of a pro singer and auto-generate a clip of the poor singer singing like a pro (no MIDI file or other structured data required).

The software required many steps including not just machine learning but also more traditional signal processing techniques (such as dynamic time warping).

Artificial Intelligence Platform (PFIA), Toulouse, France

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