1. Inside Google's AI Rewrite: Building Machine Learning into Everything

    Makoto Koike is a cucumber farmer in Japan. Koike is a former embedded systems designer who spent years working in the Japanese automobile industry, but in 2015 he returned home to help out on his parents' cucumber farm. He soon realized that the manual task of sorting cucumbers by color, shape, size, and attributes such as "thorniness" was often trickier and more arduous than growing them.

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      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD

    1. Google takes all these ML and AI models we've developed and tunes them for security.
    2. It starts with the underlying data center hardware [like the newly announced Titan chip].
    3. Think about how G Suite brings all these applications together in a naturally integrated way.
    4. A lot of users don't know what something like a pivot table is or how to use it to visualize a sheet of data.
    5. In business and with consumers, users have these natural interaction patterns. The shift to the cloud and to mobile productivity are really changing the way people work, and these applied machine learning techniques are fundamental to it.
    6. We've seen use cases across the board—from healthcare and finance to retail and agriculture.
    7. You need a strong compute offering to deal with the extreme requirements of ML jobs, and GCP's distributed fiber optics backbone moves data from node to node very efficiently.
    8. Let's say you're a customer service analytics company. Think about a speech API to transcribe the content of calls, and then sentiment analysis to improve the quality of your customer service.
    9. Think of it like a new industrial revolution.
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