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How every organization can apply product thinking

Radical Product

Case study on Datank, offering Machine Learning as a service Traditionally we think about a “product” as a physical or digital thing ; an iPhone, an app, a sofa. This way of building “product” is not a one-time effort; it’s a job that requires building repeatable, tuneable models to maximize your impact and achieve your vision.

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Top 10 Japanese Startups

freshtrax

alumni from the University of Tokyo which is a top-tier academic science lab led by Dr. Yutaka Matsuo, a pioneer of artificial intelligence (A.I.) and deep learning. Also, early this year, Kudan announced its partnership with Synopsys to accelerate the development of an intelligent computer vision processor.

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Woven CEO Tim Campos on how to spend your most important asset

Intercom, Inc.

The experience became the inspiration for his next act after Facebook: an intelligent calendar called Woven that can cut scheduling time in half. Dee: And you’ve worked in the industry for more than 22 years, so you have an incredible insight into the expansion of cloud networks, enterprise and data center technologies.

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Kevin Indig on searching for meaning and the meaning of search

Intercom, Inc.

In addition to this, our conversation also covers his time at Altassian and contrasts this with his G2 role, his thoughts on Google updates and how they impact his industry and his vision for the future of SEO. If you’re short on time, here’s some quick takeaways. Grappling with the Google game.

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Making Workplaces Work For Humans | Isa Watson, Squad | BoS USA 2018

Business of Software Conference

And so you know the founder TBD I was like You know I figured out all the other sh*t that I’ve done in my career so I can probably figure this out I’m in and the team I was able to recruit one of the earliest engineers on the Gmail team who was there from 2004 to 2016. So I said All right let’s make this happen.

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How bots, messengers and apps will define 2019 with Des Traynor, Paul Adams and Emmet Connolly

Intercom, Inc.

And then you can get smarter with machine learning and stuff. I think what happened was – with bots specifically, through 2016 and 2017 – a lot of it was on Facebook Messenger. Paul: It might be interesting to distinguish between the consumer side and the enterprise side. Then you’ve got chatbots.