We've seen a number of smart home thermostats over the past few years and Boston apparel company Ministry of Supply wants to bring this kind of personalized climate control to people on the move, launching a heated jacket that tunes its temp in line with user preferences.
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The buzzwords for the future are undoubtedly “machine learning” and “automation”. There are many things machine learning can find out and directions it can take the company. But what should machine learning actually learn about your customers and where should it focus its efforts?
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With data and its engagement going the cloud way, MLaaS will help revolutionise a paradigm of machine learning and will create a synergised result.
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Frameworks are only an intermediary step to the wider adoption of machine learning in applications. What’s needed are more visual products and those are still a couple of years away.
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Machine learning classification needs accurate DATA TO avoid bad prediction that places marketing efforts IN ethical peril. Here's an overview to help explain how data can brands at risk.
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Below are just some of the ways that this technology is taking the payments industry by storm.
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McKinsey estimates that total annual investment in AI was between $8B to $12B in 2016, with machine learning attracting nearly 60% of that investment. Deloitte Global predicts the number of machine learning pilots and implementations will double in 2018 compared to 2017, and double again by 2020.
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We are witnessing just the beginning of the artificial intelligence (AI) era.
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