Now that AI has “escaped the lab,” there are two main questions: what’s next and how is next?

One of the more pressing questions that I am occasionally asked by customers and non-AI believing developers is “AI is great and all but who else besides Microsoft, Google, Netflix, etc is actually using it?”  What they’re really asking is “How can AI really benefit my business if I’m not [insert large tech company name here]?” 

Here’s a thoughtful piece from Data Science Central that explores that very question.

We know we’ve entered the era of exploitation of AI/ML but the $64 Billion question is how far along the curve are we and who exactly has implemented and will implement? By the way, $64 Billion is a reasonable estimate of global market spend in roughly four or five years, about 6 times where we are today. And that investment should yield about $4 Trillion in business value in that same time frame according to Gartner.

Summary: Adoption of AI/ML by larger companies has more than doubled since last year according to these survey results from McKinsey and Stanford’s Human-Centered AI Institute. This new data gives us a much better idea of which global regions and which industries are adopting which AI/ML techniques. We know […]

Here’s an interesting tidbit I found on DevBlogs by Mark Wilson-Thomas.

You may know that Visual Studio IntelliCode helps you write code from commonly used libraries, based on machine learning across thousands of open sourced GitHub repos. Instead of having to search and scroll through a sorted list of methods and properties, you get suggestions on the most likely ones […]

It never hurts to practice the fundamentals and understanding SQL is fundamental to any well-rounded data scientist. Here’s an interesting closeup look at T-SQL, the SQL “dialect” found in SQL Server.

Like any programming language, T-SQL has its share of common bugs and pitfalls, some of which cause incorrect results and others cause performance problems. In many of those cases, there are best practices that can help you avoid getting into trouble. I surveyed fellow Microsoft Data Platform MVPs asking […]

TensorFlow Lite is TensorFlow’s solution for running machine learning across resource constrained platforms. In this video, learn about the current state of TFLite, as well as the roadmap, from the point of view of four core competencies: conversion, optimization, acceleration, and usability.

This week Five Things sits down with Noelle LaCharite from the Microsoft Cognitive Services team to learn how machines can translate language, perform search on unstructured data, converse like humans and more.

Best of all, you can add these cutting edge features to your applications right away; no degree in multi-dimensional calculus required.

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Unsupervised learning is the most exciting subfield of machine learning!

Finding structure in unstructured data automatically sounds like a dream come true. In this video, Siraj Raval demonstrates 2 types of unsupervised learning techniques; k means clustering and principal component analysis.