Today on In Case You Missed It: Google's Tensor Flow machine learning technology helped create a device to sort through massive amounts of cucumbers at a farm in Japan, sorting the vegetables by quality grade so that humans don't have to do it manually. Meanwhile, an Australian scientist created an ink that changes colors when exposed to sunlight, which could theoretically help people from getting a sunburn. We also touched on the new internet-connected pet toy from Acer and rounded up the biggest headlines of the week for you in TL;DR. Be sure to check out IBM Watson's movie trailer and read about SpaceX's rocket explosion. As always, please share any interesting tech or science videos you find by using the #ICYMI hashtag on Twitter for @mskerryd.

A computer's victory over a human go master this past March reminds us of the pending "singularity" -- the rapidly approaching moment in time when artificial intelligence overtakes human intelligence. Machines will learn, and we won't be their teachers. Are we prepared for it? Can we prepare for it? Many futurists declare it inevitable, probably within a generation, maybe less.

We already discussed first four steps of ML work-flow. So far, we preprocessed crude data by DICTR (Discretization, Integration, Cleaning, Transformation, Reduction), then applied a way of feature extraction procedure to convert data into machine understandable representation, and finally divided data into different bunches like train and test sets . Now, it is time to preprocess feature values and make them ready for the state of art ML model;). You may ask "Why are we so concerned about these?" Okay, I hope now we are clear why we are concerned about these. Henceforth, I'll try to emphasis some basic stuff in our toolkit for feature preprocessing. Caveat 1: One common problem of Scaling and Standardization is you need to keep min and max for Scaling, mean and variance values for Standardization for the novel data and the test time.

With the advent of artificial intelligence and machine learning, companies are going to great lengths to understand human behavior. Between virtual assistants and platforms that predict our needs, humans barely have to lift a finger to get what they need done. One person who has seriously contributed to building out products that streamline our life is Eric Colson. At Innovate! and Celebrate 2016 in September, you'll have the chance to hear him speak about everything from machine learning to shopping. Eric Colston is currently the chief algorithm officer at Stitch Fix, a women's clothing retail website that prides itself on providing personalized shopping experiences to everyone that click on their link.

Mark I tank to appear'virtually' in Torquay in honour of 100 Years of Tanks Virtual Reality's Distribution Frontier Is Heating Up Pokémon Go on the decline?

Einstein is essentially a mix of organic and acquired technologies from Salesforce. Salesforce CEO Marc Benioff believes that artificial intelligence should just be infused through software and cloud services in a way that customers barely notice. Enter Salesforce's big "Einstein" initiative. Benioff teased out Einstein, Salesforce's artificial intelligence effort, and basically pre-empted his big theme for the company's Dreamforce powwow in October. Einstein is essentially a mix of organic and acquired technologies from Salesforce.

PaddlePaddle (PArallel Distributed Deep LEarning) is an easy-to-use, efficient, flexible and scalable deep learning platform, which is originally developed by Baidu scientists and engineers for the purpose of applying deep learning to many products at Baidu.

News digest – obesity in primary schools, the Crick, artificial intelligence and… Tasmanian devils?

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The biggest challenge with AI may be designing it. That's the implication of a study designed to last until 2116, called the "One Hundred Year Study on Artificial Intelligence." The Stanford-led project aims to report on the state of AI in our world every five years for the next century, as reported by a panel of two dozen experts--currently ranging from Julia Hirschberg, a pioneer of natural language processing, to Astro Teller, leader of Google's "moonshot" division. The first report, published online yesterday, reads a bit like a half-drawn map, a mix of observations, questions, and even warnings. First of all, "the panel found no cause for concern that AI is an imminent threat to humankind," which, phew.