Emails won't literally get sucked down a vortex of analytics solutions. If you have ever bought something from an online store, chances are the store's used your email address with wanton disregard, bombarding you with email after email about its products and sales until you reach for the sweet oblivion of unsubscription. Stores and brands do this to keep customers engaged--but they don't know how many emails are too many. Adobe previewed a tool today with the promise to help alleviate what they call "customer fatigue." By using machine learning algorithms to crunch the numbers of how often emails are opened and clicked on, marketers can see whether customers are tired of getting their emails.

This image, taken from a YouTube video, shows a pilot project at Walgreens to provide 3D views of goods sought by a shopper in a store. The technology comes from Google's Project Tango and Aisle411, a shopping location application maker. A new partnership between Google and chip maker Movidius could change the way your phone looks at the world. According to Movidius, Google will be licensing Movidius' latest flagship chip, the MA2450, along with the accompanying software stack. The chip has 12 cores, and is able to perform low power, advanced computer vision processing.

Logistic Regression is one of the most powerful classification methods within machine learning and can be used for a wide variety of tasks. Think of pre-policing or predictive analytics in health; it can be used to aid tuberculosis patients, aid breast cancer diagnosis, etc. Think of modeling urban growth, analysing mortgage pre-payments and defaults, forecasting the direction and strength of stock market movement, and even predicting sport outcomes. Reading all of this, the theory[1] of Maximum Entropy Classification might look difficult. In my experience, the average Developer does not believe they can design a proper Maximum Entropy / Logistic Regression Classifier from scratch. I strongly disagree: not only is the mathematics behind is relatively simple, it can also be implemented with a few lines of code.

Deep learning is sometimes referred to as the intersection between machine learning and artificial intelligence. It is about designing algorithms that can make robots intelligent, such a face recognition techniques used in drones to detect and target terrorists, or pattern recognition / computer vision algorithms to automatically pilot a plane, a train, a boat or a car. Many deep learning algorithms (clustering, pattern recognition, automated bidding, recommendation engine, and so on) -- even though they appear in new contexts such as IoT or machine to machine communication -- still rely on relatively old-fashioned techniques such as logistic regression, SVM, decision trees, K-NN, naive Bayes, Bayesian modeling, ensembles, random forests, signal processing, filtering, graph theory, gaming theory, and many others. Some are new, such as indexation algorithms to automate digital publishing, improve search engines, or create and manage large catalogs such as Amazon's product listing. As a result, many deep learning practitioners call themselves data scientist, computer scientist, statistician, or sometimes engineer.

One day in January, Eric Wilson dashed off a message to the teaching assistants for an online course at the Georgia Institute of Technology. "I really feel like I missed the mark in giving the correct amount of feedback," he wrote, pleading to revise an assignment. Thirteen minutes later, the TA responded. "Unfortunately, there is not a way to edit submitted feedback," wrote Jill Watson, one of nine assistants for the 300-plus students. Last week, Mr. Wilson found out he had been seeking guidance from a computer.

If there's one thing that Australia's two main political parties agree on, it's that replicating Silicon Valley on local shores is a Very Good Thing. The governing Liberal/National coalition and opposition Labor party are both advocating more spending on Science, Technology, Engineering and Mathematics (STEM) teaching and back tax tweaks to replicate American arrangements felt to foster startups. If we do these things, we're told, a few weeks from now a new generation of coding-capable kids will come up with lots of great ideas that put a rocket under Australia's economy. Casey penned the piece containing that quote in response to the surfacing of the old Tweet below. And she follows up with the "overthrow of capitalism".

The critically unloved 1999 film Bicentennial Man seems like an odd turning point in the evolution of chatbots, but for Robert Hoffer, Robin Williams's performance as an intelligent robot was an inspiration. "I wanted to build that, you know?" says Hoffer, a co-creator of the SmarterChild chatbot that lived atop early messaging programs such as AOL Instant Messenger and MSN Messenger. "I wanted to have an intelligence you could talk to on the Internet that would become your best friend for life." Beyond just holding a conversation, SmarterChild wanted to be useful, tapping into web services to provide sports scores, weather forecasts, stocks, and other info. Those ambitions make it an obvious precursor to today's resurgence of chatbots, led by booming startups such as Slack and Kik, and attracting tech giants such as Facebook and Microsoft.

Eric Poindessault is founder and CEO at Biggerpan. From HAL in 2001: A Space Odyssey to Samantha in Spike Jonze's Her, for decades we have been obsessed with the idea that artificial intelligence powered-computers will one day be able to interact with people, follow spoken instructions and make decisions independently, like a human would. Since Siri hit our screens on the iPhone 4, Google, Facebook, Amazon, Microsoft and Baidu have entered the playing field, too. But while each new generation brings its lot of interesting new features or use cases, they are still far off from the AI representations in movies. It's hard to imagine anyone engaging in a romantic relationship with Siri, or NASA putting Alexa in control of a spacecraft just yet. Movies have set the bar pretty high, and we are still waiting for such ubiquitous voice-controlled AI assistants to enter the real world.

For the past few months, we have been busy working on the next major release of the big data open source software we love: Apache Spark 2.0. Since Spark 1.0 came out two years ago, we have heard praises and complaints. Spark 2.0 builds on what we have learned in the past two years, doubling down on what users love and improving on what users lament. While this blog summarizes the three major thrusts and themes--easier, faster, and smarter--that comprise Spark 2.0, the themes highlighted here deserve deep-dive discussions that we will follow up with in-depth blogs in the next few weeks. Before we dive in, we are happy to announce the availability of the Apache Spark 2.0 technical preview in Databricks Community Edition today.

"The legal industry is ripe for innovation," says attorney and journalist Robert Ambrogi, who covers the role of technology in law. In an influential April 13 blog post, Ambrogi proclaimed a boom in legal tech startups based on a more than doubling of listings on startup directory AngelList. Ambrogi has since produced his own streamlined listing that currently has nearly 500 companies offering technologies to the legal industry. Several are courting attorneys who need better, cheaper ways to sort through the avalanche of legal filings, rulings, and spiderwebs of citations between cases, from the local to federal level. The innovation upsurge may in part be generational.