Salesforce announced their shiny new AI initiative, Einstein, on the 19th September and the excitement is palpable. Understandably as AI will be able to predict how leads are likely to prosper, suggest further contacts, automate tasks, and it is an addition compatible with any of Salesforce's existing clouds, specialised versions included. However, it is crucial to note that the success and accuracy of such innovative technology is directly related to the quality of the data existing within any given Salesforce system. So, why do we love Salesforce? We love it because it can be designed around specific business processes and be customized to work for us.

Operational security is a top priority when selecting IoT platforms and we're proud to say that Watson IoT Platform regularly proves its mettle in meeting and exceeding the exacting security expectations of IoT innovators. We've blogged before about the robust security standards supported by Watson IoT Platform. These complement the use of open standard secure communications protocols, such as TLS v1.2, that ensure IoT device interactions are authenticated and encrypted. So, with robust operational platform security in place, how can you configure and manage a security environment appropriate for your device, application and user requirements? Watson IoT Platform now offers configuration and management of Roles which enable controls to be defined for Users, Applications and Gateways.

Artificial intelligence has never been as present -- or as cool -- as it is today. And, after years on the periphery, deep learning has become the most successful and most popular machine learning method around. DL algorithms can now identify objects better than most humans, outperform doctors at diagnosing diseases, and beat grandmasters at their own board game. In the last year alone, Google DeepMind's AlphaGo defeated one of the world's greatest Go player -- a feat most experts guessed would take another decade at least. Some of the finest minds in AI are at the Re•Work Deep Learning Summit in London this week to discuss the entrenched challenges and emerging solutions to artificial intelligence through deep learning. Researchers from Google, Apple, Microsoft, Oxford, and Cambridge (to name a few) are in attendance or giving talks.

General artificial intelligence, a machine that is capable of human-level expertise in multiple tasks, was the hot topic during the morning of the Rework Deep Learning Summit in London yesterday, with two of the UK's best AI companies Google DeepMind and Swifkey weighing in on the advances being made, and how far we are from a truly human AI. In his seminal piece about DeepMind for Wired magazine in June 2015, David Rowan wrote: "[DeepMind] showed that their artificial agent had learned to play 49 Atari 2600 video games when given only minimal background information. The deep Q-network had mastered everything from a martial-arts game to boxing and 3D car-racing games, often outscoring a professional (human) games tester." What this obfuscated was that the deep neural network was learning how to master each game one at a time. The same neural network couldn't, for example, flick between two different games and maintain its skill like a human would.

These changes are called "Industry 4.0" or the fourth industrial revolution. It is an industrial revolution that uses artificial intelligence and robots in such a way that manufacturing plants will become unmanned and a majority of office jobs will be made unnecessary. In March, an AI player of the board game go, developed by Google and named AlphaGo, defeated the world's leading professional go player 4 games to 1. The pro lost the first three games, and although he won the fourth, he was defeated in the fifth round. The decisive factor that led to the victory for AlphaGo was its "deep learning" capability.

Welcome back for another edition of Under the Decision Tree. As usual there were quite a number of interesting stories focused on machine learning and AI. One particularly interesting topic this week was Micorsoft and its efforts in cancer research. There are two conferences starting on Monday next week. Please send any suggestions to: Decision Tree We would love to hear from you.

Porn companies have long been early adopters of new technology; embracing videos, DVDs, internet streaming and live web chat when these mediums were in their infancy. Now, they're using facial recognition technology to create even more'personalised' experiences, but not everyone will be happy. You can now use AI to find porn stars who look like people you know. This week Megacams, a free cam site, released a new feature on it's live sex search engine called'facial recognition'. This means visitors are able to upload an image of any celebrity, or a person they know, and find a supposedly'doppelganger' performer.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

On the surface it looks like a simple public cloud infrastructure deal. But stand back from productivity app Evernote's recently announced migration of its entire infrastructure onto Google's Cloud Platform and there's a much bigger story to tell. Productivity apps are many, but Evernote's 200 million plus customers make it one of the most popular. Allowing users to store private notes on the cloud, add multimedia, and access everything from multiple devices (recently restricted to two unless you pay a fee), Evernote's back-end now contains about five billion notes. Until now, all of that was held on Evernote's own private cloud infrastructure, but from early October it's all going to be migrated to Google's Cloud Platform. Evernote's notes were already easy to integrate with Google Drive, but this goes much deeper than the mass adoption of the cloud as a place to store data.

Apple is boosting its machine learning capabilities with the acquisition of Tuplejump. The India/US-based start-up has typically operated with open source projects such as Apache Spark, Apache Cassandra, and the Apache Kafka distributed high-throughput publish-subscribe messaging system, but it is the FiloDB project which is said to be what interested Apple the most. The company describes itself as having the goal of simplifying data management technologies in order to make them simple to use. Apple has not confirmed the acquisition but told TechCrunch: "Apple buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans." The FiloDB open source project is designed to build and apply machine learning concepts and analytics to large amounts of complex streaming data.