The Turing test has always been an approximate benchmark for good AI. In the test, a human is supposed to converse with a machine over text for five minutes; if the human doesn't realize that they are talking to a machine, then the computer passes as AI "indistinguishable" from human intelligence. DON'T MISS: To make the iPhone exciting again, Apple has to launch... an Android phone? Earlier this year, Georgia Tech professor Ashok Goel noticed he was spread thin for teaching assistants for his computer science course. So Goel programmed IBM's Watson system to work as an online chatbot, answering some of the 10,000 online questions submitted by students during the course.

It has come to the attention of digital marketers that Google have ventured even further into the world of tomorrow with their latest updates to how search engine optimisation works. Utilising artificial intelligence and machine learning, the new system is called RankBrain and might be about to change SEO completely. I interviewed Owen Radford, Online Marketing Manager here at Elementary Digital to get the lowdown. JH: So what exactly is RankBrain? OR: RankBrain is the name for the artificial intelligence system that Google are using to process search engine results.

Baker & Hostetler is the first law firm to announce that it will use a ground-breaking artificial intelligence product for legal research. The law firm will license Ross Intelligence in its bankruptcy practice, report the Am Law Daily (sub. The research product uses IBM's Watson technology, which is designed to get smarter as it is used. Ross responds to lawyers' questions in natural language by reading through the law, gathering evidence and drawing inferences. The program learns from the lawyers who use it to refine its search results.

Let me think about future PLM moonshot. I want to talk about intersection of product lifecycle and AI. Have you heard about AI (artificial intelligence) recently? Grab few hours during coming weekend and do that. The AI is trending again.

Serious answer: I tend to dive deep into a particular algorithm...learning the math better, getting used to different applications of it, etc. So that's where I usually spend my time - along with the advice /u/Jigsus offered...focusing my learning around the kinds of needs I'm working on problem-/data-wise. Sounds like survival analysis, so I try to find as much material focused around that. On the flip side, I haven't done anything like sentiment analysis, so I know next to nothing about Naive Bayes text classification. I tend to read over a rather wide selection of ML and statistics blogs, so I'm not entirely unclear about such things, it's just that I don't spend a copious amount of time other than playing with a toy dataset now and then.

Which sports geek wouldn't like to create their own system for predicting matches, be it if you want to bet or just from an intellectual curiosity? Fortunately, nowadays advanced statistics are publicly available in the internet in websites like basketball-reference and awesome machine learning libraries can be used for every programming language. This is not going to be a comprehensive DIY kind of guide, I'm just going to talk about what I found when playing with this stuff for a few months and share some code that will be very useful for anyone that wants to get started with this. Machine Learning works by building models that capture weights and relationship between attributes from historical data and then use these models for predicting future outcomes. So, you need to understand the sport, think which variables are representative of future performance, build a database that contains this information and run Machine Learning algorithms on historical data to analytically assign weights to these variables.

Random forest model using Lending Club public dataset shows opportunity to improve adjusted return by 2.75% Arimo recently performed a study using a public dataset provided by Lending Club with the goal of showing how machine learning could improve investor returns. To do this we used the PredictiveEngine component of our Data Intelligence Platform, which provides the ability to easily build a variety of predictive machine learning models which scale transparently when deployed on distributed parallel computing platforms. Lending Club is an online peer-to-peer lending company that connects borrowers with investors who have capital to lend. When a loan application is submitted by a borrower, Lending Club reviews and decides whether to offer a loan at a risk-adjusted rate or to reject the application. As of the 3rd quarter of 2015, more than 12 billion in loans have been issued through Lending Club.

Open source is a disruptor that never quits, and it is seemingly penetrating and transforming every aspect of established data, analytics and application ecosystems. Give this podcast--recorded at IBM InterConnect 2016--a listen, as Tejinder Luthra, global technical ambassador, data, integration, and cloud and security, at IBM--shares his expertise and perspective on how open source initiatives are transforming machine learning. Learn how IBM Cloud Data Services is Open for Data.

In the presentation below, Dr. Demis Hassabis from Google DeepMind delivered a talk on "General Learning Algorithms" to the Royal Society in London on May 22, 2015. Hassabis is a neuroscientist and leading expert on the neural basis of memory and imagination. He was the co-founder and CEO of DeepMind, a neuroscience-inspired AI company, bought by Google in Jan 2014. He is now Vice President of Engineering at Google DeepMind and leads Google's general AI efforts. Demis is a former child chess prodigy, who finished his A-levels two years early before coding the multi-million selling simulation game Theme Park aged 17.

That's where Google comes in. The company announced Thursday that it's releasing new software capable of understanding written English – and that the software will be available to anyone for free. Called Parsey McParseface – yup, that's a play on the fact that the internet wanted to name a British research vessel Boaty McBoatface – Google's software is part of a programming toolkit called SyntaxNet, which was also released for free Thursday. The move paves the way for developers to integrate language understanding into more software. "Our hope is that people will just use this instead of building their own," Dave Orr, SyntaxNet's product manager, told the Wall Street Journal.