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.

Graphic designers, rejoice – the hours upon hours of struggling to figure out what a type face is will finally be over as Adobe is adding an artificial intelligence tool to help detect and identify fonts from any type of picture, sketch or screenshot. The DeepFont system features advanced machine-learning algorithms that send pictures of typefaces from Photoshop software on a user's computer to be compared to a huge database of over 20,000 fonts in the cloud, and within seconds, results are sent back to the user, akin to the way the music discovery app Shazam works. "You highlight the text area that you are interested in being recognised, and it will give you a list of the top five fonts that match what you highlighted," Anil Kamath, Adobe's VP of Technology and head of the data science team told the BBC. "That applies to an image that you can take with your phone. So, you might write something on a white board, take a picture of it and ask the software to suggest fonts that it corresponds to."

As much as we love to fawn over artificial intelligence (AI), it's still not great at recognizing and parsing natural language. That's why Google is open sourcing its new language parsing model for English, which it calls'Parsey McParseface.' Before you even ask, the name has no meaning. When Google was trying to figure out what to call its language parsing technology, someone suggested Parsey McParseface; it's a bit like Apple's Liam, which has no clever backstory either. The overall AI model model is called SyntaxNet (please make your SkyNet jokes now); 'ol Parsey is just for English. Our biggest ever edition of TNW Conference is fast approaching!

Every year, the Association for Computing Machinery--the world's largest scientific and educational computing society--gathers to explore the future of computer interaction in a legendary conference called CHI. It's an amazing event, in which thousands of researchers, scientists, and futurists get together to push the boundaries of what it means to interact with machines. It's a dizzying collision involving enough ideas about what the future of man and machine will look like to put the world's science-fiction authors out of their jobs for good. This year's CHI 2016 conference in San Jose was no exception--but among the hundreds of projects, here are eight that stood out. The problem: In VR, objects might look real, but they don't feel real.