As banking organisations, financial services providers and brands predict and plan for the way consumers will manage their money in the future, artificial intelligence (AI) is high on the business development strategy for 2016 and beyond. Gideon Hyde, co-founder of Market Gravity, proposition design consultancy, shares his thoughts on this emerging technology and explains how businesses can embrace AI to enhance their offerings, meet consumer demand for speed, personalisation and convenience, and launch new products and services to stand out in the competitive marketplace. Gideon Hyde is co-founder and CEO of Market Gravity, which has offices in London, Edinburgh and New York. It works with big businesses, including Boots, Barclaycard, HSBC, Aegon, Standard Life, RWE npower and The AA, to help them realise their innovation capabilities and release the start up within. AI is already around us and used everyday within payments, money management and for robo-advice, particularly in the area of intelligent digital assistants that handle regular customer service enquiries and tasks.

Building on the innovative thinking that jump-started three previous life-changing eras – steam-powered mechanised production, mass production assembly lines, and the first electronics and IT-based automations – we're about to find out how this latest evolution will take us places we can't begin to imagine. In the most disruptive industrial revolution yet, AI advancements are infiltrating our existence at every level, whether they're in the guise of IBM Watson's Jeopardy-playing prowess, or Uber's self-driving cars. The businesses we run, the jobs we perform and the lives we live will never be the same again. With every new era comes fresh concerns that the latest advancements will usurp our role as humans, and make our work redundant. From driving our cars to diagnosing the sick, if machines are being trained to do our thinking for us, will we still need to think for ourselves?

Today on In Case You Missed It: Robots have finally gotten around the'needing human assistance' thing when it comes to sewing clothing. The Sewbo is one such option, which can stitch together clothing as long as the garment is first dipped in a stiffening solution that makes it feel like cardboard. Meanwhile MIT's Computer Science and Artificial Intelligence Lab created a computer program made to simplify the process of 3D-printing with different materials in the same object. News about the Foundry is here. The video we can't stop watching from Russia is here.

President Barack Obama envisions AI as the next Apollo program--an 80 billion effort shepherded by the US government. In his interview with WIRED Editor-in-Chief Scott Dadich and MIT Media Lab director Joi Ito, President Obama said that the government should facilitate a range of research in artificial intelligence. "The way I've been thinking about the regulatory structure as AI emerges is that, early in a technology, a thousand flowers should bloom," he says. "The government should add a relatively light touch, investing heavily in research and making sure there's a conversation between basic research and applied research." That means the government should help provide a path for getting AI into the real world.

Here I put together a number of new data science techniques to solve a real life problem: identifying good articles to write and publish (or to harvest and re-post) on a website, and re-tweet them with the optimum frequency, given a specific audience. The focus is on scoring articles based on selected features (keywords in subject line, author, channel and many more), feature selection, data generation and harvesting to solve the problem, automatically categorizing and tagging articles using indexation algorithms, and predicting the lifetime value and total page views of an article. We also discuss bucketisation, and use both hidden decision trees and jackknife regression for scoring articles. The methodology applies to many contexts, not just digital publishing. It applies to situations in which a lot of unstructured text data needs to be processed (categorized and scored using natural language processing methods).

Since 1956, when computer science researchers gathered in the small town of Hanover, N.H. at Dartmouth College to talk about the field's nascent investigations into artificial intelligence, both government and industry in the U.S. have grappled with how to structure a systematic approach to research and development in the newly important field. From the government's perspective, this is increasingly important. With both federal research institutions and private companies pursuing artificial intelligence breakthroughs at breakneck speed, the federal government is frankly having a bit of an existential crisis about its role in research efforts and the priorities it has for what AI research should look like. To wit, in 2015 government spending on unclassified research and development in AI-related technologies was around 1.1 billion, according to one of the twin reports released today. But in the last five years alone, mergers and acquisitions among private companies vying for dominance in the AI market have far outstripped that figure, according to data from CB Insights.

Over the past four years, readers have doubtlessly noticed quantum leaps in the quality of a wide range of everyday technologies. Most obviously, the speech-recognition functions on our smartphones work much better than they used to. When we use a voice command to call our spouses, we reach them now. We aren't connected to Amtrak or an angry ex. In fact, we are increasingly interacting with our computers by just talking to them, whether it's Amazon's Alexa, Apple's Siri, Microsoft's Cortana, or the many voice-responsive features of Google.

Recent news about Google, Microsoft, Facebook and other tech giants teaming up to advance the capabilities of AI only confirms that the next generation of technology is going to be focused on machine learning. With artificial intelligence being at the core of all the new and upcoming programs and technologies, you can only stay relevant if you start learning about it now. Stay ahead of the pack with The Complete Machine Learning Bundle, which is now available for a special price of 25 only. The offer is valid for 24 hours, so grab the deal from WCCFtech Deals while it lasts.

Vox tech writer Timothy B. Lee used to be one of the most ardent techno-optimists. But he's had a bit of a conversion, of late, and is now on the side of those who think tech progress is slowing. Maybe it was the economist Robert Gordon who convinced him, or maybe years of observing the tech world changed his mind. In any case, Lee now broadly suggests that the inventions of tomorrow won't be as world-changing as those of yesteryear. There are a number of industries -- with health care and education being the most important -- where there's an inherent limit on how much value information technology can add.

Oracle, like so many other enterprise IT specialists, is talking up plans to integrate machine learning into its software. What the Redwood City giant hopes will set it apart from competitors is the way it plans to use those features. Where rivals have been touting AI and machine learning as a way to overhaul and redefine platforms, Oracle hopes that its Adaptive Intelligence features will instead complement its other cloud compute offerings. Introduced last month at the annual OpenWorld conference in San Francisco, Adaptive Intelligence will look to tie machine learning into Oracle's cloud to make features more personalized and better suited for individual companies. Part of Oracle's approach is based on the availability of data it has to feed into the machine learning system.