Apple has stayed true to its promise and published its first academic paper on artificial intelligence. The world's most valuable company has traditionally kept its AI research private but earlier this month Ruslan Salakhutdinov, director of AI research at Apple, made a pledge to start being more open. The new Apple paper -- published December 22 and titled "Learning from simulated and unsupervised images through adversarial training" -- gives an insight into some of the techniques that Apple is using to develop AI. In the study, which was published through the Cornell University Library, Apple researchers explain a technique that can be used to improve how an algorithm learns to "see" what is in an image. The paper's six authors state that using synthetic images (such as those seen in a video game), as opposed to real-world images, can be more efficient when it comes to training AI models known as neural networks, which are designed to think in the same way as the human brain. Because synthetic image data is already labelled and annotated while real-world images aren't.

Alexa and Siri become new retail customers: Home-based digital assistants like Amazon's Alexa and Apple's Siri will proliferate and expand into eCommerce, enabling consumers to'shop by voice'. These assistants are becoming pervasive as the ecosystems expand outside the home; Amazon is already integrating Alexa into Ford, BMW, and Hyundai autos and Apple is a growing force in cars. IoT hits a tipping point: The Internet of Things (IoT) has been talked about a lot for both in-home and in-store applications. Omnipresent omni-channel: Platforms are being put in place that provide consistent relevant content across every digital touchpoint while ensuring a cohesive and comprehensive user experience across devices and channels both inside and outside the store. Relevancy required: Strategic hyper-personalization goes from a'nice to have' to a necessity to keep customers (especially millennials and younger shoppers) engaged and to make the most efficient use of marketing budgets.

Staples Business Advantage, the B2B eCommerce unit of the office supplies company, is ramping up its attention to machine learning in an effort to boost performance. Reports this week said Staples is tapping into machine learning to more quickly engage with potential business buyers and linking sales representatives to those prospects. Staples Business Advantage is exploring machine learning and artificial intelligence to aggregate information about corporate buyers' preferences and predict their shopping needs. Zubair Murtaza, vice president of eCommerce product management and customer experience, told B2B E-Commerce World that Staples already has these technologies in place and is developing these use cases. "We have things that are very machine learning-intensive, such as self-service on the website, triggers for savings and tools for reps powered by the same algorithms used on the website," the executive said.

The process used to create a major motion picture hasn't changed much in a century. Someone has an idea that they turn into a screenplay, which is then edited, developed and handed to a director, who brings it to life. The only difference now is that there's plenty more focus-grouping, audience analysis and number crunching to ensure each film is a hit. Except that doesn't really work, since 2016 alone has seen scores of movies unceremoniously crash and burn. But maybe that will change with ScriptBook, an algorithm that its creators say can spot most turkeys before they've even been made.

Random Forest) • Used to ensemble a diverse group of strong learners • Involves training a second-level machine learning algorithm called a "metalearner" to learn the optimal combination of the base learners 5. History of Stacking • Leo Breiman, "Stacked Regressions" (1996) • Modified algorithm to use CV to generate level-one data • Blended Neural Networks and GLMs (separately) Stacked Generalization Stacked Regressions Super Learning • David H. Wolpert, "Stacked Generalization" (1992) • First formulation of stacking via a metalearner • Blended Neural Networks • Mark van der Laan et al., "Super Learner" (2007) • Provided the theory to prove that the Super Learner is the asymptotically optimal combination • First R implementation in 2010 6.

Year after year, data science techniques mature and deliver outstanding results with successful implementations. There have been various developments in the field of data science and related technologies. We saw growth in Data Scientists from all fields of profession and study. Most consulting enterprises established Analytics and Data Science as one of their key offerings, with many niche startups mushrooming to grab a space in this area. The advantage that we have is the increased contribution of the Data Scientists to open source development communities, laying out new thought processes in the analytics industry and bringing out innovative ways to solve business problems.

While artificial intelligence may replace truck dricers and beat us at chess, it also has much to offer: it can free up our minds and responsibilities for the tasks and social interactions we humans are best suited for. In this TEDx video featuring Felix Hausler, CEO of messaging interface Chatgrape, Hausler discusses how AI is becoming more a part of our daily lives, and how we can overcome the challenges this could pose. AI can be used for good as much as it can be a threat: it beats us in every technical game, exercises tireless intelligence, and yet it also helps us with research, or drives us home when we're too drunk to drive, Hausler pointed out during his talk. Because of AI, 47 percent of jobs are at risk, he said, and humans will lose their jobs to automation in the next decades. Truck driving, other transportation, production, and administrative jobs are particularly susceptible to AI.

Machine learning (ML) has received a lot of attention recently, and not without good reason. It has already revolutionized fields from image recognition to healthcare to transportation.

How do you know if you've made the right decision for a hire? Often, employers go off gut instinct and make a decision retrospectively, but it turns out AI might be able to help out in human resource management through shedding light on the best hiring decisions. In this episode, Pasha Roberts, Chief Scientist at Talent Analytics, Corp. tells us about how his company is working on helping companies make better decisions before they hire by applying machine learning and artificial intelligence to various data points on a given applicant, including information from aptitude tests that may help predict not only performance but retention.

It's hard to bet against the continued rise of automation, robots, and artificial intelligence (AI), all of which are already having major impacts on how we work, learn, shop, and play. But being able to predict that robotics and AI will change our lives is a lot easier than predicting how they will change our lives. In a recent forecast for 2017 and beyond, for instance, analyst firm IDC said we can expect to continue seeing robotic and AI technologies keep growing more affordable, more capable, and easier to use. The Obama White House said it expects the same, but also warns in a new report that "growth will not be costless" and could harm workers lacking the skills to compete in an AI-driven economy. How the incoming administration plans to address such issues is also uncertain. While President-elect Donald Trump's campaign promised to revive U.S. manufacturing and spend $1 trillion on the nation's infrastructure, he has also tapped Hardee's/Carl's Jr. chief Andrew Puzder -- who supports the use of automation to save on employment costs -- as secretary of the U.S. Department of Labor.