The company has been a leader when it comes to digital technology, and it plans to keep pushing the bar higher. Starbucks (NASDAQ:SBUX) has led the way for not just fast-casual restaurants, but all of retail when it comes to using customer-facing technology in its stores. The company was the first major chain to integrate digital payment into its app, making it a common sight to see people pay by holding up their phones to a scanner. That happened well before payment via phone become a relatively common thing, and it forced other chains to follow. Starbucks also led the way with Mobile Order & Pay.

There is no question that the portability and omnipresence of cameras in today's society has improved driver safety -- video of a vehicle crash helps people find out specifically what went wrong. But what if you could impart artificial intelligence into those camera systems in vehicles, and predict problems on the road and prevent disaster? Netradyne's Driver-I technology uses machine learning to predict and prevent accidents in the commercial transportation industry San Diego, California-based Netradyne has developed technology designed to do just that, integrating cameras and deep learning with their Driver-i, a "vision based" system, mounted in or on commercial vehicles. Rather than merely recording events triggered by the vehicle's movements, Driver-i uses a TeraFLOP processor - one trillion calculations per second - connected to cameras to identify information such as road signs, traffic lights by color, pedestrians, other vehicles, following distance, tailgating, lane prediction and even weather to learn about driving conditions. Sandeep Pandya, president of Netradyne, said he and his colleagues envisioned a driver safety system that was one step beyond simple recording.

Apple's first public research paper on AI was penned by vision expert Ashish Shrivastava and a team of engineers including Tomas Pfister, Oncel Tuzel, Wenda Wang, Russ Webb and Apple Director of Artificial Intelligence Research Josh Susskind, appleinsider.com Shrivastava holds a PhD in computer vision from the University of Maryland. Titled'Learning from Simulated and Unsupervised Images through Adversarial Training', the paper describes techniques of training computer vision algorithms to recognise objects using synthetic, or computer generated, images. However, learning from synthetic images may not achieve the desired performance owing to a gap between synthetic and real image distributions. To reduce this gap, Apple has proposed Simulated plus Unsupervised (S U) learning, where the task is to learn a model to improve the realism of a simulator's output using unlabelled real data while preserving the annotation information from the simulator.

Each of these trucks is the size of a small two-story house. None has a driver or anyone else on board. Mining company Rio Tinto has 73 of these titans hauling iron ore 24 hours a day at four mines in Australia's Mars-red northwest corner. At this one, known as West Angelas, the vehicles work alongside robotic rock drilling rigs. The company is also upgrading the locomotives that haul ore hundreds of miles to port--the upgrades will allow the trains to drive themselves, and be loaded and unloaded automatically.

Stacking (also called meta ensembling) is a model ensembling technique used to combine information from multiple predictive models to generate a new model. Often times the stacked model (also called 2nd-level model) will outperform each of the individual models due its smoothing nature and ability to highlight each base model where it performs best and discredit each base model where it performs poorly. For this reason, stacking is most effective when the base models are significantly different. Here I provide a simple example and guide on how stacking is most often implemented in practice. Feel free to follow this article using the related code and datasets here in the Machine Learning Problem Bible.

Amazon has long been at the forefront of innovation, with Amazon Prime Air, Amazon Echo and now Amazon Go all recent inventions. But where Amazon really leads the way is in its marketing and specifically how it manages to describe and sell complex technology like artificial intelligence (AI) to the public. General knowledge about AI is still low, with only 18% of consumers surveyed by Weber Shandwick feeling like they had a lot of knowledge about AI. This figure rose to 48% for those who felt that they had a little, but it's still a paltry figure given how much of our lives AI has now infiltrated. We've now got Siri and Cortana in our pockets and on our computers, Google Home and Amazon Echo is in our living rooms, and soon our roads will be filled with AI operating self-driving cars and Amazon Go-esque stores in our high streets.

Kevin Gavin (CMO at Five9): Next year the use of Artificial Intelligence will continue to rise and we'll see an increase in its potential to make our everyday lives easier. Machines will continue to learn patterns and provide answers to help eliminate some of our mundane tasks, and intelligent machine personas like the Alexa in Amazon Echo and Siri in Apple devices, are paving the way for natural language processing (NLP). However, in the customer service industry in particular, at the end of the day customers will still seek the human touch when getting complex issues and questions resolved because AI will never be able to replace the empathy that a real person can provide. Daniel Incandela (SVP Global Marketing at ReturnPath): The future of all enterprise processes will be driven by Artificial Intelligence, which requires the highest quality of data to be successful. AI is where all business processes are headed; however, with the recent push of AI technology advancements for businesses - many companies have not addressed how they will ensure that the data their AI models are built on is high quality.

Security data science is booming--reports indicate that the security analytics market is set to reach $8 billion dollars by 2023, with a growth rate of 26%, thanks to relentless cyber attacks. If you want to stay ahead of emerging security threats in 2017, it is important to invest in the right areas. In March 2016, I wrote a piece on the 4 trends to be aware of for 2016; for my 2017 trends post, Cody Rioux from Netflix joins me, bringing his platform perspective. Our goal is to help you formulate a plan for every quarter of 2017 (i.e., 4 trends for 4 quarters). For each of our trends, we provide a short rationale, why we think the time is right for investing, and how to capitalize on the investment, with pointers to specific tools and resources.

Tay, the Microsoft chatbot that pranksters trained to spew racist comments, has joined the likes of the Apple Watch and the fire-prone Samsung Galaxy Note 7 smartphone on MIT Technology Review's list of 2016's biggest technology failures. Tay had its day back in March, when it was touted as a millennial-minded AI agent that could learn more about the world through its conversations with users. It learned about human nature all too well: Mischief-makers fed its artificial mind with cuss words, racism, Nazi sentiments and conspiracy theories. Within 24 hours, Microsoft had to pull Tay offline. Other technological missteps were rated as fails because they didn't take off as expected, as was the case for Apple's smartwatch; or because they took off in flames, like the batteries in the Samsung phone.