Mediaplanet today announces distribution of its first edition of "Future of Business Technology," out in The San Francisco Chronicle as well as online. The world as we know it is run by technology. Siri manages day-to-day schedules, robots vacuum floors, Instagram suggests videos one might like and cars can self-park. Despite movies like "The Terminator" and "I, Robot," which suggest robots can only take over the world, the truth is, they're here to stay and here to help. This campaign aims to advocate for the disruptive technologies and solutions like artificial intelligence, robotics, cybersecurity, big data and Internet of Things.

We'll look at some of the most important papers that have been published over the last 5 years and discuss why they're so important. The first half of the list (AlexNet to ResNet) deals with advancements in general network architecture, while the second half is just a collection of interesting papers in other subareas. The one that started it all (Though some may say that Yann LeCun's paper in 1998 was the real pioneering publication). This paper, titled "ImageNet Classification with Deep Convolutional Networks", has been cited a total of 6,184 times and is widely regarded as one of the most influential publications in the field. Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton created a "large, deep convolutional neural network" that was used to win the 2012 ILSVRC (ImageNet Large-Scale Visual Recognition Challenge). For those that aren't familiar, this competition can be thought of as the annual Olympics of computer vision, where teams from across the world compete to see who has the best computer vision model for tasks such as classification, localization, detection, and more. The next best entry achieved an error of 26.2%, which was an astounding improvement that pretty much shocked the computer vision community. Safe to say, CNNs became household names in the competition from then on out. In the paper, the group discussed the architecture of the network (which was called AlexNet).

Read this introductory list of contemporary machine learning algorithms of importance that every engineer should understand. It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix's algorithms to make movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend books based on books you have bought before. So if you want to learn more about machine learning, how do you start?

Another common AI technique for analyzing different alternative process flows, bottlenecks, resource constraints and customer/agent/employee behavior is discrete-event and agent-based simulation modeling.

If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages.

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Artificial intelligence could just be the answer to discovering the best method for compressing images, at least according to a recent Google study. Image compression is an essential piece of the puzzle for mediums that create large files, including 4K video and 360. Functions from live-streaming to web download times are largely dependent on file sizes, but shrinking files involves a balance between quality and space. Related: Google's new Britoil algorithm is about to supercharge web browsing Google is teaching computers to recognize the best method for compressing image files, and while the system isn't (yet) able to judge the effect of a compression on the image's quality, it is able to analyze what methods are the most successful in adjusting the image's footprint. The group used 6 million random compressed photos and cropped them down to a 32 by 32-pixel chunk.

Retailers can put artificial intelligence to work in many different forms, from creating a more personalised shopping experience for the consumer, to helping adopt a data-driven approach to retail marketing. ASOS Chief Executive, Nick Beighton, predicted that by 2020, mobile sales will account for 90% of total revenue for the retailer. With this in mind, retailers have to remain vigilant to changes in trends and expectations from shoppers. As customer traffic to mobile sites increases, it's important that retailers offer a bespoke and personalised service to ensure shoppers can fluently find the products they require and thus convert to purchasing much quicker.

Are we ready for Ultron type intelligence? This is part of our special series on Artificial Intelligence (AI). If you are catching it for the first time I'd recommend that you start here for some instrumental background and here where I start building the bigger idea behind AI. In my high school years, I remember a brilliant classmate, Matthew (not quite his real name), who got the necessary points at Advanced Level to study law at a local university. I was proud to see him not long ago appearing in newspapers as a commanding Intellectual Property (IP) lawyer.

Machine Learning (ML) is an exciting new subfield of computer science. With origins in pattern recognition, today's hardware and software advances have made ML a new tool for many types of organizations I order to remain competitive. With today's hardware, massive amounts of data can be fed into a system, which can then use algorithms to determine possible outcomes of a task, and store that information for further use. As the amount of data that is ingested increases, the accuracy of the outcomes can improve. Similar to simulations that can give more accurate results with faster processing, more memory and improved algorithms, so can ML applications.