When we think about technology, we often think about physical devices that are electrical or digital. In fact technology encompasses far more than that. The dictionary definition refers to Technology as, "methods, systems, and devices which are the result of scientific knowledge being used for practical purposes." As we look to the year ahead tech disruption will be driven as much by the methods and systems as it is by the devices we associate with tech disruption. It's impossible to predict exactly which trends will become the most disruptive over the course of 2018.

On Saturday February 11, 2017, my daughter and her friend were driving from a basketball game in Chico back to our home in Palo Alto. Unfortunately, due to several days of heavier than normal rains, the Oroville Dam spillway broke and flooded many of the roads between Chico and Palo Alto. My daughter's smartphone mapping application wasn't aware of the sudden danger, and proceeded to send her into the heart of the flooding (see Figure 1). Fortunately, courtesy of some heads up "smart" driving, she was able to navigate the shallow flooding and avoid the more dangerous, deeper flooding (always helps to see cars stalled in the water before deciding to plow in). This incident highlights two significant challenges with respect to the application of artificial intelligence in the world of the Internet of Things (IoT), edge analytics and creating "smart" devices: The challenge for any autonomous device (car, truck, drone, washer, wind turbine, pace maker) is how to manage challenge #1 within the computational and storage limitations of #2.

"How can we apply Machine Learning to estimate electric loads?" Electric Load Forecasting if vital for making informed decisions about how to use energy throughout the day. And thanks to modern computational technology, we will be able to do such work by applying Machine Learning in Electric Load Forecasting. A computer algorithm can learn the past behavior of electric loads, and then make models to predict future behavior. Electric utility companies are investing large amounts of resources into developing these systems to increase their infrastructure.

The Fourth Industrial Revolution is still in its nascent state. But with the swift pace of change and disruption to business and society, the time to join in is now. The above statement was made during 2016 World Economic Forum meeting. We are now entering the year of 2018. If you have been keeping an eye on newsfeeds, you must know that a lot has changed especially in the world of marketing technology.

Growers in Illinois are looking for new ways to expand their use of technology, and artificial intelligence is emerging as their way of embracing the future of farm production. Chad Colby, an agricultural technologist and creator of Colby AgTech, said Illinois farmers are starting to look into robotics as a way of managing their crops and are using technology they wouldn't have considered a few years ago. Remote sensing in soil and from the sky has become popular, according to Colby. "Right now, today, the use is coming from satellites, drones and aircraft; but over the next couple years, you'll see those benefits expand as guys start to utilize the benefits of new technology in our soils," Colby said. The costs for using artificial intelligence has gotten cheaper, Colby said.

Artificial intelligence is a moving target. Here's how to take better aim. Artificial intelligence (AI) seems to be everywhere. We experience it at home and on our phones. Before we know it--if entrepreneurs and business innovators are to be believed--AI will be in just about every product and service we buy and use. In addition, its application to business problem solving is growing in leaps and bounds.

This paper presents OptNet, a network architecture that integrates optimization problems (here, specifically in the form of quadratic programs) as individual layers in larger end-to-end trainable deep networks. These layers encode constraints and complex dependencies between the hidden states that traditional convolutional and fully-connected layers often cannot capture. In this paper, we explore the foundations for such an architecture: we show how techniques from sensitivity analysis, bilevel optimization, and implicit differentiation can be used to exactly differentiate through these layers and with respect to layer parameters; we develop a highly efficient solver for these layers that exploits fast GPU-based batch solves within a primal-dual interior point method, and which provides backpropagation gradients with virtually no additional cost on top of the solve; and we highlight the application of these approaches in several problems. In one notable example, we show that the method is capable of learning to play mini-Sudoku (4x4) given just input and output games, with no a priori information about the rules of the game; this highlights the ability of our architecture to learn hard constraints better than other neural architectures.

We focus on wind power modeling using machine learning techniques. We show on real data provided by the wind energy company Ma{\"i}a Eolis, that parametric models, even following closely the physical equation relating wind production to wind speed are outperformed by intelligent learning algorithms. In particular, the CART-Bagging algorithm gives very stable and promising results. Besides, as a step towards forecast, we quantify the impact of using deteriorated wind measures on the performances. We show also on this application that the default methodology to select a subset of predictors provided in the standard random forest package can be refined, especially when there exists among the predictors one variable which has a major impact.

In the midst of a tech boom, productivity growth is slowing. Is the global economy simply gathering strength, or is innovation becoming elusive? In his seminal 2016 book, The Rise and Fall of American Growth, Robert Gordon of Northwestern University made a provocative claim--compared to the five waves of technological shifts of the past (electricity, urban sanitation, chemicals and pharmaceuticals, the internal combustion engine, and modern communication), the economic impact from ongoing IT developments is downright ordinary. Gordon's point is that the way we used to work and live changed fundamentally from the 1870s to the 1940s, as clean water, indoor plumbing, electricity and mechanised transportation became widely available. With these changes, we became healthier, more secure.

CES 2018 day two was interrupted by a two-hour power cut. It was as ridiculous as you'd imagine: The world's biggest tech show meets a severe lack of electricity. Rest assured, we had time on either side to delve deeper into this year's biggest incoming tech. Possibly the worst thing that could happen at a tech show: Power went down across several halls at the Las Vegas Convention Center during this morning's CES at 11:15 PT. Multiple booths suffered power outages, including swaths of TVs at LG.