Over the last two years, we have highlighted deep learning use cases in enterprise areas including genomics, large-scale business analytics, and beyond, but there are still many market areas that are still building a profile for where such approaches fit into existing workflows. Even though model training and inference might be useful, for some areas that have complex simulation-driven workflows, there are great efficiencies that could come from deep neural nets, but integrating those elements is difficult. The oil and gas industry is one area where deep learning holds promise, at least in theory. For some steps in the resource discovery workflow, deep learning could lead to faster and more accurate results for potential discovery zones. Reservoir characterization is a critical step in this discovery process and is currently a hot area for explorations into how deep learning might be applied.

I read a book today, oh boy. Actually, I read a lot of books--not quite one per day, but close to one each week in 2016. When browsing the Internet, I am continually bombarded with stories about politics that cause me to wonder what is happening in the world. I have such a love for progress, but civility, globalization and acceptance of others were seemingly going backward in 2016. As I look through the list of books that I read during the past year, apparently I was immersing myself in books about how changes in technology and social connections can propel the world in the direction I would like to see it take.

Several types of such systems exists • Closed Loop Systems – based on a predefined irrigation scheme the control system takes over and makes detailed decisions on when and how much water to apply • Open Loop Systems – based on the amount of water to be applied and the timing of the irrigation • Volume Based System – The pre-set amount of water can be applied in the field • Time Based System – works with time clock controllers (Adapted from BOMAN et al. 2006)All rights reserved. USE CASE - Face recognition systems for Domestic Cattle • Facial recognition of cows in dairy units can individually monitor all aspects of behavior in a group, as well as body condition score and feeding. Facial recognition of cows EFFECT OF USAGE • With this system it is possible to feed cows a lot less expensively if you know what they will and will not eat. It is meant to be deployed as a group or "swarm".The other three steps involve autonomous robots that tend the crops, harvest them, and finally one robot that can plant, tend, and harvest autonomously transitioning from one phase to another. Swarm Prospero EFFECT OF USAGE • The application of the system increases the productivity of land on a per unit basis. USE CASE – Strawberry Harvesting Robot • Automated harvester recently wheeled through rows of strawberry plants here, illustrating an emerging solution to one of the produce industry's most pressing problems: a shortfall of farmhands.

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated weights are not essential for learning deep representations. Random BP replaces feedback weights with random ones and encourages the network to adjust its feed-forward weights to learn pseudo-inverses of the (random) feedback weights. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations in neuromorphic computing hardware. The rule requires only one addition and two comparisons for each synaptic weight using a two-compartment leaky Integrate & Fire (I&F) neuron, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving nearly identical classification accuracies compared to artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

Imagine a marketing executive, late at night, flipping through a slide deck from a company that promises to use artificial intelligence to automate many of her lead generation and lead scoring processes, optimize her advertising spend, and increase her marketing spend ROI by fifty percent. What could go wrong, she wonders? What could go wrong, indeed. As someone whose own company uses AI to make marketers' lives easy, I'll be the first to admit that the promises of AI are often overhyped. While the benefits are great, the sales pitch often leaves out the risks.

Imagine a building that tells you – before it happens – that the heating is about to fail. Some companies are using machine learning to do just that. Software firm CGnal, based in Milan, Italy, recently analysed a year's worth of data from the heating and ventilation units in an Italian hospital. Sensors are now commonly built into heating, ventilation and air conditioning units, and the team had records such as temperature, humidity and electricity use, relating to appliances in operating theatres and first aid rooms as well as corridors. They trained a machine learning algorithm on data from the first half of 2015, looking for differences in the readings of similar appliances.

Businesses expect AI to boost revenues by 39 percent in 2020. Will artificial intelligence deliver on its bold promise for businesses? AI technologies are already profitable for many companies, who have high hopes for the technology looking ahead, according to a recent report from Infosys. The report, titled "Amplifying Human Potential: Towards Purposeful Artificial Intelligence" polled 1,600 senior decision makers from large businesses all over the world. The companies surveyed invested an average of $6.7 million in AI in the past 12 months and have been actively using AI for an average of 24 months.

The 3-D research was seminal in the fields of computer vision and artificial intelligence. Known as "constraint propagation," the technique is now used in industry for solving problems like route scheduling, package routing and construction scheduling. At M.I.T., Dr. Waltz was taught by Marvin Minsky, a pioneer in artificial intelligence. Dr. Waltz graduated in 1972, then taught computer science at the University of Illinois at Urbana-Champaign and, later, at Brandeis University in Massachusetts. But it was as a member of a group of researchers at the Thinking Machines Corporation, in Cambridge, Mass., that Dr. Waltz made his breakthrough in information retrieval.

Toshiba Corp. unveiled a robot Wednesday that it says can withstand high radiation and help in nuclear disasters. What exactly the new machine will be capable of doing, however, when it gets the go-ahead to enter Japan's crippled Fukushima Dai-ichi nuclear plant, remains unclear. The four-legged robot can climb over debris and venture into radiated areas off-limits to human workers. One significant innovation, Toshiba said, is that its wireless network can be controlled in high radiation, automatically seeking better transmission when reception becomes weak. But the machine, which looks like an ice cooler on wobbly metal legs, also appears prone to glitches.

A critical phase of oil well completion involves positioning cement between the outer surface of a metal casing and the sides of the well. This task is done by injecting a specially formulated cement slurry down the center of the casing and up the sides of the bore hole. Designing these slurry systems is time consuming and expensive because of the variability of the conditions between wells and the variability of the raw materials and techniques used in geographically diverse locations. SlurryMinder is a design tool to aid field engineers in creating globally consistent cement slurry formulations and to rapidly disseminate current well-cementing techniques. We describe the implementation of this system and why AI technology was used; we also discuss corporate benefits of the system, both real and projected.