When people think of artificial intelligence (AI), they might envision an evil supercomputer or a lifelike robot plotting to overtake its owner. In fact, you are probably using it already. Salesforce, Gmail and even Netflix utilize AI in some capacity. AI also plays a large role in marketing. Algorithms can now process data to optimize the best marketing strategy for lead generation. There are valid concerns that AI will encroach on human productivity and make certain jobs obsolete.

As part of the first SAP Esri Spatial Hackathon, GIS developers, enterprise architects, data scientists, BI developers, and students got together to solve a variety of challenges through the use of geospatial analytics and machine learning technology. With the help of NextGen and the SAP Co-Innovation Lab – who ran the technology behind the hackathon – teams were given 40 hours to define the issue at hand and develop compelling platforms that provide an effective solution that can be applied in real-world scenarios while 89% of participants had no prior experience with SAP HANA Spatial. Finalist team, We're Working on It, was a corporate team from Southern California Edison (SCE), the primary electricity supply company for much of Southern California. This team developed a solution to predict grid usage for solar, using SAP HANA, ArcGIS Pro, and R-ArcGIS Bridge to show which parts of the grid may need modernization to maintain reliability and support clean energy. The picture below shows the SAP HANA as the enterprise geodatabase for ArcGIS Pro.

Last month, the music streaming arm of telecom company Jio, Reliance Jio Music, and music and audio streaming service Saavn merged to jointly strengthen their foothold in the digital music market, which is expected to cross $460 million (Rs.3,100 crore) in revenue by 2020. The two music streaming companies will be integrated into a combined $1 billion entity that leverages the media streaming expertise of Saavn with the 4G internet connectivity of telecom company Jio. Soon after the announcement of this merger, Reliance Industries Limited (RIL), also agreed to pick up a 72.69% stake in the AI-based online education startup Embibe, with a proposal to invest $180 million over the next three years. This foray of India's petrochemical giant Reliance in the startup industry was the culmination of a plan put into motion in 2016, when RIL's owner, the richest Indian on Forbes Billionaires 2018 list, Mukesh Ambani, addressed its 42nd annual general meeting in Mumbai. During the address he stated that Reliance Industries was setting up a $743 million (Rs.5,000 crore) fund, called the Jio Digital India Startup Fund, to invest in digital businesses.

The U.S. Naval Research Laboratory (NRL) will use Alta Devices' "highly efficient, flexible, and light-weight" solar technology to help power the "breakthrough" Hybrid Tiger UAV. The Hybrid Tiger is a project designed to create a Group-2 UAV that will stay aloft for at least three and a half days, and Alta Devices says that technologies developed for the project will be applicable to other unmanned vehicles. "Widespread use of small UAVs in both the military and industry has been limited to-date by endurance. The Hybrid Tiger will demonstrate that very long endurance flights, with sophisticated telemetry and capabilities, can be achieved with the inclusion of solar arrays," says Jian Ding, Alta Devices CEO. "This project will open the door for many new solar powered UAV applications, and we look forward to achieving next generation breakthroughs via this cooperative effort."

Decision maker's preferences are often captured by some choice functions which are used to rank prospects. In this paper, we consider ambiguity in choice functions over a multi-attribute prospect space. Our main result is a robust preference model where the optimal decision is based on the worst-case choice function from an ambiguity set constructed through preference elicitation with pairwise comparisons of prospects. Differing from existing works in the area, our focus is on quasi-concave choice functions rather than concave functions and this enables us to cover a wide range of utility/risk preference problems including multi-attribute expected utility and $S$-shaped aspirational risk preferences. The robust choice function is increasing and quasi-concave but not necessarily translation invariant, a key property of monetary risk measures. We propose two approaches based respectively on the support functions and level functions of quasi-concave functions to develop tractable formulations of the maximin preference robust optimization model. The former gives rise to a mixed integer linear programming problem whereas the latter is equivalent to solving a sequence of convex risk minimization problems. To assess the effectiveness of the proposed robust preference optimization model and numerical schemes, we apply them to a security budget allocation problem and report some preliminary results from experiments.

Pollen recognition has a wide range of industrial and scientific applications. It guides the energy industry to potential oil and gas deposits, it is proxy data for climate-change scien- tists, and it increases agricultural production. However, pollen recognition is time consuming because it is usually done by visual inspection. Current automated solutions rely on pre-designed measurements of texture and contours, which require tuning for optimal features of a dataset. Also, most methods classify pollen using single-focus images, which require pollen grains to be captured at specific focal planes. We take a difference approach. Instead of using single-focus images, we use stacks of multifocal images (i.e., z-stack) to account for both visual characteristics and 3-D information. We automatically learn from the data the best visual characteristics for classifying pollen using deep-learning methods. Here, we train convolutional and recurrent neural networks (CNN and RNN) to learn the optimal features and recognize a pollen grain as a sequence of multifocal images acquired by an optical microscope. Additionally, we transfer the knowledge pre-trained network to ours to improve its classification and convergence speed. We evaluated our method using 392 stack sequences of 10 types of pollen grains with 10 images for each sequence. Our method achieved a remarkable classi- fication rate of 100%.

A bstract ： The paper intends to model the stability of power system with a deep learning algorithm to the problem, aiming to delay the removal of the fault. The so - called "fail - delay cut - off" refers to the occurrenc e of N - 1 backup protection action on the backbone network of the system, resulting in longer time for the removal of the fault. In practice, through the analysis and calculation of a large number of online data, we have found that the N - 1 failure system of the main protection action will not be unstable, which is also a guarantee of the operation mode arrangement. In the case of the N - 1 backup protection action, there is an approximately 2.5% probability that the system will be destabilized. Therefore, rese arch is needed to improve the operating arrangement.

The University of Nevada, Las Vegas drew attention in recent years with its rollout of a gaming laboratory, and while some people looked at it as a waste of academic resources, UNLV touted the addition as an appropriate response to an industry annually grossing billions and growing. This is the kind of approach institutions should consider in answering the call from elected officials and corporations that are eager to hire entry-level and credentialed talent, but have increasing doubts about how well higher education helps students to be work-ready upon graduation. Specialized degrees and training programs often invite investment from corporate partners, like the University of Hawaii System's workforce pipeline partnership with a green energy development company and community colleges offering manufacturing technology programs. And with these investments, governing bodies scramble to find ways to support such programs so that they can move closer to self-supporting offerings, which helps in reducing public spending.

To power RoboFly, the engineers pointed an invisible laser beam (shown here in red laser) at a photovoltaic cell, which is attached above the robot and converts the laser light into electricity.Mark Stone/University of Washington Insect-sized flying robots could help with time-consuming tasks like surveying crop growth on large farms or sniffing out gas leaks. These robots soar by fluttering tiny wings because they are too small to use propellers, like those seen on their larger drone cousins. Small size is advantageous: These robots are cheap to make and can easily slip into tight places that are inaccessible to big drones. But current flying robo-insects are still tethered to the ground. The electronics they need to power and control their wings are too heavy for these miniature robots to carry.

Though insect-sized flying robots have been around for a while, none had been able to take untethered fight until now. Engineers at the University of Washington have revealed that the RoboFly has taken its first untethered flaps, marking the first time a wireless flying robotic insect has flown. Previously, the electronics the insects carried to power and control their wings were too heavy for the robots to fly with, meaning they had to remain connected to a wire. RoboFly is only slightly heavier than a toothpick and is powered by an onboard circuit that converts the laser energy into enough electricity to operate its wings. 'Before now, the concept of wireless insect-sized flying robots was science fiction.