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The " Hors d'Oeuvres, Anyone? " Event

AI Magazine

Five teams entered their robotic waiters into the contest. After a preliminary round to judge the safety of the robots, the robots served conference attendees at the opening reception of the Fourteenth National Conference on Artificial Intelligence. Robots from five teams acted as waiters, serving snacks to the AAAI conference attendees at the opening reception. The robots served a variety of food items, including sandwiches, pretzels, peanuts, and candy. The primary criterion for food selection was that no item could be messy, preventing potential damage to the robotic hardware.


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#artificialintelligence

Researchers have developed an artificial intelligence (AI) capable of identifying diseases in plants; specifically, the cassava plant, which is the most widely grown root on the planet and a decent source of carbohydrates. In May, the startup Abundant Robotics developed an apple-picking robot. Earlier this month, John Deere bought Blue River Technology, a startup that uses AI and machine learning to care for plants and eliminate weeds. Additionally, combining AI with the CRISPR gene editing used to make more crops could make the idea of eliminating world hunger more realistic.


Why artificial intelligence is stuck in the backseat

#artificialintelligence

"Artificial intelligence is only as smart as the data it is receiving, which means that the biggest gains you can make in quality will come from improving data input," explains Vijay Chittoor, a thought leader in marketing A.I. It is important to look at it this way, because powerful data sets will create powerful A.I. Today, the cutting edge of marketing technology is audience-of-one targeting, where you launch marketing campaigns using A.I. in the driver seat in marketing, you are able to remove marketers from the role of button pushing," Chittoor explains.


google-ai-helps-make-safer-baby-food

Engadget

Japanese food producer Kewpie Corporation has revealed that it's using Google's TensorFlow to quickly inspect ingredients, including the diced potatoes it uses in baby food. The result was an inspection system with "near-perfect" accuracy, culling more defective ingredients than humans alone -- even with a conveyor belt shuttling potatoes along at high speed. The TensorFlow system handles the initial removal, but there are still people waiting to inspect the potatoes afterward. The tech is currently limited to Kewpie's baby food, but the company expects to spread it to other foodstuffs before long.


fred-destin-artificial-intelligence-will-wipe-out-white-collar-jobs-2017-6?r=UK&IR=T

#artificialintelligence

According to Destin, Hudack used automation to make Deliveroo's ordering process more efficient. The startup refers to live orders as "orders in flight," and Hudack specifically looked at the number of times Deliveroo has contact with an order, from the moment a customer puts in a request for food through to the moment it's delivered by a driver. Through automation, Deliveroo reduced that number "by 98%" -- making 25 people redundant in the process. March figures from PwC suggest Destin is probably right about automation destroying jobs, though the actual numbers are more conservative.


FarmBot Will Grow Your Food For You; Just Press Go

Huffington Post - Tech news and opinion

What if you could grow food in your backyard with little to no understanding of gardening? A new robotic system may make this a reality for everybody. Farmbot Genesis is an open source, autonomous farming system that is supposed to fit virtually anyone's backyard, greenhouse or rooftop.


Exploiting Semantic Annotations for Clustering Geographic Areas and Users in Location-based Social Networks

AAAI Conferences

Location-Based Social Networks (LBSN) present so far the most vivid realization of the convergence of the physical and virtual social planes. In this work we propose a novel approach on modeling human activity and geographical areas by means of place categories. We apply a spectral clustering algorithm on areas and users of two metropolitan cities on a dataset sourced from the most vibrant LBSN, Foursquare. Our methodology allows the identification of user communities that visit similar categories of places and the comparison of urban neighborhoods within and across cities. We demonstrate how semantic information attached to places could be plausibly used as a modeling interface for applications such as recommender systems and digital tourist guides.