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The success and fast expansion of Amazon Go has led other retailers and venues to seek startup help ... [ ] for their own cashierless checkout-free stores. On Amazon's jobs site, a keyword search query for Amazon Go yielded 3,500 results, seeking to fill positions manning the cashierless stores and looking for a head of marketing for the concept and a wide variety of engineers and program managers. Meanwhile, six months after the first Amazon Go opened in New York in May, six stores are in operation in the city, including four located less than a mile from one another in Midtown Manhattan. Two more are scheduled to open soon in the city. Those job postings and the fact that Amazon Go is cropping up in busy commercial sections of New York are just the latest signal of the Seattle giant's ambition to further expand its Just Walk Out Shopping concept, which features in-house-built computer vision, sensor fusion and deep machine learning technologies similar to those used in self-driving cars.

IMAGE: Philip Thomas at UMass Amherst, with colleagues there and at Stanford, says they say they hope that machine learning researchers will go on to develop new and more sophisticated... view more Guaranteeing safe and fair machine behavior is still an issue today, says machine learning researcher and lead author Philip Thomas at the University of Massachusetts Amherst. "When someone applies a machine learning algorithm, it's hard to control its behavior," he points out. This risks undesirable outcomes from algorithms that direct everything from self-driving vehicles to insulin pumps to criminal sentencing, say he and co-authors. Writing in Science, Thomas and his colleagues Yuriy Brun, Andrew Barto and graduate student Stephen Giguere at UMass Amherst, Bruno Castro da Silva at the Federal University of Rio Grande del Sol, Brazil, and Emma Brunskill at Stanford University this week introduce a new framework for designing machine learning algorithms that make it easier for users of the algorithm to specify safety and fairness constraints. "We call algorithms created with our new framework'Seldonian' after Asimov's character Hari Seldon," Thomas explains.

BARCELONA, SPAIN – A growing push to put cities on a digital path to a greener future risks excluding groups like the poorest, disabled and elderly, and will fail to benefit those people unless technology is used to help meet their needs, rights advocates have warned. They also called for women to be given a bigger say in urban planning that is based on high-tech tools such as big data and artificial intelligence, while speaking at an international conference on "smart cities" in Barcelona this week. "My fear is that smart cities end up benefiting the elite white men," said Catherine D'Ignazio, an assistant professor at Massachusetts Institute of Technology. In the United States, she said, national politics and other social spheres are shaped by "the privilege hazard," in which a small, dominant group -- often of rich, older men -- make decisions for others whose lives and experiences they know little about. One way to counteract that is to produce and use data that dive into key areas of discrimination, such as gender and race, she added. Berkeley-based consultancy World Enabled, which works for the inclusion of people with disabilities, collaborated with the University of California to analyze nearly 1,200 projects with digital elements run by six international development agencies.

Ever heard of the Nasca Lines? They're literal lines etched in the sands of southern Peru covering an area of nearly 1,000 square kilometers, which depict over 300 different figures including animals and plants. The best evidence suggests that they're pre-Columbian in origin, dating from between roughly 500 BC and 500 AD, and that they might mark solstice points or serve as offerings to ancient deities. Although the Nasca Lines have been studied for decades (and more intensely since they were designated a UNESCO World Heritage site in 1994), they've yet to be fully mapped. Yamagata used IBM's Watson Machine Learning Accelerator (WMLA) -- a framework designed to handle large-scale workloads spanning clusters of machines -- to expedite their analyses.

Environmental conflicts have emerged as major issues that deeply affect the socio-economic state of a region and/or an entire nation. These conflicts are related to natural resources, land, wildlife, supply chains etc. The crises are widespread around the globe and it is increasing rapidly. According to the Environmental Justice Atlas, India has the most number of environmental conflicts, followed by Colombia and Nigeria. For instance, approximately 66% of all civil cases in the Supreme court of India are related to the land disputes for more than 2.5 million hectares of land.

We can already see the potential for artificial intelligence (AI) in international development: the seemingly endless possibilities to enhance productivity and innovation across healthcare, agriculture, education, transportation, and governance. Yet it is also becoming abundantly clear that AI could have negative repercussions as well, particularly in countries with weaker institutional capacity and legal protections. AI has the potential to threaten democratic processes, employment, human rights and -- because of the weaponization of AI tools -- privacy, policing, and defense. Apart from these potential benefits and threats, the transformative potential of AI for both good and harm will be magnified in the Global South, where existing gender and socio-economic inequalities could either be tempered or exacerbated. Given the opportunities and potential consequences of new automation and mechanization techniques and advanced analysis through machine learning and neural networks, IDRC is investing in applied research across a number of domains to advance the public good with the use of artificial intelligence for development (AI4D).

In this chapter, we give an introduction to symbolic artificial intelligence (AI) and discuss its relation and application to multimedia. We begin by defining what symbolic AI is, what distinguishes it from non-symbolic approaches, such as machine learning, and how it can used in the construction of advanced multimedia applications. We then introduce description logic (DL) and use it to discuss symbolic representation and reasoning. DL is the logical underpinning of OWL, the most successful family of ontology languages. After discussing DL, we present OWL and related Semantic Web technologies, such as RDF and SPARQL. We conclude the chapter by discussing a hybrid model for multimedia representation, called Hyperknowledge. Throughout the text, we make references to technologies and extensions specifically designed to solve the kinds of problems that arise in multimedia representation.

Joint models are a common and important tool in the intersect ion of machine learning and the physical sciences, particularly in contex ts where real-world measurements are scarce. Recent developments in rainfall-run off modeling, one of the prime challenges in hydrology, show the value of a joint m odel with shared representation in this important context. However, curren t state-of-the-art models depend on detailed and reliable attributes characteriz ing each site to help the model differentiate correctly between the behavior of diff erent sites. This dependency can present a challenge in data-poor regions. In this p aper, we show that we can replace the need for such location-specific attributes w ith a completely data-driven learned embedding, and match previous state-of-the -art results with less information.

Improvement of statistical learning models in order to increase efficiency in solving classification or regression problems is still a goal pursued by the scientific community. In this way, the support vector machine model is one of the most successful and powerful algorithms for those tasks. However, its performance depends directly from the choice of the kernel function and their hyperparameters. The traditional choice of them, actually, can be computationally expensive to do the kernel choice and the tuning processes. In this article, it is proposed a novel framework to deal with the kernel function selection called Random Machines. The results improved accuracy and reduced computational time. The data study was performed in simulated data and over 27 real benchmarking datasets.