Google plans to open an artificial-intelligence research center in Accra, Ghana, the latest in a string of investments the tech company has made in Africa. The research center will focus on using AI in areas such as healthcare, agriculture and education, Google said. "We're committed to collaborating with local universities and research centers, as well as working with policy makers on the potential uses of AI in Africa," the company In a blog post on Wednesday. The new AI center in Ghana will open later this year and include machine learning researchers and engineers, Google said, thought it did provide details on the number of staff it will hire. Google CEO Sundar Pichai promised last year during a visit to Lagos that Google would continue raising its profile on the continent.

Researchers from the University of Wyoming have shown how images, collected by motion-sensor cameras, can be rapidly interpreted by deep neural networks. This has led to a system that can automate animal identification with an accuracy of 99.3 percent of images. When compared to humans (as part of a citizen science project) the artificial intelligence was more accurate (the human volunteers came in with a 96.6 percent accuracy rate). This type of machine learning could be used to assist with nature conservation. Achieving this level of accuracy was not straightforward.

In Africa, it's not just Kenya that knows the value of AI. Aubra Anthony, a strategy and research lead for the Center for Digital Development within the U.S. Agency for International Development, wrote on 24 May 2018 in techcrunch.com about his discovery in Johannesburg, South Africa. This is what he says: "On a recent work trip, I found myself in a swanky-but-still-hip office of a private tech firm. I was drinking a freshly frothed cappuccino, eyeing a mini-fridge stocked with local beer and standing amidst a group of hoodie-clad software developers typing away diligently at their laptops against a backdrop of Star Wars and xkcd comic wallpaper. I wasn't in Silicon Valley: I was in Johannesburg, South Africa, meeting with a firm that is designing machine learning (ML) tools for a local project backed by the U.S. Agency for International Development."

We present a first attempt to perform attentional word segmentation directly from the speech signal, with the final goal to automatically identify lexical units in a low-resource, unwritten language (UL). Our methodology assumes a pairing between recordings in the UL with translations in a well-resourced language. It uses Acoustic Unit Discovery (AUD) to convert speech into a sequence of pseudo-phones that is segmented using neural soft-alignments produced by a neural machine translation model. Evaluation uses an actual Bantu UL, Mboshi; comparisons to monolingual and bilingual baselines illustrate the potential of attentional word segmentation for language documentation.

Multiwinner voting rules are used to select a small representative subset of candidates or items from a larger set given the preferences of voters. However, if candidates have sensitive attributes such as gender or ethnicity (when selecting a committee), or specified types such as political leaning (when selecting a subset of news items), an algorithm that chooses a subset by optimizing a multiwinner voting rule may be unbalanced in its selection -- it may under or over represent a particular gender or political orientation in the examples above. We introduce an algorithmic framework for multiwinner voting problems when there is an additional requirement that the selected subset should be "fair" with respect to a given set of attributes. Our framework provides the flexibility to (1) specify fairness with respect to multiple, non-disjoint attributes (e.g., ethnicity and gender) and (2) specify a score function. We study the computational complexity of this constrained multiwinner voting problem for monotone and submodular score functions and present several approximation algorithms and matching hardness of approximation results for various attribute group structure and types of score functions. We also present simulations that suggest that adding fairness constraints may not affect the scores significantly when compared to the unconstrained case.

An entrepreneur with a background in verifying the provenance of so-called conflict minerals is applying that expertise to keep tabs on one of the world's most widely traded commodities: coffee. Tracking this kitchen staple requires a mélange of emerging technologies such as blockchain, artificial intelligence and the internet of things. The goal of his venture, bext360, is to help coffee buyers automate their dealings with fair-trade farmers, allowing them to more closely track the source and quality of the fair trade beans they're buying while speeding up payments for local growers. For buyers, the service promises deeper transparency, as well as a way of automating the verification process. For harvesters and growers -- largely women -- the service could mean more ready access to investment capital, according to bext360 CEO Daniel Jones.

Feature engineering is one of the most important but most tedious tasks in data science. This work studies automation of feature learning from relational database. We first prove theoretically that finding the optimal features from relational data for predictive tasks is NP-hard. We propose an efficient rule-based approach based on heuristics and a deep neural network to automatically learn appropriate features from relational data. We benchmark our approaches in ensembles in past Kaggle competitions. Our new approach wins late medals and beats the state-of-the-art solutions with significant margins. To the best of our knowledge, this is the first time an automated data science system could win medals in Kaggle competitions with complex relational database.

In a recent blog post and tweet, Google announced that it'd be launching a new Artificial Intelligence research centre to be opened later this year in Accra, Ghana. We're continuing to expand our @GoogleAI teams around the world. We'll be opening our first research center in Africa in #Ghana later this year! If you're a machine learning researcher interested in working in Accra, Ghana, apply: https://t.co/YgntDigTJt With this, Accra will be the first African city to join the likes of New York, Montreal, Tokyo, San Francisco, Paris, Beijing, Zurich, Toronto, Seattle, Cambridge/Boston, and Tel Aviv/Haifa, in hosting Google AI centres.

Google has announced that it will establish an artificial intelligence research center in the Ghanaian capital later this year -- a first for the company on the continent. Expert machine learning researchers and engineers will work together at the new AI research center in Accra which will be dedicated to artificial intelligence research and its applications in a wider African context, according to Quartz. The research center will focus on using artificial intelligence applications in areas that include healthcare, agriculture and education. Announcing the AI research center in a Google blog post, senior Google AI fellow Jeff Dean and staff research scientist Moustapha Cisse wrote about the growing interest artificial intelligence has experienced across Africa. "In recent years, we've witnessed an increasing interest in machine learning research across the continent," the pair wrote in the Google blog post.

In a business where shaving off a mile per day per driver can result in savings of up to $50 million per year, UPS has plenty of incentive to incorporate technology to drive efficiencies in every area of its operations. Here are just a few of the ways UPS uses big data and artificial intelligence (AI) to prepare for the 4th Industrial Revolution. UPS was founded in 1907 and has a history of embracing change and evolving as new technologies arise. It's the use of big data and artificial intelligence that allows the company to operate its global logistics network in more than 220 countries and territories. On an average day, there are typically 96,000 UPS vehicles on the road handling 19 million packages.