The problem of Knowledge Base Completion can be framed as a 3rd-order binary tensor completion problem. In this light, the Canonical Tensor Decomposition (CP) (Hitchcock, 1927) seems like a natural solution; however, current implementations of CP on standard Knowledge Base Completion benchmarks are lagging behind their competitors. In this work, we attempt to understand the limits of CP for knowledge base completion. First, we motivate and test a novel regularizer, based on tensor nuclear $p$-norms. Then, we present a reformulation of the problem that makes it invariant to arbitrary choices in the inclusion of predicates or their reciprocals in the dataset. These two methods combined allow us to beat the current state of the art on several datasets with a CP decomposition, and obtain even better results using the more advanced ComplEx model.

A group of Amazon shareholders is asking CEO Jeff Bezos to stop selling and marketing facial recognition technology to governments after civil liberties groups warned of the potential for abuse. Earlier this year, a group of advocacy organisations led by the American Civil Liberties Union (ACLU) published a report detailing how Amazon was marketing its Rekognition tool to American law enforcement agencies. In addition to touting the technology as helping to find suspects, Amazon has said it could be used to preemptively identify "persons of interest" and prevent crimes. A letter signed by 19 shareholders - and provided to The Independent by the ACLU - urges Mr Bezos to halt the tool's expansion until those concerns can be addressed. Amazon supplier investigated over'mistreatment' of workers in China How Alexa recorded a family's conversation then sent it to someone Amazon told to stop selling facial recognition tools to police Amazon supplier investigated over'mistreatment' of workers in China How Alexa recorded a family's conversation then sent it to someone Furnishing police and sheriff's departments with the tool would bolster "government surveillance infrastructure technology" and could drive down Amazon's value, the letter warned. It also echoed concerns about the potential for misuse. "While Rekognition may be intended to enhance some law enforcement activities, we are deeply concerned it may ultimately violate civil and human rights", the letter said.

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.