Roughly half of the 821 million people considered hungry by the United Nations are those who dedicate their lives to producing food for others: farmers. This is largely attributed to the vulnerability of farmers to agricultural risks, such as extreme weather, conflict, and market shocks. Smallholder farmers, who produce some 60-70% of the world's food, are particularly vulnerable to risks and food insecurity. Emerging technologies such as Artificial Intelligence (AI), however, have been particularly promising in tackling challenges such as lack of expertise, climate change, resource optimization and consumer trust. AI assistance can, for instance, enable smallholder farmers in Africa to more effectively address scourges such as viruses and the fall armyworm that have plagued the region over the last 40 years despite extensive investment, said David Hughes, Co-Founder of PlantVillage and Assistant Professor at Penn State University at a session on AI for Agriculture at last week's AI for Good Global Summit.

We present RUSLAN -- a new open Russian spoken language corpus for the text-to-speech task. RUSLAN contains 22200 audio samples with text annotations -- more than 31 hours of high-quality speech of one person -- being the largest annotated Russian corpus in terms of speech duration for a single speaker. We trained an end-to-end neural network for the text-to-speech task on our corpus and evaluated the quality of the synthesized speech using Mean Opinion Score test. Synthesized speech achieves 4.05 score for naturalness and 3.78 score for intelligibility on a 5-point MOS scale.

Our USP in the PR market is to research to find the next influential technology and business leader who can support POC data to the media. In today's digital thunderstorm of news, publications, are more than ever, reliant on the principles of journalism. That is why we are excited to announce we have been nominated for Best Integrated Agency in the 2019 Prolific London Awards. Our mission to create dynamic client campaigns through digital innovation, keeps us at the forefront of leading business and technology media conversations and we are proud to share this nomination with our dedicated international team. Our work with UK based WAN Data Acceleration company Bridgeworks Ltd., has produced a thriving external communications strategy to attract multi-million dollar business contracts in global markets like the US, Europe and South Africa.

Today, can all companies use artificial intelligence (AI), regardless of their size or sector of activity? Nozha Boujemaa: Yes, in fact the difficulty does not come from AI itself but from the data. People have not yet understood how important their structuring is. A company that wants to use AI must be able to exploit data even if they are multi-source, so they must be structured. We always talk about algorithms, but the algorithm is only the engine.

In the last five years, private companies, research institutions as well as public sector organisations have issued principles and guidelines for ethical AI, yet there is debate about both what constitutes "ethical AI" and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analyzed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted; why they are deemed important; what issue, domain or actors they pertain to; and how they should be implemented. Our findings highlight the importance of integrating guideline-development efforts with substantive ethical analysis and adequate implementation strategies.

We consider the problem of answering queries about formulas of first-order logic based on background knowledge partially represented explicitly as other formulas, and partially represented as examples independently drawn from a fixed probability distribution. PAC semantics, introduced by Valiant, is one rigorous, general proposal for learning to reason in formal languages: although weaker than classical entailment, it allows for a powerful model theoretic framework for answering queries while requiring minimal assumptions about the form of the distribution in question. To date, however, the most significant limitation of that approach, and more generally most machine learning approaches with robustness guarantees, is that the logical language is ultimately essentially propositional, with finitely many atoms. Indeed, the theoretical findings on the learning of relational theories in such generality have been resoundingly negative. This is despite the fact that first-order logic is widely argued to be most appropriate for representing human knowledge. In this work, we present a new theoretical approach to robustly learning to reason in first-order logic, and consider universally quantified clauses over a countably infinite domain. Our results exploit symmetries exhibited by constants in the language, and generalize the notion of implicit learnability to show how queries can be computed against (implicitly) learned first-order background knowledge.

President Trump's last-minute decision to pull back from a retaliatory strike on Iran underscored the absence of appealing options available to him as Tehran races toward its next big challenge to the United States: building up and further enriching its stockpile of nuclear fuel. Two weeks of flare-ups over the attacks on oil tankers and the downing of an American surveillance drone, administration officials said, have overshadowed a larger, more complex and fast-intensifying showdown over containing Iran's nuclear program. In meetings in the White House Situation Room in recent days, Secretary of State Mike Pompeo contended that the potential for Iran to move closer to being able to build a nuclear weapon was the primary threat from Tehran, one participant said, a position echoed by Mr. Trump on Twitter on Friday. Left unsaid was that Iran's moves to bolster its nuclear fuel program stemmed in substantial part from the president's decision last year to pull out of the 2015 international accord, while insisting that Tehran abide by the strict limits that agreement imposed on its nuclear activities. Mr. Trump has long asserted that the deal would eventually let Iran restart its nuclear program and did too little to curb its support for terrorism.

Tuesday of this past week was quite fortuitous: In my Data Science Cohort at Lambda School, we are working a predictive maintenance competition on Kaggle regarding Water pumps in Tanzania. And, I went to a Data Science networking event at a defense contractor who spoke of the importance of Predictive Maintenance Solutions -- in their case, they were predicting the failure rates of parts of the F35 Joint Strike Fighter. According to IoT world, The Predictive Maintenance report forecasts a compound annual growth rate for Predictive Maintenance of 39% between 2016–2022, with annual technology spending reaching US$10.96 This has a large positive impact on Data Science and Machine Learning if the industry can keep up with the needs of predictive maintenance problems. What is predictive maintenance and why is it so important to different domains?

Healthcare Cognitive Computing market report is based on present industry situations, market demands, business strategies utilized by prominent players involved in this market along with their growth synopsis. This report has been segmented into types, applications and regions. The report also comprises major drivers boosting this market. Healthcare Cognitive Computing market worth about XX million USD in 2018 and it is expected to reach YY million USD in 2026 with a CAGR of AA% during the forecast period. Cognitive computing (CC) describes technology platforms that are based on the scientific disciplines of artificial intelligence and signal processing.

You may not be using machine learning, often referred to as artificial intelligence, for business applications yet, but there is little doubt you have read or heard about how it could or should be used. The issue is not that there are not legitimate business uses for machine learning (ML) options, the challenge is knowing which types of ML may work best for your business needs and finding the right provider or recruiting the right people to implement it. Initially understanding machine learning is hard, but with a few big concepts under your belt, it becomes easy. It then gets complicated again, but by then you will be ready to deal with generative adversarial networks! This is the most basic version about the content and should get you ready to listen to our special A Word On Artificial Intelligence podcast hosted by Primedia Broadcasting Head of Digital Allan Kent.