One is improving women's access to pre-natal healthcare in the Democratic Republic of Congo. "A pregnant woman has to walk 17 hours to her nearest, rural, prenatal clinic to get a check-up. What if she could get a diagnosis on her phone instead?" asked Sharma. Another example is that of a woman in South Africa who is suffering domestic abuse. Sharma asked: "What could AI do for those one in three women? If it isn't safe to talk out loud, they could get an AI service to raise the alarm or get financial and legal advice."

I envisioned an old Cadillac with massive Texas longhorns adorning the hood meandering along a dusty road. This road was in Egypt, and Stringfield was behind the wheel, sweat glistening on his brow as he hauled a load of freshly-baked sesame seed bagels. No, I hadn't been experimenting with some designer hallucinogen. But my conversation with him, as happens with particularly captivating sources, conjured evocative concepts and imagery, the kind of stuff that begs to be illustrated in word pictures. Thing is, although his latest enterprise encompassed many of the issues I aimed to address in my most recent feature story in MIT Technology Review -- such as fair labor in the AI industry, data ethics and the future of work -- his background as a former Halliburton executive who became a bagel-making entrepreneur during his time in Cairo as an HR consultant with the oil giant never made it into the story.

While machine learning today is dominated by deep neural network research, in the 1990s neural approaches were not recognized as reliable for real-world applications. Back then, researchers put their efforts into kernel methods and support vector machines (SVM). One of the most notable and respected contributors to kernel methods and SVM is John Shawe-Taylor, a professor at University College London (UK) and Director of the Centre for Computational Statistics and Machine Learning (CSML). His main research area is Statistical Learning Theory, but his contributions range from neural networks to machine learning and graph theory. Shawe-Taylor has published over 300 papers with over 42000 citations.

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. In Nigeria, AI has the power to reduce election fraud, improve infrastructure design and provide greater government security for intelligence, military and foreign relations. It would create jobs and expand job growth as it is expected to become more deeply embedded in science, healthcare, global business and trade. AI will evolve with greater transparency to resolve trust issues and lack of understanding. AI has the ability to "learn" when information is acquired.

Are you interested in using AI & ML and their Techniques to solve Nigeria's education, agriculture, healthcare and financial inclusion problems? Artificial Intelligence is expected to transform various industries just as Electricity did over 10 Decades ago and global consulting firm, McKinsey has stated that non-internet sectors like Agriculture, Education, Energy, Logistics & Manufacturing will see a $13 Trillion GDP growth driven by AIby 2030. For this reason, The Rise Labs by Rise Networks, Nigeria's first AI powered Learning, Research and Work Readiness Centre based in Lagos, Nigeria's commercial capital in partnership with Business Day and other Key Private Sector Stakeholders has launched the Nation's first Artificial Intelligence Ideathon by calling for revolutionary ideas that have the potential of solving Nigeria's Education, Agriculture, Healthcare and financial inclusion problems using Artificial Intelligence and Machine Learning models and methodologies. Winners of the Ideathon will get the rare opportunity of a veritable springboard to showcase and share their AI and ML models and applications to a broad community of stakeholders especially Policy Makers, Technology Experts and Organizations, Leaders in the Private Sector and Venture Capitalists to facilitate the scaling and execution of their ideas. To submit an entry, complete the registration form and submit your idea for using Artificial Intelligence and/or Machine Learning ("AI" and/or "ML") to transform education, agriculture, healthcare and financial inclusion in Nigeria.

Recently, the development and implementation of phishing attacks require little technical skills and costs. This uprising has led to an ever-growing number of phishing attacks on the World Wide Web daily. Consequently, proactive techniques to fight phishing attacks have become extremely necessary. In this paper, we propose a deep learning model HTMLPhish based on the HTML analysis of a web page for accurate phishing attack detection. By using our proposed HTMLPhish, the experimental results on a dataset of over 300,000 web pages yielded 97.2% accuracy, which significantly outperforms the traditional machine learning methods such as Support Vector Machine, Random Forest and Logistics Regression. We also show the advantage of HTMLPhish in the aspect of the temporal stability and robustness by testing our proposed model on a dataset collected after two months when the model was trained. In addition, HTMLPhish is a completely language-independent and client-side strategy which can, therefore, conduct web page phishing detection regardless of the textual language.

One prominent approach for data collection has been to automatically generate pseudo-language paired with logical forms, and paraphrase the pseudo-language to natural language through crowdsourcing (Wang et al., 2015). However, this data collection procedure often leads to low performance on real data, due to a mismatch between the true distribution of examples and the distribution induced by the data collection procedure. In this paper, we thoroughly analyze two sources of mismatch in this process: the mismatch in logical form distribution and the mismatch in language distribution between the true and induced distributions. We quantify the effects of these mismatches, and propose a new data collection approach that mitigates them. Assuming access to unlabeled utterances from the true distribution, we combine crowdsourcing with a paraphrase model to detect correct logical forms for the unlabeled utterances. On two datasets, our method leads to 70.6 accuracy on average on the true distribution, compared to 51.3 in paraphrasing-based data collection. 1 Introduction Conversing with a virtual assistant in natural language is one of the most exciting current applications of semantic parsing, the task of mapping natural language utterances to executable logical forms (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005; Liang et al., 2011). Semantic parsing models rely on supervised training data that pairs natural language utterances with logical forms. Alas, such data does not occur naturally, especially in virtual assistants that are meant to support thousands of different applications and use-cases. Thus, efficient data collection is per-Figure 1: An overview of G RA NNO, a method for annotating unlabeled utterances with their logical forms.

Recently, hybrid metaheuristics have become a trend in operations research. A successful example combines the Greedy Randomized Adaptive Search Procedures (GRASP) and data mining techniques, where frequent patterns found in high-quality solutions can lead to an efficient exploration of the search space, along with a significant reduction of computational time. In this work, a GRASP-based state-of-the-art heuristic for the Minimum Latency Problem (MLP) is improved by means of data mining techniques for two MLP variants. Computational experiments showed that the approaches with data mining were able to match or improve the solution quality for a large number of instances, together with a substantial reduction of running time. In addition, 88 new cost values of solutions are introduced into the literature. To support our results, tests of statistical significance, impact of using mined patterns, equal time comparisons and time-to-target plots are provided.

A new smartphone tool developed for banana farmers scans plants for signs of five major diseases and one common pest. In testing in Colombia, the Democratic Republic of the Congo, India, Benin, China, and Uganda, the tool provided a 90 percent successful detection rate. This work is a step towards creating a satellite-powered, globally connected network to control disease and pest outbreaks, say the researchers who developed the technology. The findings were published this week in the journal Plant Methods. "Farmers around the world struggle to defend their crops from pests and diseases," said Michael Selvaraj, the lead author, who developed the tool with colleagues from Bioversity International in Africa.

When HSBC Holdings Plc thwarted a $500 million central-bank heist, sophisticated computer software didn't raise the alarm. The funds flowed undetected from Angola's reserves to a dormant company's account in London. It was a teller at a suburban bank branch who became suspicious, declined a request to transfer $2 million, and triggered a review that uncovered the scam, according to one account of the episode. That was two years ago, and the finance industry's battle to stop the illicit transfer of as much as $2 trillion a year around the globe hasn't become any easier. At least a half-dozen lenders in Europe have found themselves at the center of fresh allegations of dirty money schemes in the past year.