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.

DeepMind's New AI Tracks Serengeti Herds from Images Alone DeepMind, the U.K.-based AI research subsidiary acquired by Alphabet in 2014 for $500 million, today detailed ecological research its science team is conducting to develop AI systems that'll help study the behavior of animal species in Tanzania's Serengeti National Park. They extend the popular BERT architecture to a multi-modal two-stream model, processing both visual and textual inputs in separate streams that interact through co-attentional transformer layers.

Artificial intelligence-powered tools are rapidly becoming more accessible, including for people in the more remote corners of the globe. This is good news for smallholder farmers, who can use handheld technologies to run their farms more efficiently, linking them to markets, extension workers, satellite images, and climate information. The technology is also becoming a first line of defense against crop diseases and pests that can potentially destroy their harvests. 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 oldest known knitting item dates back to Egypt in the Middle Ages, by way of a pair of carefully handcrafted socks. Although handmade clothes have occupied our closets for centuries, a recent influx of high-tech knitting machines have changed how we now create our favorite pieces. These systems, which have made anything from Prada sweaters to Nike shirts, are still far from seamless. Programming machines for designs can be a tedious and complicated ordeal: When you have to specify every single stitch, one mistake can throw off the entire garment. In a new pair of papers, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have come up with a new approach to streamline the process: a new system and design tool for automating knitted garments. In one paper, a team created a system called "InverseKnit," that translates photos of knitted patterns into instructions that are then used with machines to make clothing.