A common need when you are analyzing real-world data-sets is determining which data point stand out as being different to all others data points. Such data points are known as anomalies. This article was originally published on Medium by Davis David. In this article, you will learn a couple of Machine Learning-Based Approaches for Anomaly Detection and then show how to apply one of these approaches to solve a specific use case for anomaly detection (Credit Fraud detection) in part two. A common need when you analyzing real-world data-sets is determining which data point stand out as being different to all others data points.
American University of Beirut is developing a tool that farmers in the Middle East and Africa can use to irrigate fields at the optimum times to save water. At Colegio Mayor de Nuestra Señora del Rosario, a university in Colombia, researchers will use satellite images to detect illegal mines that are polluting community drinking water. Crisis Text Line, a nonprofit that connects people experiencing a crisis with volunteer counselors by text message, uses AI to evaluate messages and move the people who are in most danger to the front of the line. In Australia, a public health service called Eastern Health will use AI to comb through clinical records from ambulances and find patterns in suicide attempts–and ways to intervene earlier. Full Fact, an independent fact-checking organization in the U.K., is using AI to help human fact-checkers more quickly assess claims made by politicians and the media.
The humanity has been facing a plethora of challenges associated with infectious diseases, which kill more than 6 million people a year. Although continuous efforts have been applied to relieve the potential damages from such misfortunate events, it is unquestionable that there are many persisting challenges yet to overcome. One related issue we particularly address here is the assessment and prediction of such epidemics. In this field of study, traditional and ad-hoc models frequently fail to provide proper predictive situation awareness (PSAW), characterized by understanding the current situations and predicting the future situations. Comprehensive PSAW for infectious disease can support decision making and help to hinder disease spread. In this paper, we develop a computing system platform focusing on collective intelligence causal modeling, in order to support PSAW in the domain of infectious disease. Analyses of global epidemics require integration of multiple different data and models, which can be originated from multiple independent researchers. These models should be integrated to accurately assess and predict the infectious disease in terms of holistic view. The system shall provide three main functions: (1) collaborative causal modeling, (2) causal model integration, and (3) causal model reasoning. These functions are supported by subject-matter expert and artificial intelligence (AI), with uncertainty treatment. Subject-matter experts, as collective intelligence, develop causal models and integrate them as one joint causal model. The integrated causal model shall be used to reason about: (1) the past, regarding how the causal factors have occurred; (2) the present, regarding how the spread is going now; and (3) the future, regarding how it will proceed. Finally, we introduce one use case of predictive situation awareness for the Ebola virus disease.
Today, Zipline is officially opening the first of four distribution centers in Ghana, inaugurating a drone-delivery network that will eventually serve 2,000 hospitals and clinics covering 12 million people. Here's what Zipline says in a press release about the new operation: The revolutionary new service will use drones to make on-demand, emergency deliveries of 148 different vaccines, blood products, and life-saving medications. The service will operate 24 hours a day, seven days a week, from 4 distribution centers--each equipped with 30 drones--and deliver to 2,000 health facilities serving 12 million people across the country. Together, all four distribution centers will make up to 600 on-demand delivery flights a day on behalf of the Government of Ghana. Each Zipline distribution center has the capacity to make up to 500 flights per day.
We use an adversarial expert based online learning algorithm to learn the optimal parameters required to maximise wealth trading zero-cost portfolio strategies. The learning algorithm is used to determine the relative population dynamics of technical trading strategies that can survive historical back-testing as well as form an overall aggregated portfolio trading strategy from the set of underlying trading strategies implemented on daily and intraday Johannesburg Stock Exchange data. The resulting population time-series are investigated using unsupervised learning for dimensionality reduction and visualisation. A key contribution is that the overall aggregated trading strategies are tested for statistical arbitrage using a novel hypothesis test proposed by Jarrow et al. on both daily sampled and intraday time-scales. The (low frequency) daily sampled strategies fail the arbitrage tests after costs, while the (high frequency) intraday sampled strategies are not falsified as statistical arbitrages after costs. The estimates of trading strategy success, cost of trading and slippage are considered along with an offline benchmark portfolio algorithm for performance comparison. In addition, the algorithms generalisation error is analysed by recovering a probability of back-test overfitting estimate using a nonparametric procedure introduced by Bailey et al.. The work aims to explore and better understand the interplay between different technical trading strategies from a data-informed perspective.
Artificial intelligence (AI) could displace millions of jobs in the future, damaging growth in developing regions such as Africa, says Ian Goldin, professor of globalisation and development at Oxford University. I have spent my career in international development, and in recent years have established a research group at Oxford University looking at the impact of disruptive technologies on developing economies. Perhaps the most important question we have looked at is whether AI will pose a threat - or provide new opportunities - for developing regions such as Africa. Optimists say that such places could use rapidly advancing AI systems to boost productivity and leapfrog ahead. But I am becoming increasingly concerned that AI will, in fact, block the traditional growth path by replacing low-wage jobs with robots.
The intention of the Workshop within the AI4D initiaitve in sub-Saharan Africa is to scope out the African ML/AI landscape, provide inputs for an African AI research roadmap, and support the development of cross-continent cooperation on AI for sustainable development. It is anticipated that the outcome of the workshop will be a Network of Excellence on AI for sub Saharan African researchers who focus on applications and research relating to AI and human development.
AI can be applied in sectors such as agriculture, health, and education, and Moustapha Cisse, the research scientist heading up Google's AI efforts in Africa, says his team's goal is to provide developers with the necessary research needed to build products that can solve problems that Africa faces today. "Most of what we do in our research centers at Google and not just in Accra, we publish it and open-source code, so that everybody can use it to build all sorts of things," he said. Cisse mentioned the app used by the Tanzanian farmer, to diagnose her cassava's disease as an example of the type of product his team plans to collaborate on with relevant institutes across various sectors. "A team of Pennsylvania University and the International Institute of Tropical Agriculture using TensorFlow to build new artificial intelligence models that are deployed on phones to diagnose crop disease. "This wasn't done by us but by people who use the tools we built.
In seconds she gets a diagnosis of the disease affecting her plant and how best to manage it to boost her production. The farmer used an app on her phone based on TensorFlow, Google's Artificial Intelligence (AI) machine that the company opensourced to help developers create solutions to real-world problems. When people think of Artificial Intelligence, they most likely think of scenes from science fiction movies, but in reality, it applies to everyday life from virtual assistants to language translation on Google, says John Quinn, an AI researcher. Google now wants to position itself as an "AI first" company and with research centers across the globe in places such as Tokyo, Zurich, New York, and Paris. And last week, the technology company opened its first center in Africa in Ghana's capital city, Accra.
Vikram Mahidhar reminds us all that AI is only as good as the humans supervising it and programming it. The biases and artefacts that come out of the processing are reflective of the biases programmed in at the beginning. A program trained to recognise totalled car bodies for insurance purposes, for example, will need close supervision of its decision-making outputs, for regulatory and consumer confidence and acceptance of the decision. There is a call and a growth in a new class of AI--one that is explainable, and that builds trust by providing evidence. Vikram also reminds us that a governance strategy is key to engendering trust in our organisation, processes and people.