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.

This paper addresses the problem of learning the optimal control policy for a nonlinear stochastic dynamical system with continuous state space, continuous action space and unknown dynamics. This class of problems are typically addressed in stochastic adaptive control and reinforcement learning literature using model-based and model-free approaches respectively. Both methods rely on solving a dynamic programming problem, either directly or indirectly, for finding the optimal closed loop control policy. The inherent `curse of dimensionality' associated with dynamic programming method makes these approaches also computationally difficult. This paper proposes a novel decoupled data-based control (D2C) algorithm that addresses this problem using a decoupled, `open loop - closed loop', approach. First, an open-loop deterministic trajectory optimization problem is solved using a black-box simulation model of the dynamical system. Then, a closed loop control is developed around this open loop trajectory by linearization of the dynamics about this nominal trajectory. By virtue of linearization, a linear quadratic regulator based algorithm can be used for this closed loop control. We show that the performance of D2C algorithm is approximately optimal. Moreover, simulation performance suggests significant reduction in training time compared to other state of the art algorithms.

In this paper, we address the question answering challenge with the SQuAD 2.0 dataset. We design a model architecture which leverages BERT's capability of context-aware word embeddings and BiDAF's context interactive exploration mechanism. By integrating these two state-of-the-art architectures, our system tries to extract the contextual word representation at word and character levels, for better comprehension of both question and context and their correlations. We also propose our original joint posterior probability predictor module and its associated loss functions. Our best model so far obtains F1 score of 75.842% and EM score of 72.24% on the test PCE leaderboad.

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.

In the elegant quiet of the café at the Church of Sweden, a narrow Gothic-style building in Midtown Manhattan, Daniel Cohen is taking a break from explaining genetics. He moves toward the creaky piano positioned near the front door, sits down, and plays a flowing, flawless rendition of "Over the Rainbow." If human biology is the scientific equivalent of a complicated score, Cohen has learned how to navigate it like a virtuoso. Cohen was the driving force behind Généthon, the French laboratory that in December 1993 produced the first-ever "map" of the human genome. He essentially introduced Big Data and automation to the study of genomics, as he and his team demonstrated for the first time that it was possible to use super-fast computing to speed up the processing of DNA samples.

Artificial intelligence (AI) has become a critical aspect in financial services. Financial institutions around the world are making efforts to adopt AI for task automation, customer services, behavior analysis, as well as fraud finding, and are making large-scale investments in related technologies. The World Economic Forum (WEF) estimates the number to reach US$10 billion by 2020. In financial services, applications for AI technologies exist across nearly the entire spectrum of business, from algorithmic stock trading applications and credit card fraud detection, to auto investment advisors and market research and sentiment analysis. The following 10 AI fintech companies are some of Europe's rising stars to watch very closely: Swiss startup Parashift develops AI-based accounting document management technologies which it offers through a SaaS platform and APIs.

Considering the evolution of the semantic wiki engine based platforms, two main approaches could be distinguished: Ontologies for Wikis (OfW) and Wikis for Ontologies (WfO). OfW vision requires existing ontologies to be imported. Most of them use the RDF-based (Resource Description Framework) systems in conjunction with the standard SQL (Structured Query Language) database to manage and query semantic data. But, relational database is not an ideal type of storage for semantic data. A more natural data model for SMW (Semantic MediaWiki) is RDF, a data format that organizes information in graphs rather than in fixed database tables. This paper presents an ontology based architecture, which aims to implement this idea. The architecture mainly includes three layered functional architectures: Web User Interface Layer, Semantic Layer and Persistence Layer. Introduction This research study is set in an African context, where the main problem is an economic, social development and the means to achieve it. Indeed, after the failure of several development models in the recent decades, theoretical research seems to be turning to the development knowledgebased approaches (UNESCO, 2014). The place of knowledge, science and technology in the current dynamics of growth gives rise to intensify the reflection within the economic field.

The duck is famous for two things: really liking bread (even though they're not supposed to be eating it), and wielding insanely complicated reproductive bits. More specifically, male ducks have corkscrew-shaped penises, while females' reproductive tracts corkscrew in the opposite direction. But you know who tends to get short shrift for their own bizarrely complicated reproductive system? That would be the rhino. "It's composed of a number of interlocking ridges or rings that makes it look like a number of S's connected to each other for 8 to 12 inches," says Barbara Durrant, director of reproductive sciences at the San Diego Zoo.

Viral hepatitis is the regularly found health problem throughout the world among other easily transmitted diseases, such as tuberculosis, human immune virus, malaria and so on. Among all hepatitis viruses, the uppermost numbers of deaths are result from the long-lasting hepatitis C infection or long-lasting hepatitis B. In order to develop this system, the knowledge is acquired using both structured and semi-structured interviews from internists of St.Paul Hospital. Once the knowledge is acquired, it is modeled and represented using rule based reasoning techniques. Both forward and backward chaining is used to infer the rules and provide appropriate advices in the developed expert system. For the purpose of developing the prototype expert system SWI-prolog editor also used. The proposed system has the ability to adapt with dynamic knowledge by generalizing rules and discover new rules through learning the newly arrived knowledge from domain experts adaptively without any help from the knowledge engineer.