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Cote d'Ivoire


The Charlettes: An AI engineer in Ivory Coast and Ghana

Al Jazeera

Charlette Désiré N'Guessan comes from an intriguing family, where all the women share the same name: Charlette. It is confusing, and also a little ironic, since she is a software engineer who has invented a facial recognition app. In The Charlettes, by filmmaker Gauz, we see how this particular Charlette has made an impact in the tech world in Ivory Coast and Ghana, winning prizes and plaudits for her artificial intelligence (AI) identity invention. Gbaka-Brede Armand Patrick, known professionally as Gauz, is a multidisciplinary and self-proclaimed iconoclastic artist, based in Ivory Coast.


How An Automated Gesture Imitation Game Can Improve Social Interactions With Teenagers With ASD

arXiv.org Machine Learning

With the outlook of improving communication and social abilities of people with ASD, we propose to extend the paradigm of robot-based imitation games to ASD teenagers. In this paper, we present an interaction scenario adapted to ASD teenagers, propose a computational architecture using the latest machine learning algorithm Openpose for human pose detection, and present the results of our basic testing of the scenario with human caregivers. These results are preliminary due to the number of session (1) and participants (4). They include a technical assessment of the performance of Openpose, as well as a preliminary user study to confirm our game scenario could elicit the expected response from subjects.


Andile Ngcaba's inq Wants to be Africa's Number one AI Service Provider.

#artificialintelligence

ICT industry veteran Andile Ngcaba's inq., a Pan-African digital service provider, wants to be Africa's number one artificial intelligence (AI) service provider. The company has points of contacts in 12 African cities, Johannesburg, Gaborone, Lusaka, Ndola, Blantyre, Lilongwe, Mzuzu, Lagos, Abuja, Port Harcourt, Kanu and Abidjan. It has concluded the 100% acquisition of Vodacom Business Africa's operations in Nigeria, Zambia and Cote d'Ivoire with a further planned acquisition in Cameroon pending regulatory approvals. At the time of the announcement of the transaction last June, inq. said this deals represents a significant milestone to its vision to be a leading provider of cloud and digitally based services in key markets across sub-Saharan Africa and provides additional vital assets in its build-out of a regional footprint. Today, inq. said this landmark transaction grows inq.'s regional footprint to 13 cities in 7 countries across Africa including its existing operations in Botswana, Malawi and Mozambique.


Africa: MTN Group Launches Africa's First AI Service for Momo

#artificialintelligence

The MTN Group has launched Africa's first Mobile Money (MoMo) Artificial Intelligence (AI) service or "chatbot". A statement issued by the Group's Corporate Affairs on Tuesday said the chatbot went live in Ivory Coast in May and would be rolled out across MTN's MoMo footprint in the next few months. The AI mobile money "assistant" enables customers to engage with MTN's MoMo services, including payments on various social media platforms such as WhatsApp and Facebook Messenger, and via SMS. The statement said the service would also be included over time, in MTN's own newly released advanced instant messaging service "Ayoba". It said the chatbot was an AI guide that assists users to navigate MTN's MoMo services and provide other useful information.


Machine Learning in Africa – Hacker Noon

#artificialintelligence

Earlier this month, I shared the Meeshkan Machine Learning service with a friend from Côte d'Ivoire and his immediate reaction was "you have to get the word out in Africa." I'm a big believer in enthusiasm, and he was genuinely enthusiastic, so we gave it a shot. We initially ran a small advertising campaign in four countries -- Côte d'Ivoire, Senegal, Algeria, and Morocco. The organic growth from these campaigns has been much more promising than anything we've seen in Europe or North America. Why? Below are some musings.


Geographic Differential Privacy for Mobile Crowd Coverage Maximization

AAAI Conferences

For real-world mobile applications such as location-based advertising and spatial crowdsourcing, a key to success is targeting mobile users that can maximally cover certain locations in a future period. To find an optimal group of users, existing methods often require information about users' mobility history, which may cause privacy breaches. In this paper, we propose a method to maximize mobile crowd's future location coverage under a guaranteed location privacy protection scheme. In our approach, users only need to upload one of their frequently visited locations, and more importantly, the uploaded location is obfuscated using a geographic differential privacy policy. We propose both analytic and practical solutions to this problem. Experiments on real user mobility datasets show that our method significantly outperforms the state-of-the-art geographic differential privacy methods by achieving a higher coverage under the same level of privacy protection.


Facebook's AI team maps Earth to beam internet access to all

#artificialintelligence

Social networking giant Facebook is using its artificial intelligence (AI) technology and resources to map the entire Earth and launch the world's most detailed population maps that will help it beam cheap internet to remote areas. To begin with, the Facebook AI team crunched 14.6 billion images of maps from across 20 countries, including India, covering 21.6 million sq kms to come up with the first detailed map of human settlement for these countries. "This is an impressive project from our team developing solar-powered planes for beaming down internet connectivity and our AI research team. Many people live in remote communities and accurate data on where people live doesn't always exist," wrote Facebook CEO Mark Zuckerberg in a latest post. The 20 countries mapped were Algeria, Burkina Faso, Cameroon, Egypt, Ethiopia, Ghana, India, Ivory Coast, Kenya, Madagascar, Mexico, Mozambique, Nigeria, South Africa, Sri Lanka, Tanzania, Turkey, Uganda, Ukraine and Uzbekistan.


Country-scale Exploratory Analysis of Call Detail Records through the Lens of Data Grid Models

arXiv.org Machine Learning

Call Detail Records (CDRs) are data recorded by telecommunications companies, consisting of basic informations related to several dimensions of the calls made through the network: the source, destination, date and time of calls. CDRs data analysis has received much attention in the recent years since it might reveal valuable information about human behavior. It has shown high added value in many application domains like e.g., communities analysis or network planning. In this paper, we suggest a generic methodology for summarizing information contained in CDRs data. The method is based on a parameter-free estimation of the joint distribution of the variables that describe the calls. We also suggest several well-founded criteria that allows one to browse the summary at various granularities and to explore the summary by means of insightful visualizations. The method handles network graph data, temporal sequence data as well as user mobility data stemming from original CDRs data. We show the relevance of our methodology for various case studies on real-world CDRs data from Ivory Coast.