Curators of Africa's largest business focused Artificial Intelligence (AI) & Data Science community discussing the real world applications & trends driving the AI Economy in Africa. Our audience comprises; CxO decision makers, platform providers, Tier 1 or 2 deployment & service providers, entrepreneurs / investors, educators, government and AI ecosystem builders across Africa.

Yoshua Bengio: Yoshua Bengio OCFRSC (born 1964 in Paris, France) is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning.[1][2][3] He was a co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning.[4] He is a professor at the Department of Computer Science and Operations Research at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA). Geoffrey Hinton: Geoffrey Everest HintonCCFRSFRSC[11] (born 6 December 1947) is an English Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto.

Prophecy Market Insights recently presented Artificial Intelligence (AI) In Supply Chain market report which provides reliable and sincere insights related to the various segments and sub-segments of the market. The market study throws light on the various factors that are projected to impact the overall dynamics of the Artificial Intelligence (AI) In Supply Chain market over the forecast period (2019-2029). The Artificial Intelligence (AI) In Supply Chain research study contains 100 market data Tables, Pie Chat, Graphs & Figures spread through Pages and easy to understand detailed analysis. This Artificial Intelligence (AI) In Supply Chain market research report estimates the size of the market concerning the information on key retailer revenues, development of the industry by upstream and downstream, industry progress, key highlights related to companies, along with market segments and application. Global Artificial Intelligence (AI) In Supply Chain market 2020-2030 in-depth study accumulated to supply latest insights concerning acute options.

The importance of area coverage with unmanned vehicles, in other words, traveling an area with an unmanned vehicle such as a robot or a UAV completely or partially with minimum cost, is increasing with the increase in usage of such vehicles today. Area coverage with unmanned vehicles is used today in the exploration of an area with UAVs, sweeping mines with robots, cleaning ground with robots in large shopping malls, mowing lawn in a large area etc. The problem has versions such as area coverage with a single unmanned vehicle, area coverage with multiple unmanned vehicles, on-line area coverage (The map of the area that will be covered is not known before starting the coverage) with unmanned vehicles etc. In addition, the area may have obstacles that the vehicles cannot move over. Naturally, many researches are working on the problem and a lot of researches have been done on the problem until today. Spanning tree coverage is one of the major approaches to the problem. In this approach, at the basic level, the planar area is divided into identical squares according to the range of sight of the vehicle, and centers of these squares are assumed to be vertexes of a graph. The vertexes of this graph are connected with the edges with unit costs and after finding the minimum spanning tree of the graph, the vehicle strolls around the spanning tree. The method we propose suggests a way to cover a non-planar area with unmanned vehicles. The method we propose also takes advantage of the spanning-tree coverage approach, but instead of assigning unit costs to the edges, we assigned a weight to each edge using slopes between vertexes those the edges connect. We have gotten noticeably better results than the results we got when we did not consider the slope between two squares and used the classical spanning tree approach.

The coordination and cooperation of all the stakeholders involved is a decisive point for the control and the resolution of problems. In the insecurity events, the resolution should refer to a plan that defines a general framework of the procedures to be undertaken and the instructions to be complied with; also, a more precise process must be defined by the actors to deal with the case represented by the particular problem of the current situation. Indeed, this process has to cope with a dynamic, unstable and unpredictable environment, due to the heterogeneity and multiplicity of stakeholders, and finally due to their possible geographical distribution. In this article, we will present the first steps of validation of a collaborative decision-making approach in the context of crisis situations such as road accidents. This approach is based on ontologies and multi-agent systems.

A new collaborative learning, called split learning, was recently introduced, aiming to protect user data privacy without revealing raw input data to a server. It collaboratively runs a deep neural network model where the model is split into two parts, one for the client and the other for the server. Therefore, the server has no direct access to raw data processed at the client. Until now, the split learning is believed to be a promising approach to protect the client's raw data; for example, the client's data was protected in healthcare image applications using 2D convolutional neural network (CNN) models. However, it is still unclear whether the split learning can be applied to other deep learning models, in particular, 1D CNN. In this paper, we examine whether split learning can be used to perform privacy-preserving training for 1D CNN models. To answer this, we first design and implement an 1D CNN model under split learning and validate its efficacy in detecting heart abnormalities using medical ECG data. We observed that the 1D CNN model under split learning can achieve the same accuracy of 98.9\% like the original (non-split) model. However, our evaluation demonstrates that split learning may fail to protect the raw data privacy on 1D CNN models. To address the observed privacy leakage in split learning, we adopt two privacy leakage mitigation techniques: 1) adding more hidden layers to the client side and 2) applying differential privacy. Although those mitigation techniques are helpful in reducing privacy leakage, they have a significant impact on model accuracy. Hence, based on those results, we conclude that split learning alone would not be sufficient to maintain the confidentiality of raw sequential data in 1D CNN models.

Managing the quality of water for present and future generations of coastal regions should be a central concern of both citizens and public officials. Remote sensing can contribute to the management and monitoring of coastal water and pollutants. Algal blooms are a coastal pollutant that is a cause of concern. Many satellite data, such as MODIS, have been used to generate water-quality products to detect the blooms such as chlorophyll a (Chl-a), a photosynthesis index called fluorescence line height (FLH), and sea surface temperature (SST). It is important to characterize the spatial and temporal variations of these water quality products by using the mathematical models of these products. However, for monitoring, pollution control boards will need nowcasts and forecasts of any pollution. Therefore, we aim to predict the future values of the MODIS Chl-a, FLH, and SST of the water. This will not be limited to one type of water but, rather, will cover different types of water varying in depth and turbidity. This is very significant because the temporal trend of Chl-a, FLH, and SST is dependent on the geospatial and water properties. For this purpose, we will decompose the time series of each pixel into several components: trend, intra-annual variations, seasonal cycle, and stochastic stationary. We explore three such time series machine learning models that can characterize the non-stationary time series data and predict future values, including the Seasonal ARIMA (Auto Regressive Integrated Moving Average) (SARIMA), regression, and neural network. The results indicate that all these methods are effective at modelling Chl-a, FLH, and SST time series and predicting the values reasonably well. However, regression and neural network are found to be the best at predicting Chl-a in all types of water (turbid and shallow). Meanwhile, the SARIMA model provides the best prediction of FLH and SST.

We propose novel dynamic multiscale graph neural networks (DMGNN) to predict 3D skeleton-based human motions. The core idea of DMGNN is to use a multiscale graph to comprehensively model the internal relations of a human body for motion feature learning. This multiscale graph is adaptive during training and dynamic across network layers. Based on this graph, we propose a multiscale graph computational unit (MGCU) to extract features at individual scales and fuse features across scales. The entire model is action-category-agnostic and follows an encoder-decoder framework. The encoder consists of a sequence of MGCUs to learn motion features. The decoder uses a proposed graph-based gate recurrent unit to generate future poses. Extensive experiments show that the proposed DMGNN outperforms state-of-the-art methods in both short and long-term predictions on the datasets of Human 3.6M and CMU Mocap. We further investigate the learned multiscale graphs for the interpretability. The codes could be downloaded from https://github.com/limaosen0/DMGNN.

The ominous spread of coronavirus has bolstered the case for such advances as telemedicine; drones; artificial intelligence/machine learning; Big Data; and more flexible regulation of health care personnel and institutions. During a social conversation via FaceTime, her grandson, a physician, realized Mom was in the early stages of septic shock. A day's delay in treatment might have proven fatal. Similar tales emerge from professional telemedicine doctors. The advantages of telemedicine for, say, a migrant worker family on a remote ranch whose child becomes ill in the wee hours.

Timnit Gebru was wary of being labelled an activist. As a young, black female computer scientist, Gebru – who was born and raised in Addis Ababa, Ethiopia, but now lives in the US – says she'd always been vocal about the lack of women and minorities in the datasets used to train algorithms. She calls them "the undersampled majority", quoting another rising star of the artificial intelligence (AI) world, Joy Buolamwini. But Gebru didn't want her advocacy to affect how she was perceived in her field. "I wanted to be known primarily as a tech researcher. I was very resistant to being pigeonholed as a black woman, doing black woman-y things."