In this paper, we study how to learn an appropriate lane changing strategy for autonomous vehicles by using deep reinforcement learning. We show that the reward of the system should consider the overall traffic efficiency instead of the travel efficiency of an individual vehicle. In summary, cooperation leads to a more harmonic and efficient traffic system rather than competition

In recent years, art-creating AI has pushed the boundaries of how we define art. AI Expo Africa, the largest business focused AI event in Africa, have launched a grand challenge to create an original piece of artwork or music that leverages AI. There will be one winner for each category (visual art & music) and winners must be citizens of an African country and must be living in Africa. Awards will be presented to the winner at the exclusive AI Expo Africa VIP event on the opening night of AI Expo Africa in Cape Town, South Africa, on 3 September 2019 – with the winning music track(s) being played during the event and art work on display. Both works will be auctioned during an exclusive event on 4 September.

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.

Facebook is working closely with key non-profit and research partners to use artificial intelligence (AI) and big data to address large-scale social, health and infrastructure challenges in sub Saharan Africa. These efforts range from rural electrification in Tanzania to vaccinating people in remote corners of Malawi. Facebook is applying the processing muscle of its compute power, its extensive data science skills and its expertise in AI and machine learning to create the world's most detailed and accurate maps of local populations. Facebook also partners with Columbia University's Center for International Earth Science Information Network (CIESIN (http://www.ciesin.org/)) to ensure that this effort leverages the best available administrative data for all countries involved. The Boston-based Facebook team uses advanced computer vision and machine learning to combine satellite imagery from Digital Globe with public census data and other sources to create detailed population density maps of Africa.

Powered by AI technologies, businesses are transforming the way they operate. But what is the real opportunity for your business? Where can you implement tech to maximise the benefits and how do you need to adapt to take advantage? These are the questions being addressed at the cutting-edge of business transformation in Africa. Engaging with leading global multinationals as well as regional powerhouses, the AI Summit Cape Town event will uncover the true opportunity AI presents for the most forward-thinking enterprises, tackling the challenges around people-change management, upskilling the workforce, creating deeper relationships with customers and partners as well as understanding the business case.

There seems to be a general consensus that AI will by no means make the work done by financial professionals redundant since results produced by AI models still require human interpretation, says the writer. Artificial intelligence (AI) is nothing new. It has been around since the 50s. It is only now that we possess the processing power to utilise it properly. It is already embedded in many people's lives.

To describe the future of work, Richard Baldwin is developing a new lexicon. The professor of international economics at the Graduate Institute in Geneva warns that we are unprepared for the ways in which new technology is changing the nature of globalization. Baldwin's new book, The Globotics Upheaval: Globalization, Robotics, and the Future of Work, is a natural follow-up to his 2016 book, The Great Convergence. Three years ago, he explained how a third wave of globalization--a collapse in the cost of the movement of people thanks to technology--would be the most disruptive, because it hits workers in the service sector. Baldwin's new book, published earlier this year, breaks down what this disruption will entail.

Ed. note: Does pedophilia allow those involved with building A.I. technology to experiment with the very extremes in human emotions through pedophilia? While we are mesmerized by all this rapidly accelerating technology, what else is going on that might be of concern? Contributor I cover what entrepreneurs are looking to learn. Virtual gaming is about to warp through a black hole, thanks to a band of scientists in Hong Kong and a hedge funder with a zealous science background, called Jeffrey Epstein. Indeed, game programming is moving away from algorithmic robots to a twilight realm of emotional thinkers, taking online, video and toy entrepreneurs, one step closer to Star Trek's'Holodeck'. For years, in virtual gaming, the only intelligent player was the person playing the game, responding to non-reactive obstacles. At most, opponents could blow up or morph into something else.

Plasmodium falciparum malaria still poses one of the greatest threats to human life with over 200 million cases globally leading to half-million deaths annually. Of these, 90% of cases and of the mortality occurs in sub-Saharan Africa, mostly among children. Although malaria prediction systems are central to the 2016-2030 malaria Global Technical Strategy, currently these are inadequate at capturing and estimating the burden of disease in highly endemic countries. We developed and validated a computational system that exploits the predictive power of current Machine Learning approaches on 22-years of prospective data from the high-transmission holoendemic malaria urban-densely-populated sub-Saharan West-Africa metropolis of Ibadan. Our dataset of >9x104 screened study participants attending our clinical and community services from 1996 to 2017 contains monthly prevalence, temporal, environmental and host features. Our Locality-specific Elastic-Net based Malaria Prediction System (LEMPS) achieves good generalization performance, both in magnitude and direction of the prediction, when tasked to predict monthly prevalence on previously unseen validation data (MAE<=6x10-2, MSE<=7x10-3) within a range of (+0.1 to -0.05) error-tolerance which is relevant and usable for aiding decision-support in a holoendemic setting. LEMPS is well-suited for malaria prediction, where there are multiple features which are correlated with one another, and trading-off between regularization-strength L1-norm and L2-norm allows the system to retain stability. Data-driven systems are critical for regionally-adaptable surveillance, management of control strategies and resource allocation across stretched healthcare systems.

The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers, yet its performance competes with the most complex classifiers in the literature. The core of this classifier depends mainly on measuring the distance or similarity between the tested example and the training examples. This raises a major question about which distance measures to be used for the KNN classifier among a large number of distance and similarity measures? This review attempts to answer the previous question through evaluating the performance (measured by accuracy, precision and recall) of the KNN using a large number of distance measures, tested on a number of real world datasets, with and without adding different levels of noise. The experimental results show that the performance of KNN classifier depends significantly on the distance used, the results showed large gaps between the performances of different distances. We found that a recently proposed non-convex distance performed the best when applied on most datasets comparing to the other tested distances. In addition, the performance of the KNN degraded only about $20\%$ while the noise level reaches $90\%$, this is true for all the distances used. This means that the KNN classifier using any of the top $10$ distances tolerate noise to a certain degree. Moreover, the results show that some distances are less affected by the added noise comparing to other distances.