Africa's nascent artificial intelligence sector just got its biggest boost from Google which is opening its first Africa AI research center in Accra, Ghana's capital. Though Accra has a vibrant tech industry, it would not have been the obvious location for many Africa tech watchers when compared with Nairobi or nearby Lagos where Google has already announced it would open its first Launchpad Space outside the US. Last month, Facebook also opened its first startup hub in Africa there. Google had been laying the pipeline, both figuratively and physically, for future developments in Accra for a few years now. Back in 2015, the Mountain View, California tech giant started work on a fiber optic network, called Project Link, across the city to improve internet speeds.

South African-based data scientists at Principa are at it again; this time using predictive analytics and machine learning to foretell the results of the 2018 Football World Cup. The 2018 FIFA World Cup kicks off tomorrow in Russia with the host nation taking on Saudi Arabia in Group A. Principa has already predicted the results for all the first games in the first round of matches. The company's data scientists use different algorithms to develop models that can predict the outcome of the matches. Principa notes that as the objective of machine learning is to develop models that can retrain themselves to adapt when exposed to new data, the algorithms will be re-trained with the results of each match to improve the accuracy of the following round's generated prediction. It points out that the purpose is to see how well different predictive analytics techniques used successfully in other areas can outperform the best human-made predictions.

Denoising autoencoders (DAEs) have proven useful for unsupervised representation learning, but a thorough theoretical understanding is still lacking of how the input noise influences learning. Here we develop theory for how noise influences learning in DAEs. By focusing on linear DAEs, we are able to derive analytic expressions that exactly describe their learning dynamics. We verify our theoretical predictions with simulations as well as experiments on MNIST and CIFAR-10. The theory illustrates how, when tuned correctly, noise allows DAEs to ignore low variance directions in the inputs while learning to reconstruct them. Furthermore, in a comparison of the learning dynamics of DAEs to standard regularised autoencoders, we show that noise has a similar regularisation effect to weight decay, but with faster training dynamics. We also show that our theoretical predictions approximate learning dynamics on real-world data and qualitatively match observed dynamics in nonlinear DAEs.

The lack of comprehensive, high-quality health data in developing nations creates a roadblock for combating the impacts of disease. One key challenge is understanding the health information needs of people in these nations. Without understanding people's everyday needs, concerns, and misconceptions, health organizations and policymakers lack the ability to effectively target education and programming efforts. In this paper, we propose a bottom-up approach that uses search data from individuals to uncover and gain insight into health information needs in Africa. We analyze Bing searches related to HIV/AIDS, malaria, and tuberculosis from all 54 African nations. For each disease, we automatically derive a set of common search themes or topics, revealing a wide-spread interest in various types of information, including disease symptoms, drugs, concerns about breastfeeding, as well as stigma, beliefs in natural cures, and other topics that may be hard to uncover through traditional surveys. We expose the different patterns that emerge in health information needs by demographic groups (age and sex) and country. We also uncover discrepancies in the quality of content returned by search engines to users by topic. Combined, our results suggest that search data can help illuminate health information needs in Africa and inform discussions on health policy and targeted education efforts both on- and offline.

Statisticians at German technical university Technische Universitat Dortmund built a model that used machine learning to predict Spain will win the 2018 World Cup. The prediction is based on 100,000 simulations of the tournament. Spain was followed by Germany, Brazil, France and Belgium in terms of their chances of winning. And it should be a good tournament because Spain, with a 17.8 percent chance of winning, is only slightly ahead of Germany at 17.1 percent. Brazil follows with 12.3 percent, and then it's France (11.2 percent) and Belgium (10.4 percent).

Renting a car can be a time-consuming process. You have to go to the rental agency, wait for the attendant to fill out the leasing forms, which you must then check and sign before even getting sight of the keys. It's a hassle and that's even when you remember to bring along your license! We're so used to going through this process that it's hard to imagine there's a better, more convenient way. Imagine being able to walk up to a car and lease it with a micro-loan that would approve your use of it for, say, an afternoon.

Thank you, Stephen [Carter], for the introduction. Your work as a board member at BEIS has been of huge value, as has your contribution to this industry and many other spheres of public life. It is fantastic to be here at the world's largest AI Summit for businesses, which now in its third year is going from strength to strength. Quite apart from the range and reputation of our sponsors, the 10,000 visitors expected over the two days is testament to just how engaged and passionate the AI community is. Almost 70 years ago to this day, in July 1948, just 15 miles upriver from here, a document (I have a facsimile here to show you) landed on the desk of the National Physical Laboratory – then, as today, the UK government's leading research lab – that would be my nomination for the greatest research report ever written.

Tech giants are pouring money into artificial intelligence. Baidu and Google spent between $20 and $30 billion on AI in 2016 alone, according to research from McKinsey. In Google's case, a portion of that investment went to AI centers in China and France, and the Mountain View company shows no signs of slowing down. Today, Google announced its next AI research center will be in Accra, Ghana. "In recent years, we've … witnessed an increasing interest in machine learning research across the continent," senior Google AI fellow Jeff Dean and staff research scientist Moustapha Cisse wrote in a blog post.

Moving to a new country can be hard. You don't know the language. Cultural differences create conversational landmines. And you just can't be sure that everyone will like you. As it turns out, that as true for people as it is for Amazon's Alexa voice assistant, which officially sets up residence in France today.

AI is an area of computer science dedicated to creating software that can be taught to perform complex procedures. What makes AI "intelligent" is that it can learn new behaviours, improve performance as more experience is gained, and make decisions and predictions based on available data. The algorithms at the core of some AI systems are trained using the large datasets that are now available thanks to the "big data" revolution. It is the intelligent capabilities of AI systems that allow for the automation of tasks that until now required human judgement to deliver. There is enormous potential for how AI can benefit the developing world and what it can contribute towards achieving the UN's Sustainable Development Goals: AI can play a crucial role in augmenting healthcare capacity by filling gaps in human expertise, increasing productivity, and enhancing disease surveillance.