Chatbots and artificial intelligence (AI) are becoming business essentials. If utilised correctly, the continent will be able to benefit from the wealth of data its citizens generate on a daily basis. You recently participated on a panel titled Entrepreneurial Perspectives on Emerging Technologies in the Field of Artificial Intelligence and Autonomous Systems in Switzerland. With the power of big data, companies in Africa are looking for opportunities to provide more personalised customer experiences, improve core business processes and spark innovation through the use of AI. Businesses use chatbots to collect meaningful data through conversation.

A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern. Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection.

We introduce a new neural network model, together with a tractable and monotone online learning algorithm. Our model describes feed-forward networks for classification, with one output node for each class. The only nonlinear operation is rectification using a ReLU function with a bias. However, there is a rectifier on every edge rather than at the nodes of the network. There are also weights, but these are positive, static, and associated with the nodes. Our "rectified wire" networks are able to represent arbitrary Boolean functions. Only the bias parameters, on the edges of the network, are learned. Another departure in our approach, from standard neural networks, is that the loss function is replaced by a constraint. This constraint is simply that the value of the output node associated with the correct class should be zero. Our model has the property that the exact norm-minimizing parameter update, required to correctly classify a training item, is the solution to a quadratic program that can be computed with a few passes through the network. We demonstrate a training algorithm using this update, called sequential deactivation (SDA), on MNIST and some synthetic datasets. Upon adopting a natural choice for the nodal weights, SDA has no hyperparameters other than those describing the network structure. Our experiments explore behavior with respect to network size and depth in a family of sparse expander networks.

In this paper, we investigate the tendency of end-to-end neural Machine Reading Comprehension (MRC) models to match shallow patterns rather than perform inference-oriented reasoning on RC benchmarks. We aim to test the ability of these systems to answer questions which focus on referential inference. We propose ParallelQA, a strategy to formulate such questions using parallel passages. We also demonstrate that existing neural models fail to generalize well to this setting.

In October 2016, while working in Rwanda, a biologist named Jordi Galbany heard about a new online game on one of his favorite podcasts, a Catalan-language radio show called "Versió Rac 1." Playing was simple, he learned: All you did was frantically press 1's and 0's as randomly as possible. Between days of fieldwork, where he would enter the Rwandan forest to measure the growth of wild mountain gorillas, he logged on to his computer to play the game for an hour. "I put it in my agenda," Galbany says. "I really wanted to do it." In the next month--mostly on November 30--about 100,000 people around the world would play the simplistic keyboard-mashing game in response to a publicity campaign run by physicists.

Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection. The group of bacteria known as Salmonella includes many different types that vary in the severity of the disease they cause. Some types cause food poisoning, known as gastrointestinal Salmonella, whereas others cause severe disease by spreading beyond the gut, for example Salmonella Typhi which causes typhoid fever.

Two of the opening keynotes at the Artificial Intelligence Conference, hosted by O'Reilly Media, highlighted the corporate efforts by a couple of well-established technology companies to use artificial intelligence for social good. The SAS Institute's Mary Beth Ainsworth talked about how the analytics company is partnering with a nonprofit organization to study cheetah populations in southwest Africa by using computer vision. And Microsoft Corp.'s Jennifer Marsman talked about the company's efforts to use machine learning, the internet of things and new networking ideas to reduce world hunger. They are part of a growing collection of tech companies investing in projects that use artificial intelligence for social good. The list includes IBM and its IBM Watson AI XPrize, as well as Amazon and its "artificial intelligence for good manager."

The promise of oceanic discovery has intrigued scientists and explorers, whether to study underwater ecology and climate change, or to uncover natural resources and historic secrets buried deep at archaeological sites. To meet the challenge of accessing oceanic depths, Stanford University, working with KAUST's Red Sea Research Center and MEKA Robotics, developed Ocean One, a bimanual force-controlled humanoid robot that affords immediate and intuitive haptic interaction in oceanic environments.

A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern. Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection.

Would you trust a machine to manage your wealth, or do you feel more comfortable with a person? After all, it is a fact that last year, BlackRock – the world's largest asset manager with around $5.7 trillion under management – announced that it was moving away from having humans selecting and selling its stocks, at least in part, and was turning to automated systems for certain of its stock picks. Datacentrix is fostering discussions on the possibilities that the digital age brings to South Africa, and how disruptive technologies are reshaping traditional business models. Dr. Dennis Mwansa, an expert with both local and international experience in the field of stock traders and their related technologies, has contributed to the theme, "Trading billions in nanoseconds – how artificial intelligence is used to achieve this". Mwansa is the chairman of Dot Com Zambia, and holds the position of Technology Strategist and Head of Technology Research & Development at one of the largest exchanges in Africa.