Zygmunt received his PhD degree in Computer Science from the University of Adelaide, Australia in 2013, and his MSc degree in Computer Science from the University of KwaZulu-Natal, South Africa in 2009. He is a senior research fellow at the Australian Institute for Machine Learning. His research lies at the interface of computer vision, machine learning, and challenging industry problems. He develops algorithms that allow computers to perform tasks typically associated with human intelligence. In the last couple of years, his work has focused on the application of machine learning and image processing techniques for the development of smart medical devices.

We plot the judge's probability of the target answer given that sentence against how often humans also select that target answer given that same sentence. Humans tend to find a sentence to be strong evidence for an answer when the judge model finds it to be strong evidence. Strong evidence to a model tends to be strong evidence to humans as shown in Figure 7. Combined with the previous result, we can see that learned agents are more accurate at predicting sentences that humans find to be strong evidence. F Model Evaluation of Evidence on DREAM Figure 8 shows how convincing various judge models find each evidence agent. Our findings on DREAM are similar to those from RACE in §4.2. Figure 8: On DREAM, how often each judge selects an agent's answer when given a single agent-chosen sentence. The black line divides learned agents (right) and search agents (left), with human evidence selection in the leftmost column. All agents find evidence that convinces judge models more often than a no-evidence baseline (33%). Learned agents predicting p ( i) or p ( i) find the most broadly convincing evidence.

In May 2019 Deloitte surveyed 523 executives in a range of industries in 26 countries across Africa, the Americas, Asia and Europe on their intelligent automation strategies and the impact on their workforces. The analysis reveals that these organisations are not only continuing to use robotic process automation (RPA) but are moving beyond it by increasing deployment of intelligent automation. Fifty-eight per cent of surveyed executives report they have started their automation journey. Of these, 38 per cent are piloting (1-10 automations), 12 per cent implementing (11-50 automations) and eight per cent automating at scale (51 automations) – twice as many as in 2018. Organisations believe they can transform their business processes, achieving higher speed and accuracy by automating decisions on the basis of structured and unstructured inputs. They expect an average payback period of 15 months – and in the scaling phase just nine months.

The robot uprising is coming to a store near you. And no, I'm not talking about Skynet building T-1000 Terminators to take over the world (or store in this case). I am talking about how the retail store is drastically changing to become more automated, using artificial intelligence, and relying on non-human assistance for check-outs, price checks, and inventory management. Robots and drones are in stores now and retailers are looking to build upon their capabilities for the foreseeable future. Robots in the warehouse are certainly nothing new.

In one event, an electronics shop close to her university was completely destroyed a few minutes after she left her laptop for repairment. This of one of many events that did not stop Munira from following her vision to improve her skills, empower other women in STEM, and use technologies such as Machine Learning to solve problems in her country and beyond. I want to solve community problems like droughts and also improve many industries in my country using Deep Learning and Computer Vision in the near future. Munira works among 40 other Collaborators in our AI for Good challenge with the UN Refugee Agency to predict forced displacement and climate change in Somalia. Read on your own what extraordinary mindset she has.

The cumulative view provided by the confluence of machine learning and decision making in conjunction with third-party data all hosted in the cloud, has given rise to artificial intelligence (AI) in agriculture. The ability of agricultural equipment to think, predict and even advise farmers presents Africa with an historic opportunity to meet the continent's own food requirements. AI also presents Africa with the capability to integrate its agricultural sector into global agricultural value chains. According to GeoFarm South Africa, farms in the United States are 27% more productive than South African farms compared across the same area, moisture levels and soil types. While, superficially, this is because farms in the United States are more mechanised, a closer examination of what these agricultural machines are doing shows just how fundamentally AI has changed the way humans farm, and how dramatically these changes have increased agricultural yields.

We develop a general theory for the goodness-of-fit test to non-linear models. In particular, we assume that the observations are noisy samples of a sub-manifold defined by a non-linear map of some intrinsic structures. The observation noise is additive Gaussian. Our main result shows that the "residual" of the model fit, by solving a non-linear least-square problem, follows a (possibly non-central) $\chi^2$ distribution. The parameters of the $\chi^2$ distribution are related to the model order and dimension of the problem. The main result is established by making a novel connection between statistical test and differential geometry. We further present a method to select the model orders sequentially. We demonstrate the broad application of the general theory in a range of applications in machine learning and signal processing, including determining the rank of low-rank (possibly complex-valued) matrices and tensors, from noisy, partial, or indirect observations, determining the number of sources in signal demixing, and potential applications in determining the number of hidden nodes in neural networks.

Artificial intelligence (AI) can improve various aspects of healthcare. It can help reduce annual expenditure,[1] allow early detection of diseases, provide round-the-clock monitoring for chronic disorders, and help limit the exposure of healthcare professionals in contagious environments. The use of AI in healthcare systems in Africa, in particular, can eliminate inefficiencies such as misdiagnosis, shortage in healthcare workers, and wait and recovery time. However, it is important to safeguard against issues such as privacy breaches, or lack of personalised care and accessibility. The central tenet for an AI framework must be ethics. This brief discusses the benefits and challenges of introducing AI in Africa's healthcare sector and suggests how policymakers can strike a balance between allowing innovation and protecting data. This paper is for ORF's Centre for New Economic Diplomacy (CNED). Other CNED research can be found here.

Over the past decade, computer vision has had a tremendous impact. From self-automated cars to space exploration, its various uses in surveillance and image analysis has bettered the world. The in-depth analysis of video footage and images using deep machine learning models has become a powerful tool that most companies and even governments are taking notice of. Recognition and tracking - the computer tries to link any pattern or feature in the image to a previous feature or activity in its database. Prediction - lastly it predicts possible behaviours of the object, and how it will react in the future.

Experts in ethics, privacy and bias in AI spoke at the AI Expo Africa conference held in Cape Town this week. Highlights included an AI art and music challenge, and French company Rhoban who showed off their soccer-playing robots. The robots are also the RoboWorld Cup champions. Mushambi Mutuma, chief executive at tech company Altivex said AI was controlled by the people who input the data. "Last year Google's facial recognition software kept inputting African-Americans as gorillas. We have to have new voices and a bit more diversity because the data will never come back accurate and that's what we've tried to solve from bias," he said.