Coupled Matrix Tensor Factorization (CMTF) facilitates the integration and analysis of multiple data sources and helps discover meaningful information. Nonnegative CMTF (N-CMTF) has been employed in many applications for identifying latent patterns, prediction, and recommendation. However, due to the added complexity with coupling between tensor and matrix data, existing N-CMTF algorithms exhibit poor computation efficiency. In this paper, a computationally efficient N-CMTF factorization algorithm is presented based on the column-wise element selection, preventing frequent gradient updates. Theoretical and empirical analyses show that the proposed N-CMTF factorization algorithm is not only more accurate but also more computationally efficient than existing algorithms in approximating the tensor as well as in identifying the underlying nature of factors.

As machine learning algorithms are increasingly deployed for high-impact automated decision making, ethical and increasingly also legal standards demand that they treat all individuals fairly, without discrimination based on their age, gender, race or other sensitive traits. In recent years much progress has been made on ensuring fairness and reducing bias in standard machine learning settings. Yet, for network embedding, with applications in vulnerable domains ranging from social network analysis to recommender systems, current options remain limited both in number and performance. We thus propose DeBayes: a conceptually elegant Bayesian method that is capable of learning debiased embeddings by using a biased prior. Our experiments show that these representations can then be used to perform link prediction that is significantly more fair in terms of popular metrics such as demographic parity and equalized opportunity.

Microsoft is looking for entrepreneurs to join its latest programme. Specifically social entrepreneurs as part of its recently launched Global Social Entrepreneurship programme, which has been made available in 140 countries, including South Africa. The programme forms part of the Microsoft for Startups initiative and aims to give entrepreneurs access to the necessary technology they need in order to get their socially-focused projects running. "The Global Social Entrepreneurship programme has benefits aimed specifically at elevating startups addressing an important social and/or environmental challenge through their products, services or operations," Microsoft explained regarding the announcement. "Solving global social and environmental challenges requires synergy of the right technology, partners, conducive environment and technology. When startups work together with investors, enterprises, governments, non-profits and communities, we are able to unlock new potentials," adds Microsoft4Afrika director, Amrote Abdella.

In the service of business, of society at large, artificial intelligence (AI) can be effective. Can it also be ethical? The wisdom of crowds, gleaned from social media, can paint a gestalt picture of how a government agency's, bank's or retailer's efforts are being received on the ground, so to speak. And it can also (perhaps), fed through models and analytics, can bolster decision-making for the greater, common good. Public opinion matters, after all, but across the social media platforms, the chatrooms -- the chatbots, even -- making sense of qualitative data is a challenge for most enterprises.

Artificial intelligence (AI) is "completely living up to its expected hype and hysteria," with 70% of daily digital interactions being AI-based, and businesses generating multimillion-dollar revenue streams from it. This was the word from Mike Bugembe, founder of UK-based AI consultancy, Lens.ai, delivering a keynote at the ITWeb Business Intelligence Summit 2020, in Johannesburg, today. Bugembe is a bestselling author, international speaker and executive advisor, helping organisations use data and AI to transform their businesses and grow. Discussing the importance of an AI strategy to gain business value, Bugembe noted that companies across the globe are ramping up investments in AI-related technologies and gaining multimillion-dollar-revenue streams, cutting costs, managing risk, improving operations, and finding innovative ways to develop products and strengthen customer intimacy. However, he warned that without an intelligent roadmap, companies risk focusing on the wrong opportunities, resulting in failure to tap into the true promise of AI. "Business and technology experts believe AI will be the most significant technological revolution that businesses have ever experienced," said Bugembe.

BLISS Team Educational Services, Bred Hub, has called on the Federal Government to introduce Artificial Intelligence in school curriculum as part of measures to prepare this generation for the future. Speaking ahead of train-the-trainer programme in Lagos, the General Manager, Bliss Team Educational Services, Christian Chime, said that government must champion the initiative that would introduce children and youths to the world of innovation through the teaching of Robotics, Artificial Intelligence, Coding and Science, Technology, Engineering and Mathematics (STEM) education. Chime noted that in the nearest future, the world would expect AI/Robotics to be a way of life and would play great roles in human existence on earth; adding: "Whoever leads in Artificial intelligence in 2030 will rule the world until 2100. We want this skill to be part of school curriculum, we want every student to be able to build and programme robotics. Irrespective of what a child wants to become, he or she needs to understand how to use technology because it is taking over every industry it makes process faster in whatever industry they are going to find themselves. "Bred Hub in partnership with UBTECH is organizing an interactive Artificial Intelligence and Robotics training for teachers and educators so that they can educate their students.

Pathology, the study and diagnosis of disease, is a growth industry. As the global population ages and diseases such as cancer become more prevalent, demand for keen-eyed pathologists who can analyze medical images is on the rise. In the U.K. alone, about 300,000 tests are carried out daily by pathologists. In the U.S., there are only 5.7 pathologists for every 100,000 people. By 2030, this number is expected to drop to 3.7.

With the world gripped by the Coronavirus (COVID-19) epidemic, it's clear that the main ways to mitigate the impact are through personal hygiene (hand washing), increasing social distance (keeping away from people) and imposing quarantine for effected areas and self-quarantine. This is, however, after the event has taken hold and spread. What is really needed is an effective early warning system. This can then allow authorities to identify areas at risk, potential transmission routes and allow systems to be put in place to cope with a large-scale outbreak. Step forward Artificial Intelligence (AI) based systems able to process massive amounts of seemingly unrelated data and pick out trends.

You can't miss The Commodore. Dressed in black, from his hat to his shoes, he stands out in any crowd. But he is no fashion celebrity. His real name is Tokologo Phetla, and he is a rare breed in South Africa: an entrepreneur in the field of artificial intelligence (AI). He has developed an artificial intelligence software system, which he named Christopher, in honour of the machine developed by legendary computer scientist Alan Turing during the Second World War to crack the German encryption machine, Enigma.

We introduce sentenceMIM, a probabilistic auto-encoder for language modelling, trained with Mutual Information Machine (MIM) learning. Previous attempts to learn variational auto-encoders for language data have had mixed success, with empirical performance well below state-of-the-art auto-regressive models, a key barrier being the occurrence of posterior collapse with VAEs. The recently proposed MIM framework encourages high mutual information between observations and latent variables, and is more robust against posterior collapse. This paper formulates a MIM model for text data, along with a corresponding learning algorithm. We demonstrate excellent perplexity (PPL) results on several datasets, and show that the framework learns a rich latent space, allowing for interpolation between sentences of different lengths with a fixed-dimensional latent representation. We also demonstrate the versatility of sentenceMIM by utilizing a trained model for question-answering, a transfer learning task, without fine-tuning. To the best of our knowledge, this is the first latent variable model (LVM) for text modelling that achieves competitive performance with non-LVM models.