Valuing human capital not only serves to equip individuals with the knowledge and skills to respond to systematic shifts, it also empowers them to take part in creating a more, equal and sustainable world. Education is and will remain critical for promoting inclusive economic growth and providing a future of opportunity for all. It is in line with this, that Coven Works' has partnered with The Deutsche Gesekkschaft fur Internationale Zusammenarbeit (GIZ) to successfully train 100 youths from underserved communities in Nigeria on Data Science and Artificial Intelligence (AI). Coven Works', the leading Data Science and Artificial Intelligence education organization in Nigeria, schooled and mentored these 100 participants for a period of twelve weeks and secured placements for each trainee in a three months internship, where they will work in Data Science and Artificial Intelligence related job functions. While speaking at the Project Demonstration day of the Beneficiaries, Coven Works' Country Director, Mr. Dunsin Fatuase said that "Coven Works through Coven Labs will continue to focus on upskilling youths in underserved communities, by providing cutting edge knowledge in data science and Artificial Intelligence for Africans, and other working professionals to the point where we can say that Africa has a refined workforce fully ready for the future of work."

Google is quickly putting its wildlife-spotting AI to good use. The internet giant has launched a Wildlife Insights tool that helps conservationists track wildlife by not only parsing their photos, but sharing them in a searchable public website. The AI automatically tosses out photos that are highly unlikely to include animals and tries to label the animals it does spot, dramatically speeding up a laborious task. That, in turn, helps researchers track animal populations as they're affected by climate change and direct human intrusion. The website, meanwhile, is powerful whether or not you're a researcher.

The Hamlyn Centre for Robotic Surgery, Imperial College London, UK 2. Institute of Medical Robotics, Shanghai Jiao Tong University, ChinaAbstract Artificial Intelligence (AI) is gradually changing the practice of surgery with the advanced technological development of imaging, navigation and robotic intervention. In this article, the recent successful and influential applications of AI in surgery are reviewed from preoperative planning and intra-operative guidance to the integration of surgical robots. We end with summarizing the current state, emerging trends and major challenges in the future development of AI in surgery. Keywords: Artificial intelligence, Surgical autonomy, Medical robotics, Deep learning 1. Introduction Advances in surgery have made a significant impact on the management of both acute and chronic diseases, prolonging life and continuously extending the boundary of survival. These advances are underpinned by continuing technological developments in diagnosis, imaging, and surgical instrumentation. Complex surgical navigation and planning are made possible through the use of both pre-and intra-operative imaging techniques such as ultrasound, Computed Tomography (CT), and Magnetic Resonance Imaging Preprint submitted to Frontiers of Medicine January 6, 2020 arXiv:2001.00627v1 Many terminal illnesses have been transformed into clinically manageable chronic lifelong conditions and increasing surgery is focused on the systematic level impact on patients, avoiding isolated surgical treatment or anatomical alteration, with careful consideration of metabolic, haemodynamic and neurohormonal consequences that can influence the quality of life. For recent advances in medicine, AI has played an important role in clinical decision support since the early years of developing the MYCIN system [5]. AI is now increasingly used for risk stratification, genomics, imaging and diagnosis, precision medicine, and drug discovery. The introduction of AI in surgery is more recent and it has a strong root in imaging and navigation, with early techniques focused on feature detection and computer assisted intervention for both preoperative planning and intra-operative guidance. Over the years, supervised algorithms such as active shape models, atlas based methods and statistical classifiers have been developed [1]. With recent successes of AlexNet [6], deep learning methods, especially Deep Con-volutional Neural Network (DCNN) where multiple convolutional layers are cascaded, have enabled automatically learned data-driven descriptors, rather than ad hoc handcrafted features, to be used for image understanding with improved robustness and generalizability.

Yes!!! Our Data Science and Artificial Intelligence Masterclass Cohort 1 will commence with three tracks for January -February 2020. Members who participate at our cohorts will have the opportunity of doing an internship programme with Reispar Analytics Academy.

An effective method to generate a large number of parallel sentences for training improved neural machine translation (NMT) systems is the use of back-translations of the target-side monolingual data. Tagging, or using gates, has been used to enable translation models to distinguish between synthetic and natural data. This improves standard back-translation and also enables the use of iterative back-translation on language pairs that underperformed using standard back-translation. This work presents a simplified approach of differentiating between the two data using pretraining and finetuning. The approach - tag-less back-translation - trains the model on the synthetic data and finetunes it on the natural data. Preliminary experiments have shown the approach to continuously outperform the tagging approach on low resource English-Vietnamese neural machine translation. While the need for tagging (noising) the dataset has been removed, the approach outperformed the tagged back-translation approach by an average of 0.4 BLEU.

Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities. However, controlling attributes of the generated language (e.g. switching topic or sentiment) is difficult without modifying the model architecture or fine-tuning on attribute-specific data and entailing the significant cost of retraining. We propose a simple alternative: the Plug and Play Language Model (PPLM) for controllable language generation, which combines a pretrained LM with one or more simple attribute classifiers that guide text generation without any further training of the LM. In the canonical scenario we present, the attribute models are simple classifiers consisting of a user-specified bag of words or a single learned layer with 100,000 times fewer parameters than the LM. Sampling entails a forward and backward pass in which gradients from the attribute model push the LM's hidden activations and thus guide the generation. Model samples demonstrate control over a range of topics and sentiment styles, and extensive automated and human annotated evaluations show attribute alignment and fluency. PPLMs are flexible in that any combination of differentiable attribute models may be used to steer text generation, which will allow for diverse and creative applications beyond the examples given in this paper.

Anything you do online can come back to bite you. It's been a decade full of lessons: who to trust, when to speak out and how to stream big events online after you've broken up with your cable company. In 2010, the first iPhone was only three years old. Uber and Lyft didn't exist, and neither did Google Assistant and Siri, Instagram or streaming video. We've come a long way since then, but the next 10 years won't be easy.

The International Conference on Learning Representations ICLR 2020 is four months away but has already attracted more than its share of drama with a deluge of submissions and doubts about the qualifications of some reviewers. Yesterday the conference programme chairs finally put the selection process behind them, announcing 687 out of 2594 papers had made it to ICLR 2020 -- a 26.5 percent acceptance rate. ICLR 2020 will be held in Addis Ababa, Ethiopia from April 26 to 30. This will be the first trip to Africa for a major AI conference, a move long-encouraged by many leading AI researchers. All accepted papers will be presented as posters as usual, while 23 percent will have an oral presentation.

Every time some genius decides to apply AI where it doesn't belong, the world collectively rolls its eyes and puts another ballot in the AI-Is-A-Fad box. If your dictionary defines AI as magic or robots (or magical robots), of course you'll be disappointed when it doesn't deliver the cure to all that ails you. Let's look at three common gripes using simple examples everyone can grasp. A respectable software engineer once asked me with a straight face, "Can AI know that Canada is a country?" Hold your horses there, cowboy.

--Operating envelope is an important concept in industrial operations. Accurate identification for operating envelope can be extremely beneficial to stakeholders as it provides a set of operational parameters that optimizes some key performance indicators (KPI) such as product quality, operational safety, equipment efficiency, environmental impact, etc. Given the importance, data-driven approaches for computing the operating envelope are gaining popularity. These approaches typically use classifiers such as support vector machines, to set the operating envelope by learning the boundary in the operational parameter spaces between the manually assigned'large KPI' and'small KPI' groups. One challenge to these approaches is that the assignment to these groups is often ad-hoc and hence arbitrary. However, a bigger challenge with these approaches is that they don't take into account two key features that are needed to operationalize operating envelopes: (i) interpretability of the envelope by the operator and (ii) implementability of the envelope from a practical standpoint. In this work, we propose a new definition for operating envelope which directly targets the expected magnitude of KPI (i.e., no need to arbitrarily bin the data instances into groups) and accounts for the interpretability and the implementability. We then propose a regularized'GA penalty' algorithm that outputs an envelope where the user can tradeoff between bias and variance. The validity of our proposed algorithm is demonstrated by two sets of simulation studies and an application to a real-world challenge in the mining processes of a flotation plant. In industrial operations, an important concept is that of the operating envelope. Conceptually, the operating envelope is a set of operational parameters, such that some KPI is optimized. In the industrial context, typical KPIs include product quality, operational safety, equipment efficiency, environmental impact, etc [1]-[4]. The operating envelope has wide application since it directly targets the business outcome and yields actionable recommendations in the operations space.