Lagos – Educationists, researchers and others have said that Nigeria must include relevant skills acquisition in its education curricula to remain relevant in the comity of nations. They made their views known in a communique they issued at the end of a three-day University of Lagos International Research Conference and Fair. The International Conference of the Humanities and Science which began on Wednesday had the theme: "Automation and Artificial Intelligence: Opportunities for 21st Research and Development''. The communique was signed by the Director, Academic Planning of the institution, Prof. Obinna Chukwu. The News Agency of Nigeria (NAN) reports that the conference – the 14th edition – was attended by more than 400 scholars, captains of industries and others from within and outside Nigeria. A total number of 168 oral paper presentations and 49 poster presentations were made the conference which had Air Peace and Access Bank as major sponsors. The participants observed that many countries suffered from significant skill mismatch due to inability of the education system to accurately reflect the demands of the labour market. The participants noted that technology, specifically, artificial intelligence, was rapidly changing trends and perceptions in different facets of life including education, employment, economy, communication and healthcare. They added that artificial intelligence would aid resource utilisation and development of smart cities. They urged that researches in the academia that cut across disciplines should be carrief out in partnership with industries to build required competencies. They also recommended that organisations in emerging markets should make investments in automation to bridge the gap between them and their counterparts in developed markets. "Higher education should help students compete in artificial intelligence age by including it in the curriculum.

During The AI Summit London 2019, TechXLR8's own Tech TV team sat down with Anthony Impey, CEO of Optimy and a Board Member of the London Chamber of Commerce and Industry, to discuss the role AI has to play in the future of London's business landscape. With flagship shows in San Francisco, London, New York, Munich, Hong Kong, Singapore, and Cape Town, 2019 will see over 30,000 delegates from businesses globally joining the AI revolution through The AI Summit events. The AI Summit series uniquely has the support of tech's elite, with our 2019 Industry Partners featuring Agorai, AWS, IBM Watson, Microsoft, Oracle, Google, HCL, Publicis Sapient, Genpact, Intel alongside 300 sponsors and partners. Exclusive, inspirational insights from acclaimed speakers are frequently reported by the world's foremost press including official media partners CBS, Reuters, BBC, The Times, Quartz, Tech Radar.

Naspers is looking to invest in machine learning as Africa's largest company seeks to expand following an Amsterdam listing of assets including a stake in Tencent Holdings. The South African Internet and technology group spent about US$3-billion on its various ventures around the world last year, including Indian food-delivery service Swiggy and Russian classifieds site Avito. Machine learning -- a subset of artificial intelligence -- is another area of interest, as is online education, executives said on Friday. "We are interested in investing in machine learning," CEO Bob van Dijk told shareholders at the AGM in Cape Town. "The amount of data is exploding -- last year we collected more data in the world than ever before."

Although GAN-based methods have received many achievements in the last few years, they have not been such successful in generating discrete data. The most important challenge of these methods is the difficulty of passing the gradient from the discriminator to the generator when the generator outputs are discrete. Despite several attempts done to alleviate this problem, none of the existing GAN-based methods has improved the performance of text generation (using measures that evaluate both the quality and the diversity of generated samples) compared to a generative RNN that is simply trained by the maximum likelihood approach. In this paper, we propose a new framework for generating discrete data by an adversarial approach in which we do not need to pass the gradient to the generator. In the proposed method, the update of either the generator or the discriminator can be accomplished straightforwardly. Moreover, we leverage the discreteness of data to explicitly model the data distribution and ensure the normalization of the generated distribution and consequently the convergence properties of the proposed method. Experimental results generally show the superiority of the proposed DGSAN method compared to the other GAN-based approaches for generating discrete sequential data.

These are exciting times for computational sciences with the digital revolution permeating a variety of areas and radically transforming business, science, and our daily lives. The Internet and the World Wide Web, GPS, satellite communications, remote sensing, and smartphones are dramatically accelerating the pace of discovery, engendering globally connected networks of people and devices. The rise of practically relevant artificial intelligence (AI) is also playing an increasing part in this revolution, fostering e-commerce, social networks, personalized medicine, IBM Watson and AlphaGo, self-driving cars, and other groundbreaking transformations. Unfortunately, humanity is also facing tremendous challenges. Nearly a billion people still live below the international poverty line and human activities and climate change are threatening our planet and the livelihood of current and future generations. Moreover, the impact of computing and information technology has been uneven, mainly benefiting profitable sectors, with fewer societal and environmental benefits, further exacerbating inequalities and the destruction of our planet. Our vision is that computer scientists can and should play a key role in helping address societal and environmental challenges in pursuit of a sustainable future, while also advancing computer science as a discipline. For over a decade, we have been deeply engaged in computational research to address societal and environmental challenges, while nurturing the new field of Computational Sustainability.

When HSBC Holdings Plc thwarted a $500 million central-bank heist, sophisticated computer software didn't raise the alarm. The funds flowed undetected from Angola's reserves to a dormant company's account in London. It was a teller at a suburban bank branch who became suspicious, declined a request to transfer $2 million, and triggered a review that uncovered the scam, according to one account of the episode. That was two years ago, and the finance industry's battle to stop the illicit transfer of as much as $2 trillion a year around the globe hasn't become any easier. At least a half-dozen lenders in Europe have found themselves at the center of fresh allegations of dirty money schemes in the past year.

The robotic process automation (RPA) services market is booming around the world, according to a recently published Forrester Research report, titled'The RPA Services Market will Grow to Reach $12 billion by 2023'. In 2018, the market was estimated to be worth only $3.9 billion. Furthermore, Forrester's research indicates that over the past three years, annual revenue growth for the top services vendors has topped 100%, rising from around $0.6 billion in 2017 to an estimated $4.2 billion by 2023. After surveying 25 of the top RPA service providers about their customers, geographic focus, revenue and scale of implementations for the report, which was published last month, the authors – Leslie Joseph and Craig Le Clair – concluded that the massive growth in RPA services was a result not only of organisations adopting RPA as a cost mitigation strategy, but because automation was regarded as critical to the implementation of broader digital transformation efforts. However, while RPA adoption in terms of spend appears to be rising around the world, Forrester was unable to provide information about RPA adoption in Africa, or South Africa, as it was unable to source sufficient data.

As a subfield of machine learning, \emph{reinforcement learning} (RL) aims at empowering one's capabilities in behavioural decision making by using interaction experience with the world and an evaluative feedback. Unlike traditional supervised learning methods that usually rely on one-shot, exhaustive and supervised reward signals, RL tackles with sequential decision making problems with sampled, evaluative and delayed feedback simultaneously. Such distinctive features make RL technique a suitable candidate for developing powerful solutions in a variety of healthcare domains, where diagnosing decisions or treatment regimes are usually characterized by a prolonged and sequential procedure. This survey will discuss the broad applications of RL techniques in healthcare domains, in order to provide the research community with systematic understanding of theoretical foundations, enabling methods and techniques, existing challenges, and new insights of this emerging paradigm. By first briefly examining theoretical foundations and key techniques in RL research from efficient and representational directions, we then provide an overview of RL applications in a variety of healthcare domains, ranging from dynamic treatment regimes in chronic diseases and critical care, automated medical diagnosis from both unstructured and structured clinical data, as well as many other control or scheduling domains that have infiltrated many aspects of a healthcare system. Finally, we summarize the challenges and open issues in current research, and point out some potential solutions and directions for future research.

The recent surveys, studies, forecasts and other quantitative assessments of the health and progress of AI estimated the impact on productivity of human-machine collaboration, the number of jobs that could be automated in major U.S. cities, and the size of the future AI in retail and healthcare markets; and found AI optimism among the general population, algorithms outperforming (again) pathologists, and that our very limited understanding of how our brains learn may improve machine learning. Do you think securing your devices and personal data will become more or less complicated over the next 12 months? DeepMind has developed a machine learning model that can label most animals at Tanzania's Serengeti National Park at least as well as humans while shortening the process by up to 9 months (it normally takes up to a year for volunteers to return labeled photos) [Engadget] In a simulation, biological learning algorithms outperformed state-of-the-art optimal learning curves in supervised learning of feedforward networks, indicating "the potency of neurobiological mechanisms" and opening "opportunities for developing a superior class of deep learning algorithms" [Scientific Reports] The AI in retail market is estimated to reach $4.3 billion by 2024 [P&S Intelligence] [e.g., Nike acquires Celect, August 6, 2019] The AI in healthcare market is estimated to reach $12.2 billion by 2023 [Market Research Future] [e.g., BlueDot has raised $7 million in Series A funding, August 7, 2019] AI companies funded in the last 3 months: 417 for total funding of $8.7 billion Data is eating the world quote of the week: "Although it is fashionable to say that we are producing more data than ever, the reality is that we always produced data, we just didn't know how to capture it in useful ways"--Subbarao Kambhampati, Arizona State University AI is eating the world quote of the week: "We advocate for a new perspective for designing benchmarks for measuring progress in AI. Unlike past decades where the community constructed a static benchmark dataset to work on for the next decade or two, we propose that future benchmarks should dynamically evolve together with the evolving state-of-the-art"--Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi, Allen Institute for Artificial Intelligence and the University of Washington

We propose a novel approach for estimating the difficulty and transferability of supervised classification tasks. Unlike previous work, our approach is solution agnostic and does not require or assume trained models. Instead, we estimate these values using an information theoretic approach: treating training labels as random variables and exploring their statistics. When transferring from a source to a target task, we consider the conditional entropy between two such variables (i.e., label assignments of the two tasks). We show analytically and empirically that this value is related to the loss of the transferred model. We further show how to use this value to estimate task hardness. We test our claims extensively on three large scale data sets -- CelebA (40 tasks), Animals with Attributes 2 (85 tasks), and Caltech-UCSD Birds 200 (312 tasks) -- together representing 437 classification tasks. We provide results showing that our hardness and transferability estimates are strongly correlated with empirical hardness and transferability. As a case study, we transfer a learned face recognition model to CelebA attribute classification tasks, showing state of the art accuracy for tasks estimated to be highly transferable.