The use of artificial intelligence (AI) and machine learning is on the rise but it's important to know which reputation management processes should and shouldn't be automated. AI, using natural language processing (NLP) models, allows computers to understand and decipher what a human is saying. This is starting to be used by companies in South Africa responding to customers through online channels. It also being employed increasingly in the reputation marketing sector. The main reasons organisations turn to these technologies is that they dramatically improve efficiency, can reduce errors and they save time.

Human brains are singularly special in the animal kingdom, writes Andrew Quixley, Data Science and AI Sales Lead, IBM South Africa. We are the curious, communicative collaborators who rose from a simple foraging existence on the savannahs to build structures of incredible complexity. Sure, other animals are curious, communicative and collaborative too. An octopus will investigate and solve problems; elephants use infrasound to communicate over vast distances; termites collaborate to build structures that are millions of times larger than any individual; but none of these feats comes close to the scale of human complexity. And the key to this complexity is our ability to generate language. There are 7 099 official languages on Earth.

On 17 November, the first edition of the Mint Visionaries series, which seeks to delve into the minds of people inspiring a new future, was kicked off with entrepreneur-philanthropist Bill Gates, who is also the co-chair of the Bill and Melinda Gates Foundation, sharing his thoughts with Wipro Ltd chairman Rishad Premji. The two discussed the challenges of mitigating climate change, eliminating malnutrition, and improving the health and education infrastructure, besides the role of technology, such as artificial intelligence, for social inclusion, something Gates considers a mission statement. Rishad Premji: Climate change will be one of the defining challenges of the 21st century--the impact of weather events, rising sea level, islands getting flooded. It will affect the way people live and potentially impact health and mortality. There is a huge implication of climate change. I know you personally and the Gates Foundation is spending a lot on mitigation--on how to reduce carbon emission. I know you are spending time on breakthrough energy ventures in your personal capacity, investing in technology that can pay off, as well as around adaptation. What are you personally, and through Gates Foundation, doing in these areas? And, what can we do to learn how to leverage science and technology, as governments and as citizens, to be more informed about climate change and its impact, considering that we often have this debate on whether it is real. And, what can come out of it? Bill Gates: I am actually writing a book about climate change.

Bengaluru: Akshaya Patra provides mid-day meals to 1.8 million school children across India. The NGO came to Accenture a couple of years ago with a simple query: how do we feed more children? The consultant looked at the supply chain and then worked with three startups from different domains for a solution. One startup used data from IoT sensors to streamline cooking processes and monitor the quality of food. Another one used machine learning and artificial intelligence (AI) to predict the demand for food. And a third startup used blockchain to put feedback from schools on a distributed ledger in a tamper-proof manner.

Graph matching plays a central role in such fields as computer vision, pattern recognition, and bioinformatics. Graph matching problems can be cast as two types of quadratic assignment problems (QAPs): Koopmans-Beckmann's QAP or Lawler's QAP. In our paper, we provide a unifying view for these two problems by introducing new rules for array operations in Hilbert spaces. Consequently, Lawler's QAP can be considered as the Koopmans-Beckmann's alignment between two arrays in reproducing kernel Hilbert spaces (RKHS), making it possible to efficiently solve the problem without computing a huge affinity matrix. Furthermore, we develop the entropy-regularized Frank-Wolfe (EnFW) algorithm for optimizing QAPs, which has the same convergence rate as the original FW algorithm while dramatically reducing the computational burden for each outer iteration. We conduct extensive experiments to evaluate our approach, and show that our algorithm significantly outperforms the state-of-the-art in both matching accuracy and scalability.

Words embedding (distributed word vector representations) have become an essential component of many natural language processing (NLP) tasks such as machine translation, sentiment analysis, word analogy, named entity recognition and word similarity. Despite this, the only work that provides word vectors for Hausa language is that of Bojanowski et al. [1] trained using fastText, consisting of only a few words vectors. This work presents words embedding models using Word2Vec's Continuous Bag of Words (CBoW) and Skip Gram (SG) models. The models, hauWE (Hausa Words Embedding), are bigger and better than the only previous model, making them more useful in NLP tasks. To compare the models, they were used to predict the 10 most similar words to 30 randomly selected Hausa words. hauWE CBoW's 88.7% and hauWE SG's 79.3% prediction accuracy greatly outperformed Bojanowski et al. [1]'s 22.3%.

Past literature has been effective in demonstrating ideological gaps in machine learning (ML) fairness definitions when considering their use in complex socio-technical systems. However, we go further to demonstrate that these definitions often misunderstand the legal concepts from which they purport to be inspired, and consequently inappropriately co-opt legal language. In this paper, we demonstrate examples of this misalignment and discuss the differences in ML terminology and their legal counterparts, as well as what both the legal and ML fairness communities can learn from these tensions. We focus this paper on U.S. anti-discrimination law since the ML fairness research community regularly references terms from this body of law.

Hydroxyurea (HU) has been shown to be effective in alleviating the symptoms of Sickle Cell Anemia disease. While Hydroxyurea reduces the complications associated with Sickle Cell Anemia in some patients, others do not benefit from this drug and experience deleterious effects since it is also a chemotherapeutic agent. Therefore, to whom, should the administration of HU be considered as a viable option, is the main question asked by the responsible physician. We address this question by developing modeling techniques that can predict a patient's response to HU and therefore spare the non-responsive patients from the unnecessary effects of HU on the values of 22 parameters that can be obtained from blood samples in 122 patients. Using this data, we developed Deep Artificial Neural Network models that can predict with 92.6% accuracy, the final HbF value of a subject after undergoing HU therapy. Our current studies are focussing on forecasting a patient's HbF response, 30 days ahead of time.

New York City, NY: October 25, 2019 – Published via (WiredRelease) – Global (United States, European Union and China) Cognitive Computing Market Research Report 2019-2028. The Cognitive Computing Market report covers all the minute details related to the industry like Technological Developments, Growth Opportunities, Threats to Market Growth, Innovative Strategies and Futuristic Market Trends. Cognitive Computing market report provides a comprehensive overview of current trends and new product development in the global Cognitive Computing market. Featuring global and regional data and over top key players profiles, this report provides the ultimate guide to exploring opportunities in the keyword industry internationally. Some of the key players in the market are, Statistical Analysis System (SAS) Software Ltd, Saffron Technology Inc, Vicarious FPC Inc, IBM corporation, Enterra Solutions LLC, Oracle corporation, SAP Inc, Google LLC, Palantir Technologies Inc and Microsoft corporation.

So, imagine you are driving along in your fancy high-tech car, minding your own business, with your yet-to-be-feature-complete full self-driving software activated, and suddenly the car slams the brakes to a full stop, like a donkey that had an epiphany that what it was doing just didn't serve any purpose and therefor just refused to move any further. Sure, it's great that something useful is becoming of outdated fossil fuel vehicles. Giants having fun with them riding down hills seem like a good solution, and I guess they don't burn gas in the process, but my car might get nervous around these guys. For the time being, my car didn't stop, but when the brain of the car gets more clever than that of a cricket, it might recognize the car, and panic! It does so already when it sees broken off tree branches laying on the side of the road.