There seems to be a general consensus that AI will by no means make the work done by financial professionals redundant since results produced by AI models still require human interpretation, says the writer. Artificial intelligence (AI) is nothing new. It has been around since the 50s. It is only now that we possess the processing power to utilise it properly. It is already embedded in many people's lives.

To describe the future of work, Richard Baldwin is developing a new lexicon. The professor of international economics at the Graduate Institute in Geneva warns that we are unprepared for the ways in which new technology is changing the nature of globalization. Baldwin's new book, The Globotics Upheaval: Globalization, Robotics, and the Future of Work, is a natural follow-up to his 2016 book, The Great Convergence. Three years ago, he explained how a third wave of globalization--a collapse in the cost of the movement of people thanks to technology--would be the most disruptive, because it hits workers in the service sector. Baldwin's new book, published earlier this year, breaks down what this disruption will entail.

Ed. note: Does pedophilia allow those involved with building A.I. technology to experiment with the very extremes in human emotions through pedophilia? While we are mesmerized by all this rapidly accelerating technology, what else is going on that might be of concern? Contributor I cover what entrepreneurs are looking to learn. Virtual gaming is about to warp through a black hole, thanks to a band of scientists in Hong Kong and a hedge funder with a zealous science background, called Jeffrey Epstein. Indeed, game programming is moving away from algorithmic robots to a twilight realm of emotional thinkers, taking online, video and toy entrepreneurs, one step closer to Star Trek's'Holodeck'. For years, in virtual gaming, the only intelligent player was the person playing the game, responding to non-reactive obstacles. At most, opponents could blow up or morph into something else.

Plasmodium falciparum malaria still poses one of the greatest threats to human life with over 200 million cases globally leading to half-million deaths annually. Of these, 90% of cases and of the mortality occurs in sub-Saharan Africa, mostly among children. Although malaria prediction systems are central to the 2016-2030 malaria Global Technical Strategy, currently these are inadequate at capturing and estimating the burden of disease in highly endemic countries. We developed and validated a computational system that exploits the predictive power of current Machine Learning approaches on 22-years of prospective data from the high-transmission holoendemic malaria urban-densely-populated sub-Saharan West-Africa metropolis of Ibadan. Our dataset of >9x104 screened study participants attending our clinical and community services from 1996 to 2017 contains monthly prevalence, temporal, environmental and host features. Our Locality-specific Elastic-Net based Malaria Prediction System (LEMPS) achieves good generalization performance, both in magnitude and direction of the prediction, when tasked to predict monthly prevalence on previously unseen validation data (MAE<=6x10-2, MSE<=7x10-3) within a range of (+0.1 to -0.05) error-tolerance which is relevant and usable for aiding decision-support in a holoendemic setting. LEMPS is well-suited for malaria prediction, where there are multiple features which are correlated with one another, and trading-off between regularization-strength L1-norm and L2-norm allows the system to retain stability. Data-driven systems are critical for regionally-adaptable surveillance, management of control strategies and resource allocation across stretched healthcare systems.

The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers, yet its performance competes with the most complex classifiers in the literature. The core of this classifier depends mainly on measuring the distance or similarity between the tested example and the training examples. This raises a major question about which distance measures to be used for the KNN classifier among a large number of distance and similarity measures? This review attempts to answer the previous question through evaluating the performance (measured by accuracy, precision and recall) of the KNN using a large number of distance measures, tested on a number of real world datasets, with and without adding different levels of noise. The experimental results show that the performance of KNN classifier depends significantly on the distance used, the results showed large gaps between the performances of different distances. We found that a recently proposed non-convex distance performed the best when applied on most datasets comparing to the other tested distances. In addition, the performance of the KNN degraded only about $20\%$ while the noise level reaches $90\%$, this is true for all the distances used. This means that the KNN classifier using any of the top $10$ distances tolerate noise to a certain degree. Moreover, the results show that some distances are less affected by the added noise comparing to other distances.

Topological Data Analysis (TDA) is a recent approach to analyze data sets from the perspective of their topological structure. Its use for time series data has been limited to the field of financial time series primarily and as a method for feature generation in machine learning applications. In this work, TDA is presented as a technique to gain additional understanding of the customers' loyalty for business-to-business customer relationship management. Increasing loyalty and strengthening relationships with key accounts remain an active topic of discussion both for researchers and managers. Using two public and two proprietary data sets of commercial data, this research shows that the technique enables analysts to better understand their customer base and identify prospective opportunities. In addition, the approach can be used as a clustering method to increase the accuracy of a predictive model for loyalty scoring. This work thus seeks to introduce TDA as a viable tool for data analysis to the quantitate marketing practitioner.

This article describes REBA, a knowledge representation and reasoning architecture for robots that is based on tightly-coupled transition diagrams of the domain at two different levels of granularity. An action language is extended to support non-boolean fluents and non-deterministic causal laws, and used to describe the domain's transition diagrams, with the fine-resolution transition diagram being defined as a refinement of the coarse-resolution transition diagram. The coarse-resolution system description, and a history that includes prioritized defaults, are translated into an Answer Set Prolog (ASP) program. For any given goal, inference in the ASP program provides a plan of abstract actions. To implement each such abstract action, the robot automatically zooms to the part of the fine-resolution transition diagram relevant to this action. The zoomed fine-resolution system description, and a probabilistic representation of the uncertainty in sensing and actuation, are used to construct a partially observable Markov decision process (POMDP). The policy obtained by solving the POMDP is invoked repeatedly to implement the abstract action as a sequence of concrete actions. The fine-resolution outcomes of executing these concrete actions are used to infer coarse-resolution outcomes that are added to the coarse-resolution history and used for subsequent coarse-resolution reasoning. The architecture thus combines the complementary strengths of declarative programming and probabilistic graphical models to represent and reason with non-monotonic logic-based and probabilistic descriptions of uncertainty and incomplete domain knowledge. In addition, we describe a general methodology for the design of software components of a robot based on these knowledge representation and reasoning tools, and provide a path for proving the correctness of these components. The architecture is evaluated in simulation and on a mobile robot finding and moving target objects to desired locations in indoor domains, to show that the architecture supports reliable and efficient reasoning with violation of defaults, noisy observations and unreliable actions, in complex domains.

Unlike major Western languages, most African languages are very low-resourced. Furthermore, the resources that do exist are often scattered and difficult to obtain and discover. As a result, the data and code for existing research has rarely been shared. This has lead a struggle to reproduce reported results, and few publicly available benchmarks for African machine translation models exist. To start to address these problems, we trained neural machine translation models for 5 Southern African languages on publicly-available datasets. Code is provided for training the models and evaluate the models on a newly released evaluation set, with the aim of spur future research in the field for Southern African languages.

Researchers at the University of Washington plan to release their AI algorithm – GROVER -- that they say can generate extremely convincing, text-based fake news. The system is also able to write in the style of highly respected publications like The New York Times, The Washington Post and Wired. Researchers say their motivation for releasing the algorithm is to alert the public that such technology can be easily created and deployed. Data journalists formulate the appropriate story templates, and human editors review each story, according to Jason Hwang, head of partnerships, Hoodline. Essentially, they want AI-generated writing to be more human.

Opportunities made possible through Artificial Intelligence (AI) and analytics were brought to life at the annual AI Roadshow hosted by analytics company SAS at Hyatt Regency in Rosebank on 30 May. Business people, media and civil stakeholders attended the event which helped them gain insight into the current state of the analytics market in South Africa and how analytics, cloud and AI could be leveraged for business value. Speakers at the event included SAS South Africa country manager Akesh Lalla and IDC SA senior research manager for cloud and IT services Jon Tullett, among others. Keynote speaker and business philosopher Anders Indset began the event by encouraging delegates to think, ask questions and put people first in order to gain real value out of technology such as AI and use it to solve problems. Indset aims to show leaders how to cope with the 21st century by bridging the philosophy of the past with the technology and science of the future. Keynote speaker, business philosopher and futurist Anders Indset address business leaders at the AI Roadshow.