Artificial intelligence is set to swell the GCC and Egypt's economies to the tune of $320 billion by 2030, according to a report. Globally, the economic uplift could be to the magnitude of $15.7 trillion, more than the current output of China and India combined, according to a report by professional services firm PwC. Within that increase, $6.6 trillion is likely to come from increased productivity, while $9.1 trillion is likely to come from benefits to consumers. Artificial intelligence (AI) is a collective term for computer systems that can sense their environment, think, learn, and take action in response to what they are sensing and their objectives. AI is rapidly evolving, with current technology including autopilots, digital assistants and chatbots.

A significant number of South African startups have revolutionized in 2017 and embraced the Artificial Intelligence (AI) related technologies in their software. The list below contains some South African startups that have either potentially disruptive technologies using AI or developed cutting AI solutions. The Cape Town-based startup was established in 2013 by Daniel Schwartzkopff and Francis Cronje. The startup gives consulting and product development services to some industries from law to finance. The startup was founded in 2011 by Dayne, Ryan Falkenberg, and Mark Pederson.

LONDON: Artificial intelligence is set to swell the GCC and Egypt's economies to the tune of $320 billion by 2030, according to a report.Globally, the economic uplift could be to the magnitude of $15.7 trillion, more than the current output of China and India combined, according to a report by professional services firm PwC. Within that increase, $6.6 trillion is likely to come from increased productivity, while $9.1 trillion is likely to come from benefits to consumers....

LONDON: Artificial intelligence is set to swell the GCC and Egypt's economies to the tune of $320 billion by 2030, according to a report. Globally, the economic uplift could be to the magnitude of $15.7 trillion, more than the current output of China and India combined, according to a report by professional services firm PwC. Within that increase, $6.6 trillion is likely to come from increased productivity, while $9.1 trillion is likely to come from benefits to consumers. Artificial intelligence (AI) is a collective term for computer systems that can sense their environment, think, learn, and take action in response to what they are sensing and their objectives. AI is rapidly evolving, with current technology including autopilots, digital assistants and chatbots.

In a time when data has been labelled'the new oil', businesses are scrambling to implement effective forward-thinking data management strategies that can deliver real-time insights and business value to decision-makers to drive business strategy, increase revenue, and grow profits. Data modellers have become indispensable assets to enterprises wishing to leverage their data to drive competitive advantage. However, there is often a disconnect between the data modellers analysing and extracting value from data, and the business decision-makers who need to utilise data as a strategic asset to drive business outcomes. Historically, businesses owned vast amounts of structured and unstructured data in their ERP, transactional, and other business systems, which was brought together in a data warehouse. Here, a range of different data modelling tools, from the conceptual (showing relationships between different entities) to the logical (looking at certain attributes within the data) and physical (referring to how data is represented and stored using a database management system) were applied to create a framework within which analysts could extract business value.

The United Nations Development Programme (UNDP) helps countries implement the United Nations (UN) Sustainable Development Goals (SDGs), an agenda for tackling major societal issues such as poverty, hunger, and environmental degradation by the year 2030. A key service provided by UNDP to countries that seek it is a review of national development plans and sector strategies by policy experts to assess alignment of national targets with one or more of the 169 targets of the 17 SDGs. Known as the Rapid Integrated Assessment (RIA), this process involves manual review of hundreds, if not thousands, of pages of documents and takes weeks to complete. In this work, we develop a natural language processing-based methodology to accelerate the workflow of policy experts. Specifically we use paragraph embedding techniques to find paragraphs in the documents that match the semantic concepts of each of the SDG targets. One novel technical contribution of our work is in our use of historical RIAs from other countries as a form of neighborhood-based supervision for matches in the country under study. We have successfully piloted the algorithm to perform the RIA for Papua New Guinea’s national plan, with the UNDP estimating it will help reduce their completion time from an estimated 3-4 weeks to 3 days.

The potential for agents, whether embodied or software, to learn by observing other agents performing procedures involving objects and actions is rich. Current research on automatic procedure learning heavily relies on action labels or video subtitles, even during the evaluation phase, which makes them infeasible in real-world scenarios. This leads to our question: can the human-consensus structure of a procedure be learned from a large set of long, unconstrained videos (e.g., instructional videos from YouTube) with only visual evidence? To answer this question, we introduce the problem of procedure segmentation---to segment a video procedure into category-independent procedure segments. Given that no large-scale dataset is available for this problem, we collect a large-scale procedure segmentation dataset with procedure segments temporally localized and described; we use cooking videos and name the dataset YouCook2. We propose a segment-level recurrent network for generating procedure segments by modeling the dependencies across segments. The generated segments can be used as pre-processing for other tasks, such as dense video captioning and event parsing. We show in our experiments that the proposed model outperforms competitive baselines in procedure segmentation.

The state of the art in bidirectional search has changed significantly a very short time period; we now can answer questions about unidirectional and bidirectional search that until very recently we were unable to answer. This paper is designed to provide an accessible overview of the recent research in bidirectional search in the context of the broader efforts over the last 50 years. We give particular attention to new theoretical results and the algorithms they inspire for optimal and near-optimal node expansions when finding a shortest path.

In this case, we would be interested used in biological and social sciences to predict events and to in identifying a segment of the population for which describe possible relationships between variables. When addressing a linear rule is highly predictive of the price of certain cars, the task of prediction, machine learning and statistics whereas this linear rule may not provide a good prediction commonly focus on capturing the vast majority of data, overall in the larger population. Let us imagine that for this occasionally ignoring a segment of the population as "outliers" data set, and for a target fraction of the population, we found or "noise," which could be helpful to better understand a simple rule that describes the subpopulation, along with the data. Previous work by Juba (2016) gave an algorithm its linear fit.

Our work extends Juba’s formulation of learning abductive reasoning from examples, in which both the relative plausibility of various explanations, as well as which explanations are valid, are learned directly from data. We extend the formulation to consider partially observed examples, along with declarative background knowledge about the missing data. We show that it is possible to use implicitly learned rules together with the explicitly given declarative knowledge to support hypotheses in the course of abduction. We observe that when a small explanation exists, it is possible to obtain a much-improved guarantee in the challenging exception-tolerant setting.