CAPE TOWN – One of the major drivers of the Fourth Industrial Revolution (4IR) or the age of intelligentisation is the major advances in Artificial Intelligence (AI) that are supporting and even taking over from humans in many situations. AI is increasingly replacing humans where knowledge could be learned or the decision-making formula is known. It is in particular the AI abilities of machine learning and deep learning that makes AI so powerful in numerous fields. Machine learning refers to the ability of computer systems to learn by itself and to adapt accordingly, allowing them to perform a specific task without explicit instructions. In the Business Report of last Friday I illustrated that AI even transforms the disciplines based on "human touch" such as social work and is used to predict successful youth influencers in an HIV campaign; match homeless people with the best-suited housing and most effective social interventions; and select vulnerable families and children in need of intervention.

The founders of Vise AI met when they were 13, a couple of teenagers more interested in applied artificial intelligence than English class. Fast-forward several years and the pair has relocated from the Midwest to San Francisco to raise money for a financial technology business they've been self-funding since 2016. As teenagers with an inordinate amount of AI knowledge, Samir Vasavada and Runik Mehrotra proved to be quite useful to large businesses, investment bankers and other financiers. Leveraging their AI know-how, they were paid $700 per hour by a consulting firm to teach financial "experts" about AI. Mehrotra, according to Vasavada, is a mathematical prodigy: "And that translates extremely well to AI, right, because what underlies AI is math," Vasavada, co-founder and chief executive officer of Vise AI, tells TechCrunch.

But as these organizations grapple with growing demand, erratic temperatures, aging infrastructure, and the threat of cyberattacks, many struggle to maintain a high level of service in an uncertain and unpredictable landscape. Artificial intelligence (AI) and machine learning (ML), as powered by big data, have the potential to modernize energy and utilities organizations by identifying ways to reduce waste and redundancy, protect and manage assets, and detect performance anomalies – all while realizing valuable cost savings, both for the organization and the customer. In this blog, we explore the three main areas where AI is making a mark on the energy and utilities sector today and how such investments may impact the future. Each year in the U.S. alone, trillions of gallons of water are lost due to aging pipes, broken water mains, and faulty meters. Replacing the entire system would be massively expensive, time-consuming, and impractical, which means that utility companies must take a localized approach to repairs.

In the United States, it seems we never have to go more than a few weeks without hearing about another mass shooting. With each new incident comes renewed calls to strengthen gun control laws, expand federal background checks, and get rid of assault rifles. Though the opposing faction promptly dismisses each appeal by citing 2nd Amendment rights, other discussions of practicality often emerge. Specifically, the efficacy of such laws is often called into question. How do we know which laws work and which ones don't?

Fior Markets offers a latest published report on Global Machine Learning in Finance Market Growth (Status and Outlook) 2019-2024, providing key insights and giving a competitive advantage to consumers through a detailed report. The researchers have included essential figures associated with the production and consumption forecast for the major regions that the market is separated into consumption forecast by application and production forecast by type. The research study is a source of methodical information rich in both quantity and quality. It shows upcoming as well as future opportunities, revenue growth, pricing, and profitability, focusing on both global and the regional market. The report identifies the key trends related to the different sectors of the market. Various important players have mentioned in the report are: Ignite Ltd, Yodlee, Trill A.I., MindTitan, Accenture, ZestFinance A top-to-bottom research wraps the market dynamics such as growth drivers, threats, opportunities, and challenges.

With mass tort style litigations reaching new highs in 2019, insurance and reinsurance companies should increasingly be thinking about how to capitalise on this emerging opportunity, according to Robert Reville, CEO of insurtech risk modelling and analytics firm Praedicat. Speaking at the Reinsurance Rendezvous event in Monte Carlo, Reville explained that a number of factors had come together to drive the increase in mass litigation activity this year. For example, hedge funds and other sources of capital are now helping to finance litigation in the US, making it possible for the plaintiff's to continue to drive very expensive forms of litigation. What's more, Praedicat believes there is a sense that the jury pool in the US is increasingly suspicious of corporations, and are more willing to accept that they could have been involved in nefarious actions. Finally – and of most relevance to the re/insurance industry – is the decades of accumulated science that is being brought to bear to describe what corporations are selling and doing that could potentially cause bodily or property damage, or environmental damage.

Machine Learning in the medical field will improve a patient's health with minimum costs. Use cases of ML are making a near-perfect diagnosis, recommend best medicines, predict readmissions and identify high-risk patients. These predictions are based on the dataset of anonymized patient records and symptoms exhibited by a patient. Adoption of ML is happening at a rapid pace despite many hurdles, which can be overcome by practitioners and consultants who know the legal, technical, and medical obstacles. Diagnostic errors account for about 10% of yearly patient deaths, mostly due to issues like poor tracking, misinformation, and miscommunication.

What is driving the demand, how incumbents are responding, and how startups are already tackling explainability 2.0 Explainable AI helps a user understand the machine's decision-making process. Instead of discussing methods of explainable AI (e.g., LIME, SHAP, etc.), below are some dimensions to wrap our heads around the concept. What explainable AI means depends on the user, the object being explained, and the underlying data. It is such a broad and rapidly developing field that when discussing explainable AI in-depth, it is good to have a mental framework of how it fits these dimensions. Most examples in this article are products built for business decision makers analyzing tabular data.

Web pages indexed by Google (plus gazillion of temporal snapshots) Embedded markup (RDFa, Microdata, Microformats) for annotation of Web pages Supports Web search & interpretation Pushed by Google, Yahoo, Bing et al (schema.org Factual errors, annotation errors (see also [Meusel et al, ESWC2015]) o Ambiguity & coreferences. Relevance: supervised coreference resolution 2.) Quality & redundancy: data fusion through supervised fact classification (SVM, knn, RF, LR, NB), diverse feature set (authority, relevance etc), considering source- (eg PageRank), entity-, & fact-level KnowMore: data fusion on markup 02/10/19 11 1. Relevance: supervised coreference resolution 2.) Quality & redundancy: data fusion through supervised fact classification (SVM, knn, RF, LR, NB), diverse feature set (authority, relevance etc), considering source- (eg PageRank), entity-, & fact-level KnowMore: data fusion on markup 02/10/19 12 1. Rich Context & Coleridge Initiative building (yet another) KG of scholarly resources & datasets 13Stefan Dietze Context/corpus: publications (currently: social sciences, SAGE Publishing) Tasks: I. Extraction/disambiguation of dataset mentions II.

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