Sebastian Klovig Skelton's article in this week's CW ezine issue, 'Auditing for algorithmic discrimination' could hardly be more timely. The fall-out from the algorithmic exams fiascos of recent weeks – with the Highers in Scotland and the A-levels in the rest of the UK – is set to continue. These were not "black box algorithms" based on straightforwardly biased data, of the kind we have often heard about. The Scottish Qualifications Authority and Ofqual published the statistical models they chose (after what seems like a lot of head-scratching and brains-cudgelling) and it is clear what data they chose to use. However, it is also clear those models generated unjust results at the level of individual candidates.

For a country that holds 12 percent of the planet's water supply, Brazil faces significant water management issues. In addition to its commonly known sanitation problems, the country's infrastructure lends itself to distribution issues, including fraudulent use. Fraudulent water use can be particularly hard to track and identify, and often goes unaddressed for significant periods of time – especially in highly populated areas where physically checking people's homes and water meters isn't an option. Instead, companies need to find ways to swiftly identify and eliminate fraudulent water activity which impacts an already scarce supply and costs communities money. To address this challenge, a utilities company from Mato Grosso, Brazil recently worked with a group of data engineers at ScientificCloud. The goal was to develop a solution that could better locate fraudulent water usage by tracking data patterns based on home location and property attributes. As a Sao Paolo-based data science company that develops and deploys machine learning (ML) and artificial intelligence (AI)-powered applications, ScientificCloud understood these problems first hand.

While you might not consider ethics in AI a primary concern for your business, consider this: A whopping 50% of business processes will be fully automated by 2022, compared to around 30% today. Most of the advantages in digital transformation are enabled by task augmentation through artificial intelligence (AI) or through AI powered Robotic Process Automation (RPA). Using such vast quantities of data in automated processes is already having a massive impact on business and society. Many analysts have projected that by 2020, around 70% of the data that a company uses will come from Internet of Things (IoT) enabled devices and external data streams – in other words, external, non-transactional sources. This rising impact can be both a blessing and a concern.

Imagine the possibilities when AI can handle ambiguity, she says. Business and governments are turning to Artificial Intelligence (AI) to automate and improve their decision-making and uncover multiple opportunities. The problem is that AI has been effective in powerful, but narrow, contexts, on applications where it can do one thing extremely well. An increasingly promising approach for teaching AI systems to be more intelligent is by extending their power with graph technology. Because graphs help us better understand and work with complexity, as it's a technology uniquely suited to managing connections. Context is the information that frames something to give it meaning.

Artificial Intelligence is the biggest threat to mankind, right? Even if robots aren't taking over the planet by force, the yarn goes, computers will surely push us all into unemployment in the next decade or so. Let's meet someone who can give us a slightly different perspective. This is Joel, standing in front of his house, a few kilometers outside Gulu, Uganda, where he lives with his 14 brothers and sisters. Joel works for Zillow, the leading online real estate marketplace in the US with 1.1B of revenue in 2017.

The post-big data landscape has been shaped by two emergent, intrinsically related forces: the predominance of cognitive computing and the unveiling of the data fabric architecture. The latter is an overlay atop the assortment of existing distributed computing technologies, tools and approaches that enable them to interact for singular use cases across the enterprise. Gartner describes the data fabric architecture as the means of supporting "frictionless access and sharing of data in a distributed network environment." These decentralized data assets (and respective management systems) are joined by the data fabric architecture. Although this architecture involves any number of competing vendors, graph technology and semantic standards play a pivotal role in its implementation.

"Every aspect of our lives will be transformed [by AI]", potentially "the biggest event in the history of our civilization" -Stephen Hawking We are already seeing the tremendous inroads that Artificial Intelligence (AI) has made in virtually every industry. Despite AI's rapid expansion, the Artificial Intelligence technology itself is still evolving. AI points towards a future where machines not only do physical work, as they have done since the industrial revolution, but also the "thinking" work – planning, strategizing, prioritizing and making decisions. In fact, the definition of what is considered Artificial Intelligence keeps shifting. What used to be called AI even several years ago is now just widely used and familiar technology, and no longer resides under the AI umbrella.

Artificial Intelligence is all over the place. If you attended our post-summit AI symposium we held during Global Summit 2018 in San Antonio, you certainly got a taste of the varied use cases where AI can make a difference. But what does it take to build an AI-powered application? Do you start implementing tedious data-gathering processes for training your models? Or do you first scour the job market for a handful of those elusive data scientist unicorns, which itself may take years?

This is a guest blogpost by Matt Jones, lead analytics strategist at Tessella, in which he argues companies with physical products and infrastructure cannot simply cut and paste the tech giant's AI strategy Every conference this year contains a dead human genius reincarnated as software system or a robot. Yes, there is a lot of hype, but there is real worth in AI and Machine Learning. Read our counseling on how to avoid adopting "black box" approach. You forgot to provide an Email Address. This email address doesn't appear to be valid.

The business world has largely forgotten why we need analytics: for action. Employees spend hours each week, month and quarter crunching hundreds of thousands of data points, but all too often the pretty charts and insights are never looked at again. Maybe those insights are on a slide that is presented and discussed in a meeting, but since analytics aren't incorporated in the workstream, nothing ends up happening. Or maybe the data is about last quarter, and it's too late to do anything with the knowledge. The point is, analysis is useless when it doesn't result in specific actions.