Data Mining

This article was written by Monica Rogati. Monica is an independent data science executive and advisor. She built key data products and teams at Jawbone and LinkedIn; she now helps companies make the most out of their data. Do a project you care about. Make it good and share it.

Gradually yet steadily, technology has taken over all aspects of our life. And the financial services sector is no exception. Financial Services spanning investments, lending and management of assets are a fundamental part of fund management for individuals as well as corporations. One of the nuances of this sector is the volatility associated with it owing to factors such as prevailing market conditions, political scenario, performance of stocks, taxation norms, etc. Since this condition is a given, companies dealing with such financial instruments need to glean reams of data before counselling clients on the right investment choice or the right kind of loan to opt, for instance.

Gartner's recently released "Magic Quadrant for Industrial IoT Platforms" outlines how organizations can leverage the Internet of Things (IoT) to drive their digital transformation initiatives. In particular, Gartner believes that "By 2020, on-premises Internet of Things (IoT) platforms coupled with edge computing will account for up to 60% of industrial IoT (IIoT) analytics, up from less than 10% today."[1] More real-time sensor and device data coupled with more computational power are driving analytics "to the edge", which will yield new business and operational monetization opportunities for organizations looking to become more effective at leveraging data and analytics to power their business models (see Figure 1). IoT will be a significant Digital Transformation enabler – enabling new opportunities to integrate digital capabilities into the organization's assets, products and operational processes in order to improve efficiency, enhance customer value, mitigate risk, and uncover new monetization opportunities. IoT value creation occurs when the IoT Analytics collide with IoT Applications (like predictive maintenance, manufacturing performance optimization, waste reduction, reducing obsolete and excessive inventory, and first-time-fix) to deliver measurable sources of business and operational value (see Figure 2).

Verizon's FIOS fiber optic broadband keeps millions of US homes online. However, monitoring stability and reacting to faults and outages which affect customer experience takes huge amounts of resources. Until recently, Verizon primarily relied on customer feedback to understand when the speed and quality of its service was falling short of expectations. In recent years, however, following a large investment in analytics and AI-driven technology such as machine learning – in part subsumed through the company's 2017 acquisition of Yahoo! and it's research units – a different approach is bringing impressive results. Now it's predictive analytics algorithms monitor 3GB of data every second streaming from millions of network interfaces – from customers' routers to an array of sensors gathering temperature and weather data, and software which "listens in" on operational data, such as billing records.

There's no doubt about it: The future will be machine driven, and central to this future are the advanced algorithms, which are fueled by the data they're trained on. Every ad you see, every car driving itself, every medical diagnosis provided by a machine will be based on your data – and lots of it. Without your data, we inherit a world without machine learning, and most would argue that companies without machine learning will fail. At least that's where we're heading; it sounds like a big problem, and it is. Concepts around "big data" are completely incompatible with how people expect their data to be protected and how laws are shaping those protections.

Materials for five new AI-oriented courses have been published to the LearnAI Materials site.

For all their stunning frequency, school shootings remain a confounding horror. Not only is there little consensus on how to stop them--with suggestions ranging from restricting gun access to arming teachers--but there's even less certainty about why a student would open fire on his classmates. Now, some scientists are starting to explore if artificial intelligence (AI) could help find answers. The idea is that algorithms might be able to better analyze data related to school shootings, and perhaps even identify patterns in student language or behavior that could foreshadow school violence. The research is still in its early stages, and the prospect of using machines to predict who might become a school shooter raises privacy issues and other ethical questions associated with any kind of profiling, particularly since the process would involve children.

Business Intelligence (BI) solutions have steadily taken the business world by storm. Originating nearly two decades ago, BI has since morphed and evolved numerous times to meet ever-increasing demands for more in-depth analysis, increased speed and easier-to-digest outputs. With each new evolution, new use cases were born, and the resulting business value increased. Today, many consider the progression of BI solutions to have developed into three main categories. Machine learning and AI are largely responsible for the latter two types of analytics.

The rise of big data and the subsequent centrality of analytics has started to have an impact on nearly every industry and occupation in the globe – a trend that is only set to continue. With developing technology and more data to fuel analytics engines, we are probably going to find smart data tools everywhere – even in the court room. It may seem a little alien at first, but analytics and AI are already helping lawyers to do their job more effectively. Here is a look at early indications of just how useful it might be to use advanced data technology in the court room. Unlike glamorous TV shows of unforgettable court room battles, the reality of the law is that lawyers spend most their time reading.

One of the important fields of Artificial Intelligence is Computer Vision. Computer Vision is the science of computers and software systems that can recognize and understand images and scenes. Computer Vision is also composed of various aspects such as image recognition, object detection, image generation, image super-resolution and more. Object detection is probably the most profound aspect of computer vision due the number practical use cases. In this tutorial, I will briefly introduce the concept of modern object detection, challenges faced by software developers, the solution my team has provided as well as code tutorials to perform high performance object detection.