If you know me, you know I love podcasts. If you don't indulge in a podcast or two already, I highly recommend you start. Listening during a commute or when you're taking a break from the hustle and bustle of your day is an effective way to keep yourself updated in any field you're interested in and to quickly and easily gain knowledge-- currently, my interests are machine learning (ML) and artificial intelligence (AI). Technology is driving innovation in ML and AI at a rapid pace. It has become exceedingly vital for me to keep up with the ongoing trends in this area, so I decided to find the best podcasts out there to help me out.
Credit scoring is without a doubt one of the oldest applications of analytics. In recent years, a multitude of sophisticated classification techniques have been developed to improve the statistical performance of credit scoring models. Instead of focusing on the techniques themselves, this paper leverages alternative data sources to enhance both statistical and economic model performance. The study demonstrates how including call networks, in the context of positive credit information, as a new Big Data source has added value in terms of profit by applying a profit measure and profit-based feature selection. A unique combination of datasets, including call-detail records, credit and debit account information of customers is used to create scorecards for credit card applicants. Call-detail records are used to build call networks and advanced social network analytics techniques are applied to propagate influence from prior defaulters throughout the network to produce influence scores. The results show that combining call-detail records with traditional data in credit scoring models significantly increases their performance when measured in AUC. In terms of profit, the best model is the one built with only calling behavior features. In addition, the calling behavior features are the most predictive in other models, both in terms of statistical and economic performance. The results have an impact in terms of ethical use of call-detail records, regulatory implications, financial inclusion, as well as data sharing and privacy.
Amazon's first attempt at a set of true wireless earbuds gets a lot right, with Bose active noise reduction technology and hands-free Alexa. At £119.99, the Echo Buds undercut rivals, some of which cost more than twice as much. Their design is generic: large, kidney-shaped with a glossy touch panel on the outside and a standard silicone eartip on the inside. The eartip supports the earbud with the majority of the rest of the body sitting outside the ear. But the earbuds are large and heavy at 7.6g each, meaning they sit proud of your ear.
Advances in Data Science are lately permeating every field of Transportation Science and Engineering, making it straightforward to imagine that developments in the transportation sector will be data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a "story" intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers' personal devices act as sources of data flows that are eventually fed to software running on automatic devices, actuators or control systems producing, in turn, complex information flows between users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. The present work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded on this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the everchanging phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within the Data Science realm that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.
CES 2020 is kicking off into full gear in Las Vegas this week and that means the world's top technology minds have gathered in one place to share their vision of the future. In the mobility space, particularly with autonomous vehicles and electric vehicles, advanced or new technology is playing a major role in shaping the vision of this industry. Data, or rather the analysis of big data through AI, is being laid as the foundation to the future of mobility. This topic was the point of discussion for three industry experts at CES – Hardik Bhatt, Leader Digital Government, Amazon; Seleta Reynolds, General Manager, Los Angeles DOT; and Marcus Welz, CEO Siemens Mobility Intelligent Traffic Systems, Siemens Mobility. Seleta Reynolds leads one of the most populous department of transportation agencies in the U.S., which means she has access to a lot of transportation data to improve the function of the area she manages.
We live in an age where payments to other individuals or companies can be made with a single push of a button on a smartphone, where purchases can be made in an instant, and where loans can be applied for without meeting a person face-to-face. In addition, cars drive themselves, robots mimick human movements, and privately funded rockets soar into space while sending their boosters back to earth to land themselves for reuse. While modern technologies, data, advanced analytics and automation are transforming every facet of daily life, most organizations and executives are not fully prepared for what these massive changes mean to the role of leaders and for the future of work. Employees at all levels of the organization fear what these lightning fast changes will mean for their occupations. Will these jobs simply be modified based on new applications of data and insights and new digital tools?
At the Spark AI Summit, Europe, Enterprise Times sat with Ali Ghodsi, CEO and co-founder, Databricks to talk AI and big data. Ghodsi started as an AI researcher and took that knowledge and experience into Databricks when it was founded. It gives him an interesting perspective on the state of the, often overhyped, AI market. For example, Ghodsi says that one of the reasons for founding Databricks was to: "democratise artificial intelligence and bring it to the masses." One of the problems that AI faces is that it is not a new discipline, it's been around for literally decades.
Seven decades ago, agricultural scientists used high-yielding, dwarf varieties of wheat and rice to revolutionize agriculture across Asia and Latin America – and now European data scientists are teaming up with Kenyan farmers to use the fruits of the Fourth Industrial Revolution to drive the next agricultural one. The Green Revolution produced massive increases in crop yields throughout Asia and Latin America, but even today, many smallholders –farmers who produce crops on small pieces of land – struggle to afford and utilize the mechanized equipment and agricultural chemicals that came with that revolution. When it comes to Africa, there is still great potential for productivity increases in agriculture. The number of small-holder farmers in Kenya could be between 5 million and 9 million people according to some estimates. In order to see how artificial intelligence, machine learning and big data could help those farmers, French consultancy firm Capgemini teamed up with a Kenyan social enterprise in the Kakamega region in Western Kenya.
Whatever the country is, when the mobile commerce data from online retailers and brands is compiled, the key findings states that purchase and transaction through mobile devices are continuously increasing. There is nothing surprising as it's a logical extension of the way smartphones are in the use. The way mobile devices are creeping into our lives portrays a positive picture of mobile usage in the future. This is the reason that the retailers have increasingly put their sights on and investing heavily their resources into E-commerce app development. Advocates believe that it's a golden ticket to potentially augment the sales and customer experience.
One industry that's always been at the cutting edge of technology is telecommunications. If we consider some of the breakthroughs made in human achievements, the most significant involve communication technology – from wired telegraph relays crossing vast continents to the invention of the telephone, to the world-crossing internet and the ubiquitous mobile phone. The need to communicate being at the heart of human behavior means that modern communications service providers (CSPs) operate in a highly competitive market; everyone needs to connect. Unless companies can find differentiation from one another, the technology that underpins the many services (like landlines, internet and mobile) is interchangeable, for businesses and consumers alike. After all, changing one's cellphone provider can be as simple as flicking a software toggle switch in a phone's settings to use SIM B, not SIM A. Retaining profitability is essential for CSPs as it is in every other vertical, of course, and with a saturated market that's based on technology, the challenges in the sector are very specific.