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Databricks and Summit Gold Sponsor AWS Present on a wide variety of topics at this year's premier data and AI event. Amazon Web Services (AWS) is sponsoring Data AI Summit Europe 2020 and our work with AWS continues to make Databricks better integrated with other AWS services, making it easier for our customers to drive huge analytics outcomes. As part of Data AI Summit, we want to highlight some of the top sessions of interest for AWS customers. The sessions below are relevant to customers interested in or using Databricks on the AWS cloud platform, demonstrating key service integrations. If you have questions about your AWS platform or service integrations, visit the AWS booth at Data AI Summit.
Amazon Web Services' new visual data preparation tool for AWS Glue allows users to clean and normalize data with an interactive point-and-click visual interface without writing custom code. AWS Glue DataBrew helps data scientists and data analysts get the data ready for analytics and machine learning (ML) 80 percent quicker than traditional data preparation approaches, according to the cloud provider, which made the tool generally available on Wednesday. The new offering builds on AWS Glue, which AWS generally released in April of 2017. AWS Glue is a serverless, fully managed, extract, transform and load (ETL) service to categorize, clean, enrich and move data between various data stores. It has a central data repository called the AWS Glue Data Catalog, an ETL engine that generates Python code automatically and a flexible scheduler to handle dependency resolution, job monitoring and retries.
My recent claim that fashion needs more imagination when it comes to using artificial intelligence has been unexpectedly answered by a project combining e-commerce data and artisanship. Not an obvious pairing, but the brainchild of passionate'dataphile' YOOX NET-A-PORTER GROUP Chairman and CEO, Federico Marchetti, and HRH The Prince of Wales, whose appreciation and support of artisanal craftsmanship (and dedication to safeguarding its future) is decades-long. Marchetti and the YOOX NET-A-PORTER team worked with The Prince's Foundation to create a unique year-long apprenticeship to cultivate the next generation of luxury fashion artisans, informed and guided by customer shopping data and AI analysis of millions of images of historically successful products. To breathe life into artisanship as a viable and attractive career option, underpinned by data that empowers it to deliver the right product, for the right customer on the right sales platform, crucially sustaining the artisans' craft methods and their livelihood. The Modern Artisan project brought together six designers from Milan's Politecnico Di Milano Fashion in Process (FiP) research laboratory and four apprentices undergoing certified training in small batch production and hand-craft skills at The Prince's Foundation, Dumfries House, Scotland.
While many know UK company Ocado as an online grocery retailer, it's really one of the most innovative tech companies in the world. Ocado was founded in 2000 as an entirely online experience and therefore never had a brick-and-mortar store to serve its customers, who number 580,000 each day. Its technology expertise came about out of necessity as it began to build the software and hardware it needed to be efficient, productive, and competitive. Today, Ocado uses artificial intelligence (AI) and machine learning in many ways throughout its business. Since 2000, Ocado tried to piece together the technology they needed to succeed by purchasing products off the shelf.
The healthcare sector, that contains a diverse array of industries with activities ranging from research to manufacturing to facilities management (pharma, medical equipment, healthcare facilities), generated in 2013 something like 153 exabytes (1 exabyte 1 billion gigabytes). It is estimated that by year 2020 the healthcare sector will generate 2,134 exabytes. To put that into perspective data centres globally will have enough space only for an estimated of 985 exabytes by 2020. Meaning that two and a half times this capacity would be required to house all the healthcare data. Big data have four V's volume, velocity (real time will be crucial for healthcare), variety and veracity (noise, abnormality, and biases). Poor data quality costs the US economy $ 3,1 trillion a year. And 1 in 3 business leaders don't trust the information they use to make decisions, and this is true also for the healthcare sector.
A global panel will consider how to define ethical leadership and the particular challenges posed by emerging technologies in a virtual event from 1-1:45 p.m. ET on Oct. 28. "Defining Ethical Leadership" is free and open to the public. Those who wish to participate may register online. The event is made possible by a grant from Lilly Endowment Inc. to support Leading Ethically in the Age of AI and Big Data, an initiative designed to develop curricula to foster character and ethical values in future leaders, preparing them to respond appropriately to the challenges posed by rapidly evolving technologies, such as artificial intelligence and Big Data management. "As we embark upon the work of our Lilly Endowment grant, a thoughtful conversation about how we define ethical leadership offers an appropriate starting point," said David Reingold, the Justin S. Morrill Dean of Liberal Arts and professor of sociology at Purdue, principal investigator for the grant.
Financial crime as a wider category of cybercrime continues to be one of the most potent of online threats, covering nefarious actives as diverse as fraud, money laundering and funding terrorism. Today, one of the startups that has been building data intelligence solutions to help combat that is announcing a fundraise to continue fueling its growth. Ripjar, a UK company founded by five data scientists who previously worked together in British intelligence at the Government Communications Headquarters (GCHQ, the UK's equivalent of the NSA), has raised $36.8 million (£28 million) in a Series B, money that it plans to use to continue expanding the scope of its AI platform -- which it calls Labyrinth -- and scaling the business. Labyrinth, as Ripjar describes it, works with both structured and unstructured data, using natural language processing and an API-based platform that lets organizations incorporate any data source they would like to analyse and monitor for activity. It automatically and in real time checks these against other data sources like sanctions lists, politically exposed persons (PEPs) lists and transaction alerts.
The development of the internet over the last few decades has resulted in a massive increase in the production of data and the unprecedented availability of computing power for corporate applications. Machine Learning and artificial intelligence (AI) techniques have been fuelled by these revolutions to emerge from being purely academic topics of investigation to be the basis for a new wave of products and services for the digital age. The paradigm-shifting opportunities presented to corporates by this emerging technology range from the ability to expose and extract insights and patterns from data lakes to replacing human beings in critical decision-making scenarios. However, with these opportunities also come novel risks and concerns that must be considered when contemplating the development and deployment of AI and machine learning agents. These include understanding how their trustworthiness may be measured, the ethics and policies required for their deployment and the cybersecurity implications of their widespread adoption.