Europe, while making progress, is behind the US and China in capturing the opportunities of artificial intelligence and automation. Digitization is everywhere, but adoption is uneven across companies, sectors, and economies, and the leaders are capturing most of the benefits. Accelerating progress in AI and automation now bring further opportunities for users, businesses, and the economy. Europe, while making progress, is behind the United States and China. This briefing note was prepared for the European Union Heads of State Tallinn Digital Summit, which brought together heads of state and CEOs to discuss the steps needed to enable people, enterprises, and governments to fully tap into the potential of innovative technologies and digitization.
We estimate that Europe has captured only 12 percent of its potential from digital technologies, ranging from 10 percent in Germany to 17 percent in the United Kingdom. As a consequence, Europe is a net importer of digital services, running a digital trade deficit with the United States amounting to nearly 5.6 percent of total EU-US services trade. Digital cross-border flows beyond e-commerce, including web and video applications that have the potential to be produced domestically, could also increase as companies take advantage of the single market's scale. These advances will provide national economies with a much-needed boost to productivity and enable companies to realize substantial performance gains.
Oracle Autonomous Data Warehouse Cloud is a next-generation cloud service built on the self-driving Oracle Autonomous Database technology using machine learning to deliver enhanced performance, reliability and ease of deployment for data warehouses. The Oracle Autonomous Database Cloud eliminates the human labor associated with tuning, patching, updating and maintaining the database and includes self-driving that provides continuous adaptive performance tuning based on machine learning. Unlike traditional cloud services with complex, manual configurations that require a database expert to specify data distribution keys and sort keys, build indexes, reorganize data or adjust compression, Oracle Autonomous Data Warehouse Cloud is a simple "load and go" service. Unlike traditional cloud services, which use generic compute shapes for database cloud services, Oracle Autonomous Data Warehouse Cloud is built on the high-performance Oracle Exadata platform.
Smart bidding uses advanced machine learning to amend bids based on a wide range of real-time signals including device, location, time of day, remarketing list, language, and operating system. My agency has even created a script that modifies bids for every product group on Google Shopping based on a target ROI figure, saving our account managers huge amounts of time manually amending bids. A great example of how to utilize the power of automation to help monitor account performance is an AdWords Script like Google's Account Anomaly Detector. At Hallam, we use the Google Analytics API to populate our PPC reports in Google sheets, ensuring that our account managers automatically get the data they need to send their clients, and that their monthly "reporting time" is dedicated to analyzing the information and planning necessary actions for the next period.
Humatics, an MIT spinout, is developing an indoor radar system that should give robots and other industrial systems the ability to track people's movements very precisely. This could make industrial systems significantly safer, make it possible to track worker performance in greater detail, and lead to more effective new forms of collaboration between people and machines. The technology might improve the efficiency of an industrial manufacturing line because workers could grab something a robot has finished working on without fear of being injured. Meanwhile, inside many warehouses and fulfillment centers such as those operated by Amazon, robots are increasingly helping people move items around more efficiently (see "Inside Amazon's Warehouse: Human-Robot Symbiosis").
By modeling human testers, including manual and test automation tasks such as scripting, Appvance has developed algorithms and expert systems to take on those tasks, similar to how driverless vehicle software models what a human driver does. The Appvance AI technology learns from various existing data sources, including learning to map an application fully on its own, various server logs, Splunk or Sumo Logic production data, form input data, valid headers and requests, expected responses, changes in each build and others. The resulting test execution represented real user flows, data driven, with near 100% code coverage. Built from the ground up with DevOps, agile and cloud services in mind, Appvance offers true beginning-to-end data-driven functional, performance, compatibility, security and synthetic APM test automation and execution, enabling dev and QA teams to quickly identify issues in a fraction of the time of other test automation products.
Most importantly, AI in advertising will enhance unaided brand awareness across the search engine, taking the customer to brand's landing page based on purchase queries and clickable ads. AI can virtually replace the human factor in key stacks of marketing automation, including Email, Social Media, Data Management, Content Analytics, CRM, and Search. Based on the real-time recommendations to marketers, marketing automation tools can create customized content to drive personalized digital experiences for prospects and customers. Synchronized to customer journey, the similar video experience platforms based on intent data will significantly improve business results.
Summary: With only slight tongue in cheek about the road ahead we report on the just passed House of Representative's new "Federal Automated Vehicle Policy" as well as similar policy just emerging in Germany. Just today (9/6/17) the US House of Representatives released its 116 page "Federal Automated Vehicles Policy". Equally as interesting is that just two weeks ago the German federal government published its guidelines for Highly Automated Vehicles (HAV being the new name of choice for these vehicles). On the 6 point automation scale in which 0 is no automation and 5 is where the automated system can perform all driving tasks, under all conditions, the new policy applies to level 3 or higher (though the broad standards also apply to the partial automation in levels 1 and 2).
In this model, human employees augment and assist AI software leveraging its natural language processing (NLG), analytics, image recognition or other ML functionality to run business processes and make important decisions. Companies can also leverage the power of complex image recognition software to automatically assess the performance of employees in contexts where it can not be properly measured by human supervisors. AI software is in charge of important business decisions, planning and performance assessment in many on-demand mobility and delivery services that make up the so called gig economy. Deliveroo's algorithmic system carefully monitors a courier's performance calculating his/her average "time to accept orders", "travel time", and "unassigned orders".
In the supply chain, AI can analyze large data sets and recommend customer service and operations improvements while supporting better working capital management. As corporate systems become more interconnected, providing access to a wider breadth of supply chain data, the opportunity to leverage AI increases. Let's look at the potential benefits of using AI to link transportation data with order data: A logistics enterprise ensures the delivery of a product within two days. This information supplies customer service and supply chain professionals with proactive alerts of potential fulfillment challenges.