The goal of newly-formed AI teams is to build intelligent systems, focused on quite specific tasks, that can be integrated into the scalable data transformations of Data Engineering work and the data products and business decisions of Data Science work. The differences between Artificial Intelligence, Data Science, and Data Engineering can vary considerably among companies and teams. Artificial Intelligence, or AI, focuses on understanding core human abilities such as vision, speech, language, decision making, and other complex tasks, and designing machines and software to emulate these processes. These models typically require very large datasets, so while efficient manipulation and use of large amounts of data is a fundamental aspect of Data Engineering work, it is crucial for state-of-the-art AI systems.
The advent of IoT technologies--and the more general move to digital tools that support operations, communication, analysis, and decision making in every part of the modern organization--won't change the fundamental purpose of production systems. With the introduction of comprehensive, real-time data collection and analysis, production systems can become dramatically more responsive. Highly integrated, digitally enabled production systems won't just work differently from today's--they'll be built differently, too. Automated optimization systems will adjust manufacturing sequences and speeds to help balance lines and match production more closely to customer demand.
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.
In my previous blog post "How I stopped worrying and embraced docker microservices" I talked about why Microservices are the bees knees for scaling Machine Learning in production. If only there was a tool that made this decision easy and allowed you to even go to the extreme case of writing a monolith, without sacrificing either HTTP performance (and pretty HTTP server semantics) or ML performance and relevance in the rapid growing Deep Learning market. WebTorch is the freak child of the fastest, most stable HTTP server, nginx and the fastest, most relevant Deep Learning framework Torch. Now of course that doesn't mean WebTorch is either the best performance HTTP server and/or the best performing Deep Learning framework, but it's at least worth a look right?
Read next: Splunk brings machine learning capabilities into its tools and launches toolkit for customer's own algorithms NRAI is built into New Relic's SaaS application performance monitoring (APM) platform and driven by the trillions of event data points that New Relic processes from its customers' critical systems every day, all underpinned by the cloud. New Relic launched three new NRAI features at FutureStack: Radar, NRQL Baseline Alerting, and New Relic APM Error Profiles. Each individual Radar user receives personalised recommendations based on their individual requirements. The company has extended this to New Relic Query Language (NRQL) Baseline Reporting, which lets users receive alerts based on any query written in NRQL.
In view of falling oil prices and the resulting squeeze on cash flows, the oil and gas industry has been challenged to adapt and optimize its performance to remain profitable while maintaining a long-term investment and operating outlook. Additionally, geoscientists can better assess variables such as the rate of penetration (ROP) improvement, well integrity, operational troubleshooting, drilling equipment condition recognition, real-time drilling risk recognition, and operational decision-making. AI can help to create tools that allow asset teams to build professional understanding and identify opportunities to improve operational performance. By using AI software to analyze the company's large collection of historical well performance data, the company is drilling in better locations and has seen production rise 30% over conventional methods.
The Fourth Industrial revolution brings us all kinds of great innovation, with huge impact on everyday life. Already you see that the combination of increased computing strength and artificial intelligence, brings changes to things we do every day. Business models change due to the technological innovations who have a key focus on amplifying yourself with the help of Artificial Intelligence, which is something Tom Goodwin nicely pointed out in his article. Uber uses data analytics and machine learning to determine where customers are, will be, and want to go, and then when the customer is in the car, it uses (and shows) Google Maps to determine what the best route is.
But imagine if, instead, a computer could have stepped in, watched all of Walker's performances in the previous Furious films, learning the minute details of how he walked, talked and even raised an eyebrow. And then imagine that artificial intelligence took over and itself helped to create a digital performance for Walker's character. Potential AI-driven applications -- in which the machine takes over and can learn and think for itself -- could function as script supervisors, take a first pass at film editing, even create performances either for digital characters that resemble actual humans or more fantastic CG creatures. But, theoretically, AI could do the job of creating a digital "Paul Walker" faster and more economically than current methods.
For example, for personalized recommendations, we have been working with learning to rank methods that learn individual rankings over item sets. Figure 1: Typical data science workflow, starting with raw data that is turned into features and fed into learning algorithms, resulting in a model that is applied on future data. This means that this pipeline is iterated and improved many times, trying out different features, different forms of preprocessing, different learning methods, or maybe even going back to the source and trying to add more data sources. Probably the main difference between production systems and data science systems is that production systems are real-time systems that are continuously running.
While AI and machine learning are normally used for number crunching applications, more complex analytical projects such as hiring were assumed to be a task requiring human reasoning. This requires a commitment to evaluating the full extent of what machine learning can do in their organization, finding agreement on a machine learning strategy among all top executives and bringing in external experts to advise the company on executing that strategy. The second type has a breadth of knowledge on how to communicate the potential of machine learning, converting results into insights and visualizations that make sense to managers on the front lines. In the end, as the WEF's Fourth Industrial Revolution analysis would suggest, human intelligence and machine learning will merge to create something we don't yet have a name for.