Computers have become adept at extracting patterns from very large collections of data. For example, shopping transactions can reveal consumers' preferences and message traffic on social networks can reveal political trends.
Automation has been used for decades in a wide range of industries to boost efficiency and productivity, reduce waste and ensure quality and safety. Emerging technologies such as Artificial Intelligence (AI), Natural Language Processing (NLP) and big data analytics are now being combined with automation, to deal with more complex problems and bring further improvements to business processes. This convergence of automation and intelligence is known as hyper automation. Also known as cognitive or smart automation, hyper automation is at the forefront of the 4th Industrial Revolution and is gradually making its way into every aspect of business, delivering unprecedented results. There are a number of factors driving the adoption of hyper automation among enterprises, including the ability to improve operational and service performance.
Talend has released the latest update to its Talend Data Fabric platform is adding several new features, including AI/ML, to more quickly reveal latent intelligence held inside dispersed enterprise data. The Talend Winter '20 release delivers trusted data quickly, reliably and at first sight for faster business outcomes, according to Talend execs. "The innovations introduced in Talend Data Fabric will provide our customers with dramatically improved efficiency, optimized productivity and scale, and accelerated path to revealing value from data," said Talend's Ciaran Dynes senior vice president products in a statement. Here's a list of notable features in Talend's Winter '20 release, and how they deliver value. Data Inventory: This new cloud-based app automatically inventories and quality checks data to reveal trusted data quickly and easily.
The insurance industry is way past its time when timely response and a balanced price-quality relationship were enough to define customer experience. The advent of Artificial Intelligence, Machine Learning, and Advanced Analytics have disrupted the insurance industry and have reshaped the way it operates. Insurtech firms these days are using their AI and ML capabilities to drive high-quality customer experiences, increased loyalty, generate new revenue while simultaneously reducing the costs. The vision of the insurance firms today and for the future is where customers and customer experience comes first. The combination of AI and ML models built on top of the Customer Data Platform leads to improved customer experience through hyper-personalization.
This course focuses on how to use KNIME Analytics Platform for in-database processing and writing/loading data into a database. Get an introduction to the Apache Hadoop ecosystem and learn how to write/load data into your big data cluster running on premise or in the cloud on Amazon EMR, Azure HDInsight, Databricks Runtime or Google Dataproc.. Learn about the KNIME Spark Executor, preprocessing with Spark, machine learning with Spark, and how to export data back into KNIME/your big data cluster. This course lets you put everything you've learnt into practice in a hands-on session based on the use case: Eliminating missing values by predicting their values based on other attributes. This course consists of four, 75-minutes online sessions run by one of our KNIME data scientists. Each session has an exercise for you to complete at home and together, we will go through the solution at the start of the following session.
A major marketing firm has turned to IBM Watson Studio, and its data, to create an interactive platform that predicts the risk, readiness and recovery periods for counties hit by the coronavirus. Global digital marketing firm Wunderman Thompson launched its Risk, Readiness and Recovery map, an interactive platform that helps enterprises and governments make market-level decisions, amid the coronavirus pandemic. The platform, released May 21, uses Wunderman Thompson's data, as well as machine learning technology from IBM Watson, to predict state and local government COVID-19 preparedness and estimated economic recovery timetables for businesses and governments. The idea for the Risk, Readiness and Recovery map, a free version of which is available on Wunderman Thompson's website, originated two months ago as the global pandemic accelerated, said Adam Woods, CTO at Wunderman Thompson Data. "We were looking at some of the visualizations that were coming in around COVID-19, and we were inspired to really say, let's look at the insight that we have and see if that can make a difference," Woods said.
Capstone (3 Credits): A semester-long group project in which teams of students propose and select project ideas, conduct and communicate their work, receive and provide feedback (in informal group discussions and formal class presentations), and deliver compelling presentations along with a web-based final deliverable. Includes relevant readings, case discussions, and real-world examples and perspectives from panel discussions with leading data science experts and industry practitioners.
B2B software sales and marketing teams love hearing the term "artificial intelligence" (AI). AI has a smoke and mirrors effect. But, when we say "AI is doing this," our buyers often know so little about AI that they don't ask the hard questions. In industries like the DevTools space, it is crucial that buyers understand both what products do and what their limitations are to ensure that these products meet their needs. If the purpose of AI is to make good decisions for humans, to accept that "AI is doing this" is to accept that we don't really know how the product works or if it is making good decisions for us.
Last Tuesday, Google shared a blog post highlighting the perspectives of three women of color employees on fairness and machine learning. I suppose the comms team saw trouble coming: The next day NBC News broke the news that diversity initiatives at Google are being scrapped over concern about conservative backlash, according to eight current and former employees speaking on condition of anonymity. The news led members of the House Tech Accountability Caucus to send a letter to CEO Sundar Pichai on Monday. Citing Google's role as a leader in the U.S. tech community, the group of 10 Democrats questioned why, despite corporate commitments over years, Google diversity still lags behind the diversity of the population of the United States. The 10-member caucus specifically questioned whether Google employees working with AI receive additional bias training.
Machine learning might be high on the agenda for the data science team at Coles, but according to Richard Glew, Coles head of engineering and operations, they are currently limited by the existing on-premise environment. "Even if we can do something, being able to do something quickly is another matter. We've got a lot of issues [like] where is our data, do we have the right hardware, how long does it take to get it … all the usual stuff with an on-prem environment," he said, speaking as part of the Databricks Data and AI APAC virtual conference. In a move to expand the possibility of enabling machine learning, advanced analytics, and data exchange, the company is currently developing an electronic data processing platform (EDP) to change the way it manages and stores data. "Our EDP platform is designed to be a universal data repository for all the data we want to share internally or externally as an organisation, and we fully catalogue that," Glew said.