Alan Gibson, VP EMEA at Alteryx explains that these technologies have real business-altering potential and that's why Gartner's Enter the Age of Analytics report predicts that by 2023, AI and deep-learning techniques will be the two most common approaches for new applications of data science. But despite the promise, few companies have been able to successfully implement and deploy this technology as part of their overall data and analytics strategy--according to Gartner, 46 percent of CIOs have developed plans to deploy AI but just 4 percent have made the concept a reality. The truth is that it will take years before many organisations realise the true potential of AI and ML, but it is never too early to lay the groundwork now for an AI-driven future. In fact, if an organisation is not already thinking about what an AI strategy looks like, its competition is likely one step ahead. There's no time to waste, so here are five important points to consider when getting started with AI and ML.

Recognizing the pervasivetalent gapthat exists between data scientists and data workers in the line of business, Assisted Modeling helps teach data science with a guided walk-through and aims to help all data workers, regardless of technical acumen, advance their skill sets in the process of building machine learning models. Our approach in building Assisted Modeling is to advance the skills of the data worker, creating next-level citizen data scientists capable of building the machine learning models required to tackle the advanced analytic challenges of the future. Assisted Modeling provides users the transparency and control needed to build trustworthy machine learning models that drive business outcomes without writing a line of code. As an output of the application, users can access code-free machine learning tools directly within the Alteryx Designer interface. Assisted Modeling allows any data worker to construct machine learning models, understand how and why their models work, and capture modeling decisions, turning raw data into informed business decisions with unprecedented speed and confidence.

In Australia, the Scientists-in-Schools program partners professional scientists with teachers from K-12 schools to improve early engagement and educational outcomes in the sciences and mathematics.  An overview of the developing syllabus of a K-6 course resulting from the pairing of a senior AI researcher with teachers from a K-6 (primary) school is presented. Now entering its third year, the course introduces the basic concepts, vocabulary and history of science generally and AI specifically in a manner that emphasises student engagement and provides a challenging but age appropriate syllabus. Reflecting on the course at this time provides an action research basis for ongoing maturation of the syllabus, and the paper is presented in that light.