As your self-driving car navigates this exhilarating course, decelerating for twists and turns, the stakes are high. That's because on this test track, no variable in any given driving scenario is left unturned, and every variable is repeatably and reliably measured. The mission is to train your car's autonomous driving algorithm so that it makes the optimal decision every time, with no catastrophic errors in the process. This sounds like an impossible feat in the real world, where virtually every variable surrounding a self-driving car is unpredictable. Moreover, accidents during test runs are bound to happen.

Founded in 2002, Quantium combines the best of human and artificial intelligence to power possibilities for individuals, organisations and society. Our solutions make sense of what has happened and what will, could or should be done to re-shape industries and societies around the needs of the people they serve. As one of the world's fully diversified data science and AI leaders we operate across every sector of the economy and we're growing fast - with growth comes opportunity! We're passionate about building out our team of smart, fun, diverse and motivated people. We combine a team of experts that spans data scientists, actuaries, statisticians, business analysts, strategy consultants, engineers, technologists, programmers, product developers, and futurists – all dedicated to harnessing the power of data to drive transformational outcomes for our clients.

A year after Mage's creation in early 2021, we have officially launched into general availability. Mage is built on the mission to equip product developers with accessible AI technology so they can build magical products for their users. After working closely with early paying customers, we are confident in Mage's ability to deliver a user-friendly, intuitive, and easy-to-use tool. At its core, Mage's product is a solution for developers to integrate AI into their apps by building ranking models that increase user engagement and retention. Mage works by first connecting existing data sources into a Mage workspace.

Mage, an archaic term for a magician or someone who makes magic, is now also the name of a Silicon Valley startup that's demonstrating some magic of its own. The Santa Clara, California-based company today released to general availability its prize low-code tool for product developers to build AI ranking models. Year-old Mage has been in private beta for the last 12 months working closely with early paying customers to make its tool user-friendly, intuitive, and simple to use, the company said. After working with hundreds of product developers at Airbnb, CEO and cofounder Tommy Dang saw that those developers knew how AI could be used to improve their product, but that they also had to rely on data science resources to help implement their ideas. Data scientists do not come inexpensively anywhere in the world.

When working with AI, it's important to know how to import data sets, read through tables, and understand what the structure is. Welcome to the "Product developer's guide to getting started with AI". In this series, we'll go over key concepts and run through examples using Pandas. First, we will cover setting up your development environment and learning how to inspect your data. Then, you'll be ready to tackle the more exciting parts of AI throughout this series.

Artificial intelligence (AI) and machine-learning (ML) have quickly grown beyond a few major tech companies and hardcore academic researchers. Every marketing organization can tap into the power of AI to streamline operations and grow the business. The new book The AI Marketing Canvas: A Five-Stage Road Map to Implementing Artificial Intelligence in Marketing provides a growth framework for business and marketing leaders to implement AI using a five-stage model called the "AI Marketing Canvas." On this episode of Marketing Smarts, I speak with co-author Rajkumar Venkatesan about how he and his co-writer developed those stages by studying leading global brands. We cover examples of brands―including Google, Lyft and Coca-Cola―that have successfully woven AI into their marketing strategies. This is not a conversation about coding for AI models. Raj and I talk about how marketing leaders can go from "zero to hero" with AI in marketing, and what that means for your team and your company culture. Listen to the entire show now from the link above, or download the mp3 and listen at your convenience.

Digital disruption is affecting nearly every industry, from financial services to healthcare -- and the food and beverage sector is no exception. Historically, flavor profiles, trends and new food products have largely been attributed to chefs and product developers, and it would take months or years before an idea could be translated into a product and introduced to the market. In more recent years, however, the answer to the next big food or flavor trend has had less to do with humans and more with the power of big data and artificial intelligence (AI), which learns and mimics human behavior by collecting and analyzing millions of data sets concurrently. So how does harnessing technology translate into the next flavor or trend? As an example, spice company McCormick partnered with IBM in 2019 to leverage AI to predict new flavor combinations.

What do Tuscan Chicken, Bourbon Pork Tenderloin and New Orleans Sausage all have in common? They're all new spice mix flavours that have been developed by the world's biggest spice firm using artificial intelligence (AI). But with taste such a subjective experience, can machines really do the job better than humans? And what does this mean for cultures that see spice as a clear token of identity? Spice giant McCormick, which sells spices to consumers but also develops flavours for the food industry, says it spent four years crunching through more than 40 years of flavour-related data, using machine learning to come up with new flavour combinations that human scientists might not have considered. After all, would you have thought of trying cumin on pizza?

Artificial intelligence (AI) has a perception problem, as many people think of the technology primarily as a job killer. However, collaboration between humans and AI opens the opportunity of putting the design and manufacturing of goods of all kinds on a new, better foundation by curating intelligence. That's why we should rethink our expectations for machine intelligence and how it will affect our future. The role of a human as the most intelligent creature on earth may not last much longer. Technologies like artificial intelligence and machine learning are taking on operations that could previously only be conducted with human intelligence – and in some cases they're doing even better than we do.

Artificial Intelligence is quickly becoming ubiquitous in personal and professional lives in ways we both observe and others we don't see as readily. Artificial Intelligence is used to influence life-changing decisions, such as whether or not you get hired to that dream job, who you will date, and whether or not you'll be approved for a loan for your first home. However, we have little insight into how critical decisions are made with AI. As a result, there is increasing demand (and legislation) to ensure the influence of these technologies is understood. What is it we seek when we ask for explainability in AI, as in the GDPR's Article 22? Explainable by whom and to whom?