These investments are paying off, according to fraud prevention specialists, as 80 per cent of experts say AI reduces payments fraud and 63.6 per cent of financial institutions cite AI as a valuable tool for halting fraud before it succeeds. These systems are commonplace at large banks that have more than $100 billion in assets -- 72.7 per cent of which leverage AI -- but only 5.5 per cent of all financial institutions reportedly have an AI-based system in place.

We know full well that AI is one of those acronyms that gets chucked around so often that it begins to lose some of its wonders. But that belies its power and significance because when it comes to the value added to video marketing by both artificial intelligence and its subset machine learning (ML), it really is worth your undivided attention. "Marketers, through AI technologies, have been both challenged and empowered," sums up Rob Freedman, VP of marketing at Fourlane. "We have greater access to data and can custom tailor our video content to fit the exact demographic of our ideal customer persona. This is an extremely powerful way to get our message in front of the right people, at the ideal time."

TensorFlow Serving is an easy-to-deploy, flexible and high performing serving system for machine learning models built for production environments. It allows easy deployment of algorithms and experiments while allowing developers to keep the same server architecture and APIs. TensorFlow Serving provides seamless integration with TensorFlow models, and can also be easily extended to other models and data. Open-source platform Cortex makes execution of real-time inference at scale seamless. It is designed to deploy trained machine learning models directly as a web service in production.

I've just read this article about how a revolution in sales is going to take place with Artificial Intelligence (AI). Is this really the case or is the author selling AI? In this 2 minute video I explain what social selling is about, it's about being human on social. Let's imagine, tomorrow I pick you up and drive you to a place where all your prospects and customers hang out. All you have to do is have conversations with them and be human.

Society is entering the age of artificial intelligence.

It seems that we have two very different problems here: There is the PAC-learning problem from theoretical computer science discussing whether or not machines can learn certain functions. And there is the Continuum Problem asking whether there are infinite sets of a certain size. What does this have to do with each other? In 2019, a group of researchers, Ben-David et al., published an article entitled "Learnability can be undecidable" in Nature Machine Intelligence: We describe simple scenarios where learnability cannot be proved nor refuted using the standard axioms of mathematics. Our proof is based on the fact the continuum hypothesis cannot be proved nor refuted.

The world today is thriving on artificial intelligence and the branch technologies associated with it. It is a truth universally acknowledged that the survival of business organizations is heavily contingent on technological advancements induced by AI integration. One such platform is Automation Anywhere that leverages AI and RPA to accelerate and empower business conductions. Automation Anywhere is a reputed global leader in robotic process automation that specializes in offering cloud-native, web-based intelligent automation solutions to empower business operations for companies. Founded in 2003, Automation Anywhere holds a strong legacy of setting benchmarks by AI and RPA adoption.

If the potential and possibility of artificial intelligence has always fascinated you, get ready for the perfect bundle to fill the next few weeks with! Humble Bundle teamed up with Morgan & Claypool to bring you insights into AI and its applications into autonomous vehicles, conversational systems, and more! Pick up this bundle and you'll enjoy discovering eBooks like Why AI/Data Science Projects Fail: How to Avoid Project Pitfalls, Deep Learning Systems: Algorithms, Compilers, and Processors for Large-Scale Production, and Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots. Your purchase of this bundle helps support a charity of your choice. This bundle launched on June 14 at 11:00 am PST and lasts through July 05, 2021.

In the last decade, advances in data science and engineering have made possible the development of various data products across industry. Problems that not so long ago were treated as very difficult for machines to tackle are now solved (to some extent) and available at large scale capacities. These include many perceptual-like tasks in computer vision, speech recognition, and natural language processing (NLP). Nowadays, we can contract large-scale deep learning-based vision systems that can recognize and verify faces on images and videos. In the same way, we can take advantage of large-scaled language models to build conversational bots, analyze large bodies of text to find common patterns, or use translation systems that can work on nearly any modern language.

The authors of this blog are Stan Zwinkels & Ted de Vries Lentsch. This blog aims to present our attempt to create a detection algorithm for detecting ripe flowers of the Alstroemeria genus Morado. Throughout this blog, we explain our process to create a dataset and detection model that achieves an F1 score of more than 0.75. This blog is part of the course Seminar Computer Vision By Deep Learning (CS4245) 2021 from the Delft University of Technology. Creating the dataset has been carried out in collaboration with the company Hoogenboom Alstroemeria.