When Sodexo, a company that operates over 400 university dining programs, was looking for a futuristic, seamless experience to provide students in place of the usual buffet meal options, it wasn't necessarily thinking of AI and computer vision. The only thing the corporation knew was that they wanted to build shop-and-go platforms, a.k.a shops with no cashiers, similar to Amazon Go. That is a store where customers may stroll in, choose products off the shelves, and leave without waiting in line at the register or swiping a code at a self-checkout. "Students today want things they can partially or fully prepare in their room or apartment, with organic, highly-local options. We also wanted to remove friction, but many solutions still require the interaction of the guest with a cashier – this generation really doesn't want to talk to a lot of people in their service interactions," said Kevin Rettle, global vice president of product development and digital innovation at Sodexo.

Could artificial intelligence (AI) enhance the ability of clinicians to detect prediabetes and diabetes? Findings from a recently published study show that adjunctive use of DiaBeats, a machine learning-based algorithm, with electrocardiogram (ECG) recordings may provide an alternative noninvasive imaging approach to screening for prediabetes and diabetes. "In theory, our study provides a relatively inexpensive, non-invasive, and accurate alternative (to current diagnostic methods) which can be used as a gatekeeper to effectively detect diabetes and prediabetes early in its course," wrote study co-author Anoop R. Kulkarni, Ph.D., who is affiliated with Innotomy Consulting in Bengaluru, India, anbd colleagues. "Nevertheless, adoption of this algorithm into routine practice will need robust validation on external, independent data sets." As the intrinsic risk of prediabetes has become increasingly recognized by the medical community, identification of low-cost, noninvasive methods for diagnosis or risk stratification for prediabetes and future diabetes has become paramount.

Without a question, artificial intelligence (AI) is a popular issue right now.

To recap, XGBoost stands for Extreme Gradient Boosting and is a supervised learning algorithm that falls under the gradient-boosted decision tree (GBDT) family of machine learning algorithms. They make their predictions based on combining a set of weaker models and evaluate other decision trees through if-then-else true/false feature questions. They are created in sequential form to assess and estimate the probability of producing a correct decision. Before we get into the tuning of XGBoost hyperparamters, let's understand why tuning is important Hyperparameter tuning is a vital part of improving the overall behavior and performance of a machine learning model. It is a type of parameter that is set before the learning process and happens outside of the model.

"AI for Ukraine" is a series of workshops and lectures held by international artificial intelligence experts to support the development of Ukraine's tech community during the war. Montreal), Alex J. Smola (Amazon Web), Sebastian Bubeck (Microsoft), Gaël Varoquaux (INRIA), and many other well-known specialists have joined the initiative. This is a non-commercial educational project by AI HOUSE – a company focused on building the AI/ML community in Ukraine and is part of the Roosh tech ecosystem. All proceeds collected upon registration will be donated to the biggest Ukrainian charity fund "Come Back Alive". It's been five months of a completely new reality for every single Ukrainian, one with sirens, bombings, pain, and war.

Over the last four months, I have proactively worked with and around some of the most competent people in their respective fields of tech in Europe, and while it is very flattering for them to assume that I understand everything, it's not true. With that in mind, I decided to start a weekly article marathon WDIC (Why Do I Care?) -- a series of articles describing all the possible buzzwords and fields of science and tech that I hear about often but have no idea about what they really mean. And I'd want to start with the one that rules them all -- AI or Artificial intelligence. Artificial intelligence, or AI for short, is a term that people use to describe machines that can think like humans. To be precise, they cannot "think" like humans (that's AGI, and we'll talk about it later on), but they can emulate human thinking really well, especially the latest technologies.

The University of the Philippines recently opened a doctorate degree in the National School of Engineering for Artificial Intelligence at their Diliman campus. According to the university's official press release, they created the course in hopes of "developing graduates who had the mindset to expand the field of artificial intelligence". We have the command bot Alexa, our iPhones have Siri, and some laptops have Cortana. There's even the new Google Home that can control our air-conditioner and lights! All those names we just mentioned are forms of Artificial Intelligence. It takes a lot of programming to create these bots.

Avoid data leaking from the test set into the training set. “How to Avoid Data Leakage in Data Preprocessing” is published by Rukshan Pramoditha.

What might unfold in the future for AI.

IT leaders seeking to derive business value from the data their companies collect face myriad challenges. Perhaps the least understood is the lost opportunity of not making good on data that is created, and often stored, but seldom otherwise interacted with. This so-called "dark data," named after the dark matter of physics, is information routinely collected in the course of doing business: It's generated by employees, customers, and business processes. It's generated as log files by machines, applications, and security systems. It's documents that must be saved for compliance purposes, and sensitive data that should never be saved, but still is.