The validation of a data-driven model is the process of asses sing the model's ability to generalize to new, unseen data in the population o f interest. This paper proposes a set of general rules for model validation. T hese rules are designed to help practitioners create reliable validation plans and report their results transparently. While no validation scheme is flawle ss, these rules can help practitioners ensure their strategy is sufficient for pr actical use, openly discuss any limitations of their validation strategy, and r eport clear, comparable performance metrics. Keywords: Validation, Cross-validation 1. Introduction Model validation is a fundamental task in all modern data-dr iven systems, whether they fall under the broad categories of Statistics, Machine Learning (ML), Artificial Intelligence (AI), or more specialized fiel ds like chemometrics. Validation has become a major focus for regulatory and stand ardization bodies, with key reports and standards highlighting the growing con cern for ensuring the trustworthiness and reliability of data-driven models: NIST AI Risk Management Framework (AI RMF 1.0, 2023): Publi shed by the U.S. Department of Commerce, this framework provides management techniques to address the risks and ensure the trustwor thiness of AI systems, with validation as a core component. The EU AI Act of 2024, landmark piece of EU legislation that c ategorizes AI systems by risk level, where validation is not defined as a b est practice but a legal requirement within the conformity assessment. The ISO/IEC TS 4213:2022, by the International Organizati on for Standardization (ISO), describes approaches and methods to ens ure the rele-Email address: josecamacho@ugr.es The IEEE P2841 -2022 is a recommended practice for the fram ework and process for deep learning evaluation.

Technology can be a topic difficult to understand and make predictions on, even for those with a strong technical background and perceived expertise. From Ethernet's creator Robert Metcalfe's 1995 prediction that the internet would "catastrophically collapse" by the next year to Intel's prediction that 3D TV was the future, it is clear that predicting tech trends is a difficult endeavor. No matter how hard predicting the future of technology is, every new technology that creates disruption will go through this cycle. Most recently, we have gone through multiple hype cycles with innovations like blockchain, cryptocurrency, the metaverse, VR, and now, AI. Every single of these technologies has captivated not only the public but also developers and investors, blurring the line between facts and fiction.

As AI models continue to advance into many real-life applications, their ability to maintain reliable quality over time becomes increasingly important. The principal challenge in this task stems from the very nature of current machine learning models, dependent on the data as it was at the time of training. In this study, we present the first analysis of AI “aging”: the complex, multifaceted phenomenon of AI model quality degradation as more time passes since the last model training cycle. Using datasets from four different industries (healthcare operations, transportation, finance, and weather) and four standard machine learning models, we identify and describe the main temporal degradation patterns. We also demonstrate the principal differences between temporal model degradation and related concepts that have been explored previously, such as data concept drift and continuous learning. Finally, we indicate potential causes of temporal degradation, and suggest approaches to detecting aging and reducing its impact.

We all might agree that we have entered the golden age of artificial intelligence, however, no AI or machine learning project is easy to implement and comes without challenges. One of the core problems is when students wish to make a career in the tech space, they often face difficulties in learning data structure and algorithms. Without having proper knowledge of data structure and algorithms, a programmer isn't efficient enough to write the right code for its software. Moreover, it's not just from the application point of view, but data structures and algorithms are often used to test candidates in a job interview. Interviews, in general, give data structure and algorithms problems to solve to test the candidate's problem-solving and analytical skills.

Artificial intelligence is here for a long time in many forms and ways. In recent years significant progress has been made in some areas of AI. This doesn't mean that AI, in general, is evolving as fast, just those fields. And some of them are increasingly used for different domains of digital transformation. Instead of talking about artificial intelligence (AI), some describe the current wave of AI innovation and acceleration with – admittedly somewhat differently positioned – terms and concepts such as cognitive computing. Others focus on several real-life applications of artificial intelligence that often start with words such as "smart" (omnipresent in anything related to the Internet of Things and AI), "intelligent," "predictive" and, indeed, "cognitive," depending on the exact application – and vendor.

The 4 Most Common Probability Distributions Used in Data Science. Probability distributions are one of the most used concepts of maths that are used in various real-life applications. From weather prediction to the stock market to machine learning applications, different probability distributions are the basic building blocks of all these applications and more. Probability distributions are one of the most used concepts of maths that are used in various real-life applications. From weather prediction to the stock market to machine learning applications, different probability distributions are the basic building blocks of all these applications and more.

Andrew Ng has called it the "new electricity" while Google CEO Sundar Pichai even went beyond that: "artificial intelligence is going to have a bigger impact on the world than some of the most ubiquitous innovations in history", like electricity (sorry Andrew…) and even fire. So, I feel pretty confident to state that AI will lie at the basis of the (re-)development of business, and even society in general. If the world wide web built the substructure of the age of networks and disruption, then AI will drive the same type of revolution in the cognitive age. Google, Apple, Facebook, Amazon, Alibaba, IBM: everyone is racing to get a piece of that very delectable and rich pie. But who will win the race?

Encouraged by media portrayals of AI, a widespread myth is that AI simply learns by itself. For example, a common misconception represents AI as a digital brain that can be plugged and played into a given scenario, learning to solve X, Y, Z challenges on its own. Such representations are based on fiction, not fact. While AI is a robotic brain that can learn, it learns in a different way than a human brain. AI uses mathematics and pre-classified data to learn. Crucially, AI needs a human brain to guide it through the learning process by pre-classifying data into categories that it can examine and categorize.

Encouraged by media portrayals of AI, a widespread myth is that AI simply learns by itself. For example, a common misconception represents AI as a digital brain that can be plugged and played into a given scenario, learning to solve X, Y, Z challenges on its own. Such representations are based on fiction, not fact. While AI is a robotic brain that can learn, it learns in a different way than a human brain. AI uses mathematics and pre-classified data to learn.

Amazon is every online retailer's forbidding nightmare. Last year, it dominated 44 percent of the US eCommerce market and about 4 percent of all domestic retail sales. One Click Retail, an eCommerce analysis provider, explains its dominance with the fact that millennials, Amazon's core demographic, are getting older and starting to spend more. Moreover, advanced marketing capabilities for sellers, developments in Alexa, and pioneering in applications of the hottest technologies make it impossibly hard for smaller competitors to actually… well, compete. Amazon is not only a simple and familiar platform selling everything you can think of – it's also one of the most innovative players on the market.