AI technologies like ChatGPT are necessitating a fundamental overhaul of our educational systems and institutions. Getting the right answers to predetermined tests is no longer sufficient in an age where AI can access, integrate, and recite knowledge billions if not trillions of times faster than the human mind. So, what are the skills, capabilities, and experiences that our students and citizens will need to prosper in an age where personal and professional success will be based on the application, not the memorization and regurgitation, of knowledge? Let's continue that conversation here in Part II to define the requirements for humans to excel in creating organizational and societal value in a world dominated by AI and Big Data. Many organizations engage in a "wear'em down" decision-making process when dealing with wicked hard challenges with multiple opposing views.

Many cancer patients undergo treatment with multiple drugs, each of which attacks cancer in a different way, so the combination fights cancer on many fronts. But more drugs mean higher risks of side effects. "Most cancer therapy is now a combination treatment," says Avinash (Avi) Sahu, Ph.D., assistant professor at The University of New Mexico Comprehensive Cancer Center. Sahu joined UNM from Harvard and Dana-Farber Cancer Institute. "We wanted to find drugs that could suppress two cancer-causing pathways at the same time."

TWO YEARS ago, when Elinor Lobel was 16, a "smart" insulin pump was attached to her body. Powered by artificial intelligence (AI), it tracks her glucose levels and administers the right dose of insulin at the right time to keep her healthy. It is a miraculous innovation for diabetes sufferers and just one of myriad new ways that data and ai can help improve lives. Your browser does not support the audio element. Books that decry the dark side of data abound.

The pandemic accelerated the use of AI-powered technologies and may have hastened the arrival of Industry 5.0, which places a strong emphasis on personalization. Big data and AI has the potential to transform education and are referred to as Education 4.0 AI, along with big data, blockchain, augmented reality, and the Internet of Things (IoT), has been credited with ushering in the Fourth Industrial Revolution, or Industry 4.0. The pandemic accelerated the use of AI-powered technologies and may have hastened the arrival of Industry 5.0, which places a strong emphasis on personalization. Big data and AI has the potential to transform education and are referred to as Education 4.0. The Indian EdTech market has expanded significantly and is expected to reach $10.4 billion by 2025.

Developers today have a fantastic assortment of tools and technology available to them, which they use to construct the digital world around us. However, the sheer number of choices in the DevOps and CICD toolchains introduces a vast amount of complexity, which leads to multiple inefficiencies. Now a new discipline called developer productivity engineering (DPE) is emerging to tackle this problem, and advanced analytics and AI play big roles. While the advent of DevOps and continuous integration, continuous deployment (CICD) methods has made developers lives simpler in many respects, they have also unleashed new forces that hinder developer productivity, according to Hans Dockter, the CEO of Gradle, the for-profit company behind the leading open source build tool of the same name. For starters, testing is critical to ensure software is bug free and doesn't pose a security risk.

As my colleagues and I attend 3 major data and technology industry events in New York City this coming week – ML Ops, Finovate, and the granddaddy of them all, Strata Data Conference – it is interesting to note how far we have come in a few short decades. It may be hard to imagine today, but there was a time not too very long ago when data analysts, with a few notable exceptions, were relegated to the hidden recesses of most corporations. Better to toil away in the bowels than to be shown the light of day. For many decades, even as information technology (IT) emerged as a critical business function, data was viewed more as something that firms filed away in vaults for the mandatory seven years to comply with regulators, and not a business asset that could be mined to unlock critical business insights. Data was perceived as the purview of those who were sometimes derisively referred to as data geeks or "propeller heads". This was long before Silicon Valley, or Wall Street, embraced the term "geek".

Today's world is full of sensors, and the higher your nation-state is on the advanced-industrial food chain, the more likely it is that you carry a sensor on your person every minute of every day (and for many, even while asleep). That matters: the data collected by these sensors can be stored, analyzed, and weaponized. And although most of today's data collectors are for-profit corporations, there are dire risks alongside the potential for breakthroughs in areas such as medicine and global warming. The collection, analysis, storage, and theft of information about you have lethal implications; both for you as an individual and for all of us in terms of interstate war. In his 2018 book AI Superpowers, author and entrepreneur Kai-Fu Lee likened big data to the new crude oil and noted that insofar as the analogy holds, that would make the People's Republic of China (PRC) the world's data Saudi Arabia.

Big Data and AI are, perhaps, the most important business technologies of the century, and they are intrinsically related. Every year, the use of AI algorithms and information sets grows and improves. Because of this, businesses become faster and more effective, and the public gets what they want quicker and more often. But what is the state of AI and Big Data, right now? In this article, we take a snapshot look at the world of information processing as it stands in the present.

Area under the ROC curve, a.k.a. AUC, is a measure of choice for assessing the performance of a classifier for imbalanced data. AUC maximization refers to a learning paradigm that learns a predictive model by directly maximizing its AUC score. It has been studied for more than two decades dating back to late 90s and a huge amount of work has been devoted to AUC maximization since then. Recently, stochastic AUC maximization for big data and deep AUC maximization for deep learning have received increasing attention and yielded dramatic impact for solving real-world problems. However, to the best our knowledge there is no comprehensive survey of related works for AUC maximization. This paper aims to address the gap by reviewing the literature in the past two decades. We not only give a holistic view of the literature but also present detailed explanations and comparisons of different papers from formulations to algorithms and theoretical guarantees. We also identify and discuss remaining and emerging issues for deep AUC maximization, and provide suggestions on topics for future work.

A year ago, the economic situation in LatAm seemed dire. But in the second half of 2021 and well into 2022, the business and economic outlook for Latin America has not only bounced back, but in many areas is surging forward. The picture, of course, is uneven. Exciting new technologies are being adapted swiftly, while sharp, sudden increases in some consumer prices reflect now-familiar supply chain constraints. Meanwhile, the region's rich natural resources and the upside ripple effects of high energy prices and commodity exports – thanks to some nearshoring efforts – are enabling continuity and growth, offsetting some of the inflationary pressures we are seeing elsewhere.