In recent years, between lockdowns, curfews, supply chain disruptions, and energy crunches, retailers must have felt like dinosaurs trying to dodge a rain of asteroids and avoid extinction. But unlike those giant prehistoric reptiles, the retail industry could count on a full array of technological innovations to better meet the challenges of these difficult times. One of the most impactful tools in this arsenal has certainly turned out to be artificial intelligence, including its powerful sub-branch known as machine learning (ML). Let's briefly frame the nature of this technology and explore the key use cases of machine learning in retail. Machine learning in retail relies on self-improving computer algorithms created to process data, spot recurring patterns and anomalies among variables, and autonomously learn how such relations affect or determine the industry's trends, phenomena, and business scenarios.

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Artificial intelligence (AI) technology has become a critical disruptor in almost every industry and banking is no exception. The introduction of AI in banking apps and services has made the sector more customer-centric and technologically relevant. AI-based systems can help banks reduce costs by increasing productivity and making decisions based on information unfathomable to a human agent. Also, intelligent algorithms are able to spot anomalies and fraudulent information in a matter of seconds. A report by Business Insider suggests that nearly 80% of banks are aware of the potential benefits that AI presents to their sector. Another report suggests that by 2023, banks are projected to save $447 billion by using AI apps.

Majority of the traditional, conventional software development environment follows the usual phases which include analyze, plan, design, build, quality assurance, and deployment. The environment of development of artificial intelligence, however, works differently.

The healthcare sector is no different--particularly in the wake of the global pandemic, during which rapid and remote healthcare practices have had to take shape almost overnight. Healthcare software development services and data science solutions have become an integral part of the industry today. In fact, data science in healthcare represents arguably one of the most critical and long-overdue sector revolutions of modern times. With data science, healthcare institutions can harness analytics to bring about faster and far more accurate diagnoses while providing treatments that carry a higher efficacy and lower risk to patients' health. And with over a billion clinical documents being produced every year in the US alone, there's a deep mine of healthcare data out there to be drilled.

Opportunity / Framework Generation defining technology – AI will ultimately have an impact on productivity on the magnitude of steam power, electrification, computing, etc. Core tech done by others – the frameworks (e.g. Microsoft LUIS) will either be open source & collaborative or otherwise require immense amounts of capital and data to develop and therefore are better left to the Internet Giants and Silicon Valley-based investors. AI includes any tech that allows machines to simulate the cognitive capabilities of a human. However, consensus has changed over time; as AI technologies go from leading edge to commercially accepted to mundane, those technologies are often dismissed as "not real AI". For the purposes of this paper, we define AI to be the generation of technologies that have been enabled by advances in machine learning ("ML").

The applications of the Internet of Things (IoT) have been growing dramatically in recent a few years. According to IDC, the transportation sector will be among the first to see a significant growth from the IoT, and the global IoT market in the transportation sector is expected to reach $195 billion by 2020. The smart IoT is dramatically accelerating the pace of innovation and transforming the way of operations in transportation and infrastructure. The ubiquitous deployment of smart, connected sensors and things, combined with artificial intelligence (AI) and big data analytics, can enable us to gather insightful knowledge, make real-time and even predictive computing to help us reaching better decisions and developing better plans to improve the safety, efficiency, and reliability of smart transportation. Here we take a look at some important applications of the IoT in intelligent transportation systems and smart cities.