Artificial intelligence is driving the technological revolution, and experts in this field believe it has the potential to be the game-changing technology that will change the world. If you want to pursue a career in artificial intelligence, Python is one of the most important skills you should learn. If you want to learn Python for artificial intelligence, you must understand what Python is and how it can be used across multiple fields in the technology industry. This article will cover the quickest and most dependable educational paths for learning Python, as well as a step-by-step guide for learning Python for artificial intelligence. Python is an object oriented, interpreted general-purpose programming language.

Inspired by the demands of real-time climate and weather forecasting, we develop optimistic online learning algorithms that require no parameter tuning and have optimal regret guarantees under delayed feedback. Our algorithms -- DORM, DORM+, and AdaHedgeD -- arise from a novel reduction of delayed online learning to optimistic online learning that reveals how optimistic hints can mitigate the regret penalty caused by delay. We pair this delay-as-optimism perspective with a new analysis of optimistic learning that exposes its robustness to hinting errors and a new meta-algorithm for learning effective hinting strategies in the presence of delay. We conclude by benchmarking our algorithms on four subseasonal climate forecasting tasks, demonstrating low regret relative to state-of-the-art forecasting models.

For the insurance industry, the health and safety benefits of wearables and other IoT-connected devices is well established. But meeting new customer demands for protection goes beyond capturing user-generated data. What matters now is how an insurer and their ecosystem partners use the data shared with them by the customer. And whether they have the right mix of talent and technology to optimize its use. Analytics capabilities, including predictive and prescriptive analytics, can enable data-driven insurance offers in real-time.

The emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to understand such large amounts of data. Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the data, and make more informed decision based on the resulting analysis. ML has applications in various fields. This review focuses on some of the fields and applications such as education, healthcare, network security, banking and finance, and social media. Within these fields, there are multiple unique challenges that exist. However, ML can provide solutions to these challenges, as well as create further research opportunities. Accordingly, this work surveys some of the challenges facing the aforementioned fields and presents some of the previous literature works that tackled them. Moreover, it suggests several research opportunities that benefit from the use of ML to address these challenges.

This paper investigates the problem of designing data-driven stochastic Model Predictive Control (MPC) for linear time-invariant systems under additive stochastic disturbance, whose probability distribution is unknown but can be partially inferred from data. We propose a novel online learning based risk-averse stochastic MPC framework in which Conditional Value-at-Risk (CVaR) constraints on system states are required to hold for a family of distributions called an ambiguity set. The ambiguity set is constructed from disturbance data by leveraging a Dirichlet process mixture model that is self-adaptive to the underlying data structure and complexity. Specifically, the structural property of multimodality is exploit-ed, so that the first- and second-order moment information of each mixture component is incorporated into the ambiguity set. A novel constraint tightening strategy is then developed based on an equivalent reformulation of distributionally ro-bust CVaR constraints over the proposed ambiguity set. As more data are gathered during the runtime of the controller, the ambiguity set is updated online using real-time disturbance data, which enables the risk-averse stochastic MPC to cope with time-varying disturbance distributions. The online variational inference algorithm employed does not require all collected data be learned from scratch, and therefore the proposed MPC is endowed with the guaranteed computational complexity of online learning. The guarantees on recursive feasibility and closed-loop stability of the proposed MPC are established via a safe update scheme. Numerical examples are used to illustrate the effectiveness and advantages of the proposed MPC.

In the Internet of Things (IoT) era, billions of sensors and devices collect and process data from the environment, transmit them to cloud centers, and receive feedback via the internet for connectivity and perception. However, transmitting massive amounts of heterogeneous data, perceiving complex environments from these data, and then making smart decisions in a timely manner are difficult. Artificial intelligence (AI), especially deep learning, is now a proven success in various areas including computer vision, speech recognition, and natural language processing. AI introduced into the IoT heralds the era of artificial intelligence of things (AIoT). This paper presents a comprehensive survey on AIoT to show how AI can empower the IoT to make it faster, smarter, greener, and safer. Specifically, we briefly present the AIoT architecture in the context of cloud computing, fog computing, and edge computing. Then, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Next, we summarize some promising applications of AIoT that are likely to profoundly reshape our world. Finally, we highlight the challenges facing AIoT and some potential research opportunities.

Analysis of a large amount of data has always brought value to institutions and organizations. Lately, people's opinions expressed through text have become a very important aspect of this analysis. In response to this challenge, a natural language processing technique known as Aspect-Based Sentiment Analysis (ABSA) has emerged. Having the ability to extract the polarity for each aspect of opinions separately, ABSA has found itself useful in a wide range of domains. Education is one of the domains in which ABSA can be successfully utilized. Being able to understand and find out what students like and don't like most about a course, professor, or teaching methodology can be of great importance for the respective institutions. While this task represents a unique NLP challenge, many studies have proposed different approaches to tackle the problem. In this work, we present a comprehensive review of the existing work in ABSA with a focus in the education domain. A wide range of methodologies are discussed and conclusions are drawn.

The "Global Artificial Intelligence in Supply Chain Market Analysis to 2027" is a specialized and in-depth study of the technology, media and telecommunication industry with a special focus on the global market trend analysis. The report aims to provide an overview of the Artificial Intelligence in Supply Chain market with detailed market segmentation by components, technology, application, and industry vertical, and geography. The global artificial intelligence in supply chain market is expected to witness high growth during the forecast period. The report provides key statistics on the market status of the leading artificial intelligence in supply chain market players and offers key trends and opportunities in the market. The reports cover key developments in the artificial intelligence in supply chain market as organic and inorganic growth strategies.

As one of the leading logistics companies worldwide, FedEx is well-regarded in the supply chain industry. FedEx operates a portfolio of solutions; FedEx Express, FedEx Ground, FedEx Freight, FedEx Services, FedEx Logistics and FedEx Office. The company enables businesses to access over 99% of the world's GDP

The SaaS company will explore how it can leverage AI to improve its software's lead conversion capabilities Is IIT-M becoming India's new-age technology hub? New-age technology like artificial intelligence (AI) has come a long way in bringing unique solutions for companies working across multiple sectors. The revolutionary technology has also become a go-to innovation tool for software-as-a-service (SAAS) companies as well. This time SaaS unicorn Freshworks has partnered with Robert Bosch Centre for Data Science and Artificial Intelligence in Indian Institute of Technology, Madras (IIT Madras) to improve the predictive capability of its customer relationship management (CRM) software. Freshworks will initially work with the centre exploring how it can leverage AI to improve its software's lead-conversion capabilities, helping its clients to increase their business.