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Artificial intelligence (AI) is playing an increasingly important part in our daily lives, whether it is providing our personalised social media feeds, online shopping or streaming movie suggestions, or even the mapping apps that route us around traffic jams. On a bigger scale, AI is already having a major impact on healthcare, finance, farming and many other sectors and its influence is predicted to expand rapidly in the coming years. One area where there is considerable untapped potential for AI is in the field of Earth observation, where it can be used to help manage large datasets, find new insights in data and generate new products and services. With this in mind, EUMETSAT, ECMWF, Mercator Ocean International and the EEA have joined up to develop a new massive open online course (MOOC) on AI and Earth monitoring. The idea for the course is to introduce participants to the wealth of Copernicus Earth observation data and the AI and machine learning techniques that can be used to work with it.

Last March, Zoom, the ubiquitous online conferencing platform, became a staple of daily life for many students and educators as learning shifted online. Millions downloaded it--and first learned of it--back in early 2020, when lockdowns forced billions of students online, and at least 100,000 schools onto Zoom. But as the company itself will tell you, it didn't spring up overnight. Zoom is actually a decade old, and the first conferences launched in 2012, limited to a mere 15 participants. While post-pandemic growth has slowed as schools resume in-person learning, the company is still flush with cash, reporting over $1 billion in revenue in the second quarter of 2021.

This research is aimed to propose an artificial intelligence algorithm comprising an ontology-based design, text mining, and natural language processing for automatically generating gap-fill multiple choice questions (MCQs). The simulation of this research demonstrated an application of the algorithm in generating gap-fill MCQs about software testing. The simulation results revealed that by using 103 online documents as inputs, the algorithm could automatically produce more than 16 thousand valid gap-fill MCQs covering a variety of topics in the software testing domain. Finally, in the discussion section of this paper we suggest how the proposed algorithm should be applied to produce gap-fill MCQs being collected in a question pool used by a knowledge expert system.

Search engines answer users' queries by listing relevant items (e.g. documents, songs, products, web pages, ...). These engines rely on algorithms that learn to rank items so as to present an ordered list maximizing the probability that it contains relevant item. The main challenge in the design of learning-to-rank algorithms stems from the fact that queries often have different meanings for different users. In absence of any contextual information about the query, one often has to adhere to the {\it diversity} principle, i.e., to return a list covering the various possible topics or meanings of the query. To formalize this learning-to-rank problem, we propose a natural model where (i) items are categorized into topics, (ii) users find items relevant only if they match the topic of their query, and (iii) the engine is not aware of the topic of an arriving query, nor of the frequency at which queries related to various topics arrive, nor of the topic-dependent click-through-rates of the items. For this problem, we devise LDR (Learning Diverse Rankings), an algorithm that efficiently learns the optimal list based on users' feedback only. We show that after $T$ queries, the regret of LDR scales as $O((N-L)\log(T))$ where $N$ is the number of all items. We further establish that this scaling cannot be improved, i.e., LDR is order optimal. Finally, using numerical experiments on both artificial and real-world data, we illustrate the superiority of LDR compared to existing learning-to-rank algorithms.

The field of data science is growing with increasing demand. Data science is not limited to only consumer goods or tech or healthcare. There is a high demand to optimize business processes using data science from banking, transport to manufacturing. Organizations are now hiring data science professionals to deal with complex data. To become an expert in data science read the article and check out the list of top-rated data science courses on Coursera.

We propose a detailed analysis of the online-learning problem for Independent Cascade (IC) models under node-level feedback. These models have widespread applications in modern social networks. Existing works for IC models have only shed light on edge-level feedback models, where the agent knows the explicit outcome of every observed edge. Little is known about node-level feedback models, where only combined outcomes for sets of edges are observed; in other words, the realization of each edge is censored. This censored information, together with the nonlinear form of the aggregated influence probability, make both parameter estimation and algorithm design challenging. We establish the first confidence-region result under this setting. We also develop an online algorithm achieving a cumulative regret of $\mathcal{O}( \sqrt{T})$, matching the theoretical regret bound for IC models with edge-level feedback.

With the popularity and success of our first Introduction to Embedded Machine Learning course, we decided to launch another! We listened to feedback from students, engineers, and industry leaders about which areas in tinyML were most interesting and useful. One topic stood above the rest: vision. Shawn Hymel returns as the main instructor, and we teamed up with OpenMV, Seeed Studio, and the tinyML Foundation to create a new course: Computer Vision with Embedded Machine Learning. The course covers important concepts in computer vision, including how digital images are constructed, stored, and manipulated.

Data Science is an interdisciplinary field that uses scientific methods, algorithms to extract clean information from raw data for the formulation of actionable insights. The Data Science field is growing so rapidly, and revolutionizing so many industries. Data Science has incalculable benefits in business, research, and our everyday lives. Your route to work, your most recent Google search for the nearest coffee shop, your Instagram post about what you ate, and even the health data from your fitness tracker are all important to different data scientists in different ways. Sifting through massive lakes of data, looking for connections and patterns, data science is responsible for bringing us new products, delivering breakthrough insights, and making our lives more convenient.

With the high rise in demand for talent in the field of Artificial Intelligence (AI), the need for professionals who have expertise in this field has also increased immensely. Worldwide, many organizations are on the lookout for individuals who possess a great skillsets in the field of AI. This demand gave rise to the artificial intelligence engineer certification program, which is offered by several online learning institutes. If a person wants to enhance their skill set and also stay ahead in the growing populations then doing a certification program in the field of AI is the best choice. In this article, let's understand the most affordable and industry-recognized top AI certifications that one can do to jump the career ladder.