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Artificial Intelligence Data Science Course – In recent years, an explosion of workshops, conferences and symposia, in addition to books, reports and blogs, have covered the use of data in various fields, including aviation. The use of aviation data to derive new processes to improve, for example, airport or airline operations is becoming a fashionable topic. Some of these activities, trying to attract attention in an already flooded field, use a variation of the words “data”, “data-driven”, “big data”. Some refer to techniques: “data analysis”, “machine learning” or “artificial intelligence”. Other related terms are “deep learning”, “operations research” and even “the internet of things”.

Given this large pool of efforts, it may be useful to clarify the difference between some of the terms used: statistical analysis, machine learning, artificial intelligence and data science.

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First of all, the term “data-driven” is probably one of the most used terms, and paradoxically, probably the one to avoid the most. Most classical techniques use data as the core of their design, such as Monte Carlo simulations available since the 1930s. An engineering or operational concept can be claimed to be “data-driven” if the data is at the core of the design. However, “data-driven” as a concept is considered very generic today and does not clarify how data is actually used. We recommend avoiding this sentence because it is often meaningless.

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Statistics are even older, with initial developments dating back to the 1600s. As an academic discipline, statistics is a branch of mathematics concerned with the collection, analysis, interpretation, presentation and organization of data. The field adds significant value to any research effort and it can be argued that statistics can be the first and basic step of any other related technique that will be developed. Still, many reports today are based on statistics and provide a lot of information to the professionals in the aviation industry. Statistics can show some predictability, but only if the relationships between the variables are simple enough to see and immediately human-readable.

Artificial intelligence is also a broad field, but more modern. Although some ideas were introduced in ancient times, the field is documented as being born in a workshop at Dartmouth College in 1956. Artificial intelligence is presented in relation to the intelligence displayed by humans and other animals, and the field can include not only techniques that’ t use large amounts of data, but also others such as agent-based models that have been used in aviation.

Different techniques are part of this broad field of artificial intelligence, but in general, artificial intelligence refers to providing techniques to solve tasks that are easy for humans but difficult for computers. Autonomous driving comes to mind as the obvious example in current times. Lately, the field of robotics offers very good examples of tasks that can be performed by humans and that have traditionally been difficult for machines. Aviation should quickly learn from them.

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In the field of artificial intelligence, machine learning is a set of techniques and algorithms that use large amounts of data to achieve certain AI goals. Machines gain the ability to learn various tasks, some of which are highly sophisticated yet trivial to humans, such as vision or character recognition. These machines are not specifically programmed for these tasks, but perform them by using general algorithms designed to train with data. Depending on whether the training data has been previously labeled by humans or not, the learning process will be called supervised or unsupervised learning. However, these algorithms, once trained, can make predictions or decisions, weighing the features that provide the best result. Although machine learning began in the 1980s, computers have only recently introduced the capabilities to store and process the large amounts of data needed to train these algorithms. Artificial neural networks come to mind and there are a few examples of research projects available in aviation, such as SafeClouds. Within machine learning, deep learning is a subset of algorithms that use multiple layers to extract features, mostly based on artificial neural networks.

However, data analysis involves more than one challenge. In order for the most successful algorithms (eg artificial neural networks) to flourish, we need to solve several problems. First, we need specific techniques for acquiring, cleaning, merging and semantically describing the various available datasets. Safeclouds.eu uses more than 10 different data sources to describe some operational challenges. Merging all these data sets requires specific data management techniques. All of these datasets require running on a scalable computer (processing) architecture that puts the data scientist at the center. Analyzing aviation data requires a certain infrastructure that we have written about before. The data is often proprietary and/or confidential and protecting the data while performing analytics requires specific techniques. Cryptography helped in this event. A variety of techniques, such as secure multiparty computation or fully homomorphic encryption, show promise in solving some of the privacy barriers to broader data use, although they still require some research. Last but not least, data must be consumed by people, and visualization, as well as user experience, must be considered in a complete data solution.

All these fields, along with deep analytics, make up the field of Data Science. We feel that this term includes all the techniques, tools and methods necessary to advance the knowledge of the use of data in aviation.

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The advancement of data science, including all its subfields, is the main objective of the series of air data science workshops launched by the SESAR ComplexWorld network in 2013 and which will celebrate its sixth edition on 11 October 2018 at EASA HQ.

A final note: The Oxford English Dictionary (OED) traces the first use of the terms ‘digitisation’ and ‘digitalization’ in connection with computers to the mid-1950s. In the OED, digitization refers to “the act or process of digitizing; converting analogue data (especially for later use in images, videos and text) into digital form”. Digitization, on the other hand, refers to “the adoption or increased use of digital or computer technology by an organization, industry, country, etc.” Recently, “going digital”, although it is gaining buzz as a phrase, does not make it clear whether the focus is on digitization or digitization. In any case, it is much more confusing than any of the terms mentioned in this post, and as such warrants a degree of caution in use. This commit does not belong to a branch in this repository, and may belong to a fork outside the repository.

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Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and imitate their actions. The term can also be applied to any machine that exhibits functions associated with the human brain, such as learning and problem solving.

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Artificial intelligence is advancing by leaps and bounds. Recent research in data science, machine learning, natural language processing, and other subfields of AI has already begun to impact the lives of ordinary people. AI is no longer a superficial concept. It is already used by tech giants, companies and startups to solve everyday problems. Therefore, choosing AI as a career path is truly rewarding in the long run.

Even if your profession is not directly related to technology, it is still said that artificial intelligence will disrupt every field in one way or another. This is why you need at least a basic understanding of how AI works.

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