AI and Education Sciences
This lecture overviews the impact of AI on Education Sciences. First an overview of Machine Learning is presented, focusing on the use of data in learning. Then Natural Language Processing is detailed, starting with word embedding, namely the transformation of words in vectors. This approach enabled the development of the Large language Models that are trained on huge data texts and exploit statistical relations between words to model text. GPT and ChatGPT are overviewed, as well as their qualities and shortcomings. The use of LLMs and AI in education are also presented. The distinction between morphosis and education is detailed, as well as its application on citizen and scientist morphosis.Educational systems modeling is overviewed from a Systems and Information Theory point-of-view. Finally, the effects of AI on University education are presented, particularly on Education Sciences.
AI and Book Publishing
This lecture overviews the relation between AI and book publishing. First, an in informative summary of “What is AI?” is presented, containing topics such as Symbolic AI, Data, Machine Learning (Clustering, Classification and Neural Networks). Topics that are related to book content creation, e.g., image processing, computer vision and natural language processing, are presented. Various Generative AI approaches, e.g., Large Language Models (LLMs such as ChatGPT), which are used for book content creation are detailed. As human knowledge is foremost communicated through books, its forms are also detailed in this lecture. The role book authors and publishers in the AI era is analyzed, together with several novel issues, such as digital book IP handling, self-publishing, NFT-publishing. Finally, a new book/journal publishing mode called Global Intellectual Property Market is presented..
AI in Medical Imaging
This lecture overviews the relation between AI and medical imaging and diagnosis. First, an in informative summary of “What is AI?” is presented, containing topics such as Symbolic AI, Data, Machine Learning (Clustering, Classification and Neural Networks). Topics that are related to book content creation, e.g., image processing, computer vision and natural language processing, are presented. The use of AI in proteomics (protein folding prediction) is also overviewed. The role of AI in medical diagnosis and education is also detailed. Finally, the creation of a medical data market is presented.
Interoperability with JSON-LD and NGSI-LD via Orion Context Broker
The short course aims to provide the foundation for understanding NGSI-LD Context Broker(s). The course will begin with a brief introduction to JSON-LD (JSON Linked Data) since it is the data format used by NGSI-LD. Next, there will be a brief mention of the history of NGSI-LD and who are the organizations that are currently using it. After that, the key components and aspects of an NGSI-LD Context Broker will be described. Specifically:
· NGSI-LD Tenant and Scope
· NGSI-LD Entity, Attributes and SubAttributes
· The @context field(s) – related to JSON-LD
· The possible formats and modes of presentation of a JSON-LD (e.g., normalized)
· The API exposed by an NGSI-LD Context Broker
· As an NGSI-LD Context Broker allows querying entities by filtering them for different attributes
· NGSI-LD Subscription
· Key Differences between NGSIv2 and NGSI-LD
· Short Demo about NGSI-LD Subscriptions
Lecture by Dr. A. Filograna and M. Basile.
Uploaded by Aristotle University of Thessaloniki
Introduction to Apache Airflow: Workflow Automation with DAGs and Tasks
In today’s technological landscape, process automation has become a fundamental component for improving the efficiency and productivity of organizations. In this introductory conference on Apache Airflow, we will explore how to use DAGs (Directed Acyclic Graphs) together with tasks to orchestrate and automate workflows effectively.
During the session, we will delve into the concept of DAGs and how to use them to define workflows, organizing tasks into a logical and sequential flow. We will also explore the role of tasks within DAGs, explaining how they represent individual units of work and how they can be configured to perform specific actions, such as data processing, script execution, or notification sending.
We will also explore how Airflow simplifies the programming and execution of tasks, offering a wide range of predefined operators and the flexibility to create custom operators to meet the specific needs of the workflow. We will demonstrate how to define tasks within a DAG, schedule them, and monitor them to ensure reliable and smooth execution.
Lecture by Dr. M. Palese.
Uploaded by Aristotle University of Thessaloniki.
Introduction to Kubernetes
Lecture by Prof. Lorentzo Carnevale.
Uploaded by Aristotle University of Thessaloniki.