AI Science and High School Mathematics

This lecture addresses the challenge to explain the AI science basics using only High school Mathematics. Luckily enough, it has been proven to be a successful and very interesting undertaking. The lecture covers the following topics in detail: definition of AI science, Data and Vectors, Clustering, Classification, Neural Networks, Computer Vision and Natural Language Processing. Of course the treatment would not be complete without reference to some subtle notions, e.g., knowledge and abstraction. Finally, the relation of AI science, society and the environment are overviewed.

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Large Language Models, ChatGPT and University Education

This lecture overviews the impact of the Large Language Models, particularly of ChatGPT language model in Education, e.g., in Universities. First it present the ChatGPT transformer structure and ChatGPT training. It also overviews ChatGPT capabilities in language processing (e.g., text translation, summarization, text sentiment analysis, dialogue tasks, misinformation detection, code understanding and generation). The ChatGPT implications on education are presented, notably its capabilities to reply exam questions, including mathematical and programming ones.  ChatGPT raises serious issues with respect to both its constructive use in education environments and its malicious use in course projects and exams. The impact of Large Language Models, and more generally Generative AI, in the structure of University education is detailed, as all sciences are increasingly mathematized and new scientific disciplines emerge (e.g., AI Science and Engineering) or are expected to emerge (e.g., Mind and Social Science and Engineering).  LLM memory/inference capabilities, limitations (e.g., hallucinations), and open questions and regulatory proposals are presented. Finally, a possible overhaul of the education system at all levels is proposed to address social challenges coming out of the extensive LLM and Generative AI use.

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Social Impact of AI Science and Engineering: Information Filtering and Disinformation

This lecture addresses several important questions on the interface between technology and society:

• Why our world becomes ever more complex?
• Can we cope with world complexity?
• What is the relation between freedom of speech and information filtering?
• What is the psychological background of on-line cults and conspiracy theories?
• Why negative views propagate faster?
• What is the relation of irrationalism, and anti-elitism, to social media disinformation?
• How can we valorize our private data?

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AI and Computational Politics

The aim of this lecture is to a) define Computational Politics as a discipline lying at the intersection of Political science and Computer science and b) present the use of AI and IT tools in political data analysis.

Computational Politics has various subtopics, e.g.,:

• Political system modeling and design
• Community and citizen modeling
• Information flow
• Political discourse analysis
• Election campaigns
• Political history
• Politics and Economics.

Computational Politics employs several AI and IT tools, e.g.,

• Natural language Processing
• Text sentiment analysis
• Time series prediction.
• Political network data analysis.

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AI Science and Engineering: A new scientific discipline? Lecture

No matter their exact form, AI Science and Engineering and its sister disciplines have many great challenges to address. Here is a partial list:

• Is AI Science and Technology a scientific discipline in its own right?
• How can we quantify knowledge?
• Can Virtual Reality truly empower meta-societies or is it just a hype?
• Can AI-powered human-centred computing surpass human intelligence?
• Can we create self-conscious machines?
• Can Mind and Social Engineering manipulate human behaviour and social functions?
• How do social media facilitate disinformation?
• What are the envisaged effects of AI and IT on our personal relations and sexual life?
• How can we not only protect but also monetize our personal data?
• Can AI help devising new political systems?
• How are irrationalism, anti-elitism, and social media disinformation related?
• Can new technologies ignite social revolutions?
• Is life and intelligence due to matter complexity?
• Can we patch parts of our brain?
• Is climate controllable through Geoengineering?
• Can humanity progress without resorting to energy-intensive technologies?

As each of them needs an entire book to be properly addressed, this lecture will simply introduce few of these challenges for further discussion and debate.

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AI, System Complexity, Life, Intelligence and Environment

This lecture overviews the relation between matter and system complexity on one hand and Life, Intelligence and Environment on the other one. First the theoretical tools (systems, graph and network theory) are overviewed. Then their relation to: a) life structure, b) biological neural networks, c) AI and artificial neural networks, d) social structure and evolution and e) environment is presented. System and matter complexity measures are investigated and the Law of Complexity is presented. Finally, philosophical issues related to life evolution-by-design are introduced.

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