Introduction

Introduction to AI (I2AI)

Andy Weeger

Neu-Ulm University of Applied Sciences

February 20, 2025

Introduction

You x AI

Which thoughts and feelings come to your mind when you think of AI?

SciFi x AI

Fiction
or future?

The perception of AI changed over the decades. This is well reflected in Sci-Fi Movies, e.g.

• R2D2 in Star Wars
• The Terminator
• Agent Smith in the Matrix
• Sonny in I,Robot
• Wall·E
• etc.

Quotes

Will the fictions become reality? We will see. In any case, the intellectual frontiers of AI are wide open. The subfields span from artificial general intelligence (learning, reasoning, perception, etc.) to specific fields (e.g., translating, playing go) (Russel & Norvig, 2022).

The development of full artificial intelligence could spell the end of the human race. Stephen Hawking

I believe it’s going to change the world more than anything in the history of mankind — even more than electricity. Kai-Fu Lee

AI is neither magic nor monster — it is a mirror reflecting our own choices. Kate Crawford

Forget artificial intelligence—in the brave new world of big data, it’s artificial idiocy we should be looking out for. Tom Chatfield

Terms & definitions

Intelligence

What can intelligent beings do?

know   learn
create
act   predict   plan   decide
recognize   assess   infer   percept

What is AI?

AI is the science of making machines (i.e., computer systems) to

think (thought processes and reasoning)
and to
act (behavior)
humanly and/or rationally

AI is the science of making machines (i.e., computer systems) to think and to act humanly and/or rationally

AI tries to integrate cognitive abilities, such as inference, planning, problem solving, learning etc. in computer systems (i.e., think).

The goal of AI research is to enable machines to autonomously process and solve abstract tasks and problems even under changed conditions — without a human being programming an explicit solution (i.e., act).

All AI systems that are technically feasible today enable problem solving in limited contexts (e.g. speech or image recognition) and thus belong to the so-called weak AI.

What is rational?

The term rational is used here in a very specific, technical way:

• Rational: maximally achieving pre-defined goals
• Rationality only concerns what decisions are made (not the thought process behind them)
• Goals are expressed in terms of the utility of outcomes
• Being rational means maximizing your expected utility

Source: Russel & Norvig (2022)

Think humanly

Cognitive science is the study of the human brain and its processes — it examines how the human brain may be functioning. Cognitive science requires analytical observation and experimentation.

We can learn about human thought in three ways (Russel & Norvig, 2022):

• introspection (trying to catch our own thoughts as they go by)
• experiments (observing a person in action)
• brain imaging (observing the brain in action)

As a result of the cognitive modelling approach, some of the most powerful AI models are a result from observing human thinking experimentally (e.g., deep neural networks).

Differences between cognitive computing and AI

Cognitive science is about making computers solve complex problems similar to how humans solve problems. Cognitive computing tries to replicate how humans would solve problems, while AI is not bound to human cognitive processes.

The measurement problem

Why is “thinking like a human” so difficult to measure?

In his famous paper “What Is It Like to Be a Bat?”, Thomas Nagel (Nagel, 1974) argues that consciousness has a subjective character that cannot be captured by physical descriptions. We might map the neurons of a bat (or an AI), but we cannot know the experience of being one.

Even if we have perfect brain imaging, we face the problem of subjective experience.

In his famous paper “What Is It Like to Be a Bat?”, Thomas Nagel (Nagel, 1974) argues that consciousness has a subjective character that cannot be captured by physical descriptions. We might map the neurons of a bat (or an AI), but we cannot know the experience of being one.

This creates a “hard problem” for AI: we can measure behavior (acting humanly) or logic (thinking rationally), but we cannot truly measure consciousness (thinking humanly).

Rationality

What is rational thinking about?

Think rationally

Thinking rationally means following the laws of thought (i.e., rules for correct reasoning) — if your premises are true, your conclusion must be true.

Socrates is a man and all men are mortal, thus, it can be concluded that Socrates is mortal Aristotle (384-322 BCE)

These rules can be encoded — computers can solve any solvable problem, provided:

• we can describe objects in the world (“Socrates is a man”),
• we can describe relationships between them (“all men are mortal”), and
• there is enough computing power available

Logic, knowledge and probability

These laws of thought were supposed to govern the operation of the mind; the studies initiated the field called logic. However, human decisions are not always mathematically perfect or logical.
Logic as conventionally understood requires knowledge of the world that is certain. As this condition is seldom achieved, the theory of probability fills this gap. Probability allows for rigorous reasoning with uncertain information. (Russel & Norvig, 2022)

Act humanly

Many AI systems try to mimic human behavior — and the Turing Test (Turing, 1950) offers one way to measure how “intelligent” they are in this sense: a machine passes if it can fool a human into thinking it’s human.¹

To pass, a machine would need to:

• understand and produce language (natural language processing)
• remember facts and context (knowledge representation)
• reason and draw conclusions (automated reasoning)
• learn and adapt (machine learning)

Think you can tell humans from bots? Try it yourself — “Bot or Not”

Turing Test

The Turing Test (Turing, 1950) was designed as a thought experiment that would sidestep the philosophical vagueness of the question “Can a machine think?” A computer passes the test if a human interrogator, after posing some written questions, cannot tell whether the written responses come from a person or from a computer (Russel & Norvig, 2022).
The Total Turing Test additionally requires interaction with objects and people in the real world. This also requires computer vision and robotic capabilities.

Your thoughts

Are you able to discern Claude (Sonnet 4.6) from a human?
Why (not)?

Large language models in general, have the ability to produce human-like responses that can fool even experienced evaluators ChatGPT has shown it can. However, depending on your prompting skills, those models may still produce a lot of nonsense.

Act rationally

An agent is something that acts, an rational agent is one that acts so as to achieve the best coutcome (i.e., does the right thing), or, when there is uncertainty, the best expected outcome (i.e., does the appropriate thing) based on the objective that is provided to the agent (Russel & Norvig, 2022).

The approach goes beyond the “laws of thought” approach as it involves actions based on

• inference (deducing that a given action is the best and then to act on this conclusion) and
• other mechanisms such as reflex (when speed is more successful than careful deliberation that takes some time)

Standard model in AI

Russel & Norvig (2022) call the approach where the primary definition of success is getting better and better at achieving rigid human-specified goals the standard model of AI research. This standard model prevails not only in AI, “but also in control theory, where a controller minimizes a cost function; in operations research, where a policy maximizes a sum of rewards; in statistics, where a decision rule minimizes a loss function; and in economics, where a decision maker maximizes utility or some measure of social welfare” (p. 22).
They also criticize the standard model because it is increasingly difficult to specify the goal completely and correctly (e.g., autonomous driving involves multiple goals such as reaching the goal safely, where a strict definition of safety requires staying in the garage because driving on the road has a risk of injury due to myriad factors; how should the trade-off between reaching the goal and taking a risk of injury be made? There are so many questions that are difficult to answer a priori). Mis-specified goals most likely do not reflect what human designers intend, e.g., by not taking into account human values that are not included in the goals.

Standard model of AI

The standard model of AI (Russel & Norvig, 2022) describes the dominant approach in AI engineering: build systems that get increasingly better at achieving a fixed goal — one precisely defined by humans in advance.

But is that enough?

Discussion

Do you see any issues with the so-called standard model of AI?

Benificial machines

To create machines that are provably beneficial to humans, two refinements to the standard model of AI are needed:

• The ability of any agent to choose rational actions is constrained by the computational untractability of doing so (Russel & Norvig, 2022).
• An intelligent agent should not pursue a definite object, it should pursue objectives that benefit humans, while being uncertain as to what they are (Russel & Norvig, 2022).

When a machine knows that it does not know the entire objective, it has an incentive to act cautiously, to ask permission, to learn more about our preferences through observation, and to submit to human control.

Definition of the EU

‘AI system’ means a machine-based systems designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment and that, for explicit or implicit objectives, infers, from the input it received, how to generate output such as content, predictions, recommendations, or decisions, that can influence physical or virtual environment (European Commission, 2024).

Systems that perceive, learn, think and act human-like.

Types of AI matter

The definitions we discussed — thinking, acting, humanly, rationally — apply differently depending on what kind of AI system we are talking about.

Casual discussions often suffer “conceptual flattening” (Chalmers et al., 2026): treating all AI as one thing obscures fundamentally different logics and affordances.

AI Mode	Core Logic	Representative Systems	Organizational Affordances
Predictive	Pattern recognition & forecasting	Fraud detection, Radiology classifiers	Efficiency in routine analysis; surveillance
Generative	Synthetic production of novel content	GPT-4o, Midjourney, GitHub Copilot	Shift from production to curation; low marginal cost
Agentic	Multi-step reasoning & task decomposition	LangChain agents, Open Claw	Partial autonomy; cognitive sovereignty
Embodied	Physical manipulation & perception	Agility Robots	Labor substitution; material coordination

Table 1: Different logics and affordances of contemporary AI systems according to Chalmers et al. (2026)

The history of AI

The Thinking Machine

A series of interviews to some of the AI pioneers.

The full documentary is available here

GPT’s great-grandfather

Love Letters by Christopher Strachey 1953

A brief AI-timeline

1943—1956 The inception of AI

• 1943: McCulloch & Pitts: Boolean circuit model of brain (artificial neurons with on and off states; all logical connectives can be implemented with some network of these)
• 1950: Turing’s “Computing Machinery and Intelligence” (Turing (1950) already introduced the Turing test, machine learning, genetic algorithms, and reinforcement learning)
• 1950s: Early AI programs (e.g., Arthur Samuel’s influential checkers program that learned to play at a strong amateur level)

1966—73 A dose of reality (AI winter)

• The early AI programs failed on more difficult problems
  • Focus on “informed introspection” as to how humans perform a task
  • Lack of appreciation of the intractability of many of the problems
• Signification reduction of government funding of AI research

1970—90 Expert systems (knowledge-based approaches)

• 1969—79: Early development of knowledge-based systems (rule-based heuristic algorithms)
• 1980—88: Expert systems industry booms (nmany U.S. corporates had their own AI groups)
• Soon after that came the “AI winter” (difficulties to build expert systems for complex domains due to uncertainty and a lack of learning)

1990—present AI spring (statistical approaches)

• Focus on probabilistic reasoning (rather than Boolean logic) and machine learning
• Reunification of subfields such as computer vision, robotics, speech recognition, and natural language processing

2012—present New excitement

• Advances in computing power, WWW, and very large data sets
  (e.g., IBM Watson’s victory in Jeopardy!)
• AI is accessible to many as it enters productivity tools (e.g., ChatGPT, Microsoft Co-Pilot)

New excitement

Improvement in performance obtained from increasing the size of the data set by two or three orders of magnitude outweighs any improvement that can be obtained from tweaking the algorithm Banko & Brill (2001)

• Deep learning systems offer significant performance gains
  (e.g., AlphaGo’s victories)
• Significant focus on AI in academia and industry
• Breakthrough of generative AI (e.g., ChatGPT)
• AI systems find increasing application in the real world (e.g., robotic vehicles, machine translation, speech recognition, recommendations, autonomous planning, game playing, image understanding, medicine)

Agentic AI

Your experiences

Which AI systems are you using?

Q&A

Exercises

The exercises are (inspired) by Russel & Norvig (2022)

Concepts

Define in your own words:

• intelligence
• artificial intelligence
• agent
• rationality
• logical reasoning

Instances of AI

If and to what extent are the following computer systems instances of artificial intelligence?

• Supermarket bar code scanners
• Web search engines
• Voice-activated telephone menus
• Internet routing algorithms that respond dynamically to the state of the network

AI Contests

Various subfields of AI have held contests by defining a standard task and inviting researchers to do their best. Examples include the DARPA Grand Challenge for robotic cars, the International Planning Competition, the Robocup robotic soccer league, the TREC information retrieval event, and contests in machine translation and speech recognition.

Investigate one of these contests and describe the progress made over the years.

• To what degree have the contests advanced the state of the art in AI?
• To what degree do they hurt the field by drawing energy away from new ideas?

Statements

Read the statements (one after the other) and discuss if the second sentence of each statement is true and if it does imply the first.

Surely computers cannot be intelligent
—they can do only what their programmers tell them.

Surely animals cannot be intelligent
—they can do only what their genes tell them.

Surely animals, humans, and computers cannot be intelligent
—they can do only what their constituent atoms are told to do by the laws of physics.

Literature

Banko, M., & Brill, E. (2001). Scaling to very very large corpora for natural language disambiguation. Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics, 26–33.

Chalmers, D., Hunt, R. A., Pachidi, S., Potočnik, K., & Townsend, D. M. (2026). The acceleration of artificial intelligence: Rethinking organization and work in an era of rapid technological change. Journal of Management Studies, 63(2), 285–314.

European Commission. (2024). Artificial intelligence in the european commission — a strategic vision to foster the development and use of lawful, safe and trustworthy artificial intelligence systems in the european commission. C(2024) 380.

Nagel, T. (1974). What is it like to be a bat? The Philosophical Review, 83(4), 435–450.

Russel, S., & Norvig, P. (2022). Artificial intelligence: A modern approach. Pearson Education.

Turing, A. (1950). Computing machinery and intelligence. Mind, 59, 433–460.

Footnotes

1. Good read: New AI may pass the famed Turing Test. This is the man who created it