Introduction

🧠 Introduction to AI

Andy Weeger

Neu-Ulm University of Applied Sciences

February 12, 2024

How is AI received?

Your thoughts

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

SciFi

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 and Norvig 2022).

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

The pace of progress in artificial intelligence is incredibly fast. Unless you have direct exposure to groups like Deepmind, you have no idea how fast—it is growing at a pace close to exponential. The risk of something seriously dangerous happening is in the five-year timeframe. 10 years at most. Elon Musk

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

Recent developments

What is AI?

Towards a definition of AI

AI is the science of making machines to

• think (though processes and reasoning)
  • humanly and/or
  • rationally
• and to act (behavior)
  • humanly and/or
  • rationally (Russel and Norvig 2022)

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

Think humanly

The cognitive modelling approach

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 and 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)

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.

Rationality

What is rational thinking about?

Think rationally

The laws of thought approach

The “laws of thought” refer to fundamental axiomatic rules upon which rational discorse itself is often considered to be based.

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

Computers have been able to solve any solvable problem, as long as

• there are statements about any objects in the world,
• statements about the relations among them, and
• there is sufficient 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 and Norvig 2022)

Act humanly

The Turing Test approach

The Turing Test¹ (Turing 1950) tests if a computer has the ability to mimic peoples’ behavior.

To pass the test, it would need following capabilities:

• natural language processing (communicate)
• knowledge representation (store information)
• automated reasoning (answer questions, draw new conclusions)
• machine learning (adapt to new circumstances)

Want to do a Turing Test? Play “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 and 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

Would ChatGPT pass the turing test? 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

The rational agent approach

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 and 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 and 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.

Issues

Do you see any issues with the rational agent approach?

Benificial machines

Machines that are provably beneficial to humans

According to Russel and Norvig (2022) 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
• An intelligent agent should not pursue a definite object, it should pursue objectives that benefit humans, while being uncertain as to what they are

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.

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 and 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)

Co-pilot

Your experiences

Which AI-powered systems are you using?

✏️ Exercises

The exercises are (inspired) by Russel and 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, Michele, and Eric Brill. 2001. “Scaling to Very Very Large Corpora for Natural Language Disambiguation.” In Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics, 26–33.

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.

Russel, Stuart, and Peter Norvig. 2022. Artificial Intelligence: A Modern Approach. Harlow: Pearson Education.

Turing, Alan. 1950. “Computing Machinery and Intelligence.” Mind 59: 433–60.

Footnotes

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