{"id":1906,"date":"2020-01-26T19:50:21","date_gmt":"2020-01-26T18:50:21","guid":{"rendered":"http:\/\/www.k-braungardt.de\/blogkb\/?p=1906"},"modified":"2020-01-26T19:50:21","modified_gmt":"2020-01-26T18:50:21","slug":"elements-of-ai-kuenstliche-intelligenz-demystifiziert","status":"publish","type":"post","link":"https:\/\/www.k-braungardt.de\/blogkb\/?p=1906","title":{"rendered":"Elements of AI: K\u00c3\u00bcnstliche Intelligenz demystifiziert"},"content":{"rendered":"

Was sollte man als Laie \u00c3\u00bcber KI wissen? Der finnische MOOC „Elements of AI<\/a>“ setzt sich zum Ziel das Thema der k\u00c3\u00bcnstlichen Intelligenz zu demystifizieren und ein paar Grundlagen zu vermitteln.
\nDer Kurs kommt sehr gut an. Auch ich habe ihn gemacht, sogar bis zum Ende.<\/p>\n

Hier ein paar Eckdaten zum MOOC:<\/p>\n

MOOC: Elements of AI: https:\/\/www.elementsofai.com\/<\/a>
\n \u00e2\u20ac\u00a2 entwickelt von der University of Helsinki und der Firma Reaktor
\n \u00e2\u20ac\u00a2 Ziel: Verst\u00c3\u00a4ndnis f\u00c3\u00bcr KI entwickeln: Verst\u00c3\u00a4ndliche Erkl\u00c3\u00a4rungen mit Beispielen und Visualisierungen und Aufgaben
\n \u00e2\u20ac\u00a2 > 230.000 TN
\n \u00e2\u20ac\u00a2 Ziel: frei verf\u00c3\u00bcgbar in allen europ\u00c3\u00a4ischen Sprachen; \u00c3\u0153bersetzungen kommen von der EU-Kommission
\n \u00e2\u20ac\u00a2 6 Kapitel, 25 Aufgaben<\/p>\n

Hier ein \u00c3\u0153berblick der Inhalte<\/p>\n

Kapitel 1 What is AI?<\/strong><\/p>\n

Lernziele
\n \u00e2\u20ac\u00a2 Explain autonomy and adaptivity as key concepts for explaining AI
\n \u00e2\u20ac\u00a2 Distinguish between realistic and unrealistic AI (science fiction vs. real life)
\n \u00e2\u20ac\u00a2 Express the basic philosophical problems related to AI including the implications of the Turing test and Chinese room thought experiment<\/p>\n

1) Definitionen \/ Anwendungsbeispiele<\/p>\n

Inhalte:
\nBeispiele: Selbst fahrende Autos, Inhaltsempfehlungen, Bild- und Videoverarbeitung
\nCharakteristische Eigenschaften: Autonomie und Adaptivit\u00c3\u00a4t<\/p>\n

Aufgabe 1: \u00c2\u00a0Is this AI or not?
\nFrage:
\nIs this AI? Which of the following are AI and which are not. Choose yes, no, or \u00e2\u20ac\u0153kind of\u00e2\u20ac\u009d where kind of means that it both can be or can’t be, depending on the viewpoint.<\/p>\n

2) Verwandte Gebiete<\/p>\n

Inhalte:
\nMachine learning, deep learning, data science, robotics<\/p>\n

Aufgabe 2: KI-Taxonomie anhand eines Euler-Diagramms konstruieren
\nFrage:
\nConstruct a taxonomy in the Euler diagram example given below showing the relationships between the following things: AI, machine learning, computer science, data science, and deep learning.<\/p>\n

Aufgabe 3: Zuordnung von Beispielen zu Gebieten
\nFrage:
\nConsider the following example tasks. Try to determine which AI-related fields are involved in them.\u00c2\u00a0Select all that apply.<\/p>\n

3) Philosophie<\/p>\n

Inhalte:
\nTuring Test, Chinesisches Zimmer; Schwache \/ starke KI<\/p>\n

Aufgabe 4: Eigene Definition von KI bzw. Bewertung von Definitionen
\nFrage:
\nWhich definition of AI do you like best? How would\u00c2\u00a0you\u00c2\u00a0define AI? <\/p>\n

Kapitel 2 AI problem solving<\/strong><\/p>\n

Lernziele
\n \u00e2\u20ac\u00a2 Formulate a real-world problem as a search problem
\n \u00e2\u20ac\u00a2 Formulate a simple game (such as tic-tac-toe) as a game tree
\n \u00e2\u20ac\u00a2 Use the minimax principle to find optimal moves in a limited-size game tree<\/p>\n

1) Suche und Probleme l\u00c3\u00b6sen<\/p>\n

Inhalte:
\nSearch in practice: getting from A to B; Spielzeugproblem: Chicken crossing, T\u00c3\u00bcrme von Hanoi <\/p>\n

Aufgabe 5: A smaller rowboat
\nFrage:
\nUsing the diagram with the possible states below as a starting point, draw the possible transitions in it; Having drawn the state transition diagram,\u00c2\u00a0find the shortest path from NNNN to FFFF, and calculate the number of transitions on it.<\/p>\n

Aufgabe 6: Die T\u00c3\u00bcrme von Hanoi
\nFrage:
\n Draw the state diagram.<\/p>\n

2) Probleme mit AI l\u00c3\u00b6sen<\/p>\n

Inhalte:
\nGeschichtlicher R\u00c3\u00bcckblick <\/p>\n

3) Suche und Spiele<\/p>\n

Inhalte:
\nBeispiel tic tac toe; Game trees; Minimax Algorithmus; <\/p>\n

Aufgabe 7: tic tac toe
\nFrage:
\nEnter the value of the game as your answer.<\/p>\n

Kapitel 3 Real world AI<\/strong><\/p>\n

Lernziele
\n \u00e2\u20ac\u00a2 Express probabilities in terms of natural frequencies
\n \u00e2\u20ac\u00a2 Apply the Bayes rule to infer risks in simple scenarios
\n \u00e2\u20ac\u00a2 Explain the base-rate fallacy and avoid it by applying Bayesian reasoning<\/p>\n

1) Odds and probability<\/p>\n

Inhalte:
\nProbability, odds<\/p>\n

Aufgabe 8: probabilistische Vorhersagen
\nFrage:
\nConsider the following four probabilistic forecasts and outcomes. What can we conclude based on the outcome about the correctness of the forecasts?\u00c2\u00a0<\/p>\n

Aufgabe 9: Odds
\nFrage:
\nFor the first three items 1\u00e2\u20ac\u201c3, convert from odds to probabilities expressed as natural frequencies; for example from 1:1 to 1\/2. Give your answer as a fraction, for example 2\/3.
\nFor the last three items 4\u00e2\u20ac\u201c6, convert the odds into probabilities expressed as percentages (e.g. 4.2%). Give your answer in percentages using a single decimal, for example 12.2%.<\/p>\n

2) The Bayes rule<\/p>\n

Inhalte:
\nPrior and posterior odds, likelihood ratio<\/p>\n

Aufgabe 10: Bayes rule (part 1 of 2)
\nFrage:
\nApply the Bayes rule to calculate the\u00c2\u00a0posterior odds for rain\u00c2\u00a0having observed clouds in the morning in Helsinki.<\/p>\n

Aufgabe 11: Bayes rule (part 1 of 2)
\nFrage:
\nCalculate the posterior odds for her having breast cancer using the Bayes rule.\u00c2\u00a0<\/p>\n

3) Naive Bayes classification<\/p>\n

Inhalte:
\nSpam filters<\/p>\n

Aufgabe 12: One word spam filter
\nFrage:
\nCalculate the\u00c2\u00a0posterior odds\u00c2\u00a0for spam given this word using the table above, starting from prior odds 1:1.<\/p>\n

Aufgabe 13: Full spam filter
\nFrage:
\nNow use the naive Bayes method to calculate the posterior odds for spam given the message \u00e2\u20ac\u0153million dollars adclick conferences\u00e2\u20ac\u009d.<\/p>\n

Kapitel 4 Machine learning<\/strong>
\nLernziele
\n \u00e2\u20ac\u00a2 Explain why machine learning techniques are used
\n \u00e2\u20ac\u00a2 Distinguish between unsupervised and supervised machine learning scenarios
\n \u00e2\u20ac\u00a2 Explain the principles of three supervised classification methods: the nearest neighbor method, linear regression, and logistic regression<\/p>\n

1) The types of machine learning<\/p>\n

Inhalte:
\nKlassisches Beispiel Handschriftenerkennung; Typen des Machine learning<\/p>\n

2) Nearest neighbor classifier<\/p>\n

Aufgabe 14: Customers who bought similar products
\nFrage:
\nCalculate the similarity of Travis relative to the six users in the training data, identify the user who is most similar to Travis by selecting the largest of the calculated similarities. Predict what Travis is likely purchase next by looking at the most recent purchase<\/p>\n

Aufgabe 15: Filter bubbles
\nFrage:
\nDo you think that filter bubbles are harmful? After all, they are created by recommending content that the user likes. What negative consequences, if any, may be associated with filter bubbles?
\nThink of ways to avoid filter bubbles while still being able to recommend content to suit personal preferences. Come up with at least one suggestion.\u00c2\u00a0<\/p>\n

3) Regression<\/p>\n

Inhalte:
\nlinear \/ logistic regression<\/p>\n

Aufgabe 16: Linear regression
\nFrage:
\nCalculate the life expectancies for the following example cases<\/p>\n

Aufgabe 17: Life expectancy and education (part 1 of 2)
\nFrage:
\nGiven the data, what can you tell about the life expectancy of people who have 15 years of education?<\/p>\n

Aufgabe 18: Life expectancy and education (part 2 of 2)
\nFrage:
\nWhich of the following options would best match your estimate of the life expectancy for people with 15 years of education? Choose the most specific option that you think is justified by fitting the straight line model to the above data.<\/p>\n

Aufgabe 19: Logistic regression
\nFrage:
\nIf you wanted to have an 80% chance of passing a university exam, based on the above figure, how many hours should you approximately study for?<\/p>\n

Kapitel 5 Neural networks<\/strong><\/p>\n

Lernziele
\n \u00e2\u20ac\u00a2 Explain what a neural network is and where they are being successfully used
\n \u00e2\u20ac\u00a2 Understand the technical methods that underpin neural networks<\/p>\n

1) Neural network basics <\/p>\n

Inhalte:
\nElements of a neural network, deep learning<\/p>\n

Aufgabe 20: Elements of a neural network
\nFrage:
\nLabel the different components of a neuron into the diagram below<\/p>\n

2) How neural networks are built<\/p>\n

Aufgabe 21: Weights and inputs
\nFrage:
\nIn this exercise, consider the following expression that has both weights and inputs: 10.0 + 5.4 \u00c3\u2014 8 + (-10.2) \u00c3\u2014 5 + (-0.1) \u00c3\u2014 22 + 101.4 \u00c3\u2014 (-5) + 0.0 \u00c3\u2014 2 + 12.0 \u00c3\u2014 (-3) = -543.0<\/p>\n

Aufgabe 22: Activations and outputs
\nWhich of the activations described above gives:
\nthe largest output for an input of 5?
\nthe smallest output for an input of -5?
\nthe largest output for an input of -2.5?<\/p>\n

3) Advanced neural network techniques<\/p>\n

Inhalte:
\nConvolutional neural networks, generative adversarial networks.<\/p>\n

Kapitel 6 Implications<\/strong><\/p>\n

Lernziele
\n \u00e2\u20ac\u00a2 Understand the difficulty in predicting the future and be able to better evaluate the claims made about AI
\n \u00e2\u20ac\u00a2 Identify some of the major societal implications of AI including algorithmic bias, AI-generated content, privacy, and work<\/p>\n

1) About predicting the future<\/p>\n

Aufgabe 23: What is the perception of AI?
\nFrage:
\nFor this exercise, we want you to think about how AI is portrayed. Do an online\u00c2\u00a0image search\u00c2\u00a0for the term \u00e2\u20ac\u0153AI\u00e2\u20ac\u009d and see what kinds of pictures come up. If you are using Google search, you should choose „Images“ in the top of the screen.
\nWhat’s the general impression you get about AI from the image search results? Is this an accurate representation of AI? Why or why not?<\/p>\n

2) The societal implications of AI<\/p>\n

Inhalte:
\nAlgorithmic bias, Seeing is believing, Privacy, Work<\/p>\n

Aufgabe 24: Implications of AI
\nFrage:
\nWhat kind of articles (in newspapers and magazines or other popular science outlets such as blogs, …) are being written about AI – and do you think they are realistic? Do an online search about AI related to one of your interests.\u00c2\u00a0Choose one of the articles and analyze it.
\nMention the\u00c2\u00a0title of the article\u00c2\u00a0along with its author and where it was published (as a URL if applicable) in your answer.
\nExplain the central idea in the article\u00c2\u00a0in your own words\u00c2\u00a0using about a paragraph of text (multiple sentences.)
\nBased on your understanding, how accurate are the AI-related statements in the article?\u00c2\u00a0Explain your answer.\u00c2\u00a0Are the implications (if any) realistic?\u00c2\u00a0Explain why or why not.<\/p>\n

3) Summary<\/p>\n

Aufgabe 25: AI in your life
\nFrage:
\nHow do you see AI affecting you in the future, both at work and in everyday life? Include both the positive and possible negative implications.<\/p>\n","protected":false},"excerpt":{"rendered":"

Was sollte man als Laie \u00c3\u00bcber KI wissen? Der finnische MOOC „Elements of AI“ setzt sich zum Ziel das Thema der k\u00c3\u00bcnstlichen Intelligenz zu demystifizieren…<\/p>\n

WeiterlesenElements of AI: K\u00c3\u00bcnstliche Intelligenz demystifiziert<\/span><\/a><\/div>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[390],"tags":[],"class_list":["post-1906","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence","entry"],"_links":{"self":[{"href":"https:\/\/www.k-braungardt.de\/blogkb\/index.php?rest_route=\/wp\/v2\/posts\/1906","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.k-braungardt.de\/blogkb\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.k-braungardt.de\/blogkb\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.k-braungardt.de\/blogkb\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.k-braungardt.de\/blogkb\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1906"}],"version-history":[{"count":2,"href":"https:\/\/www.k-braungardt.de\/blogkb\/index.php?rest_route=\/wp\/v2\/posts\/1906\/revisions"}],"predecessor-version":[{"id":1908,"href":"https:\/\/www.k-braungardt.de\/blogkb\/index.php?rest_route=\/wp\/v2\/posts\/1906\/revisions\/1908"}],"wp:attachment":[{"href":"https:\/\/www.k-braungardt.de\/blogkb\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1906"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.k-braungardt.de\/blogkb\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1906"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.k-braungardt.de\/blogkb\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1906"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}