Unterschiede

Hier werden die Unterschiede zwischen zwei Versionen angezeigt.

--- p:ki:fische_nachlese [2024/05/04 10:56] – [🐟 Rückschau: Künstliche Intelligenz für echte Fische] Ralf Kretzschmar
+++ p:ki:fische_nachlese [2026/03/29 11:33] (aktuell) – Ralf Kretzschmar
@@ Zeile 1: / Zeile 1: @@
 {{ gem/pageinfo}}{{gem/mgr}}
 ====== 🐟 Rückschau: Künstliche Intelligenz für echte Fische ======
-[{{ :p:pasted:fish0191.jpg?320|Fischsortieren [NOAA Photo Library, CC BY 2.0](([[https://www.flickr.com/photos/noaaphotolib/5102531763/|fish0191]] by
+<figure right>{{:p:pasted:fish0191.jpg?320}}
-[[https://www.flickr.com/photos/noaaphotolib/|NOAA Photo Library]] on flickr, CC BY 2.0)) }}]
+<caption>Fischsortieren [NOAA Photo Library, CC BY 2.0](([[https://www.flickr.com/photos/noaaphotolib/5102531763/|fish0191]] by
+[[https://www.flickr.com/photos/noaaphotolib/|NOAA Photo Library]] on flickr, CC BY 2.0))</caption></figure>
 👩‍🦰 Erinnerst du dich an Sigrún, welche vor Island auf einem Hochseeschiff die beiden Fischsorten Hering und Lodde bei Wind und Wetter von Hand sortieren musste (siehe nebenstehende Abbildung)? Du hattest ihr geholfen, mithilfe eines neuronalen Netzes einen Fischsortierapparat zu entwickeln. Diese Seite fasst das Wichtigste noch einmal zusammen.
@@ Zeile 8: / Zeile 9: @@
 ⚠️ Solltest du dich nicht mehr daran erinnern (oder dieses Abenteuer noch nicht durchlebt haben) so raten wir dir, die ersten 4 Teile der Story [[:p:ki:fische1|🐟 Künstliche Intelligenz für echte Fische]] durchzuarbeiten (ca. 45 Minuten) und anschliessend weiter unten mit Kapitel "4. Spezialfälle" weiterzufahren.
-| [{{:p:pasted:clupea_harengus1.jpg?320|Hering [Citron, Public Domain](([[https://commons.wikimedia.org/wiki/File:Clupea_harengus1.jpg|Clupea harengus]] by [[https://commons.wikimedia.org/wiki/User:Citron|Citron]] on wikimedia, Public Domain))}}] | [{{:p:pasted:mallotus_villosus.gif?320|Lodde [Fb78, Public Domain](([[https://commons.wikimedia.org/wiki/File:Mallotus_villosus.gif|Mallotus villosus]] by [[https://commons.wikimedia.org/wiki/User:Fb78|Fb78]] on wikimedia, Public Domain))}}] |
+<figure center>
+<subfigure>{{:p:pasted:clupea_harengus1.jpg?320}}<caption>Hering [Citron, Public Domain](([[https://commons.wikimedia.org/wiki/File:Clupea_harengus1.jpg|Clupea harengus]] by [[https://commons.wikimedia.org/wiki/User:Citron|Citron]] on wikimedia, Public Domain))</caption></subfigure>
+<subfigure>{{:p:pasted:mallotus_villosus.gif?320}}<caption>Lodde [Fb78, Public Domain](([[https://commons.wikimedia.org/wiki/File:Mallotus_villosus.gif|Mallotus villosus]] by [[https://commons.wikimedia.org/wiki/User:Fb78|Fb78]] on wikimedia, Public Domain))</caption></subfigure>
+<caption>Die Fische in diesem Kapitel</caption>
+</figure>
 ~~INTOC~~
 ===== - Daten zusammenstellen =====
-[{{ :p:pasted:fischsamples.png?150px|Vermessene Fische((eigene Darstellung, [[https://creativecommons.org/publicdomain/zero/1.0/deed.de|CC0 1.0]])) }}]
+<figure right>{{:p:pasted:fischsamples.png?150px}}<caption>Vermessene Fische((eigene Darstellung, [[https://creativecommons.org/publicdomain/zero/1.0/deed.de|CC0 1.0]])) </caption></figure>
 Der Fischsortierapparat unterscheidet Hering und Lodde aufgrund mehrerer Messgrössen der Fische (z.B. Gewicht, Länge, Lichtreflexivität etc.). Für den Erfolg des Apparats ist die Auswahl dieser Messgrössen entscheidend. Wenn die Messgrössen schlecht gewählt sind, können die Fischsorten nicht oder nur schlecht voneinander getrennt werden und jeder noch so „intelligente“ Apparat wird versagen. Ebenso müssen alle "Varianten" einer jeden Fischsorte in den Daten berücksichtigt werden (z.B. kleine, mittlere und grosse Fische). Wird der Apparat z.B. nur auf grosse Fische geeicht, so wird dieser bei kleinen Fischen versagen.
@@ Zeile 26: / Zeile 31: @@
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 </WRAP>
-<WRAP left box width=320px>{{gdraw>1mpAnvy6xbGSf-w0wHHanRW0LrmPxhFF-7qmc4ajmHu0 width=292 left}}\\
+<figure left>{{gdraw>1mpAnvy6xbGSf-w0wHHanRW0LrmPxhFF-7qmc4ajmHu0 width=292 left}}
-Das von uns verwendete neuronale Netz((eigene Darstellung, [[https://creativecommons.org/publicdomain/zero/1.0/deed.de|CC0 1.0]]))</WRAP>
+<caption>Das von uns verwendete neuronale Netz((eigene Darstellung, [[https://creativecommons.org/publicdomain/zero/1.0/deed.de|CC0 1.0]]))</caption></figure>
-[{{ :p:pasted:decisionboundaries2.png?347px|Decision Boundaries((eigene Darstellung, [[https://creativecommons.org/publicdomain/zero/1.0/deed.de|CC0 1.0]])) }}]
+<figure right>{{:p:pasted:decisionboundaries2.png?347px}}<caption>Decision Boundaries((eigene Darstellung, [[https://creativecommons.org/publicdomain/zero/1.0/deed.de|CC0 1.0]])) </caption></figure>
 Das von uns verwendete neuronale Netz besitzt zwei Input Neuronen, mehrere Hidden Neuronen und ein Output Neuron. Die beiden Eingangsgrössen der Fische werden links in die beiden Input Neuronen eingegeben, als Resultat wird rechts vom Output Neuron eine Zahl zwischen 0 und 1 ausgegeben.
@@ Zeile 34: / Zeile 40: @@
 Bevor das neuronale Netz gebraucht werden kann, muss dieses mit einem Trainingset trainiert werden. Das Trainingset besteht aus mehreren vermessenen Fischen, von welchen wir wissen, zu welcher Fischart sie gehören. Für jede Lodde soll eine 1 vom neuronalen Netz ausgegeben werden, für jeden Hering 0, diese beiden Zahlen werden als "Desired Output" bezeichnet. Aus der Differenz neuronaler Netz Output und Desired Output wird ein Fehlerwert berechnet. Im Training werden die Gewichte zuerst zufällig gewürfelt. Danach werden sie in mehreren Schritten mithilfe des Trainingssets so eingestellt, dass der Fehlerwert immer kleiner wird. Ein Durchgang des gesamten Trainingsets wird als Epoche bezeichnet. In der Regel umfasst ein Training viele Epochen.
-Ist das neuronale Netz trainiert, bleiben die Gewichte fix und das neuronale Netz kann verwendet werden, um unbekannte Fische zu "klassifizieren". Wir legen dabei fest, dass Output < 0.5 als Hering erkannt wird und ein Output ≥ 0.5 als Lodde. Der Wert 0.5 wird als Threshold (Grenzwert) bezeichnet. Um herauszufinden, wie gut die Klassifikation ist, wird ein zweites Set verwendet, das "Validationset".
+Ist das neuronale Netz trainiert, bleiben die Gewichte fix und das neuronale Netz kann verwendet werden, um unbekannte Fische zu "klassifizieren". Wir legen dabei fest, dass Output < 0.5 als Hering erkannt wird und ein Output ≥ 0.5 als Lodde. Der Wert 0.5 wird als Grenzwert bezeichnet. Um herauszufinden, wie gut die Klassifikation ist, wird ein zweites Set verwendet, das "Validationset".
 In den vier Abbildungen rechts sind alle Eingangsgrössenpaare, welche den Output 0.5 produzieren, als rote Punkte eingezeichnet. Sie bilden eine Kurve, die sogenannte "Decision Boundary". Alles oben links davon wird als Lodde, alles unten rechts davon als Hering erkannt. Für die Training- und Validationsets sind jeweils die Anzahl falsch klassifizierter Fische angegeben. In den beiden unteren Abbildungen wird die Decision Boundary von einzelnen Fischen beeinflusst. Sie ist stark gekrümmt und der Fehler für das Validationset ist viel grösser als für das Trainingsset. Dies wird als "Overfitting" (Auswendiglernen) bezeichnet. In den beiden oberen Abbildungen erfasst die Decision Boundary den groben Unterschied der beiden Fischarten relativ gut, die Fehler für Training- und Validationset sind ähnlich gross, das neuronale Netz "generalisiert" wie gewünscht.
@@ Zeile 41: / Zeile 47: @@
 ===== - Resultate einordnen =====
-[{{ p:pasted:classoverlap.png?185px|Überlappende Klassen((eigene Darstellung, [[https://creativecommons.org/publicdomain/zero/1.0/deed.de|CC0 1.0]])) }}]
+<figure right>{{p:pasted:classoverlap.png?185px}}<caption>Überlappende Klassen((eigene Darstellung, [[https://creativecommons.org/publicdomain/zero/1.0/deed.de|CC0 1.0]])) </caption></figure>
 Du hattest mit dem Fischsortierapparat, der zwischen Hering und Lodde unterscheidet, eine Klassifikationsrate von rund 90% erreicht. Das hört sich nach viel an, jedoch sind 10% falsche Fische für Sigrún unbrauchbar.
@@ Zeile 55: / Zeile 61: @@
 == ✍ Auftrag ==
   - Just for fun, klicke auf den Button ''▶Run''. Der grüne Bereich wird als Lodde erkannt, der blaue als Hering, der weisse Bereich ist der "Keine Ahnung"-Bereich. Das Ziel besteht darin, ausserhalb des "Keine Ahnung"-Bereichs 100% ✔️ Klassifikationsrate zu erreichen und den "Keine Ahnung"-Anteil (die Prozentzahl nach dem 🗑️-Symbol) so klein als möglich zu halten.
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 </WRAP>
@@ Zeile 77: / Zeile 83: @@
 === Daten ===
   * **Relevante Eingangsgrössen:** Welche und wie viele Eingangsgrössen braucht es? Bei unpassenden Eingangsgrössen versagt jedes Verfahren. Z.B. lassen sich mit den Schweizer-Lottozahlen kaum Fische unterscheiden.
-  * **Umfassende Daten:** Es braucht Daten, welche alle möglichen, relevanten Fälle beinhalten. Wenn Fälle im Training fehlen, so produziert das Verfahren anschliessend für diese Fälle unsinnige Antworten. Z.B. versagt eine Fischklassifikation für kleine Fische, wenn diese nur mit grossen trainiert wurde.
+  * **Umfassende Daten:** Es braucht Daten, welche alle möglichen, relevanten Fälle beinhalten. Wenn Fälle im Training fehlen oder in einer zu geringen Anzahl vorkommen, so produziert das Verfahren anschliessend für diese Fälle unsinnige oder einseitige Antworten. Z.B. versagt eine Fischklassifikation für kleine Fische, wenn diese nur mit grossen trainiert wurde.
   * **Genügend viele Daten:** Wenn zu wenige Daten vorhanden sind, so besteht die Gefahr eines Auswendiglernens, d.h. die Gefahr von Overfitting.
@@ Zeile 90: / Zeile 96: @@
   * **Überlappende Muster:** Besitzen mehrere zu unterscheidende Datenpunkte dieselben oder genügend ähnliche Eingangsgrössen, so können diese nicht verlässlich unterschieden werden. Überlappende Muster treten in praktisch allen nicht künstlich erzeugten Datensätzen auf.
   * **Seltene Muster:** Seltene Muster (oder seltene Ereignisse) werden tendenziell von den Verfahren ignoriert. Verlässliche Gegenmassnahmen sind bis dato nicht bekannt.
+  * **Transparenz:** Aufgrund des Blackbox-Charakters der gängigen KI-Verfahren, kann weder vernünftig erklärt noch begründet werden, warum eine KI bei der Eingabe X die Ausgabe Y produziert.
+  * **Bias:** Unausgewogene Trainingsdaten, seltene oder fehlende Muster führen zu einseitigen oder unsinnigen Antworten. Dieses Verhalten wird als "Bias" bezeichnet. Z.B. sind KI-Chatbots nicht "neutral" in ihren Aussagen, da die zugrundeliegenden Trainingsdaten (mehrheitlich Texte aus dem Internet) auch nicht "neutral" sind.
 ==== Fazit ====
@@ Zeile 103: / Zeile 111: @@
 ++++Unsere Einschätzung|KI-Systeme wie die Fischklassifikation oder KI-Chat-Anwendungen können erstaunlich gute Resultate liefern, da ist sehr wahrscheinlich noch viel zu erwarten. Jedoch werden solche Systeme auch in Zukunft praktisch nie völlig fehlerfrei arbeiten und warum ein solches System etwas entscheidet, oder ausgibt, werden wir vermutlich auch in Zukunft nicht vollständig nachvollziehen können.\\ \\
-Wir Menschen wählen Daten, Eingangsgrössen, Verfahren, Anzahl Gewichte, Fehlerwerte und Thresholds aus. Wir Menschen bestimmen, wie mit den Resultaten, die eine KI produziert, umgegangen wird. Somit sind wir für unsere KIs auch verantwortlich.++++
+Wir Menschen wählen Daten, Eingangsgrössen, Verfahren, Anzahl Gewichte, Fehlerwerte und Grenzwerte aus. Wir Menschen bestimmen, wie mit den Resultaten, die eine KI produziert, umgegangen wird. Somit sind wir für unsere KIs und deren Bias auch verantwortlich.++++
 </WRAP>

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