Unterschiede

Hier werden die Unterschiede zwischen zwei Versionen angezeigt.

--- p:ki:fische_nachlese [2023/08/14 09:57] – [Limitationen] Ralf Kretzschmar
+++ p:ki:fische_nachlese [2024/05/04 10:56] (aktuell) – [🐟 Rückschau: Künstliche Intelligenz für echte Fische] Ralf Kretzschmar
@@ Zeile 4: / Zeile 4: @@
 [[https://www.flickr.com/photos/noaaphotolib/|NOAA Photo Library]] on flickr, CC BY 2.0)) }}]
-👩‍🦰 Erinnerst du dich an Sigrun, 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.
+👩‍🦰 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.
-⚠️ Solltest du dich nicht mehr daran erinnern (oder dieses Abenteuer noch nicht durchlebt haben) so raten wir dir, die vierteilige Story [[:p:ki:fische1|🐟 Künstliche Intelligenz für echte Fische]] durchzuarbeiten (ca. 45 Minuten) und anschliessend weiter unten mit Kapitel "4. Spezialfälle" weiterzufahren.
+⚠️ 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))}}] |
-~~NOTOC~~ {{INLINETOC 2-2}} \\
+~~INTOC~~
 ===== - Daten zusammenstellen =====
 [{{ :p:pasted:fischsamples.png?150px|Vermessene Fische((eigene Darstellung, [[https://creativecommons.org/publicdomain/zero/1.0/deed.de|CC0 1.0]])) }}]
-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 können 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.
+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.
 📐✏️ Für Sigrún hattest du zwei Messgrössen als Eingangsgrössen verwendet und die Fische gemäss diesen in ein Punktediagramm eingetragen. In der Abbildung rechts entspricht jede Achse einer Eingangsgrösse. Es wurden 12 Heringe und 12 Lodde vermessen und ins Punktediagramm eingetragen - die Heringe als blaue x, die Lodde als grüne ◯.
@@ Zeile 24: / Zeile 24: @@
 == ✍ Auftrag ==
   - Starte, um dich an alles besser erinnern zu können, noch einmal ein neuronales Netz Training, indem du auf den Button ''▶Run'' klickst.
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 <WRAP left box width=320px>{{gdraw>1mpAnvy6xbGSf-w0wHHanRW0LrmPxhFF-7qmc4ajmHu0 width=292 left}}\\
@@ Zeile 55: / Zeile 55: @@
 == ✍ 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>
 ===== - Spezialfälle =====
-⚠️ Nicht nur überlappende Klassen stellen eine Fehlerquelle dar, sondern auch seltene Ereignisse (Stürme, Börsencrash, seltene Krankheit etc.).
+⚠️ Nicht nur überlappende Klassen stellen eine Fehlerquelle dar, sondern auch seltene Ereignisse (z.B. die Vorhersage eines Sturm oder Börsencrashs oder das Erkennen einer seltenen Krankheit etc.).
 <WRAP center round box >
 == ✍ Auftrag ==
-  - Hier wird angenommen, der Fang eines Herings wäre ein seltenes Ereignis. Es gibt daher im Training- und im Validationset zwölfmal mehr Lodde-Beispiele als Hering-Beispiele. Kann das neuronale Netz trotzdem den Hering erkennen? (Alles grün Hinterlegte wird als Lodde erkannt, alles blau Hinterlegte als Hering.)\\ Klicke auf den Button ''▶Run'' und versuche dein Glück!
+  - Für dieses Beispiel wird einfach mal angenommen, der Fang eines Herings wäre ein seltenes Ereignis. Es gibt daher im Training- und im Validationset zwölfmal mehr Lodde-Beispiele als Hering-Beispiele. Kann das neuronale Netz trotzdem den Hering erkennen? (Alles grün Hinterlegte wird als Lodde erkannt, alles blau Hinterlegte als Hering.)\\ Klicke auf den Button ''▶Run'' und versuche dein Glück!
   - Wie sieht das Resultat aus? {{gem/plain?0=N4XyA#bfc3655d19c5b1fe}}
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 </WRAP>
@@ Zeile 77: / Zeile 78: @@
   * **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.
-  * **Genügend viele Daten:** Wenn zu wenige Daten vorhanden sind, so besteht die Gefahr von Overfitting.
+  * **Genügend viele Daten:** Wenn zu wenige Daten vorhanden sind, so besteht die Gefahr eines Auswendiglernens, d.h. die Gefahr von Overfitting.
 === Verfahren ===
-  * **Geeigneter Fehlerwert:** Das zu lösende Problem wird nicht direkt vom Verfahren gelöst. Es wird stattdessen ein Fehlerwert minimiert. Wenn der Fehlerwert zum Problem passt, kann eine brauchbare Lösung erzielt werden. Wenn der Fehlerwert weniger zum Problem passt, dann wird vermutlich ein anderes Problem gelöst. Bei der Fischklassifikation wird aus mathematischen Gründen nicht direkt mit dem Klassifikationsfehler trainiert, sondern mit einem Fehlerwert, der auf Summe der Differenzen zwischen tatsächlichen Output und Desired Output basiert.
+  * **Geeigneter Fehlerwert:** Das zu lösende Problem wird nicht direkt vom Verfahren gelöst. Es wird stattdessen ein Fehlerwert minimiert. Wenn der Fehlerwert zum Problem passt, kann eine brauchbare Lösung erzielt werden. Wenn der Fehlerwert weniger zum Problem passt, dann wird vermutlich ein anderes Problem gelöst. Bei der Fischklassifikation wird aus mathematischen Gründen nicht direkt mit dem Klassifikationsfehler trainiert, sondern mit einem Fehlerwert, der auf der Summe der Differenzen zwischen tatsächlichen Output und Desired Output basiert.
-  * **Sinnvolle Anzahl von Gewichten:** Je mehr Gewichte eingestellt werden können, desto mehr kann sich das Verfahren merken. Bei zu wenigen Gewichten kann der Zusammenhang nur schlecht gelernt werden, bei zu vielen Gewichten besteht die Gefahr von Overfitting.
+  * **Sinnvolle Anzahl von Gewichten:** Je mehr Gewichte eingestellt werden können, desto mehr kann sich das Verfahren merken. Bei zu wenigen Gewichten kann der Zusammenhang nur schlecht gelernt werden, bei zu vielen Gewichten besteht die Gefahr von Auswendiglernen, d.h. die Gefahr von Overfitting.
   * **Blackbox:** Die meisten Verfahren der KI speichern ihr Wissen in einer Vielzahl von abstrakten Gewichten ab. Es ist daher kaum möglich, herauszufinden, warum eine KI etwas macht. Die Resultate einer KI sind kaum nachvollziehbar.
@@ Zeile 87: / Zeile 88: @@
 Aus den folgenden Gründen werden die genannten Verfahren voraussichtlich (vielleicht selten, aber) immer Fehler produzieren, egal wie viel Rechenpower und wie viele Daten zur Verfügung stehen.
   * **Näherungsverfahren:** Die genannten Verfahren sind Näherungsverfahren. Sie können Sachverhalte häufig erstaunlich gut annähern, jedoch kaum exakte Lösungen liefern.
-  * **Überlappende Muster:** Zeigen 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.
+  * **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 Ereignisse:** Seltene Ereignisse werden tendenziell von den Verfahren ignoriert. Verlässliche Gegenmassnahmen sind bis dato nicht bekannt.
+  * **Seltene Muster:** Seltene Muster (oder seltene Ereignisse) werden tendenziell von den Verfahren ignoriert. Verlässliche Gegenmassnahmen sind bis dato nicht bekannt.
 ==== Fazit ====
@@ Zeile 97: / Zeile 98: @@
   - Haltet im Textfeld fest,
     * welche Möglichkeiten und Grenzen ihr für KI in der Zukunft seht.
-    * wo ihr die Verantwortung der Menschen sehr für das, was KI-Systeme produzieren.
+    * wo ihr die Verantwortung der Menschen seht für das, was KI-Systeme produzieren.
   - Vergleicht danach eure Überlegungen mit unserer Einschätzung.
 {{gem/plain?0=N4XyA#771f311d890f3eee}}
-++++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 deren Entscheide nicht brauchbar nachvollziehbar sein.\\ \\
+++++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 KI auch verantwortlich.++++
+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.++++
 </WRAP>

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