Kunstmatige onvolkomenheid: waarom machine learning niet altijd de beste oplossing is


Kunstmatige onvolkomenheid: waarom machine learning niet altijd de beste oplossing is

Inleiding

Met de komst van kunstmatige intelligentie (AI) staan we voor nieuwe toepassingen binnen de technologie. Machine learning is een van de populairste soorten AI – het maakt gebruik van algoritmes om gegevens te analyseren en te leren van deze gegevens. Het kan echter ook problemen veroorzaken als het niet correct wordt gebruikt; machine learning heeft momenteel nog steeds beperkingen, waardoor het soms niet de beste oplossing is. In deze blogpost gaan we wat dieper in op de onvolkomenheden van machine learning en wat de alternatieve opties hier zijn.

Wat is Machine Learning?

Machine Learning (ML) is het proces waarbij computers leren uit gegevens door patronen te herkennen, modellen te maken en generieke regels te schrijven. Het is een subset van kunstmatige intelligentie (AI). De AL-algoritmes die worden gebruikt bij machine learning, zoals regressie, classificatie, recommendation engines, deep learning en neural networks, maken interessante voorspellingen over data. Met ML kunnen computers complexe taken uitvoeren zoals het voorspellen wat iemand zou willen kopen op basis van zijn of haar koopgeschiedenis, of het beoordelen van een foto op basis van de verzamelde informatie.

Wat zijn de onvolkomenheden van Machine Learning?

Machine Learning heeft nog steeds veel onvolkomenheden. De belangrijkste problemen zijn:

  • Onvoldoende gegevens: Machine Learning is afhankelijk van grote hoeveelheden gegevens om betrouwbare voorspellingen te maken. Als de hoeveelheid gegevens die wordt gebruikt te klein is, zullen de algoritmes onbetrouwbare conclusies trekken of zelfs geen conclusies trekken.
  • Te veel variabelen: Machine Learning werkt goed als de variabelen beperkt zijn. Als er te veel variabelen zijn, wordt het moeilijk om de gegevens te analyseren en voorspellingen te maken.
  • Inconsistente gegevens: Als de gegevens die worden gebruikt inconsistent zijn, zal het machine learning-model voor de verkeerde conclusies optreden. Dit kan leiden tot onbetrouwbare voorspellingen enreports.
  • Voorspellen: Machine Learning is goed in het voorspellen van toekomstige trends. Maar in sommige gevallen heeft het niet de mogelijkheid om toekomstige problemen aan te pakken, omdat de gegevens die worden gebruikt, misschien beperkt zijn.

Waarom is Machine Learning niet altijd de beste oplossing?

1. Machine Learning is slechts een manier om problemen op te lossen. Als het niet de beste tool voor het specifieke probleem is, zal het slechte resultaten opleveren.
2. ML heeft ook beperkingen als het gaat om het aanpassen of veranderen van zijn algoritmes, waardoor het moeilijk is om te reageren op veranderende omstandigheden.
3. ML-algoritmes zijn ook gevoelig voor fouten in de data die wordt gebruikt. Hierdoor is de nauwkeurigheid beperkt en kan het leiden tot onbetrouwbare resultaten.
4. Machine Learning kan ook moeite hebben met het afhandelen van data met veel variabelen, of wanneer de data incompleet is, waardoor het moeilijk is om betrouwbare modellen te maken.

Alternatieven voor Machine Learning

1. Het Expert System: Het expert systeem is een combinatie van algoritmes dat gebruikmaakt van kennis over een domein (bijv. Geneeskunde). Het is bedoeld om kennis te vergaren van experts uit het veld en te gebruiken om problemen op te lossen.
2. Linguïstische Analyse: Linguïstische analyse is de analyse van taal voor het detecteren van betekenis. Het gebruikt algoritmes om de structuur, betekenis en intentie van zinnen te analyseren. Dit is nuttig bij het verwerken van conversaties tussen mensen.
3. BesluitvormingBoom: Decision Tree analyseert gegevens om kennis te vergaren uit die gegevens. Het gebruikt verschillende algoritmes om kennis over een domein te vergaren en beslissingsbomen te genereren om intelligente beslissingen te nemen.

Conclusie

Volgens onze discussie, Machine Learning is een krachtig hulpmiddel om complexe problemen op te lossen. Maar het kan ook bepaalde nadelen hebben, zoals beperkte variabelen, inconsistentie en onvoldoende gegevens. Daarom is het belangrijk dat elke gebruiker een goed begrip heeft van de beperkingen van ML. Om de beste oplossing te vinden, zou men ook moeten overwegen om alternatieve oplossingen te combineren met ML, zoals expert systemen, linguïstische analyse en besluitvormende bomen.

Referenties

[1] Bengio, Y. (2012). Deep Learning ofrepresentation learning: Een beknopte introductie. Universiteit van Montreal, Universiteit van Toronto.
[2] Klapper, J. (2018). Beeldherkenning met machine learning. Bliksem.
[3] Lou, Y. (2020). De kracht van algoritmen, machin: een introductie tot machine learning. Codeburst.
[4] Palak, K. (2019). Expert System: Een beknopte introductie. DataFlair.
[5]Goyal, N. (2019). Besluitvormende bomen: Een beknopte introductie. KDNuggets.

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