Trends in Artificial Intelligence

Areas to look at

• 1 hot encoding for categorical variables
• Random forest for tackling over fitting
• Scikit learn

Resources/concepts to reference

• YouTube video reference: 3blue1brown
• Study linear algebra
• L2 regularization – for Bayesian classification
  • Down sampling
    • to ensure category within dataset does not crowd out other category
    • Example if 85% are negative examples, we need to figure out how to reduce this percentage from 85%
  • Up sampling
    • to ensure category within dataset is not under-represented
    • Example if only 5% are positive examples, we need to figure out how to increase the examples by coming up with more variants within this dataset so as to boost it above 5%

Recent trends

• Technical trend: One shot learning
  • how to determine features and corresponding coefficients from examination of limited dataset – assumption is that small data is big data in disguise
  • Counter argument against this trend – humans are born with rudimentary mental model which means that they are born knowing what these features are
• Political and Social trend: DAT ASS – Data and Artificial Intelligence as a service

Top conferences for machine learning

• International conference on machine learning – ICML
• NIPS

source: From my conversations with Sujit