I contributed technical content and opinions to an article titled “How AI Is Accelerating Machine Learning Development” on the December 2025 edition of the Pure AI web site. See https://pureai.com/articles/2025/12/03/how-ai-is-accelerating-machine-learning-development.aspx.

In a nutshell, AI can 1.) accurately determine which machine learning techniques are most likely to produce the best prediction results, 2.) generate machine learning program code, and 3.) tune machine learning model hyperparameters. As a crude rule of thumb, using generative AI typically reduces the time needed to create a machine learning prediction model by at least half, and often much more.

One example I that provided shows how AI can generate machine learning program code:

Suppose you submit a prompt such as, “Please give me a simple program, using the scikit-learn library, that creates a kernel ridge regression model for the dataset.”

The resulting program code is:

import numpy as np
import pandas as pd
from sklearn.kernel_ridge import KernelRidge

np.set_printoptions(precision=4, suppress=True,
  floatmode='fixed')

# Load the dataset
data = \
  pd.read_csv("synthetic_train_200.txt",
    comment="#")

X = data.iloc[:, :-1].values   # predictors
y = data.iloc[:, -1].values    # target

# Create and train the Kernel Ridge Regression model
model = KernelRidge(kernel="rbf", alpha=1.0, gamma=0.5)
model.fit(X, y)

# Predict on the data (no split)
y_pred = model.predict(X)

print("\nFirst 3 actual values:")
print(y[:3])

print("\nFirst 3 predictions:")
print(y_pred[:3])

The code is correct and when run, the output is:

First 3 actual values:
[0.1568, 0.8054, 0.1345]

First 3 predictions:
[0.1798, 0.7659, 0.1279]

In the article, I offer some opinions: “AI is definitely going to eliminate many tech jobs, but I don’t think AI will replace the data scientist role. I suspect that it’s more likely that AI will dramatically increase the speed and efficiency of data science activities.

“However, it’s quite possible that AI will negatively impact the job market for entry-level data scientist positions, which is a role that tends to be filled by recent college graduates.”

Steampunk is a sub-genre of science fiction that combines Victorian-era sensibilities with retro-futuristic, steam-powered technology. Here’s a nice AI-generated example of a steampunk car.