Using AI to perform Cryptanalysis If given a large set of examples of cyphertext and corresponding plaintext, could AI be trained to decrypt a cyphertext as the examples provided?, and if so, are there any examples online demonstrating this?

For example, lets imagine out cypher is a simple 7-shift ceaser shift, and we have a list of thousands of examples like so

olssv,hello 
ifl,bye
dvysk,world


Could a neural network be trained on these examples, and then shown a new cyphertext, not in the training set, such as "isbl" and correctly infer the result as "blue"?
Yes it could work for simple ciphers. Here's a quick example:

# dependencies
import numpy as np

# machine learning
from keras.models import Sequential
from keras.layers import Dense

# constants
BASE = 97
MAX = 26

# let's assign a = 1, z = 26, ciphertext x and decrypted value y
y = np.arange(0,MAX,1)
x = np.roll(y,-7)

model = Sequential()
model.add(Dense(256, input_dim=1, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(MAX, activation='softmax'))

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

history = model.fit(x, y, epochs = 500, verbose=0)

for x_test in [['o', 'l', 's', 's', 'v'], \
               ['i','f','l'], \
               ['d', 'v', 'y', 's', 'k']]:
  x_test = list(map(lambda x: ord(x) - BASE, x_test))
  # print (x_test)

  pred = model.predict_classes(x_test)
  print(list(map(lambda x: chr(x + BASE), pred)))


Predicted output: link here

['h', 'e', 'l', 'l', 'o']
['b', 'y', 'e']
['w', 'o', 'r', 'l', 'd']