AI made an opening entrance that we won’t forget. Opportunities and challenges in Agile Software Development arose as AI merged into this field. That’s why we offer you some food for thought: stop and capitalize!
We delve into 5 effects and must-know if you don’t want to be out of the loop. Bear in mind that AI is in a hypersonic-speed growing process, so keeping up to date with it is a must!
Data Collection and Preprocessing
AI algorithms rely heavily on data, and software developers need to collect and preprocess data before feeding it into AI models. So, you must excel in data structures and algorithms.
Here you’ll find an example if you want to explore a bit more, with some coding tips!
Image classification: developers must first collect a large dataset of labeled images, then preprocess the images to prepare them for analysis by the AI algorithm. This involves resizing the images, converting them to grayscale, and normalizing pixel values. The AI algorithm uses it to learn patterns and features in the images and classify them.
- Convolutional Neural Networks (CNNs) are a type of neural network that are often used for image classification tasks.
The basic building block of a CNN is a convolutional layer, which applies a set of filters to the input image to extract features. The output of the convolutional layer is then passed through fully connected layers to produce a final classification.
Here’s an example code snippet for a simple CNN in Python using the TensorFlow library:
import tensorflow as tf
from tensorflow.keras import layers
# Define the CNN model
model = tf.keras.Sequential([
layers.Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)),
layers.MaxPooling2D(pool_size=(2, 2)),
layers.Flatten(),
layers.Dense(10, activation='softmax')
])
# Compile the model
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
# Train the model
model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))This code defines a simple CNN with one convolutional layer, one max pooling layer, and one fully connected layer.
The input data is assumed to be 28×28 grayscale images. The model is trained using the Adam optimizer and categorical cross-entropy loss.
- Support Vector Machines (SVMs) are a type of supervised learning algorithm that are used for image classification. SVMs work by finding a hyperplane that separates the data into different classes.
In the case of image classification, the hyperplane separates images into different categories based on their features.
Here’s an example for an SVM in Python using the scikit-learn library:
from sklearn import svm
# Define the SVM model
model = svm.SVC(kernel='linear', C=1, gamma='auto')
# Train the model
model.fit(x_train, y_train)
# Evaluate the model
score = model.score(x_test, y_test)
print('Accuracy:', score)This code defines an SVM with a linear kernel and trains it using the training data. The model is then evaluated using the test data, and the accuracy is printed.
Some examples of this are Google photos, Amazon Rekognition, Microsoft Azure Cognitive Services, and Clarifai. Click on them to explore!
Speech recognition: software developers must first collect a large dataset of audio recordings, then preprocess the recordings to extract features that are relevant to speech recognition: pitch, duration, and spectral content. The AI algorithm uses the data to learn patterns and features to recognize spoken words.
- Hidden Markov Models (HMMs): HMMs are a statistical model commonly used in speech recognition. HMMs work by modeling the acoustic properties of speech as a sequence of hidden states that correspond to phonemes or other speech units, and emitting observations (acoustic features) that are associated with each state.
Here’s an example code snippet in Python using the Hidden Markov Model Toolkit (HTK):
# Create a new HTK model
model = HTKModel()
# Define the model parameters
model.num_states = 5
model.num_mixes = 3
model.vector_size = 13
# Train the model using the HTK training tool
htk_train(model, "train.list")
# Recognize a test utterance using the HTK recognition tool
result = htk_recognize(model, "test.mfc")This code creates a new HMM model using the HTK toolkit, sets the model parameters, and trains the model using a list of training utterances.
The model is then used to recognize a test utterance and output the recognition result.
- Deep Neural Networks (DNNs) are another type of neural network that are used for speech recognition. DNNs work by mapping input features (e.g., Mel-frequency cepstral coefficients or MFCCs) to units or parts of speech.
Here’s an example code snippet in Python using the Keras library:
# Define the DNN model
model = Sequential([
Dense(512, activation='relu', input_shape=(num_frames*num_features,)),
Dropout(0.5),
Dense(num_classes, activation='softmax')
])
# Compile the model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# Train the model
model.fit(train_x, train_y, validation_data=(test_x, test_y), epochs=10, batch_size=32)This code defines a DNN model using the Keras library with two fully connected layers and a dropout layer for regularization.
The model is then compiled and trained using categorical cross-entropy loss and the Adam optimizer.
Some real life examples are Alexa, Siri, Electronic Health Records, Text-to-speech or Speech-to-text.
Recommendation systems: software developers must first collect a large dataset of user preferences and behavior, then preprocess the data to extract features that are relevant to recommendations, such as ratings, purchase and search history. The AI then makes personalized recommendations to users.
Some of the most known and used apps that apply this are: Spotify, Youtube, Netflix and the like.
Here you’ll find some hands-on examples to apply into your own project:
Sample Case #1: Spotify
- Collaborative Filtering: This algorithm is used by Spotify to recommend songs based on the preferences of users with similar tastes.
The algorithm analyzes the listening history of millions of users to find patterns and preferences, and then recommends songs that users with similar tastes have enjoyed. Here is a Python code for it:
from surprise import SVD
from surprise import Dataset
from surprise.model_selection import cross_validate
# Load the dataset
data = Dataset.load_builtin('ml-100k')
# Use SVD algorithm
algo = SVD()
# Run cross-validation
cross_validate(algo, data, measures=['RMSE', 'MAE'], cv=5, verbose=True)- Natural Language Processing: Spotify also uses NLP to analyze lyrics and metadata to recommend songs based on emotions expressed in the lyrics.
Here is an example code in Python for NLP:
import spacy
# Load the English language model
nlp = spacy.load('en_core_web_sm')
# Analyze song lyrics
doc = nlp("I'm feeling so alive, I can't stop dancing")
sentiment = doc.cats['sentiment']
# Recommend songs based on sentiment
recommendations = get_songs_with_sentiment(sentiment)Sample Case #2: YouTube
- Neural Networks are used to analyze video content and recommend videos based on their visual and audio features.
The algorithm works like NLP but analyzes the characteristics of videos, such as the colors and sounds, and then recommends videos that are visually and acoustically similar to the user’s preferences.
Here is an example code in Python for neural networks:
import tensorflow as tf
from tensorflow import keras
# Load the video dataset
data = keras.datasets.cifar10
# Define the neural network model
model = keras.Sequential([
keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(32,32,3)),
keras.layers.MaxPooling2D((2,2)),
keras.layers.Conv2D(64, (3,3), activation='relu'),
keras.layers.MaxPooling2D((2,2)),
keras.layers.Flatten(),
keras.layers.Dense(10, activation='softmax')
])
# Train the neural network
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))Caveat! The actual algorithms and codes used by Spotify and YouTube are much more complex and sophisticated than what is shown here.
Even though you could be a rookie, AI models need to be developed using programming languages such as Python, R, or Java; so there’s a degree of expertise needed.
Software developers need to have expertise in API development and integration to ensure that the AI system works seamlessly and efficiently with existing systems.
Let’s say you are developing a mobile app that uses speech recognition to transcribe audio. So, you could use an API from a speech recognition service provider, such as Google Cloud Speech-to-Text or Amazon Transcribe.
Here’s how the integration could work:
- In your mobile app code, you would integrate the API by making an HTTP request to the speech recognition service, passing in the audio file as input.
- The API would then use ML algorithms to analyze the audio and convert it into text.
- The resulting transcription would be returned to your app as a text string.
- You could then use this text string in your app as needed, for example, displaying it to the user or storing it in a database.
The API provides a powerful and scalable solution for speech recognition, allowing you to leverage the advanced machine learning models and infrastructure provided by the service provider without having to build and train your own models from scratch.
Testing and Debugging:
AI models are complex, and testing and debugging them can be challenging. Software developers need to have expertise in testing and debugging AI models to ensure that they work as expected.
Here you’ll find some posts that could help you out! Chek’em!
After developing an AI model, software developers need to deploy it in a production environment and maintain it over time. This requires expertise in cloud computing, DevOps, and software maintenance.
The intersection points between AI and software development require a broad range of skills and expertise. However, don’t hesitate to take a leak of faith and get hands on.
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