What do you think about AL vs ML vs DL?
It’s almost harder to understand all the acronyms that surround artificial intelligence(AI) than the underlying technology. Couple that with the different disciplines of AI as well as application domains and it’s easy for the average person to tune out and move on.
AI is an umbrella discipline that covers everything related to making machines smarter. Machine Learning(ML) is commonly used along with AI but it is a subset of AI. ML refers to an AI system that can self-learn based on the algorithm. Systems that get smarter and smarter over time without human intervention are ML. Deep Learning(DL) is machine learning applied to large data sets. Most AI works involve ML because behavior requires considerable knowledge. Data scientists should know AL, ML, and DL.
Artificial Intelligence: Mimicking the intelligence or behavioral pattern of humans or any other living entity.
Machine Learning: A technique by which a computer can ‘learn’ from data, without using a complex set of different rules. This approach is mainly based on training a model from datasets.
Deep Learning: A technique to perform machine learning inspired by our brain’s own network of neurons.
Artificial Intelligence
Artificial Intelligence(AI) is intelligence demonstrated by machines, which is unlike the natural intelligence displayed by humans and animals. AI is concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector
Application:
- Netflix and Spotify recommendation system
- Self-driving cars
- Smart assistants(like Siri and Alexa)
- Spam filters on email
- Optimizes, personalized healthcare treatment recommendation
Machine Learning
Machine Learning(ML), is a subset of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. ML focuses on the development of a computer program that can access data and use it learn to themselves ML provides stats tools to analyze the data, prediction models, recommendation systems, etc. ML is usually of three types supervised ML, unsupervised ML, and reinforcement ML.
Supervised ML algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. In this method, each example is a pair consisting of an input object(typically a vector) and the desired output value. A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. Supervised ML is of two types: Classification and Regression.
Classification is the problem of identifying to which of a set of categories a new observation belongs, based on a training set of data containing observations(or instances) whose category membership is known. Examples are assigning a given email to the “spam” or “non-spam” class, and assigning a diagnosis to a given patient based on observed characteristics of the patient(sex, blood pressure, presence or absence of certain symptoms, etc.).
Regression analysis is a set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables. The most common form of regression analysis is linear regression. Regression analysis is primarily used for two conceptually distinct purposes. First, regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Second, in some situations, regression analysis can be used to infer causal relationships between the independent and dependent variables.
Algorithm:
- Naive Bayes classifier
- Nearest Neighbor algorithm
- Support Vector Machine
- Random Forest
- Maximum entropy classifier
- Artificial neural network
Applications:
- Handwriting recognition
- Information extraction
- Spam detection
- Pattern recognition
- Speech recognition
Unsupervised learning is a type of machine learning that looks for previously undetected patterns in a data set with no pre-existing labels and with a minimum of human supervision. Cluster analysis is used I unsupervised learning to the group, or segment, datasets with shared attributes to extrapolate algorithmic relationships. Cluster analysis is a branch of machine learning that groups the data that has not been labeled, classified, or categorized.
Approaches:
- Hierarchical clustering
- K-means
- DBSCAN
- K-nearest neighbor clustering
Reinforcement learning is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.
Deep Learning
Deep Learning(DL), is an AI function that imitates the working of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network. Artificial neural networks were inspired by information processing and distributed communication nodes in biological systems. ANNs have differences from biological brains. Early work showed that a linear perceptron cannot be a universal classifier, and then that a network with a nonpolynomial activation function with on hidden layer of unbounded width can on the other hand so be.
Deep learning architecture such as deep neural networks, convolutional neural networks, and recurrent neural networks have been applied to fields listed below:
- Computer vision
- Machine vision
- Speech recognition
- Natural language processing
- Audio recognition
- Machine translation
- Medical image analysis
Warming up
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