With the development of technology, we have been collecting more data and processing that amount of data becomes more important than ever. Restrictions on the human brain and human resources have led scientists towards a more advanced technology – Artificial Intelligence (AI). So what is the difference between AI, ML, and DL. Let’s find out the answer below.
Artificial Intelligence (AI) is any technique that aims to enable computers to mimic human behavior, including machine learning, natural language processing (NLP), language synthesis, computer vision, robotics, sensor analysis, optimization and simulation.
In Artificial Intelligence, the most important ones are:
- Data mining (DM) is also called knowledge discovery in databases, in computer science, the process of discovering interesting and useful patterns and relationships in large volumes of data. The field combines tools from statistics and artificial intelligence (such as neural networks and machine learning) with database management to analyze large digital collections, known as data sets.
- Machine Learning (ML) is a subset of AI techniques that enables computer systems to learn from previous experience (i.e. data observations) and improve their behavior for a given task. ML techniques include Support Vector Machines (SVM), decision trees, Bayes learning, k-means clustering, association rule learning, regression, neural networks, and many more.
- Neural Networks (NNs) or artificial NNs are a subset of ML techniques, loosely inspired by biological neural networks. They are usually described as a collection of connected units, called artificial neurons, organized in layers.
- Deep Learning (DL) is a subset of NNs that makes the computational multi-layer NN feasible. Typical DL architectures are deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GAN), and many more.
The business understanding: This is the phase to understand the basics of the content and purpose of learning (based on the provided quest formulations and data description)
The data understanding: This phase is to prepare documents and necessary information in order to collect data and build a training model.
The data preparation: This is an important phase because the dataset in this stage has a major impact on the training. The data preparation consists of data transformation, exploratory data analysis (EDA) and feature engineering. Each of them can be further divided into smaller sub-steps; e.g., feature engineering consists of feature extraction, feature selection.
The modeling: This phase consists of selecting algorithms, building a model set for a dataset, conducting training and selecting the most effective model. In the modeling phase, various ML algorithms can be applied with different parameter calibrations. The model train-test-evaluation cycle can be repeated again and again thanks to the combination between data and parameter variability. If the data is large-scale, the modeling phase will have time-consuming and compute-intensive requirements.
The evaluation: This phase can be performed under various criteria for thorough testing of the ML models in order to choose the best model for the deployment phase.
The deployment: After meeting the criteria, the model will be deployed for real data.
In general machine learning, most of the data preparation and modeling processes are quite time-consuming (preparation and execution). To shorten processing time, improving algorithms, or upgrading hardware is also a good method but not a highly effective one when it comes to processing large amounts of data.
Since the data set of AI is mostly independent data, subdivision and parallel processing are possible. Taking advantage of this feature, there are two methods generated to increase ML / DL processing speed with Big Data effectively.
- Using Graphics Processing Unit – GPU:
The difference between computation on GPU and normal computation on CPU is the ability to compute in parallel at once. Usually, the computation on the CPU (central processing unit) takes place in a sequential way. Computation in the GPU is completely different when the computations are performed in parallel. Therefore, adding more cores helps the GPU have tremendous computing power to implement and combine the statistical matrix in the machine learning model.
DL makes a profit of using a form of specialized hardware present in accelerated computing environments. The current mainstream solution (NVIDIA 2018) has been to use Graphics Processing Unit (GPU) as general-purpose processors. GPUs provide massive parallelism for large-scale DM problems, allowing scaling algorithms vertically to data of volumes that are not computable by traditional approaches (Cano 2018). GPUs are effective solutions for real-world and real-time systems requiring very fast decision and learning, such as DL (especially in image processing).
In addition to the GPU, we can use other devices such as Google Tensor Processing Unit 3.0 (TPU), IBM TrueNorth Neuromorphic chip, Microsoft BrainWave (Microsoft), Nervana Neural Network Processor (Kloss), AMD Radeon Instinct (AMD).
- Use Map-Reduce:
Map-Reduce was invented by Google engineers. Map-Reduce helps split data into multiple blocks and divides it into multiple storage computers (still ensuring data integrity and availability). Then handle those pieces in parallel and independently on distributed computers.
With the MapReduce model running on a large number of machines (thousands of machines), processing data up to Terabytes is normal.
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