Data analysis, machine learning, deep learning, and Artificial Intelligence: differences and synergies.

Data analysis, machine learning, deep learning, and Artificial Intelligence: differences and synergies.

July 29, 2022

Massimo Re 

Abstract: This article stems from the numerous questions written in the LinkedIn email, especially by students from bachelor to Ph. Ds, aimed at better understanding the differences and interactions between Machine Learning, Deep Learning, and Artificial Intelligence. Someone much more authoritative than me said: "Ask, and you shall receive" I humbly say to ask, and I will answer it.

NB. There are no stupid questions.

Indice

Machine learning

Deep learning

Relationship with artificial intelligence (AI)

Data and scalability in the machine and deep learning

Performance requirements for machine and deep learning

Limitazioni in machine e deep learning

Machine learning process

Machine learning is a set of methods to create computer programs that can learn from observations and make predictions.

The Machine learning programs use the process as algorithms, regressions, and methods related to Data Sciences to understand databases. 

We might think of those algorithms as generally considered pretty close to statistical models and working on data networks. 

Deep learning

Deep learning is a subset of machine learning methods.

The data is analyzed across multiple layers of a deep learning network so that the network can draw conclusions and make decisions about the data.

Deep learning methods allow for great accuracy across large datasets, but these capabilities make deep learning much more resource-intensive than classic machine learning.

Relationship with artificial intelligence (AI)

We have used machine learning for several decades to get artificial intelligence into machines. Machine learning focuses on making computers capable of learning con and making decisions, which makes machine learning suitable for AI research. We propose deep learning was in the early stages of machine learning discussions. Still, few researchers have pursued deep learning methods because the computational requirements of deep learning are much more significant than classic machine learning. Instead, the model's design approach is to research specific and limited problems. However, the computing power of computers has increased exponentially since 2000, and researchers have been making considerable improvements in machine learning and building artificial intelligence. As deep learning models scale well with increased data, it has the potential to overcome the significant hurdles in building true AI.

Data and scalability in the machine and deep learning

Machine learning traditionally uses small data sets to learn and make predictions. With small amounts of data, researchers can determine precise characteristics that will help the machine learning program understand and learn from the data. However, if the program comes across information it can't classify based on its pre-existing algorithms, researchers will typically have to manually analyze the complex data and create a new feature.

Deep learning is best suited to large datasets, and models often require large datasets to be helpful. For this reason, classic machine learning usually doesn't scale well with vast amounts of data, but it can minimize errors on smaller data sets. Due to the complexity of a deep learning network, the network requires a significant amount of training data and additional data to test the network after training. Researchers are refining deep learning networks that can be more efficient and use smaller data sets.

Performance requirements for machine and deep learning

Machine learning has varying computer performance requirements. Many models can run on an average personal computer. The more advanced the statistical and mathematical methods, the more difficult it is for the computer to process the data quickly. Deep learning tends to be very resource-intensive.

Analyzing large amounts of information across multiple levels of decision-making requires a lot of computing power as computers get faster and deep learning becomes more accessible.

Limitations in the machine and deep learning

Traditionally, machine learning has some common and significant functions.

Overfitting is a statistical problem that can update a machine learning algorithm.

A machine learning algorithm contains specific "errors" when analyzing and predicting data.

The algorithm should show a relationship between the relevant variables, but in overfitting, it also starts capturing the error, which leads to a "noisy" or inaccurate model.

Machine learning models can also become biased towards the peculiarities of the data with which they are trained algorithms. This problem is particularly evident when researchers train on the entire available dataset instead of saving part of the data to test the algorithm.

Deep learning has the same statistical pitfalls as classic machine learning and unique problems.

For many, the available data is no problem training a reasonably accurate deep learning network.

It is often cost-prohibitive or impossible to collect more data or simulate a real-world problem, limiting the current range of topics for deep learning.

Difference between machine learning and deep learning.

Machine learning and deep learning describe the methods for teaching computers to learn and make decisions. 

Deep learning is a subset of classic machine learning, and some crucial divergences make deep learning and machine learning suitable for different applications.

Classic machine learning often includes feature engineering by programmers that helps the algorithm make accurate predictions on a small data set.

Deep learning algorithms are generally designed with multiple levels of decision-making to require less specific functionality engineering.

Deep learning is traditionally used for extensive data sets to train networks or algorithms to make many layered decisions.

Classic machine learning uses smaller data sets and is not as scalable as deep learning.

Although deep learning can learn well about many data, there are many problems where insufficient data is available to make deep learning useful.

Both deep learning and machine learning to share standard statistical limits and can be influenced if the training data set is very idiosyncratic or has been collected with improper statistical techniques.