How is AI different from ML?

How is AI different from ML?

AI is not a new word. It dates back many decades, and computer scientists in the early 80s developed algorithms that learn and simulate human behavior.

On the learning side, the most important algorithm is the neural network, which has not been very successful due to over-fitting (the model is very powerful and there is not enough data). However, in some specific tasks, the idea of using data to fit a function has been a considerable success and is the foundation of machine learning today.

On the other hand, AI is very focused on image recognition, speech recognition and natural language processing. Experts spend a lot of time creating features like edge detection, color profiles, n-grams, and syntax trees. However, success is moderate.

Traditional machine learning

Machine learning (ML) techniques have played an important role in assessment, and ML has a rich set of model structures that have been subject to many generations:

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Linear regression.

Logistic Regression.

The decision tree.

Support vector machine.

The Bayesian model.

Regularization Model.

The ensemble model.

The neural network.

These models are based on some algorithmic structure for each of the attendance models, with the parameters being tunable knobs. Model Attendance model training consists of the following steps:

Select the model structure (for example, logistic regression, random forest, etc.).

Fill the form by exercise data (both input and output).

The learning algorithm generates the optimal model (i.e. a model with specific parameters that minimizes training errors).

Each model has its characteristics and works well in some tasks and is bad in others. But in general, we can classify them as a low-power (simple) model and a high-power (complex) model. Choosing between different models is a very tricky question.

Traditionally, it is preferred to use a lower power / simple model than to use a higher power / complex model for the following reasons:

Until we have huge processing power, training a high power model takes a long time.

As long as we have a large amount of data, training a high power model results in a high fitting problem (since the high power model has great parameters and fits a wide range of data structure, we can train a very specific model) Not generalized enough to make a good estimate).

However, selecting a low power model suffers from an "under-fit" problem where the model structure is very simple and cannot be matched if training data is more complex. (Note that g has a quadratic relation to the underlying data: y = 5 * x ^ 2; there is no way you can fit the linear regression: y = a * x + b, we choose $$ anonymous $$ n b.)

The main obstacle to machine learning is this feature engineering phase, which requires domain professionals to identify vital signs before feeding into the training process. The feature engineering phase demands a lot of manual and very rare domain expertise and has therefore become a major barrier to most machine learning tasks today.

In other words, if we do not have enough processing power and insufficient data, then we must use a low-power / linear model, which requires considerable time and effort to create appropriate input features. Most data scientists spend their time today.

Return of the neural network

In the beginning 2000s, computer processing power increased exponentially with cloud computing and massively parallel processing infrastructure, where large amounts of fine-grained event data were collected. We are no longer limited to a low power / simple model. For example, two of the most popular mainstream machine learning models today are random forest and gradient enhancing trees.

However, although both are very powerful and provide non-linear model fitting to training data, data scientists still need to create features to achieve better performance.

At the same time, computer scientists have re-examined the use of multiple layers of the neural network in performing these human simulations. This is a new birth for the DNN (Deep Neural Network) and provides significant advances in image classification and speech recognition. The main difference in DNN is that you can enter raw codes (for example, the RGB pixel value) directly into the DNN without creating domain specific input properties. Through several layers of neurons