All You Need To Know About Black Box Machine Learning

From the past few decades, deep learning has been encouraged very frequently as it has increased the interest of people in the field of artificial intelligence. But just like every coin has two sides deep learning to have its merits as well as demerits. One of the key problems of this field is the “Black box problem”.

This problem which is Black box machine learning is particularly the challenge of making sense in a way in which complex machine learning algorithms make decisions. If you have heard about the Apple card disaster then you will be well aware of this situation.

Meaning

In the field of deep learning, there are various kinds of projects in which a lot of variety of work done in the field of artificial intelligence involves developing techniques that usually try to let us understand the decision made by a machine learning algorithm without breaking or opening up the black box.

Many developers prefer that they should buy or choose for artificial intelligence that is inertly interpretable us those which provides their explanations. If we dive deeper into the discussion of this we get to know that there are certainly two types of artificial intelligence present; which are explainable AI and interpretable AI respectively. But there are some fundamental differences between these two.

Interpretable AI algorithm provides a clear explanation of their decision-making process that is they can be interpreted. Whereas in total contrast to this Explainable AI are the tools that usually apply to the algorithm that doesn’t provide a clear explanation of their decisions, there they are not easily interpreted by the user.

Types

There are certainly two types of black-box machine learning in here which are:-

1. Functions that are too complicated for any human to understand.
2. Proprietary functions.

The first kind of Black box AI which are the functions that are too complicated for any human to understand includes a deep neural network. As we certainly know that the deep neural network is usually composed of layers. In that the layers are of interconnected variables that may become tuned as the network provided is trained in various examples.

Neural networks may become larger and larger so that it becomes virtually impossible to trace as there are millions of parameters are combined to make a certain and specific decision. Even certain AI engineers are unable to precisely destruct them or the decisions of neural network.

The second type of Black Box AI which are functions that are proprietary algorithms. It is certainly a reference to the companies who hide the details of their AI systems for many reasons. There could be many reasons for that such as preventing details from authorities, preventing bad factors from entering the system plus many more.

In this particular sector of the Black box machine the person who created the AI logic might be able to decode it or have knowledge of its inner logic. But the people who are using it are certainly unaware of its working.

There are many kinds of Black Box Artificial Intelligence in neural networks and deep learning techniques. It includes Google search’s ranking algorithm, facebook’s newsfeed, amazon’s recommendation system, and many more which we usually use in our day-to-day life.

original source :-https://www.ftmsinternational.com/