How does AI classify information?

We understand that Artificial Intelligence (AI) has started playing a major role in almost every industry out there. We are also aware of the fact that Artificial Intelligence is capable of doing things like teaching individuals with the help of machine learning. However, how is it made possible? One way is through the classification of information. It is referred to as an algorithm that has been used for predicting what category specific data is known to belong to.

Machine learning is referred to as the component of Artificial Intelligence –the part that helps in allowing the revolutionary AI technology to learn from the respective past experiences, or data that had been processed by the same previously.

Machine learning is known to happen in either supervised or unsupervised manners. Supervised machine learning is known to make the process of predictions on the basis of existing data. A website that is known to recommend specific products or services might, for example, be making use of supervised learning.

However, how is this exactly possible? How is it possible to obtain information from the available lists of data for predictions about what you will be buying next?

It is known to make use of specific algorithms. One of the most famous one is the classification algorithm. The algorithm is regarded as useful for the automatic categorization of items in the given database. For instance, it can be used for predicting whether or not you will be marking some email that you might have received as spam.

What is Classification?

Classification in ML or Machine Learning, is when the computer or machine that makes use of an algorithm for drawing conclusions from data that is already there. Then, the given system is known to make use of the given conclusions for categorizing new data that has been received by the same. Classification algorithms are known to allow machines assign a specific category to some data point on the basis of training data.

Patrick Case at Pexels

Here are some aspects of AI and its classification of information that you should know about:

  • True positive –in terms of cricket, umpire giving a batsman Not Out when he is Not Out

  • True negative –umpire giving the batsman an Out when he might be Out

  • False positive –umpire giving the batsman a Not Out when he might be Out

  • False negative –umpire giving the batsman an Out when he might not be Not Out

The classification algorithm is capable of only determining one or two answers. This is what is known to make classification as distinct from other forms of supervised learning algorithms –for instance, in the case of regression algorithm. Though regression algorithms are also utilized for predicting some outcome, they are capable of doing so for specific values like some stock price. The given predictions are not only just some few values that the IT developer might have developed in the given database.

What are the Classification Metrics in AI?

If you are not sure of justifying whether or not, it can be regarded as something similar to the fact that you wish to get something, but you are not aware of what it is. In this post, we aim covering multiple classification metrics.

Classification Metrics in Artificial Intelligence

Introduction to Confusion Matrix

Confusion Matrix is required to be mentioned while introducing classification metrics. TP True Positive, TN True Negative, FP False Positive, and FN False Negative serves to be some of the basic elements.

  • True positive: The assumed result turns out to be positive, while the same has been labeled as positive.

  • True Negative: The assumed result turns out to be negative, while the same has been labeled as negative. It also goes by the name as Type II Error.

  • False Positive: The assumed result turns out to be positive, while the same has been labeled as negative. It is also known to go by the name as Type I Error.

  • True Negative: The assumed result turns out to be negative, while the same has been labeled as positive.

    On the basis of the given four elements, you can extend the same to multiple calculations like specificity, recall, and precision.

    Easy to Understand or Straightforward (Accuracy)

    Accuracy turns out to be one of the easiest measurements. It is simply known to analyze the total number of correct classifications out of the total count. When the classification problem happens to be balance, you can make use of the accuracy metric to serve as one of the major measurement metrics.

The Choice Dilemma (Recall & Precision)

While accuracy serves to be straightforward as well as easy to interpret, it might not always serve to be a great metric. For instance, you would want to classify in some rare cases, like whether an individual is a CEO or a billionaire. In rare cases, it is increasingly easier to achieve 99.9 percent accuracy.

Balance Dilemma (F1)

F1 is introduced in case the classification problem request for high recall and precision while accuracy is not regarded as good for measurement. From the given formula, you can observe that F1 is known to include both recall and precision result:

F1 = (2X Precision X Recall) / (Precision + Recall)

F1 can be utilized if you would want to balance recall and precision while the overall distribution is extremely uneven.

ROC (Receiver Operator Characteristic)

It is yet another metric that is utilized for representing the classification result. Unlike recall and precision, ROC includes false which implies the number of record that classifies to be negative and it is deemed to be correct. The metrics has been designed for the binary classifier. If the business problem serves to be a multi-class problem, you can look forward to converting the same to multiple problems of binary classification during the measurement.

Why is Python Programming Better for AI?


Gone are the days when JavaScript and HTML were considered the most reliable programming languages. Nowadays, the demand for Python for artificial intelligence and machine learning projects is growing rapidly. AI is all set to transform the world into a digital place where machines can automate nearly every human task. Spotify and Netflix are the best examples of AI algorithm. These apps offer song and movie suggestions based on the user’s browsing history.

As artificial intelligence is revolutionizing the world, more and more companies are planning to implement AI-based applications. With the growing demand for artificial intelligence, there comes a requirement for a coding language. The easier the programming language you choose, the faster you can develop the AI projects. That’s when Python comes into the picture. Considering its simple and effective syntax, it isn’t surprising to say that this programming language has become one of the best options for AI and ML. In this post, we are going to discuss the top benefits of using Python for AI.

· Vast Library for AI

One of the main reasons why Python is your ultimate choice for AI projects is its vast library. The programming language comes with the base-level items that are designed to help you code in a quick and efficient manner. Not only do they save you a lot of time on coding, but these libraries enable users to manage a large volume of data. The python libraries that make coding easier for the users are Pandas, Caffe, Scikit-learn, and Keras.

· Good Readability

The simple syntax and readable codes make Python a perfect option for beginners. If you are planning to learn a new programming language, then Python is your ideal option. Artificial Intelligence involves complex machines and sophisticated processes. Complex programming languages make it harder for the developer to achieve the development goals.

Python is an easy-to-read programming language. It makes it easier for the user to type lengthy codes. The sooner you are done with the coding, the faster you can test the algorithms and implement them. Besides that, an easy and readable programming language is a must when the project is managed by a group of developers.

· Great Community Support

This open-source programming language has gained a lot of attention over the past few years. Hundreds of thousands of developers have learned Python for software development, website designing, and other applications. That being said, rest assured that there is a professional and trained python community at your disposal (if you ever need help). They can guide you throughout the development process. You could find many Python tools, libraries, and documentation for free. You can talk to the professionals and Python experts to fix technical issues.

· Visualization Options

Python is known for its vast range of libraries and the visualization tools that make it a flexible and versatile programming language for artificial intelligence. No matter how complex the project is, Python can help you to accomplish your objectives.

What is the Difference between Machine Learning and Artificial Intelligence?


Machine learning and artificial intelligence have become a major trend these days. Both terms are used interchangeably. It is important to note that artificial intelligence and machine learning are two different concepts. Machine learning is a part of AI, while the latter is a broader concept that involves machines to automate the challenging tasks. AI is used in almost every industry. The best example is the online eCommerce shopping website, such as Amazon. It has implemented the AI algorithm to discover the customer’s buying history and offer them suggestions based on what they will be interested to purchase.

Contrary to the popular belief, AI isn’t new. In fact, it has been around for decades. Artificial Intelligence gained popularity after the first logical computer was launched. It had the ability to do arithmetic calculations, store data, and perform other basic tasks. However, machine learning and AI has garnered a lot of attention over the past few years. As technology is progressing, the developers are launching new devices and technical gadgets that are engineered to automate human tasks. The automated vehicles and online stock trading are other common examples of artificial intelligence.

Machine Learning Vs AI

Machine learning, on the other hand, is defined as the machine’s ability to function without being programmed. Unlike the outdated computers, the latest technical devices are designed to perform an extensive range of functions without human intervention. You don’t need to program this machine. In fact, these machines are smart enough to handle a large volume of data efficiently. Note that artificial intelligence is used to provide machines with the ability to learn from data. Machine learning is part or subset of the AI.

To put it in simple terms, AI can create machines that mimic humans. These machines can perform research, manage data, and find the required information seamlessly. As mentioned above, the machines do not need human intervention. You don’t have to program them. They use the data and algorithms to perform the basic tasks efficiently. What sets these machines apart from the standard computers is their unique ability to make predictions based on the statistical and qualitative data. They use a large volume of structured data to help people make sound decisions.

Siri and Alexa are the machines that run on AI algorithms. They extract information from the internet and complete the requested task using the data. Artificial intelligence focuses on developing smart solutions that can mimic human functions efficiently, while machine learning is based on the machine’s ability to learn from the data and experiences. These machines are made to carry out the requested tasks as efficiently as possible. While these advanced systems have the ability to perform tasks like a human, they have a limited scope. Machines cannot perform complex tasks. They can be used for the operations they are designed for. The best examples of machine learning tools are Google Search Algorithms, Amazon auto-recommendation feature, Facebook auto-tagging system, and more.

Code Code Editor Coding Computer  - Pexels / Pixabay

Organizing Data: A model from nature

When Tocsin Data gathers information from multiple sources it can get difficult to determine how to organize that data.

Many times there are false positives within the data set that are gathered, even when a specific keyword or phrase is searched for, it can lead to unexpected results.

Filtering results becomes more difficult as the internet and pool of sources expands for example, a simple search for blood type information might result in non-medical results such as the name of a heavy metal band, which in turn can end up causing a AI based system to start producing results in Metallurgy!

The solution to this can be found in nature.

Messor Ants are a genus of harvester ants that have a very simple set of instructions for organization which we can mimic in programming our search protocols. The method these ants use can be called “gather and dump”. Each worker wanders the area seeking food and supplies until it encounters an object deemed useful, it than gathers this item and wanders again until it encounters another of the same type and dumps it’s package at the same location. This is repeated continuously by that class of worker, resulting in a complex organization of supplies by very simple rules.

Data seen on the internet by humans is very limited to what is actually there, even within a known object such as an image. However, the contents of an image can contain several other data points that can aid in the sorting of the image into different piles of gathered data.

A human being manually sorting an group if images, even using tags found in exif information, might place two images of flowers into the same box, but an AI just sees data, and might place 99% of flower images in one box, and 1% in another. That action might indicate that the second pile of flower images contain strange data, such is used in steganography.

By removing human induced filters, and allowing a Messor like process, new gleems of information can be found, and better sorting into new categories can be created.

For more inspiration see: Foraging behavior in the ant genus Messor

– Dan Foscarini



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