Which of the following is a Subset of Artificial Intelligence?

Introduction;

You may have heard the term artificial intelligence, or AI, in news headlines lately, but if you’re like most people, you don’t understand what it is or how it works.

It’s important to keep up with emerging technologies to ensure that you stay competitive in your field and can apply them to your business when they are appropriate and useful.

In this article, we’ll explain what artificial intelligence encompasses, how it works, and its uses so that you can decide whether it will benefit your business endeavors or not.

What is AI?

Which of the following is a Subset of Artificial Intelligence AI stands for artificial intelligence, which is defined as a branch of computer science that deals with intelligent behavior in machines.

Some examples of AI include IBM’s Watson, Google’s DeepMind, and Amazon Alexa.

AI can be broken down into three categories: weak AI, strong AI, and superintelligence.

Weak AI refers to technologies that enable computers to do things that would require human intelligence if done manually such as facial recognition software or speech recognition software.

Strong AI refers to technologies that enable computers to do things that would require human intelligence if done manually and also exhibit some type of intelligence themselves.

This includes technologies like IBM’s Watson which can answer Jeopardy questions or Google’s DeepMind which can beat humans at the game Go.

Superintelligence is when computers can do things that would require human intelligence if done manually and also exhibit superior intellect. An example of this is the chess-playing computer Deep Blue which beat Gary Kasparov back in 1997.

What are the different types of AI?

The two different types of artificial intelligence are machine learning and deep learning. Machine learning focuses on teaching computers to learn from data, and it has been around for decades.

Deep learning, which focuses on neural networks, has seen more recent success in tasks like speech and image recognition as well as natural language processing.

The difference between machine and deep learning is that machine-learning algorithms can only do what they are programmed to do

while deep-learning algorithms use neural networks with many layers that require an input, process it, and produce an output.

Machine-learning algorithms work by finding patterns or making predictions based on previous data points or actions taken;

whereas deep-learning algorithms work by using structures called artificial neural networks that are loosely based on how human brains work.

What are some common applications of AI?

There are many applications of artificial intelligence in today’s society.

For example, some cars have an automated driving mode that can be activated when the driver wants to let the car drive.

The car will then use sensors and cameras to detect any potential hazards and avoid them by driving around them.

A subset of artificial intelligence is machine learning which has enabled computers to be able to learn without being explicitly programmed how to do so.

Machine learning can be used for tasks such as translating languages or recommending what song you might like based on your past listening preferences.

Computer vision allows for devices that are similar to human eyes to scan photos and identify items and facial expressions.

Text recognition software enables machines to read text regardless of whether it is printed, handwritten, or typed. Speech recognition allows machines to convert speech into text that they can understand.

These tasks can be difficult for humans because they require

pattern-matching skills which the brain usually develops with years of practice but people cannot put into words how they were developed or implemented in their brains.

Machine learning relies on different techniques such as supervised and unsupervised learning.

What are some ethical concerns around AI?

While artificial intelligence has the potential to help us solve some of our most complex problems, it also raises many ethical concerns.

One concern is that AI will be used to make decisions that are not easily made by humans, such as which people should receive life-saving medical care.

Another concern is that AI will be used for surveillance and censorship purposes, or maybe weaponized in ways that could lead to global catastrophe.

However, there are also ethical issues related to designing AI systems in ways that account for human biases.

In her 2016 TED Talk, computer scientist Joy Buolamwini says that despite all

The innovation happening around machine learning and AI today, we’re still largely ignoring one of the core principles on which AI is based:

fairness.

In her research, she found that facial analysis software designed for law enforcement data sets showed that darker-skinned women were more likely to be misidentified than lighter-skinned men.

That’s a bias, she points out, that does not exist when you have images of white male faces.

Conclusion

After reading this post, you should be aware that there are many subsets of artificial intelligence and not all AI technologies are used for the same things.

It’s important to understand how each subset is different from one other to use them best.

There are six subsets of artificial intelligence: –

Deep learning can do things like language translation or natural speech processing because it mimics human brain networks.

Natural Language Processing (NLP) performs tasks like question answering, automatic translation, or semantic analysis.

Computer Vision does things like recognizing faces in photos or classifying images as landscape vs animal photos. Image Recognition uses deep learning and computer vision together on common data sets to categorize or identify objects with the use of algorithms such as LBP Labeling Toolbox or DeepLab-V3+. Machine Learning refers to any type of algorithm that processes big data without being programmed by humans and includes supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and more!