Artificial Intelligence is Shaping 2024 and Beyond: Artificial Intelligence (AI) is one of the hottest terms in technology right now, and for good reason.
The past few years have seen many innovations and advancements that were previously only in the realm of science fiction, slowly turning into reality.
Experts see artificial intelligence as a factor in manufacturing, with the potential to introduce new sources of growth and change the way industries work. For example, this PWC article predicts that AI could contribute $15.7 trillion to the global economy by 2035. China and the United States will benefit the most from the coming AI boom, accounting for nearly 70% of the global impact.
This Simplilearn tutorial provides an overview of AI, including how it works, its advantages and disadvantages, its applications, certifications, and why it’s a good field to master.
1. What is Artificial Intelligence?
Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and act like humans Learning, reasoning, problem-solving, perception, and language comprehension are all examples of cognitive abilities.
Artificial intelligence is a method of making computers, computer-controlled robots, or software think intelligently like the human brain.
AI is accomplished by studying the patterns of the human brain and analyzing the cognitive process. The results of these studies lead to the development of intelligent software and systems.
2. How does Artificial Intelligence work?
1. Simply put, AI systems operate by merging large-scale with intelligent, iterative processing algorithms. This combination allows AI to learn from patterns and idiosyncrasies in the data it analyzes. Each time an artificial intelligence system performs a round of data processing, it checks and measures its performance and uses the results to develop additional skills.
2. It is machine learning that gives AI the ability to learn. This is done by using algorithms to find patterns and generate insights from the data they encounter.
3. Deep learning, which is a subcategory of machine learning, provides AI with the ability to mimic the neural networks of the human brain. It can understand patterns, noise and sources of confusion in the data.
4. Consider the image shown below:
5. Here we have differentiated different types of images using deep learning. The machine goes through the various features of the photographs and separates them through a feature extraction process. Based on the characteristics of each photo, the machine divides them into different categories such as landscape, portrait or others.
6. The image above shows the three main layers of a neural network:
Input Level
The images to be separated go to the input layer. Arrows are drawn from the image to individual points of the input layer. Each white dot in the yellow layer (input layer) is a pixel in the image. These images fill the input layer with white dots.
You should have a clear idea of these three levels while going through this Artificial Intelligence tutorial.
Hidden layer
Hidden layers are responsible for all the mathematical calculations or feature extraction on our input. In the image above, the layers shown in orange represent hidden layers. The lines appearing in these layers are called ‘weights’ Each of them usually represents a float number or a decimal number, which is multiplied by the value in the input layer. All weights are added in the hidden layer. The dots in the hidden layer represent the value based on the sum of the weights. These values are then passed to the next hidden layer.
You might be wondering why there are so many levels. Hidden layers act as an alternative to some extent. The higher the hidden layers, the more complex the data that goes in and what can be created. The accuracy of the predicted result usually depends on the number of hidden layers present and the complexity of the data.
Output level
The output layer gives us a different photo. Once all these weights given by the layer are added, it will decide whether the picture is portrait or landscape.
Example – Estimating airfare costs
This prediction is based on various factors, including:
• Airline
• Airport of origin
• Destination airport
• Date of departure
Let’s start with some historical data on ticket prices to train the machine.
Once our machines are trained, we will share new data that will lead to cost estimates. Earlier when we learned about the four types of machines, we discussed machines with memory. Here, we only talk about memory, and how it perceives patterns in data and uses it to predict new prices as shown below:
AI Programming Cognitive Skills: Learning, Reasoning, and Self-Improvement
Artificial intelligence emphasizes the three cognitive skills of learning, reasoning, and self-improvement, skills that the human brain possesses to one degree or another. We define them in the context of AI as:
Learning: the acquisition of information and the rules necessary to use that information.
Reasoning: Using rules of information to arrive at definite or approximate conclusions.
Self-Improvement: AI algorithms continuously fine-tune the process and ensure that they deliver the most accurate results possible.
However, researchers and programmers have expanded and broadened the goals of AI to:
Logical reasoning
AI programs enable computers to perform sophisticated tasks. On February 10, 1996, IBM’s Deep Blue computer won a game of chess against former world champion Garry Kasparov.
Knowledge Representation
Smalltalk is an object-oriented, dynamically typed, reflective programming language designed to support the “new world” of computing exemplified by “human-computer symbiosis”.
Planning and Navigation
The process of enabling a computer to travel from point A to point B. A prime example of this is Google’s self-driving Toyota Prius.
Natural language processing
Set up computers that can understand and process language.
Understanding
Use computers to interact with the world through sight, hearing, touch, and smell.
Emergency intelligence
Intelligence that is not explicitly programmed but emerges from the rest of the specific AI characteristics. The goal is for machines to demonstrate emotional intelligence and moral reasoning.
Some of the tasks performed by AI-enabled devices include:
• Speech recognition
• Object detection
• Solve problems and learn from given data
• Plan the approach for future tests
3. Types of Artificial Intelligence
Below are the different types of AI:
1. Fully reactive
These machines have no memory or data to work with, specializing in only one area of work. For example, in a game of chess, the machine observes the moves and makes the best decision to win.
2. Limited memory
These machines collect past data and keep adding to their memory. They have enough memory or experience to make sound decisions but lack memory. For example, this machine can suggest restaurants based on the location data collected.
3. Theory of Mind
This type of AI can understand thoughts and emotions, as well as interact socially. However, a machine based on this type is yet to be produced.
4. Self-awareness
Self-aware machines are the future generation of this new technology. They will be intelligent, sensitive and aware.
4. Weak AI Vs. Strong AI
When discussing artificial intelligence (AI), it is common to distinguish between two broad categories: weak AI and strong AI. Let’s explore the features of each type:
• Weak AI (Narrow AI)
Weak AI refers to AI systems that are designed to perform specific tasks and are limited to only those tasks. These AI systems excel at their assigned tasks but lack general intelligence.
Weak AI refers to AI systems that are designed to perform specific tasks and are limited to only those tasks. These AI systems excel at their assigned tasks but lack general intelligence. Examples of weak AI include voice assistants like Siri or Alexa, recommendation algorithms, and image recognition systems. Weak AI works within predefined boundaries and cannot generalize beyond its particular domain.
• Strong AI (Normal AI)
Strong AI, also known as general AI, refers to AI systems that have human-level intelligence or even surpass human intelligence in a wide range of tasks. Strong AI will be able to understand, reason, learn and apply knowledge to solve complex problems like human cognition. However, the development of strong AI is still largely theoretical and has not been achieved to date.
5. Deep Learning vs Machine Learning
Let’s explore the difference between deep learning and machine learning:
Machine Learning:
Machine learning focuses on the development of algorithms and models that enable computers to learn from data and make predictions or decisions without explicit programming. Here are the key features of machine learning:
1. Development of functions. In machine learning, experts manually design or select appropriate features from input data to help algorithms make accurate predictions.
2. Supervised and unsupervised learning: Machine learning algorithms can be classified into supervised learning, where models learn from labeled data with known outcomes, and unsupervised learning, where algorithms find patterns and structures in unlabeled data.
3. Broad applicability: Machine learning techniques find applications across a variety of domains, including image and speech recognition, natural language processing, and recommendation systems.
Deep Learning:
Deep learning is a subset of machine learning that focuses on training artificial neural networks inspired by the structure and function of the human brain. Here are the key features of deep learning:
1. Automatic Feature Extraction: Deep learning algorithms have the ability to automatically extract relevant features from raw data, eliminating the need for explicit feature engineering.
2. Deep Neural Networks: Deep learning uses neural networks with multiple layers of interconnected nodes (neurons), enabling the learning of complex hierarchical representations of data.
3. High performance: Deep learning has demonstrated exceptional performance in areas such as computer vision, natural language processing and speech recognition, often outperforming traditional machine learning methods.