Artificial Intelligence

What Is Artificial Intelligence

Artificial Intelligence is a method of making a computer, a computer-controlled robot, or a software think intelligently like the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. The outcome of these studies develops intelligent software and systems.

A Brief History of Artificial Intelligence

Here’s a brief timeline of the past six decades of how AI evolved from its inception.

1956 - John McCarthy coined the term ‘artificial intelligence’ and had the first AI conference.

1969 - Shakey was the first general-purpose mobile robot built. It is now able to do things with a purpose vs. just a list of instructions.

1997 - Supercomputer ‘Deep Blue’ was designed, and it defeated the world champion chess player in a match. It was a massive milestone by IBM to create this large computer.

2002 - The first commercially successful robotic vacuum cleaner was created.?

2005 - 2019 - Today, we have speech recognition, robotic process automation (RPA), a dancing robot, smart homes, and other innovations make their debut.

2020 - Baidu releases the LinearFold AI algorithm to medical and scientific and medical teams developing a vaccine during the early stages of the SARS-CoV-2 (COVID-19) pandemic. The algorithm can predict the RNA sequence of the virus in only 27 seconds, which is 120 times faster than other methods.?

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 characteristics of each type:

Weak AI (Narrow AI)

Weak AI refers to AI systems that are designed to perform specific tasks and are limited to those tasks only. These AI systems excel at their designated functions but lack general intelligence. Examples of weak AI include voice assistants like Siri or Alexa, recommendation algorithms, and image recognition systems. Weak AI operates within predefined boundaries and cannot generalize beyond their specialized domain.

Strong AI (General AI)

Strong AI, also known as general AI, refers to AI systems that possess human-level intelligence or even surpass human intelligence across a wide range of tasks. Strong AI would be capable of understanding, reasoning, learning, and applying knowledge to solve complex problems in a manner similar to human cognition. However, the development of strong AI is still largely theoretical and has not been achieved to date.

Types of Artificial Intelligence?

Below are the various?types of AI:

1. Purely Reactive

These machines do not have any memory or data to work with, specializing in just one field of work. For example, in a chess game, the machine observes the moves and makes the best possible decision to win.?

2. Limited Memory

These machines collect previous data and continue adding it to their memory. They have enough memory or experience to make proper decisions, but memory is minimal. For example, this machine can suggest a restaurant based on the location data that has been gathered.

3. Theory of Mind

This kind of AI can understand thoughts and emotions, as well as interact socially. However, a machine based on this type is yet to be built.?

4. Self-Aware

Self-aware machines are the future generation of these?new technologies. They will be intelligent, sentient, and conscious.

?Deep Learning vs. Machine Learning

Let's explore the contrast 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 key characteristics of machine learning:

Feature Engineering: In machine learning, experts manually engineer or select relevant features from the input data to aid the algorithm in making accurate predictions.
Supervised and Unsupervised Learning: Machine learning algorithms can be categorized into supervised learning, where models learn from labeled data with known outcomes, and unsupervised learning, where algorithms discover patterns and structures in unlabeled data.
Broad Applicability: Machine learning techniques find application across various 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 human brain's structure and functioning. Here are key characteristics of deep learning:

Automatic Feature Extraction: Deep learning algorithms have the ability to automatically extract relevant features from raw data, eliminating the need for explicit feature engineering.
Deep Neural Networks: Deep learning employs neural networks with multiple layers of interconnected nodes (neurons), enabling the learning of complex hierarchical representations of data.
High Performance: Deep learning has demonstrated exceptional performance in domains such as computer vision, natural language processing, and speech recognition, often surpassing traditional machine learning approaches.
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How Does Artificial Intelligence Work?

Put simply, AI systems work by merging large with intelligent, iterative processing algorithms. This combination allows AI to learn from patterns and features in the analyzed data. Each time an Artificial Intelligence system performs a round of?data processing, it tests and measures its performance and uses the results to develop additional expertise.

Ways of Implementing AI?

Let’s explore the following ways that explain how we can implement AI:

Machine Learning

It is?machine learning?that gives AI the ability to learn. This is done by using?algorithms?to discover patterns and generate insights from the data they are exposed to.?

Deep Learning

Deep learning, which is a subcategory of machine learning, provides AI with the ability to mimic a human brain’s?neural network.?It can make sense of patterns, noise, and sources of confusion in the data.

Consider an image shown below:

Here we segregated the various kinds of images using deep learning. The machine goes through various features of photographs and distinguishes them with a process called feature extraction. Based on the features of each photo, the machine segregates them into different categories, such as landscape, portrait, or others.?

Let us understand how deep learning works.?

Consider an image shown below:

The above image depicts the three main layers of a neural network:

Input Layer
Hidden Layer
Output Layer
Input Layer

The images that we want to segregate go into the input layer. Arrows are drawn from the image on to the individual dots of the input layer. Each of the white dots in the yellow layer (input layer) are a pixel in the picture. These images fill the white dots in the input layer.

We should have a clear idea of these three layers while going through this artificial intelligence tutorial.

Hidden Layer

The hidden layers are responsible for all the mathematical computations or feature extraction on our inputs. In the above image, the layers shown in orange represent the hidden layers. The lines that are seen between these layers are called ‘weights’. Each one of them usually represents a float number, or a decimal number, which is multiplied by the value in the input layer. All the weights add up in the hidden layer. The dots in the hidden layer represent a value based on the sum of the weights. These values are then passed to the next hidden layer.

You may be wondering why there are multiple layers. The hidden layers function as alternatives to some degree. The more the hidden layers are, the more complex the data that goes in and what can be produced. The accuracy of the predicted output generally depends on the number of hidden layers present and the complexity of the data going in.

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Output Layer

The output layer gives us segregated photos. Once the layer adds up all these weights being fed in, it'll determine if the picture is a portrait or a landscape.

Example - Predicting Airfare Costs

This prediction is based on various factors, including:

Airline?
Origin airport?
Destination airport
Departure date

We begin with some historical data on ticket prices to train the machine. Once our machine is trained, we share new data that will predict the costs. Earlier, when we learned about four kinds of machines, we discussed machines with memory. Here, we talk about the memory only, and how it understands a pattern in the data and uses it to make predictions for the new prices as shown below:

AI Programming Cognitive Skills: Learning, Reasoning and Self-Correction

Artificial Intelligence emphasizes three cognitive skills of learning, reasoning, and self-correction, skills that the human brain possess to one degree or another. We define these in the context of AI as:

Learning: The acquisition of information and the rules needed to use that information.
Reasoning: Using the information rules to reach definite or approximate conclusions.
Self-Correction: The process of continually fine-tuning AI algorithms and ensure that they offer the most accurate results they can.

However, researchers and programmers have extended and elaborated the?goals of AI?to the following:

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 a former world champion, Garry Kasparov.
Knowledge Representation
Smalltalk?is an object-oriented, dynamically typed, reflective programming language that was created to underpin the “new world” of computing exemplified by “human-computer symbiosis.”
Planning and Navigation?
The process of enabling a computer to get 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.
Perception?
Use computers to interact with the world through sight, hearing, touch, and smell.
Emergent Intelligence?
Intelligence that is not explicitly programmed, but emerges from the rest of the specific AI features. The vision for this goal is to have machines exhibit emotional intelligence and moral reasoning.

Some of the tasks performed by AI-enabled devices include:

Speech recognition?
Object detection
Solve problems and learn from the given data?
Plan an approach for future tests to be done
What is Artificial Intelligence: Advantages and Disadvantages of AI

Artificial intelligence has its pluses and minuses, much like any other concept or innovation. Here’s a quick rundown of some pros and cons.

Pros
It reduces human error
It never sleeps, so it’s available 24x7
It never gets bored, so it easily handles repetitive tasks
It’s fast
Cons
It’s costly to implement
It can’t duplicate human creativity
It will definitely replace some jobs, leading to unemployment
People can become overly reliant on it

Let us continue this article on What is Artificial Intelligence by discussing the?applications of AI.

What is Artificial Intelligence: Applications of Artificial Intelligence

Artificial intelligence (AI) has a wide range of applications across various industries and domains. Here are some notable applications of AI:

Natural Language Processing (NLP)

AI is used in NLP to analyze and understand human language. It powers applications such as speech recognition, machine translation, sentiment analysis, and virtual assistants like Siri and Alexa.

Image and Video Analysis

AI techniques, including computer vision, enable the analysis and interpretation of images and videos. This finds application in facial recognition, object detection and tracking, content moderation, medical imaging, and autonomous vehicles.

Robotics and Automation

AI plays a crucial role in robotics and automation systems. Robots equipped with AI algorithms can perform complex tasks in manufacturing, healthcare, logistics, and exploration. They can adapt to changing environments, learn from experience, and collaborate with humans.

Recommendation Systems

AI-powered recommendation systems are used in e-commerce, streaming platforms, and social media to personalize user experiences. They analyze user preferences, behavior, and historical data to suggest relevant products, movies, music, or content.

Financial Services

AI is extensively used in the finance industry for fraud detection, algorithmic trading, credit scoring, and risk assessment. Machine learning models can analyze vast amounts of financial data to identify patterns and make predictions.

Healthcare

AI applications in healthcare include disease diagnosis, medical imaging analysis, drug discovery, personalized medicine, and patient monitoring. AI can assist in identifying patterns in medical data and provide insights for better diagnosis and treatment.

Virtual Assistants and Chatbots

AI-powered virtual assistants and chatbots interact with users, understand their queries, and provide relevant information or perform tasks. They are used in customer support, information retrieval, and personalized assistance.

Gaming

AI algorithms are employed in gaming for creating realistic virtual characters, opponent behavior, and intelligent decision-making. AI is also used to optimize game graphics, physics simulations, and game testing.

Smart Homes and IoT

AI enables the development of smart home systems that can automate tasks, control devices, and learn from user preferences. AI can enhance the functionality and efficiency of Internet of Things (IoT) devices and networks.

Cybersecurity

AI helps in detecting and preventing cyber threats by analyzing network traffic, identifying anomalies, and predicting potential attacks. It can enhance the security of systems and data through advanced threat detection and response mechanisms.

These are just a few examples of how AI is applied in various fields. The potential of AI is vast, and its applications continue to expand as technology advances.

Artificial Intelligence Examples

Artificial Intelligence (AI) has become an integral part of our daily lives, revolutionizing various industries and enhancing user experiences. Here are some notable examples of AI applications:

ChatGPT

ChatGPT is an advanced language model developed by OpenAI, capable of generating human-like responses and engaging in natural language conversations. It uses deep learning techniques to understand and generate coherent text, making it useful for customer support, chatbots, and virtual assistants.

Google Maps

Google Maps utilizes AI algorithms to provide real-time navigation, traffic updates, and personalized recommendations. It analyzes vast amounts of data, including historical traffic patterns and user input, to suggest the fastest routes, estimate arrival times, and even predict traffic congestion.

Smart Assistants

Smart assistants like Amazon's Alexa, Apple's Siri, and Google Assistant employ AI technologies to interpret voice commands, answer questions, and perform tasks. These assistants use natural language processing and machine learning algorithms to understand user intent, retrieve relevant information, and carry out requested actions.

Snapchat Filters

Snapchat's augmented reality filters, or "Lenses," incorporate AI to recognize facial features, track movements, and overlay interactive effects on users' faces in real-time. AI algorithms enable Snapchat to apply various filters, masks, and animations that align with the user's facial expressions and movements.

Self-Driving Cars

Self-driving cars rely heavily on AI for perception, decision-making, and control. Using a combination of sensors, cameras, and machine learning algorithms, these vehicles can detect objects, interpret traffic signs, and navigate complex road conditions autonomously, enhancing safety and efficiency on the roads.

Wearables

Wearable devices, such as fitness trackers and smartwatches, utilize AI to monitor and analyze users' health data. They track activities, heart rate, sleep patterns, and more, providing personalized insights and recommendations to improve overall well-being.

MuZero

MuZero is an AI algorithm developed by DeepMind that combines reinforcement learning and deep neural networks. It has achieved remarkable success in playing complex board games like chess, Go, and shogi at a superhuman level. MuZero learns and improves its strategies through self-play and planning.

These examples demonstrate the wide-ranging applications of AI, showcasing its potential to enhance our lives, improve efficiency, and drive innovation across various industries.?