What is Artificial Intelligence? Everything in a nutshell
Introduction
Artificial intelligence (AI) is currently one of the hottest buzzwords in tech, and with good reason. The last few years have seen several innovations and advancements that have previously been solely in the realm of science fiction slowly transform into reality.
Experts regard artificial intelligence as a factor of production, which has the potential to introduce new sources of growth and change the way work is done across industries.
For instance, one PWC article predicts that AI could contribute $15.7 trillion to the global economy by 2035. China and the United States are primed to benefit the most from the coming AI boom, accounting for nearly 70% of the global impact.
Buckle up as we dive deep into the world of AI and explore what exactly artificial intelligence is and how it works!
What is Artificial Intelligence?
Today, “AI” describes a wide range of technologies that power many of the services and goods we use daily – from apps that recommend TV shows to chatbots that provide customer support in real-time.
But do all these constitute artificial intelligence as most of us envision it? And if not, then why do we use the term so often?
Artificial intelligence (AI) is the simulation of human intelligence in machines programmed to think and act like humans. Learning, reasoning, problem-solving, perception, and language comprehension are all examples of cognitive abilities.
Artificial intelligence (AI) is the theory and development of computer systems capable of performing tasks that historically required human intelligence, such as recognizing speech, making decisions, and identifying patterns.
AI is an umbrella term encompassing various technologies, including machine learning, deep learning, and natural language processing (NLP).
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.
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 or Narrow AI
Weak AI refers to AI systems 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 or General AI
Strong AI, or general AI, refers to AI systems that possess human-level intelligence or even surpass human intelligence across various tasks.
Strong AI would be capable of understanding, reasoning, learning, and applying 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.
4 Types of Artificial Intelligence
As researchers attempt to build more advanced forms of artificial intelligence, they must also begin to formulate more nuanced understandings of what intelligence or even consciousness precisely means.
In their attempt to clarify these concepts, researchers have outlined four types of artificial intelligence.
Reactive machines
Reactive machines are the most basic type of artificial intelligence. Machines built in this way don’t know previous events but only “react” to what is before them in a given moment.
As a result, they can only perform certain advanced tasks within a very narrow scope, such as playing chess, and are incapable of performing tasks outside of their limited context.
Limited memory machines
Machines with limited memory possess a limited understanding of past events. They can interact more with the world around them than reactive machines can.
For example, self-driving cars use a form of limited memory to make turns, observe approaching vehicles, and adjust their speed.
However, machines with limited memory cannot completely understand the world because their recall of past events is limited and only used in a narrow band of time.
Theory of mind machines
Machines with a “theory of mind” represent an early form of artificial general intelligence. In addition to being able to create representations of the world, machines of this type would also have an understanding of other entities that exist within the world. As of this moment, this reality has still not materialized.
Self-aware machines
Machines with self-awareness are the theoretically most advanced type of AI and would possess an understanding of the world, others, and itself. This is what most people mean when they talk about achieving AGI. Currently, this is a far-off reality.
What is Artificial General Intelligence (AGI)?
Artificial general intelligence (AGI) is a theoretical state in which computer systems can achieve or exceed human intelligence.
In other words, AGI is “true” artificial intelligence, as depicted in countless science fiction novels, television shows, movies, and comics.
As for the precise meaning of “AI” itself, researchers don’t quite agree on how we would recognize “true” artificial general intelligence when it appears.
However, the Turing Test or Imitation Game is the most famous approach to identifying whether a machine is intelligent.
This experiment was first outlined by influential mathematician, computer scientist, and cryptanalyst Alan Turing in a 1950 paper on computer intelligence.
Turing described a three-player game where a human “interrogator” is asked to communicate via text with another human and a machine and judge who composed each response.
Turing says the machine can be considered intelligent if the interrogator cannot reliably identify the human.
To complicate matters, researchers and philosophers also can’t quite agree whether we’re beginning to achieve AGI, if it’s still far off, or just impossible.
For example, while a recent paper from Microsoft Research and OpenAI argues that Chat GPT-4 is an early form of AGI, many other researchers are sceptical of these claims and argue that they were just made for publicity.
Regardless of how far we are from achieving AGI, you can assume that when someone uses the term artificial general intelligence, they’re referring to the kind of sentient computer programs and machines commonly found in popular science fiction.
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.
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, a subcategory of machine learning, allows AI to mimic a human brain’s neural network. It can make sense of patterns, noise, and sources of confusion in the data.
Machine Learning vs Deep Learning
Let’s first understand the underlying context and difference between machine learning and deep learning.
Machine Learning
Machine Learning focuses on developing algorithms and models that enable computers to learn from data and make predictions or decisions without explicit programming. Here are the key characteristics of machine learning:
1. Feature Engineering: In machine learning, experts manually engineer or select relevant features from the input data to aid the algorithm in making accurate predictions.
2. Supervised and Unsupervised Learning: Machine learning algorithms can be categorised into supervised learning, where models learn from labelled data with known outcomes, and unsupervised learning, where algorithms discover patterns and structures in unlabeled data.
3. 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 the key characteristics of deep learning:
1. Automatic Feature Extraction: Deep learning algorithms can automatically extract relevant features from raw data, eliminating the need for explicit feature engineering.
2. Deep Neural Networks: Deep learning employs neural networks with multiple layers of interconnected nodes (neurons), enabling the learning of complex hierarchical data representations.
3. 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.
How does deep learning (neural networks) work?
To understand this topic, let’s dissect the neural networks into 3 layers - input layer, hidden layer, and output layer. Now, let’s understand every layer briefly.
Input Layer
The images that we want to segregate go into the input layer. Arrows are drawn from the image onto the individual dots of the input layer.
Each white dot in the yellow layer (input layer) is a pixel in the picture. These images fill the white dots in the input layer.
Hidden Layer
The hidden layers are responsible for all our inputs' mathematical computations or feature extraction. 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 of them usually represents a float or decimal number 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.
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 to predict costs. Earlier, when we learned about four kinds of machines, we discussed machines with memory.
Applications of Artificial Intelligence
Artificial intelligence (AI) has various 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 like speech recognition, machine translation, sentiment analysis, and virtual assistants like Siri and Alexa.
Image & 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 & Automation
AI plays a crucial role in robotics and automation systems. Robots with AI algorithms can perform complex manufacturing, healthcare, logistics, and exploration tasks. 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.
Virtual Assistants & 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.
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.
Smart Homes & 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.
Cyber Security
AI helps detect and prevent cyber threats by analyzing network traffic, identifying anomalies, and predicting potential attacks. It can also enhance systems and data security through advanced threat detection and response mechanisms.
These are just a few examples of how AI is applied in various fields. AI's potential is vast, and its applications continue to expand as technology advances.
Examples of AI in our daily lives
Artificial Intelligence (AI) has become integral to our daily lives, revolutionizing various industries and enhancing user experiences. Here are some notable examples of AI applications:
Chat GPT
Chat GPT is an advanced language model developed by Open AI. It can generate human-like responses and engage 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 perform 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 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 road safety and efficiency.
Wearables
Wearable devices like 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.