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What is Artificial Intelligence (AI)?
Artificial Intelligence (AI) is a machine's ability to perform tasks commonly associated with human cognitive functions. These tasks include interpreting speech, playing games, and identifying patterns. AI systems learn to perform these tasks by processing massive amounts of data and looking for patterns to model in their decision-making.
Sometimes, humans supervise an AI's learning process by reinforcing and discouraging good decisions. However, some AI systems use designs to learn without supervision, such as repeatedly playing a video game until they figure out the rules and how to win.
AI experts distinguish between two types of AI: strong and weak. Strong AI, also known as artificial general intelligence, refers to a machine that can solve problems without training, much like a human can. This type of AI is the Holy Grail for many AI researchers, but creating it has been challenging and comes with potential risks.
In contrast, weak AI, also known as narrow or specialized AI, operates within a limited context and is a simulation of human intelligence applied to a narrowly defined problem. Although weak AI may seem intelligent, it operates under far more constraints and limitations than essential human intelligence.
Weak AI often focuses on performing a task exceptionally well, such as driving a car, transcribing human speech, or curating content on a website. Examples of weak AI include intelligent assistants like Siri and Alexa, self-driving cars, Google search, conversational bots, email spam filters, and Netflix's recommendations.
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What is the Difference Between Machine Learning vs. Deep Learning?
The terms "machine learning" and "deep learning" are often used when discussing AI, but they are distinct concepts. Machine learning is a subset of artificial intelligence that involves feeding data into an algorithm and using statistical methods to enable the algorithm to learn and improve at a given task without being explicitly programmed.
Machine learning includes supervised learning, where the expected output is due to labeled data sets, and unsupervised learning, where the expected output is unknown due to unlabeled data sets. On the other hand, deep learning is a specific type of machine learning that utilizes a neural network inspired by the human brain's structure.
Deep learning involves processing data through multiple hidden layers in the network, allowing for a more thorough and nuanced data analysis. The neural network architecture of deep learning enables the machine to learn and improve by making connections and weighting inputs for optimal results.
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4 Primary Types of AI
There are four categories of AI based on the type and complexity of tasks they can perform: reactive machines, limited memory, theory of mind, and self-awareness.
Reactive machines are the simplest form of AI, which can perceive and react to the immediate environment but cannot store any memory to inform future decisions. Since reactive machines have a narrow worldview, they are trustworthy and reliable, reacting consistently to the same stimuli.
Limited memory AI can store past data and predictions and use them to make decisions by analyzing new data. Limited memory AI is more complex than reactive machines and is achieved by continuously training AI models or creating an environment for training models and renewed automatically.
Theory of mind, a theoretical concept in AI, is based on the understanding that living beings have thoughts and emotions that influence behavior. With a theory of mind, AI could comprehend human emotions and decision-making processes, leading to a two-way relationship between people and machines.
Self-aware AI possesses human-like consciousness and understands its existence, that of others, and their emotional states. Self-awareness in AI relies on replicating human consciousness and building it into machines, which is still in the realm of theoretical research.
4 Primary Types of Machine Learning
The four types of machine learning are Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, and Reinforcement Learning.
#1: Supervised Learning involves training machines using labeled datasets and predicting output in the future based on supervision. This technique is used extensively in fraud detection, risk assessment, and spam filtering. Supervised learning falls into two types of problems: Classification and Regression.
The former solves problems when the output variable is a binary or categorical response. At the same time, the latter is for when there is a linear relationship between the input and output variables.
Five typical applications of Supervised Learning are Image classification and segmentation, Disease identification and medical diagnosis, Fraud detection, Spam detection, and Speech recognition.
#2: Unsupervised Learning involves training machines using unlabeled and unclassified datasets to predict output without supervision or human intervention. The machines are capable of finding hidden patterns and trends from the input. Unsupervised learning has two types: Clustering and Association.
Four typical applications of Unsupervised Learning are Network analysis, Plagiarism and copyright checks, Recommendations on e-commerce websites, and Detecting fraud in bank transactions.
#3: Semi-Supervised Learning combines labeled and unlabeled datasets during training. This technique uses all available data, making it highly cost-effective. Semi-supervised learning falls under hybrid learning, and two other essential learning methods are Self-Supervised learning and Multi-Instance learning.
#4: Reinforcement Learning involves machines learning only from experiences using a trial-and-error method. The AI explores the data, notes features, learns from prior experience, and improves overall performance. The AI agent gets rewarded when the output is accurate and punished when it is not.
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Stay Tuned for More!
If you want to learn more about the dynamic and ever-changing world of AI, well, you're in luck! stoik AI is all about examining this exciting field of study and its future potential applications. Stay tuned for more AI content coming your way. In the meantime, check out all the past blogs on the stoik AI blog!