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What Is Artificial Intelligence(AI) And How Does It Work?

 

By Nerly Shammah Aug 04, 2023

 

Artificial Intelligence is the ability of a computer or supermachine to complete a task in a human-like manner but with higher efficiency. 

 

AI software is designed to mimic humans' ways of problem-solving but at the same time programmed to deliver at much faster execution rates.

 

 

You've probably been hearing of the word artificial intelligence or the abbreviation A.I and have been wondering what it could be, maybe as a result of the release of large language models like ChatGPT or Google Bard, or maybe because of some science fiction movie you just watched where A.I is deployed in humanoids and leveraged to carry out different tasks or operations.

 

Whichever the case may be, this article is a complete introductory guide that will explain to you in all clarity what artificial intelligence is, how it works and how it is being leveraged to make work or problem-solving more efficient. 

 

Definition Of Artificial Intelligence

 

Artificial Intelligence is defined as the simulation or application of human intelligence by software to carry out specific tasks. 

 

The word "artificial" signifies that these programs or computer software are created by humans to fulfil "intelligence" like humans and even beyond. 

 

Intelligence here typically refers to the ability of these software or systems to perceive, understand, reason, and learn from data to solve problems and perform tasks. 

 

It encompasses various aspects, including natural language processing, pattern recognition, decision-making, and problem-solving, among others. AI systems aim to mimic human intelligence and often employ machine learning algorithms to improve their performance over time.

 

Understanding Artificial Intelligence(AI) 

 

Artificial Intelligence(AI) is often confused with machine learning, while the two share some similarities, they are not exactly the same thing.

 

Artificial Intelligence(AI) is the overarching concept of creating intelligent machines while machine learning is simply a subset or specific approach used to attain a specific level of intelligence within AI systems or regular applications that allows machines to learn from data and improve their performance over time without explicit programming for every possible scenario. 

 

So AI here represents a full building for example, and machine learning here could represent the electrical wirings within the building, so it is only a piece of the entire system and sometimes machine learning software is used in applications that do not require advanced AI software to perform certain tasks like social media platforms. 

 

Machine learning is used in "recommendation engines" in social media platforms, the "For You" tab on TikTok and X(formerly Twitter), and your typical Facebook homepage. 

 

Machine learning algorithms are being used in these systems to enable them to learn from user data and recommend contents that are appropriate to the data it has received. 

 

How Does Artificial Intelligence(AI) Work? 

 

Artificial Intelligence (AI) works by simulating human-like intelligence in machines using algorithms and data. As previously hinted, this process has a lot of pieces coming together with each level contributing differently to the attaining of human-like intelligence in machines. 

 

As already depicted in science fiction movies, the ultimate goal of Artificial Intelligence(AI) developments is to attain highly functional machines that perform human-like tasks like cleaning, writing, conversing with customers and a lot more, with even more efficiency than humans. 

 

By this, a new system of problem-solving is attained because AIs are being put to work where humans would normally have to operate in their "limited to time" state. Artificial Intelligence can typically complete specific tasks much quicker than humans as these systems are laced to read and process specific data within nanoseconds. 

 

 

Here are some steps to how Artificial Intelligence works with machine learning as a core focus:

 

1. Data Collection

 

AI systems work on a lot of data, they require vast amounts of data to learn from. This data can be structured, that is, organized and formatted data or unstructured data for example random texts, images, and audio.

 

How and where this data is collected depends on the function of the machine being developed. For example, if artificial intelligence is being designed into a cryptocurrency trading engine, a huge chunk of on-chain data will be required. 

 

Typically, data will be extracted from blockchain tracking systems like price and market trackers like coinmarketcap and coingecko to have enough relevant on-chain information to train on the machine with. 

 

2. Data Preprocessing

 

Before feeding the data into an artificial intelligence model, it often undergoes preprocessing, which involves cleaning, transforming, and preparing the data for analysis.

 

Collected raw data often contains errors due to many reasons that could be attributed to human faults or machines. That said, common instances of errors include missing values or inconsistencies that could negatively impact the performance of the AI model. Data cleaning involves identifying and correcting these issues to ensure the data is accurate and reliable.

 

Data transformation typically involves converting or transforming these data into suitable formats before training. For instance, converting categorical data into numerical values or scaling numerical data to a standardized range.

 

What are the differences? Categorical data represent distinct categories or groups and are typically represented using labels or strings, that said, numerical data represents measurable numerical values and can be continuous or discrete, while standardized data refers to normalized data that has been scaled to have a consistent mean and standard deviation, often useful in machine learning algorithms and are represented by a mean of 0 and a standard deviation of 1.  

 

3. Training

 

AI models, especially in machine learning, are trained on preprocessed data. During training, the model tries to identify patterns, correlations, and relationships within the data to learn from it.

 

Remember the social media recommendations engine example? 

 

How does TikTok know you'd like to see yet another dance video? Because you happened to have stayed to watch only a dance video among the first five videos you scrolled through. 

 

These systems are trained to understand your engagement patterns and recommend content accordingly so that by doing so, you are satisfied by the content you see and are compelled to keep coming back, making their training quite efficient and their performance much better with each new data to work with. 

 

4. Algorithms

 

AI relies on various algorithms based on specific tasks and goals. These algorithms can be decision trees, neural networks, support vector machines, or many others, depending on the application.

 

Using neural networks as an example, these are a class of machine learning algorithms which is supposedly inspired or designed as a mirror of the structure and functioning of the human brain. 

 

They are a fundamental component of deep learning, a subfield of artificial intelligence (AI) applied in machine learning algorithms. Neural networks consist of interconnected artificial neurons - also called nodes or units - and are organized in layers. 

 

The layers are categorized as input layer, hidden layers, and output layer and the connections between neurons have associated weights, which represent the strength of the connection. 

 

Picture the same example of social media networks, AI systems rely on these algorithms to be able to match appropriate output data to the received data. You must understand that there are really many layers to how artificial intelligence gets to task completion, this is why sometimes, these machines make mistakes which can be attributed to the inability to build a consistency of value in the input data. 

 

This is an event I would quite frankly consider to be good considering that "humans" the supposed models for these systems are also prone to make mistakes. 

 

5. Learning and Optimization

 

AI models iteratively learn from the training data and adjust their internal parameters to optimize performance. This process involves minimizing errors and maximizing accuracy.

 

Just like humans, artificial intelligence models are designed to learn from experiences and optimize for future tasks, this is typically applied to improve performance over time. 

 

Think of how as humans we tend to change certain routines or adopt certain activities into our daily operations to improve performance in certain fields, and in the process of this, we discover what works and what doesn't so we apply more of what works in the future - this is typically the optimization point and is quite similar with these systems. 

 

6. Inference and Prediction

 

After training, the AI model can make predictions or decisions based on new, unseen data. This is the inference stage, where the model applies what it has learned to new situations.

 

Think of examination and creativity here. 

 

Inference here is the examination, typically, Inference refers to the process of using a trained AI model to make predictions or classifications on new input data. 

 

After an AI model has undergone the training phase, it has learned patterns and relationships from the training data. During inference, the model applies this learned knowledge to analyze and interpret the features of the new data.

 

For example, in image recognition, an image classification model trained on a dataset of various objects can perform inference on a new image to identify what objects it contains. The model analyzes the pixels of the image and decides which object class is most likely to be present in the image based on its learned knowledge.

 

That said, prediction on the other hand is likened to the creativity of humans to use pre-acquired knowledge to make assumptions or build layers of analysis. 

 

Prediction is a specific type of inference, often associated with regression tasks. In regression, the AI model learns to predict numerical values based on input features. The goal is to estimate a continuous target variable rather than categorizing data into predefined classes.

 

For instance, in a crypto price prediction scenario, as we previously touched on leveraging on-chain data, the AI model is trained on historical data containing information like coin supply, price movements, and on-chain growth in users and wallets with long-term holding practices. 

 

With this trained data, the AI can make predictions of a new cryptocurrency or even the same cryptocurrency, predicting price targets, potential user growth, and likely moving curves of long-term holders. 

 

The above steps are commonly used in machine learning, which is a significant component of artificial intelligence, remember the building and wiring analogy. 

 

That said, AI as a technology is a much broader field that encompasses various other techniques, including rule-based systems, expert systems, natural language processing, and more. 

 

The ultimate goal of AI is to develop intelligent systems that can solve complex problems, perform tasks, and make decisions with human-like reasoning but with higher efficiency.