7.12.1. Algorithms and models of AI

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Recently, there was an article in a some magazine about the car 'Tesla'. On some fast road, there was a collision between two cars in front of the Tesla car. The first car continued to drive, but the second car's engine was damaged and the car immediately stopped and crossed the road. The driver of the 'Tesla' did not manage immediately, and the collision avoidance algorithm in the 'Tesla' took control of the car's movement and went around the obstructed car. Namely, the average driver needs about 0.75 seconds to react to the observed situation (the F1 driver needs about 0.15 seconds), but the computer system in 'Tesla' reacts much faster through the existing sensors and applied its algorithm when it found that there was no reaction from the driver. In this way, another collision was avoided. The driver of the 'Tesla' could have fallen asleep or he could have felt ill, but 'Tesla' makes sure that no one gets hurt. Nice! So, it really benefits from artificial intelligence.

The relationship between algorithms and models in artificial intelligence is that algorithms are the computational procedures or techniques used to train or implement models. Algorithms provide the step-by-step instructions for processing data, making decisions, and learning from examples. They define the logic and rules that govern how a model learns, predicts, or performs a specific task. Different algorithms can be applied to train or optimize models based on the specific problem and the available data.

On the other hand, models represent the learned or implemented representations of knowledge or behavior. They are the result of applying algorithms to training data, and they capture the patterns, relationships, or characteristics of the data that are relevant to the specific task. Models can be considered as the operational implementations of the algorithms, which can make predictions, classify inputs, generate outputs, or perform other tasks based on the learned knowledge.

In some cases, the terms 'algorithm' and 'model' may be used interchangeably, depending on the context. However, it is useful to distinguish between them to understand the relationship. Algorithms are the underlying computational techniques, while models are the specific instantiations or implementations of those algorithms, trained or optimized for a particular task or problem. For example, in the context of neural networks, the algorithm refers to the learning algorithm such as backpropagation or stochastic gradient descent (SGD), which defines how the network adjusts its weights during training. The model, on the other hand, refers to the specific architecture and parameters of the neural network, which are determined by the training process and define how the network makes predictions or performs tasks.

Artificial intelligence (AI) algorithms are the computational procedures or mathematical formulas used to enable machines to mimic or simulate intelligent behavior. There are several basic artificial intelligence (AI) algorithms that are commonly used in a variety of applications. Here are a few examples:

These are just a few examples of AI algorithms, and the field is continuously evolving with new algorithms and techniques being developed to tackle various challenges in artificial intelligence. The speed of an algorithm can depend on various factors, such as the problem it is solving, the size of the input data, the hardware on which it is executed, and the specific implementation details. Additionally, algorithmic efficiency is not the only factor to consider in real-world scenarios. Other factors like memory usage, scalability, and ease of implementation can also play a role in determining the best algorithm for a given situation.

In the context of artificial intelligence, models refer to specific implementations or architectures that learn from data to perform tasks such as prediction, classification, or generation. These models can be trained on various algorithms and techniques. Models of AI that have been developed over the years. Here are a few of the most commonly used models:

These are just a few examples of basic models of AI. Each model has its own strengths, limitations, and areas of application. AI research continues to explore and develop new models to tackle complex problems and improve the capabilities of AI systems.

In summary, algorithms provide the computational techniques and procedures used to train or implement models, while models represent the learned or implemented knowledge or behavior that enables machines to perform specific tasks. A general structure of a AI learning based predictive model considering both the training and testing phase, as shown in the next Figure.

 AI learning
Figure 7.12.4. General structure of a AI learning.

There are two main types of learning at AI system: Machine learning and Deep learning. Next Figure shows a general performance of deep learning over machine learning considering the increasing amount of data. However, it may vary depending on the data characteristics and experimental set up.

 Machine & deep learning
Figure 7.12.5. Machine learning and deep learning performance.

Machine learning and deep learning are both subfields of artificial intelligence (AI) that involve training algorithms to learn from data and make predictions or decisions. While they are related, there are some key differences between the two.

Machine learning is a broader field that focuses on algorithms and techniques to enable computers to learn from data and make predictions or decisions. It encompasses various approaches like supervised, unsupervised, and reinforcement learning. Supervised learning is typically the task of machine learning to learn a function that maps an input to an output based on sample input-output pairs. Unsupervised learning analyzes unlabeled datasets without the need for human interference, i.e., a data-driven process. Reinforcement learning is a type of machine learning algorithm that enables software agents and machines to automatically evaluate the optimal behavior in a particular context or environment to improve its efficiency, i.e., an environment-driven approach.

Deep learning is a subset of machine learning that uses artificial neural networks inspired by the human brain. It excels at learning complex patterns and relationships in data by automatically extracting hierarchical representations. It has achieved remarkable success in areas like computer vision and natural language processing.

In short, machine learning is a general term, while deep learning is a specific approach within machine learning that leverages neural networks for complex tasks.


Is there a fear of the development of artificial intelligence or not?

Who can forbid the army or limit them in what they do? Nobody! Try to imagine what 'military experts' are able to do with an aircraft of the type shown in Figure 9.1. Using artificial intelligence for military purposes is the same as building intercontinental missiles with nuclear warheads. Then the military leaders will 'agree' on limiting armaments at some summits?

What can be done, since time immemorial it has been in the nature of man to kill and make war. Why should it be different now?


Citing of this page:
Radic, Drago. " IT - Informatics Alphabet " Split-Croatia.
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