neural network

When you train a model, you send data through the network multiple times. Think of it like wanting to become the best basketball player. You aim to improve your shooting, passing, and positioning to minimize errors. Similarly, machines use repeated exposure to data to recognize patterns. This article will focus on a fundamental concept called backward propagation. After reading, you'll understand: What backward propagation is and why it's important Gradient descent and its type Backward propagation in machine…

In the realm of machine learning and deep learning, activation functions play a pivotal role in neural networks' ability to make complex decisions and predictions. Among these, the softmax activation function stands out, especially in classification tasks where outcomes are mutually exclusive. This article delves into the softmax function, offering insights into its workings, applications, and significance in the field of artificial intelligence (AI). Softmax Activation Function Image credits: Towards Data Science The…

The impacts and power of generative AI were realized at scale in businesses last year. Now, every software company, supermarket brand, and business, even tangentially related to tech, appears to be building an AI-based solution of their own for 2024. But how much do we know about the foundations of these technologies and what they're doing with our data behind the curtain? How well is the AI black box we’re all learning to trust really understood outside of niche tech circles? What is there to know about something that is…

The evolution of natural language processing with Large Language Models (LLMs) like ChatGPT and GPT-4 marks a significant milestone, with these models demonstrating near-human comprehension in text-based tasks. Moving beyond this, OpenAI's introduction of Large Multimodal Models (LMMs) represents a notable shift, enabling these models to process both images and textual data. This article will focus on the core text interpretation techniques of LLMs — tokenization and embedding — and their adaptation in multimodal…

In today's world, we are bombarded with a vast amount of information, much of which is in the form of text. To make sense of this data, it's important to be able to extract the most important information quickly and efficiently. Natural Language Processing (NLP) provides a range of techniques for text summarization, allowing users to identify the key insights and make informed decisions. However, implementing these techniques is not always straightforward. This article takes a detailed look at text summarization,…

 In a new research, an AI model was trained to learn words and concepts through the eyes and ears of a single child, using headcam video recordings from when the child was six months and through their second birthday.Researchers showed that the artificial intelligence (AI) model could learn a substantial number of words and concepts using limited slices of what the child experienced. Even though the video captured only one per cent of the child's waking hours, they said that was enough for genuine language learning. "By…

To ensure the safety of rail traffic, non-destructive testing of rails is regularly carried out using various approaches and methods. One of the main approaches to determining the operational condition of railway rails is ultrasonic non-destructive testing. The assessment of the test results depends on the defectoscopist. The need to reduce the workload on humans and improve the efficiency of the process of analyzing ultrasonic testing data makes the task of creating an automated system relevant. The purpose of this work…

Since the 1940s, neural networks have evolved significantly, from the early concepts of McCulloch and Pitts to Frank Rosenblatt's perceptron in the 1950s. Major advancements in the late 20th century, like the development of the Backpropagation method, set the stage for modern deep learning. The introduction of the transformer architecture in 2017's "Attention is All You Need" by Google researchers marked a turning point in Natural Language Processing (NLP), leading to the development of powerful models like BERT and GPT.…

Artificial Intelligence (AI) has undeniably transformed various aspects of our lives, from automating mundane tasks to enhancing medical diagnostics. However, as AI systems become increasingly sophisticated, a new and concerning phenomenon has emerged – AI hallucination. This refers to instances where AI systems generate outputs or responses that deviate from reality, posing significant challenges and raising ethical concerns. In this article, we will delve into the problems associated with AI hallucination, exploring its…

First Steps and Evolution Imagine a world where machines cannot only see but also understand, where their "eyes" are powered by artificial intelligence, capable of recognizing objects and patterns as adeptly as the human eye. Thanks to the evolution of artificial intelligence, particularly the advent of deep learning and neural networks, we find ourselves at the threshold of this breathtaking reality. Computer Vision, a field that originated in 1959 with the advent of the first digital image scanner, has undergone a…