TECHNOLOGY SIMPLIFIED

  MAKING OF CHATGPT History and Making of ChatGPT Background and Development 1. Evolution of AI and NLP: The development of ChatGPT is a part of the broader evolution of artificial intelligence ( AI ) and natural language processing ( NLP ) . Early AI research in the mid - 20th century focused on symbolic AI and rule-based systems . The shift towards machine learning, especially deep learning , started in the late 2000s, leveraging large datasets and significant computational power . 2. Introduction of Transformers: The transformer model, introduced by Vaswani et al. in 2017, revolutionized NLP. The paper titled "Attention is All You Need" proposed a new architecture that significantly improved the performance of various NLP tasks. Transformers use self-attention mechanisms to weigh the importance of different words in a sentence, allowing for better context understanding. 3. Generative Pre-trained Transformer (GPT): OpenAI developed the Generative Pre-trained Transformer (GP...

     REINFORCEMENT  LEARNING What is reinforcement learning ? Reinforcement learning is a machine learning training method based on rewarding desired behaviors and / or punishing undesired ones . In general , a reinforcement learning agent is able to perceive and interpret its environment , take actions and learn through trial and error . How does reinforcement learning work ? In reinforcement learning , developers devise a method of rewarding desired behaviors and punishing negative behaviors . This method assigns positive values to the desired actions to encourage the agent and negative values to undesired behaviors . This programs the agent to seek long-term and maximum overall reward to achieve an optimal solution . These long - term goals help prevent the agent from stalling on lesser goals . With time , the agent learns to avoid the negative and seek the positive . This learning method has been adopted in artificial intelligence ( AI ) as a way...

BIG  DATA  ANALYTICS WHAT IT IS AND WHY IT MATTERS Big data analytics examines large amount of data to uncover hidden patterns  , correlations and other insights . With today’s technology , it’s possible to analyse your data and get answers from it almost immediately – an effort that’s slower and less efficient with more traditional business intelligence solutions . History and evolution of big data analytics : The concept of big data has been around for years ; most organizations now understand that if they capture all the data that streams into their businesses , they can apply analytics and get significant value from it . But even in the 1950s , decades before anyone uttered the term “ big data , ” businesses were using basic analytics ( essentially numbers in a spreadsheet that were manually examined ) to uncover insights and trends .  The new benefits that big data analytics brings to the table , however , are speed and efficiency . Whereas a few years ago...

  COMPUTER VISION WHAT IS COMPUTER VISION : Computer vision is a field of Artificial Intelligence (AI) that enables computers and systems to derive meaningful information from digital images , videos and other visual inputs - and take actions or make recommendations based on that information . If AI enables computers to think , computer vision enables them to see , observe and understand . Computer vision works much the same as human vision , except humans have a head start . Human sight has the advantage of lifetimes of context to train how to tell objects apart , how far away they are , whether they are moving and whether there is something wrong in an image . Computer vision trains machines to perform these functions , but it has to do it in much less time with cameras , data and algorithms rather than retinas , optic nerves and a visual cortex . Because a system trained to inspect products or watch a production asset can analyze thousands of products or processes a minute , no...

MACHINE LEARNING INTRODUCTION : Machine Learning is a sub-area of artificial intelligence, whereby the term refers to the ability of IT systems to independently find solutions to problems by recognizing patterns in databases. In other words : Machine Learning enables IT systems to recognize patterns on the basis of existing algorithms and data sets and to develop adequate solution concepts. Therefore, in Machine Learning, artificial knowledge is generated on the basis of experience. In order to enable the software to independently generate solutions , the prior action of people is necessary. For example, the required algorithms and data must be fed into the systems in advance and the respective analysis rules for the recognition of patterns in the data stock must be defined. WORKING : Machine Learning works in a similar way to human learning. For example, if a child is shown images with specific objects on them, they can learn to identify and differentiate between them. Machine Learn...