DeepLearning5 How to Use Bard: A Large Language Model from Google AI How to Use Bard Bard is a large language model from Google AI, trained on a massive dataset of text and code. It can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. Here are some tips on how to use Bard: Be specific in your requests. The more specific you are, the better Bard will be able to understand you. For examp.. 2023. 4. 20. AI and Natural Language Processing: Revolutionizing Human-Computer Interaction 1. Intro Natural Language Processing (NLP) is an interdisciplinary field that combines linguistics, computer science, and artificial intelligence to enable computers to understand and interpret human language. NLP technology has advanced significantly in recent years due to the development of artificial intelligence and machine learning algorithms, which have made it possible to analyze large am.. 2023. 3. 7. Deepfakes & AI The rapid advancement of artificial intelligence (AI) technology has brought about many breakthroughs and conveniences that have made our lives easier. However, one of the negative consequences of this progress is the emergence of "deepfakes" - highly realistic videos or images created using machine learning techniques. What are Deepfakes? Deepfakes are manipulated media that appear to be real. .. 2023. 2. 13. AI and Machine Learning Artificial Intelligence (AI) and Machine Learning (ML) have become buzzwords in recent years, as they have rapidly advanced and expanded into various fields such as healthcare, finance, and transportation. Despite being often used interchangeably, these two technologies are not the same, and it's essential to understand their differences. In this article, we'll explore what AI and ML are, how th.. 2023. 2. 6. Generative Adversarial Networks (GANs) Generative Adversarial Networks (GANs) are a type of deep learning architecture designed to generate new and realistic data samples, such as images, sounds, and text, by training on a large dataset. Working Principle GANs consist of two components: a generator and a discriminator. The generator takes random noise as input and generates new samples. The discriminator, on the other hand, takes bot.. 2023. 2. 6. 이전 1 다음
