OpenAI’s GPT-4o, the latest addition to its line of advanced AI models, stands out with its enhanced capabilities across text, voice, and vision. Designed to deliver faster and more cost-effective solutions, GPT-4o is rapidly becoming a preferred choice for various AI applications. In this article we will explore GPT-4o, its main capabilities and limitations. And if you would like to read more about GPT-4o vs GPT Turbo, you’re invited to read the article in the link.
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OpenAI’s continuous evolution in AI technology has brought about significant improvements and variations in its models, specifically between GPT-4o and GPT-4 Turbo. Here’s a detailed comparison to help understand the key differences between these two advanced models.
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This was my “remodeling my living room” project 🙂 So, I took a picture of our small living room and asked DALL-E to change the accenting wall behind sofa to yellow. What I expected DALL-E, a model that generates images, is to edit the existing photo I uploaded. But is that even possible?
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Are you embarking on the journey of creating your own AI model? Yes, that can be both exciting and daunting, but with the proliferation of AI technology, platforms like GPT Builder (You will need GPT-4 to use this feature) offer users the opportunity to tailor AI models to their specific needs and preferences. In this article, we’ll take a deep dive into the intricacies of GPT Builder, shedding light on its functionalities, interfaces, and the process of customizing your AI model.

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In this short tutorial about summarizing text we worked on Google Colab, so go get an account here if you don’t have one already.

Here’s a link to this small project, where we basically summarized a chunk of text with a Facebook/bart-large-cnn model. BART is particularly effective when fine-tuned for text generation, such as summarization, translation but also works well for comprehension tasks, for example text classification and question answering. This particular checkpoint has been fine-tuned on CNN Daily Mail, a large collection of text-summary pairs.

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In this article we will learn what are sentence embeddings and transformers and how they typically work. We will also take a look at the practically applications of sentence embeddings in theory. However, we will roll our sleeves and take get our hands dirty with applying SentenceTransformer(“all-MiniLM-L6-v2”) model, which works on tasks like semantic similarity comparison, clustering, and classification. In this exercise, which is in-depth described in Google Colab we delve into the functionalities of the sentence-transformers Python library and explore how it can be utilized to generate and compare sentence embeddings.
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In this article we are going to learn what sentence embeddings are and how they work in LLMs. We will learn how we can use sentence embeddings in practice, and we will take a look at a few real-life examples of sentence embeddings in different industries and domains.

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In this short tutorial we’ve worked on Google Colab, so go get an account here if you don’t have one already.

Here’s a link to this small project, where we basically translated the text from English to Croatian language with a Facebook NLLB-200 model. This is a machine translation model primarily intended for research in machine translation, – especially for low-resource languages. It allows for single sentence translation among 200 languages.

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In today’s interconnected world, effective translation is key to breaking down language barriers and fostering communication across diverse cultures. ChatGPT, a cutting-edge language model, offers a unique solution to this challenge. By harnessing its deep understanding of languages, context, and natural language processing capabilities, ChatGPT facilitates seamless translation between different languages. You can even learn a foreign language with ChatGPT. This article explores how ChatGPT revolutionizes the translation process, its strengths compared to traditional methods like Google Translate, and the diverse range of translation tasks it can perform.
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Text transformation encompasses altering text structure, content, or style to achieve specific objectives such as summarization, expansion, translation, or paraphrasing. Its significance lies in its widespread applications across industries and domains, including improving search engine efficiency through summarization for information retrieval, generating personalized content for enhanced user engagement, facilitating natural language understanding tasks like sentiment analysis and entity recognition, bridging language barriers and adapting content to different cultural contexts through translation and paraphrasing, streamlining data analysis by extracting key insights from large datasets, assisting in content moderation and filtering online, and supporting educational initiatives through the generation of study materials and personalized learning experiences. Overall, text transformation plays a pivotal role in enhancing information processing, communication, and decision-making, contributing to efficiency, accessibility, and innovation in various applications.

In this article, we will learn how LLMs, such as ChatGPT and many others can help you transform text and enhance information processing, therefore help you communicate your message to the target audience.
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