OpenAI's Dev Day has been a showcase of announcements that have the developer and AI communities buzzing. With the introduction of GPT-4 Turbo and a suite of new APIs, the future of AI seems ever more accessible and boundless. Let's dive into these updates with real-world examples of how they could transform our interaction with AI.
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The trek into the expansive realm of AI is both captivating and laden with obstacles. Despite the promises made by Large Language Models (LLMs) to offer a seamless navigation through this intricate landscape, their challenges remain evident. Whether it's their ambiguity or inaccuracies, the current state of LLMs isn't the silver bullet. However, as technology evolves, a promising beacon of hope emerges.
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Recently, I had the incredible opportunity to attend DefCon 31, the annual security conference held in Las Vegas. With attendees hailing from around the globe, DefCon serves as a vibrant hub for knowledge-sharing and collaboration within the cybersecurity domain. This year, my focus was on delving into the innovative ways in which security experts are leveraging artificial intelligence (AI), Large Language Models (LLMs), and machine learning to enhance their practices.
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In the dynamic landscape of natural language processing (NLP), transformers have become a shining star, paving the way towards revolutionizing the way machines comprehend human language. These powerful neural network architectures, introduced to the world by Vaswani et al. in their enlightening 2017 paper “Attention is All You Need”, have significantly altered the landscape of NLP, turning it into a vibrant and bustling field that is constantly pushing the boundaries of what was previously thought possible.
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Open source models have revolutionized the field of machine learning by providing accessible and powerful tools for developers and researchers worldwide. However, getting started with these models can sometimes be a daunting task, particularly when it comes to moving beyond the provided examples.
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Large language models have opened up incredible possibilities in natural language text generation and image synthesis. However, running these models can present challenges, particularly when dealing with unrunnable examples and platform-specific limitations. In this installment of the It Doesn’t Need to Be This Hard series, we will take the shortest path toward running large language models.
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Spoiler alert! Back in 2017, the TV show Silicon Valley had a gag where one of the characters was tasked to create an image recognition app. Dubbed as the “Shazam” for food it’s purpose was to give the user the ability to take a picture of any type of food and immediately be told what it was. The punch line was that the creator (Jìan-Yáng) couldn’t be bothered to train the app for every type of food in existence so he instead decided to make an app...
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Artificial intelligence has become a transformative force across numerous industries within the rapidly changing technology landscape. Its potential to automate tasks, enhance decision-making processes, and improve overall efficiency has captured the attention of organizations worldwide. Eager to harness the power of AI, companies often find themselves navigating uncharted territory, experimenting with new tools and technologies to stay ahead in this digital age.
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In the ever-evolving landscape of artificial intelligence (AI), a quiet yet powerful player is steadily reshaping the landscape, eclipsing even industry leaders like Google and OpenAI. This silent giant is the open source community, a globally scattered group of dedicated and passionate individuals who are showing that the future of AI may lie not in the hands of a few tech giants, but in the spirit of global, collaborative innovation.
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