Digital Mindset Technologies’ Post

OpenAI has just debuted SearchGPT, a trailblazing AI search engine powered by GPT-4! This avant-garde tool is poised to revolutionize our online search and information interaction. 🤖🔍 Leveraging sophisticated natural language processing, SearchGPT promises to deliver razor-sharp and contextually nuanced search results, elevating our efficiency in discovering and utilizing information. This marks a pivotal moment in AI advancement, pushing the boundaries of intelligent search capabilities. Key features of SearchGPT: - Powered by GPT-4, one of the most sophisticated language models in existence. - Engineered for pinpoint accuracy and contextual awareness in search results. - Potential to dramatically boost productivity and information retrieval. I'm ecstatic to see how this groundbreaking technology will shape the future of search and influence our digital landscape. Kudos to the OpenAI team for their relentless pursuit of innovation #TechyTuesday #AB_2024 #ITMBusinessSchool #ESMEducation

🌟 Excited to share my latest blog post: "Discovering the Future of AI: Transformers, GPT, and How They're Changing Everything." 🚀"Understanding Transformers: From Basic Models to GPT-1”🚀 In this blog, I've gone back in time to explore how Seq2Seq models and the famous NMT paper shaped the way we understand language. Then, I've introduced Transformers, the real game-changers! 🌟 I've explained why these new models are so exciting for language processing. We've looked at things like self-attention (which helps the model focus on important words) and positional encodings (which help it understand the order of words) .🤖 and how we can train GPT-1 from scratch, taking tips from other smart models like BERT and the original GPT. 📚 Check out the post here: https:https://lnkd.in/gVti3kSW Innomatics Research Labs🌐🔍 hashtag #AI hashtag #NLP hashtag #Transformers hashtag #DeepLearning Kanav Bansal sir, big thanks for your help along the way throughout this enlightening journey!

OpenAI and Meta are set to release new AI models capable of reasoning and planning, a significant step towards more advanced artificial intelligence. Meta's Llama 3 and OpenAI's expected GPT-5 model are part of a wave of large language models being developed this year. These advancements aim to enhance AI's ability to handle complex tasks, moving beyond simple one-off activities to more sophisticated reasoning and planning capabilities. For more information, you can read the full article : https://lnkd.in/gAeU8e8F

#Blog: AI PDF Data Extraction Discover how rapid advancements in Large Language Models, like OpenAI's GPT, are transforming #AI-powered systems. Learn about the challenges and solutions in extracting semi-structured data from #PDFs, and how to streamline this process. This blog offers valuable insights into enhancing data accuracy and efficiency for innovative AI applications. https://lnkd.in/e2tsNhS4 #21cfrpart11 #validation #qualified #compliance #clinicaltrials #clinicaldata #ai

Small language models are scaled-down versions of larger AI models like GPT-3 or GPT-4. They are designed to perform similar tasks—understanding and generating human-like text—but with fewer parameters. They are cheaper and more environmentally responsible. Subscribe to our weekly AI newsletter to stay updated with everything that matters in data and #AI: https://lnkd.in/dCNQMGtB

In the ML community, effective prompting of performant large language models (LLMs) like GPT-4 and Llama 3 can already solve many problems. But, there are indeed times when fine-tuning is necessary. When to fine-tune an LLM? Consider these scenarios: - Accuracy Needs: The default model's accuracy isn't sufficient for your specific use case. - Latency Concerns: The model's response time is too slow for practical use. - Cost Issues: The operational costs of using the model are too high. A practical example is davinci, a company developing a co-pilot tool for IP professionals to help draft patents more efficiently. Initially, we used GPT-4 to power this tool. However, despite good performance, the response times were too slow. davinci achieved better performance with lower latency and reduced costs by fine-tuning a smaller model. Understanding when and how to fine-tune LLMs can be crucial for improving the performance and efficiency of AI applications. Learn how Kili Technology does this for our LLM partners: https://lnkd.in/eezUWjYY #AI #LLM #Finetuning

#Blog: AI PDF Data Extraction Discover how rapid advancements in Large Language Models, like OpenAI's GPT, are transforming #AI-powered systems. Learn about the challenges and solutions in extracting semi-structured data from #PDFs, and how to streamline this process. This blog offers valuable insights into enhancing data accuracy and efficiency for innovative AI applications. https://lnkd.in/e2tsNhS4 #21cfrpart11 #validation #qualified #compliance #clinicaltrials #clinicaldata #ai

The Impact of Hardware on Large Language Model Performance. In the burgeoning field of artificial intelligence (AI), large language models (LLMs) like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers) have become cornerstones of technological advancement. These models, which leverage vast amounts of data to understand and generate human-like text, are integral in driving innovations from automated customer service to sophisticated analytical tools. However, the efficacy of these models is heavily dependent on the underlying hardware that powers them. This blog aims to delve into the critical role that hardware plays in the performance of LLMs, examining how different hardware configurations can significantly affect outcomes in AI applications. By understanding the synergy between hardware and software, stakeholders can make informed decisions that propel the capabilities of AI forward. #Algomox  #AIOps Anil A. Kuriakose  Princy P  Valerian Fernandes https://lnkd.in/gQN5gvDi

https://lnkd.in/esMmZhp4 An excellent presentation, given by Grant Sanderson, on the internal workings of AI based on GPT3 type language models