Chris Columbkille Biddle’s Post

The improvement of Generative AI LLM accuracy seem to reach a limit. From one side, access to quality data is being limited more and more, over multiple reliable and quality data sources disallowing AI companies from using their data to train their model (Many examples, including newspapers like New York Times or Blogs like Medium). One idea emerged to train the models on AI generated content. But, this seems to cause a model collapse! Here is an article on this topic, that explains the main challenges faced by AI Large Language Models to continue improving their training data sets. Link : https://lnkd.in/eYssxh4F #LLM #GenAI #GenerativeAI #ArtificialIntelligence

Is your data in "mint condition"? Today’s most advanced Large Language Models, like GPT-4, make parsing unstructured and unpredictable text data much easier to execute - providing more flexibility and delivering a higher degree of accuracy. Learn how 👉https://lnkd.in/gAGsQ5WW #LLM #GPT4 #Dataiku #AI

Here's a fun article showing how LLMs + Dataiku make a powerful pair to more quickly and easily uncover insights from unstructured data.

In the rapidly evolving field of AI, two popular methods for enhancing the capabilities of language models are retrieval-augmented generation (RAG) and fine-tuning. https://lnkd.in/gj34nRaz #AI #LargeLanguageModels by Asmitha Rathis thanks to QueryPal

Why AI Struggles to Spell "Strawberry": A Look Into Language Model Limitations Even though AI models like GPT-4 and Claude can write essays and solve complex problems, they sometimes stumble on surprisingly simple tasks, like spelling "strawberry." When asked how many "R"s are in the word, many models incorrectly say two instead of three. This isn't just a quirky mistake but reveals a deeper issue with how AI handles language. Instead of recognizing words as a collection of letters, AI processes them as abstract tokens, which can lead to errors in simple letter-counting tasks. It's a fascinating limitation that shows AI, while incredibly advanced, still has gaps in basic human-like understanding. As generative AI continues to evolve, it’s critical to understand its boundaries and work towards bridging these knowledge gaps. Key Takeaways: AI processes words as tokens, not individual letters. Simple tasks like counting letters highlight inherent limitations in current models. Addressing these gaps is key to making AI more robust and reliable in everyday applications. https://lnkd.in/ds_gJMCw #AI #MachineLearning #TechInnovation #NaturalLanguageProcessing #AIChallenges

In the rapidly evolving field of AI, two popular methods for enhancing the capabilities of language models are retrieval-augmented generation (RAG) and fine-tuning. https://lnkd.in/gj34nRaz #AIEngineering #LLMs #LargeLanguageModels by Asmitha Rathis thanks to QueryPal

Read this incredible article about ATLA - a new machine learning start-up that's revolutionizing the language models. 🤖🤯 #MachineLearning #AI #LargeLanguageModels

💡 Did you know that large language models (LLMs) are designed to generate coherent text, not necessarily correct information? 🧪 LLMs are often referred to as "stochastic parrots", they produce content based on patterns in their training data. This means hallucinations—where the model generates incorrect or nonsensical information—are a feature, not a bug. The reason LLMs often produce good content is because they are trained on high-quality data and replicate what they've seen. However, their goal isn't to create truthful statements, but rather coherent text that resembles their training data. 🔍 When an LLM generates a true statement, it's a fortunate coincidence that we shouldn't take for granted. Always verify the outputs from GPT and other LLMs and avoid blindly copying and pasting their content. 👩🏻💻 For professionals using AI, it's crucial to cross-check the information produced by LLMs. This ensures the reliability and accuracy of the content being used. 📃 Source: https://lnkd.in/eVBv_X_X Do you find this development as exciting as I do? Let's discuss 💬 📰 Check out my blog for more on AI and related fields: https://lnkd.in/es_BxAQN #AI #LLM #Data #Innovation #TechResearch #MachineLearning #Hallucinations #Veracity

The Importance of Prompt Engineering in LLMs (Large Language Models) Prompt engineering is critical when working with Large Language Models (LLMs) like GPT-4, as it helps you maximize the potential of these powerful AI tools. Here’s why mastering prompt engineering can transform your AI experience: Optimized Output: Well-crafted prompts lead to precise and relevant responses. The more detailed and specific your instructions, the better the AI can generate high-quality, accurate output. Efficiency: By refining prompts, you can reduce unnecessary back-and-forths, saving time. You get closer to the desired result in fewer iterations, making your workflow smoother and faster. Task Specificity: Different tasks like content creation, data analysis, or reasoning require tailored prompts. By adjusting the wording, tone, or structure, you guide the LLM to focus on the unique requirements of each task. Cost-Effective: With large models, more tokens often mean more computational cost. Effective prompts can help achieve desired results with fewer tokens, optimizing both resource usage and performance. Bias Reduction: By being deliberate about your prompt design, you can reduce model biases, ensuring the output aligns more with ethical standards or user expectations. Pro Tip: Experiment and Iterate! Prompt engineering is an evolving process, so don’t hesitate to test and refine your prompts for the best results. #AI #PromptEngineering #LLM #ArtificialIntelligence #MachineLearning

🚀New Article Alert: Qwen/QwQ-32B vs GPT-O1 and Sonnet: What's Different? 🤖 In the ever-evolving landscape of artificial intelligence, specialized language models are becoming game-changers in how we tackle complex tasks. In my latest blog post, I dive deep into Qwen/QwQ-32B, a powerful AI model developed by Alibaba, and compare it with other well-known models like GPT-O1 and Sonnet. 🔍 🔑 Key Takeaways: Qwen/QwQ-32B: A 32B parameter model designed for technical problem-solving and domain-specific tasks. How it differs from GPT-O1 in terms of specialization and general-purpose tasks. A look at Sonnet's strength in creative applications and its limitations. If you're interested in the future of specialized AI models, deep learning, and the potential for domain-specific applications, this article provides an insightful comparison. 🔗 Read the full article here-https://lnkd.in/gTAxCgS5 #AI #MachineLearning #DeepLearning #Qwen #GPT #Sonnet #ArtificialIntelligence #TechInnovation #LanguageModels #DataScience #AIResearch