Nick Pike’s Post

Small language are very powerful at doing specific tasks.In machine learning, particularly for large language models (LLMs), improving performance while reducing training costs is crucial. One effective way to do this is by enhancing the quality of the pretraining data through a process called data pruning. Data pruning involves selecting the best parts of a large dataset to train the model, removing noisy and irrelevant data. This helps streamline the training process and boosts the model's performance. A common problem with training LLMs is that the data can be vast and messy. Poor-quality data can make the models perform poorly, so it's important to filter out the bad data and keep only the good stuff. Traditional methods for this include basic filtering rules and simple classifiers, but these often fall short when dealing with large, diverse datasets. A new, more advanced method has been developed by researchers from Databricks, MIT, and DatologyAI. They use small reference models to measure something called perplexity, which helps determine how well a model can predict a piece of text. Lower perplexity scores mean better quality data. Here's how it works: 1.Train a Small Model: A small model is trained on a random subset of the data. 2.Compute Perplexity: This small model then evaluates the perplexity of each sample in the larger dataset. 3.Prune Based on Perplexity: Data samples with the lowest perplexity scores (indicating high quality) are selected. 4.Train the Larger Model: The larger model is then trained using this high-quality, pruned dataset. This method has shown to improve the performance of large models significantly. For example, using perplexity scores from a smaller 125 million parameter model to prune data improved the performance of a much larger 3 billion parameter model by up to 2.04%. It also reduced the pretraining steps needed to reach a good performance level by up to 1.45 times. This perplexity-based pruning is effective in various scenarios and datasets, demonstrating its robustness. It enhances model performance and reduces the computational resources required, making it a valuable tool for data researchers. In essence, by using smaller models to filter out bad data, researchers can train bigger models more efficiently and effectively. Link to paper: https://lnkd.in/dE6KkrTn #llm #ai #generativeai

🚀 Exciting news in the world of data analytics! 📊✨ MIT researchers have introduced generative AI for databases, a groundbreaking tool that simplifies complex statistical analyses of tabular data. [3] [2] [1] 🤯🔬 Traditionally, performing statistical analyses required a deep understanding of the underlying algorithms and processes. But with generative AI, database users can now dive into complicated data analyses without needing to know what's happening behind the scenes. 📈💡 This transformative technology is set to revolutionize the field of data analytics. 💥 By leveraging generative AI, organizations can unlock valuable insights from their vast volumes of data, empowering them to make data-driven decisions with ease. 📚🔍 Generative AI is not just limited to data analytics; it has a wide range of applications in various industries. 🌐 For instance, it can be used to create natural language chatbots that intelligently respond to complex questions, providing custom knowledge and enhancing customer experiences. 🤖💬 In our data-centric world, where enterprises and institutions are accumulating massive amounts of data at an unprecedented velocity, generative AI offers a solution to extract meaningful insights from this overwhelming sea of information. 🌊💡 Thanks to generative AI, we are getting closer to the promise of democratizing data. 🌍🔓 It enables anyone, not just highly skilled data scientists, to make data-driven decisions. This empowers individuals and organizations to harness the power of data and drive innovation. 💪💡 But with the exponential growth of data, it's crucial to stay updated on the latest generative AI data analytics tools. Forbes has compiled a list of 17 tools that everyone should know about, providing a comprehensive resource for those looking to explore this exciting technology further. 📚🔧 Let's embrace the power of generative AI and unlock the full potential of data analytics! 💥🔓 Share your thoughts and experiences with generative AI in the comments below. How do you envision it transforming your industry? Let's start a conversation and drive innovation together! 🗣️💡 #GenerativeAI #DataAnalytics #Innovation #MITResearch #TechnologyRevolution #DataDrivenDecisions #TransformingIndustries #UnlockingInsights #DemocratizingData #FutureOfAnalytics References: [1] Generative AI Dos and Don’ts for Information Managers: https://lnkd.in/dEifzKNQ [2] AI is Eating your Algorithms: https://lnkd.in/dB8hKufD [3] Infusion of generative AI into analytics a work in progress: https://lnkd.in/dJ5prKVj

• A McKinsey report underscores the crucial importance of viable and high-quality data for the successful adoption of generative AI. • IT and data managers must cultivate a comprehensive understanding of the data implications and invest in advanced labeling and tagging strategies. • Organizations must reconsider their data architecture to support generative AI initiatives and establish a robust data foundation. Source: [https://lnkd.in/e8pxQicK] —————- P.S.: This article underscores the crucial role of data in the effective deployment of AI technologies. Navigating the challenge of managing unstructured data necessitates innovative solutions to handle the growing diversity of data types. Organizations should proactively address data governance, quality, and storage to leverage the advantages of generative AI and other AI-driven innovations fully. Embracing AI for data management represents a forward-thinking approach that can streamline operations and enhance the value derived from data assets. #ArtificialIntelligence #AI #Innovation #TechNews #DataScience #Analytics #AICommunity #Productivity #LLM #OpenAI #DeepLearning #Courses #Learning #GPT #Bard #Charts #Insights #Analysis #Data #Python #Coding #Enhancement #Tips #NLP #TextProcessing #LLMs #AIInnovation #DataSecurity #CyberSecurity

The Release of LLaMA-3 Highlights Data Curation as the Key Challenge in Training Powerful LLMs Meta's recent launch of LLaMA-3, their latest state-of-the-art large language model, serves as a prime example of why data quality is the most critical factor in developing high-performing LLMs. While the technical details are still emerging, a significant portion of the available information revolves around the meticulous data curation process employed by Meta's researchers. Key Insights on LLaMA-3's Data-Centric Approach: 1. Massive, High-Quality Pretraining Data: LLaMA-3 was pretrained on a staggering 15 trillion tokens of curated data, with a focus on including more code samples to enhance reasoning capabilities. 2. Advanced Data Filtering Techniques: Meta employed sophisticated filtering methods, including NSFW filters, semantic deduplication, and text quality classifiers powered by previous LLaMA models. 3. Efficient Tokenization: LLaMA-3 introduces a more efficient tokenizer with a larger 128K vocabulary, leading to improved performance and inference efficiency. 4. Empirical Analysis of Data Mixture: Extensive experimentation was conducted to determine the optimal composition of the pretraining data. 5. High-Quality Prompts for Alignment: During the alignment phase (using techniques like Supervised Fine-Tuning and Reinforcement Learning), Meta emphasized the critical role of prompt and preference data quality. 6. Multiple Rounds of Quality Assurance: Significant effort went into curating and repeatedly validating the quality of human annotations used for alignment. The LLaMA-3 release underscores that while model architectures are becoming more standardized, the real challenge lies in constructing and filtering the vast datasets required to train these powerful language models effectively. As AI systems continue to advance, this data-centric approach will likely become even more crucial, driving the development of sophisticated data curation pipelines and quality assurance processes. The ability to leverage high-quality, diverse, and well-curated data will be a key differentiator in the race to build the next generation of AI agents and applications.

Check this latest case study related to Artificial General Intelligence titled Transforming Data Queries with AI: The Rise of Text-to-SQL #AI #TextToSQL #DataScience #ArtificialIntelligence #DataQueries #TASDesignGroup #ArtificialGeneralIntelligence

Data science vs. machine learning: How are they different? - TechTarget: Data quality fuels analytics, AI. Data quality is tied to organizations' ability to gain actionable data from analytics and AI processes, and orgs ...

Data-Centric AI? Is that just rebranding Machine Learning? This confusion is common in an age of buzzwords. Here's our view: Machine Learning = Iterating on modeling/training recipes to produce the best model for a given dataset (the concepts from any ML textbook/course). Data-Centric AI = Iterating on the dataset to improve any ML model trained on it. While this is often done manually, the key is automated algorithms (many invented by our scientists) to systematically find and fix dataset issues. Most of these algorithms use AI, which led us to a second definition: Data-Centric AI = AI that diagnoses flaws in data and suggests remedies. Mastering all 3 concepts is vital as datasets grow in size + diversity and AI grows in power. Learn from Business Insider how our automated platform integrates all 3 concepts in a virtuous cycle that works universally across data modalities: https://lnkd.in/gQpNY8gf #DataCentricAI #MachineLearning #DataScience #DataQuality #AI

Hey Data Community! I just wrote a blog post with my thoughts on how Data Quality affects LLMs applications and what to do about it. Feel free to take a look!

Ready to become an AI data specialist? Discover the top 10 skills you'll master in our comprehensive AI Data Specialist Course! From data analysis to machine learning and ethical AI practices, we've got you covered. Dive into the world of AI and data science and transform your career today. Read more in our latest blog post! 📊🤖 #AI #DataScience #MachineLearning #BigData #DeepLearning #CareerGrowth #TechInnovation

Gretel AI Releases Largest Open Source Text-to-SQL Dataset to Accelerate Artificial Intelligence AI Model Training Quick read: https://lnkd.in/g4gYQty5 HF Page with Dataset: https://lnkd.in/ghkYAf6s Gretel.ai #artificialintelligence #ai #dataset