Gretel’s Post

The use of vector databases in AI technologies offers numerous benefits.

Synthetic Data: Meet the Unsung Catalyst in AI Acceleration Synthetic data is not a new phenomenon. Rules-based synthetic data has been around longer than most people realize. It is commonly used in analytics for data augmentation, conjoint model analysis, and simulation testing. Rules-based methods, however, lack flexibility and struggle with complex data distributions. Assumptions made during rule creation don’t always hold universally, and manually defining rules can become impractical as datasets grow.  Generative AI (GenAI) models such as generative adversarial networks (GANs) and variational autoencoders (VAEs) are making it easier to generate more realistic synthetic data quickly by learning complex distributions directly from that data and generating much more realistic and higher-quality synthetic data, which can then train better-performing AI models. Forrester defines synthetic data as: Generated data of any type (e.g., structured, transactional, image, audio, etc.) that duplicates, mimics, or extrapolates from the real world but maintains no direct link to it, particularly for scenarios where real-world data is unavailable, unusable, or strictly regulated. #ai #syntheticdata #llms

Who knew that sometimes fiction is better than fact? 📊💭 Synthetic data might just be the game-changer you've been waiting for: fake data based on the real thing to train and test models. Synthetic data is pivotal in accelerating AI development, thanks to its diverse, secure datasets that enhance model performance. You might want to use synthetic data if the actual data you need is unavailable, unusable, or strictly regulated. 🚧 #AI #Data #AITraining

In this guest post, Jimmy Whitaker, Data Scientist in Residence at Human Signal, introduces Adala, an Autonomous Data Labeling Agent framework that harmonizes AI's computational power with human judgment. It challenges conventional AI paradigms with dynamic agents that not only learn and adapt but also heavily... Artificial Intelligence | Read more at The Digital Insider.

Data quality, lineage and #security matter more than ever to unlock the power of #AI large language models. Our Bharti Patel shares why enterprises need a lean, mean #data machine to fuel their enterprise AI strategy. 🔗 Read her POV in The New Stack: https://ow.ly/mHjB50StNpk #GenAI #Storage #DataManagement

🚀 Exciting News from Salesforce 🚀 What is MINT-1T Dataset and why it matters? in 🇬🇧 🇺🇸 Salesforce has released the groundbreaking MINT-1T open-source dataset, featuring one trillion text tokens and 3.4 billion images. This dataset can be a game-changer for advancing multimodal learning, enabling AI to process and integrate text, images, audio, and video seamlessly. With sources like web pages and scientific articles, MINT-1T offers free access to vast data, fostering innovation and democratizing AI research. This also promotes ethical data use and accelerates AI development, particularly in computer vision and cross-modal reasoning. Explore the future of AI with MINT-1T! 🌐🤖 #AI #DataScience #Innovation #EthicalAI #OpenSource #TechInnovation #MachineLearning Read more: https://lnkd.in/d6WeEJ_V

Wild to think that we are just starting to feel the impact of generative AI on many parts of the business world and some companies are running out of data to train on. Ironically the next big thing in the Gen AI space is going to be synthetic data (data artificially generated) and companies that provide it. As companies start to trawl their own internal data storage it will create massive new datasets but they will be limited to internal use only.

Synthetic Data And LLMs Synthetic data has become a hot topic in generative AI and has come to refer to data generated by one LLM that is then used in training another. This is often done because the smaller more optimized model is faster and less expensive to operate than the larger foundational model. For instance, you may be trying to classify short texts such as tweets but not have enough hand-labeled examples of particular cases and no inexpensive or easy way to find more. In that case you may want to use an LLM to generate enough examples of each class of interest. This seems like a great idea but there are two general groups of concerns. https://lnkd.in/gcgSK4Fb

Is the high cost of data locking out smaller players in the AI game? Training data is the secret sauce that fuels increasingly capable and sophisticated AI systems. But there's a catch. The cost of these data licenses is skyrocketing, creating a formidable barrier for smaller players in this field. Because they cannot afford these licenses, they are left standing on the sidelines, unable to develop or study AI models. The question is, are we creating a marketplace where only big tech can afford to play? Dig into this more at TechCrunch: https://lnkd.in/gdrjqXq9

Synthetic data is revolutionizing the AI landscape and in our most recent article we let know why and how you can adopt it effectively! Synthetic data benefits: 1️⃣ Data Accessibility: Synthetic data makes AI development more accessible, reducing the need for vast real-world datasets. 2️⃣ Models: Enhances model robustness through diverse and comprehensive data inputs. 3️⃣ Endless Possibilities: It unlocks limitless experimentation by generating data for various scenarios and edge cases. 4️⃣ Privacy Preservation: Protects sensitive information while still enabling robust AI training and testing. 5️⃣ Accelerated Development & innovation catalyst: Speeds up AI projects by eliminating data collection bottlenecks. 6️⃣ Adaptive AI: Enables AI systems to adapt and perform better in dynamic real-world environments. As we harness the power of synthetic data, AI's potential knows no bounds. Exciting times ahead! 💪📈 #AI #dataquality #MachineLearning #softwaredevelopment #SyntheticData #datascience  https://lnkd.in/g2XBFaZT