Nuno Seixas’ Post

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.

AI's strained relationship with the truth, also called hallucinations, could easily get worse. Using facts to train artificial intelligence models is getting tougher, as companies run out of real-world data. AI-generated synthetic data is touted as a viable replacement, but experts say this may exacerbate hallucinations, which are already one of the biggest pain points of machine learning models.

The success of enterprise AI initiates hinges on data quality. Developers are grappling with a host of data quality issues right now, most of which can be addressed with synthetic data. My article in InfoWorld this morning explains how. Check it out here: https://lnkd.in/gBEQN-Fd Gretel.ai

Why Enterprises are Choosing RAG for AI 🤔 Analyst Dion Hinchcliffe recently highlighted how Retrieval-Augmented Generation (RAG) is transforming enterprise AI by combining database data with generative LLMs for richer, more accurate responses. Jerry Liu of LlamaIndex emphasizes RAGApp’s ease in deploying AI chatbots without coding. RAG reduces hallucinations, cuts compute costs, and adapts to dynamic data, providing precise outputs. Techniques like self-supervised learning and synthetic data are on the rise, but human-annotated datasets still set the gold standard. Companies like OpenAI and Google rely on manual labeling, especially in countries like India. Experts agree that RAG and fine-tuning are complementary; RAG offers real-time info, while fine-tuning customizes models for specific domains, optimizing performance and cost. Read more - https://lnkd.in/ge7XHkBG

Harvard Business School has an interview on the difficulty of making AI models forget private data https://lnkd.in/eEjsnJri. #artificialintelligence #forgetdata #trainingdata #harvardbusinessschool

Should AI models be designed with the ability to "unlearn" or "forget"? What if a company has trained a model on sensitive and private data and has no customer consent? What if regularion kicks in and requires that companies / owners of AI models have them "unlearn" certain specific data? Probably smart to start thinking of how to design our AI models with this capability. What do you think and why? Great Harvard Business School article on the topic. #artificialintelligence #genai #dataprivacy

The rapid rise of #generativeAI like OpenAI’s GPT-4 brings advancements and risks, particularly in privacy and data scarcity. Model collapse is a significant issue as AI systems degrade without diverse, high-quality data. Synthetic data, which mimics real-world data without exposing personal information, is emerging as a solution. It’s transforming industries by: -Training AI models -Enhancing diagnostic tools in healthcare -Predicting market trends in finance -Improving AI-driven customer support However, challenges remain. Ensuring data quality and preventing reverse engineering are crucial. Synthetic data must also avoid introducing biases. Check out my new piece, which unpacks these tensions, exploring how we can harness synthetic data's potential while navigating its complexities. https://lnkd.in/gGPc_Akv

As we see more and more companies looking towards synthetic data as a potential solution to their data liquidity problems. In the right hands synthetic data is a powerful tool however we need to take a step back and understand the potential risks such as model collapse and the preventive measures to mitigate them. Find out more in the article below to see how we at Valyu takle this growing issue.

As AI models face challenges accessing quality data, synthetic data is gaining attention as a potential solution. Tech giants are already using AI-generated data to train models, but it comes with risks like bias and decreased model diversity. While synthetic data offers promising cost and scalability benefits, it’s not yet perfect and still requires human oversight to avoid long-term issues like model degradation. The future may hold fully self-trained models, but for now, the human touch remains essential. #AI #SyntheticData #TechInnovation #DataScience #AITraining

Struggling with unreliable AI? Proper model validation makes machine learning dependable, aiding in future-proofing and unlocking growth. TikTok! ⏰🚀 #AIValidation #MachineLearningReliability Get the crucial steps here 👇: bit.ly/3hCv1Ff