Inflexion Points Technology Partners (IPTP)’s Post

🌟 Unlocking the Potential of Synthetic Data in AI Development 🌟📊🤖 The future of AI development is here, and it's powered by Synthetic Data. Our latest article delves into the fascinating world of synthetic data, exploring its transformative impact on AI and machine learning. 🚀🔍 🔍 In this article, you’ll discover: 🚀 1. What is Synthetic Data? Get a clear understanding of synthetic data and how it is generated to simulate real-world data while maintaining privacy and security. 📊🔐 💡 2. Advantages of Using Synthetic Data: Explore the numerous benefits synthetic data offers for AI and machine learning projects: Data Privacy: Protect sensitive information while training models with realistic data. 🔒🛡️ Cost Efficiency: Reduce the costs associated with data collection and labeling. 💰📉 Bias Reduction: Create diverse datasets to mitigate biases and improve model fairness. ⚖️🌍 Scalability: Easily generate large volumes of data to scale AI applications. 📈🔄 🌐 3. Real-World Applications of Synthetic Data: Discover how synthetic data is revolutionizing various industries: Healthcare: Enhancing medical research and diagnostics with privacy-preserving data. 🏥🔬 Autonomous Vehicles: Improving safety and performance with diverse training datasets. 🚗🛤️ Finance: Developing robust fraud detection systems and financial models. 💳📊 Retail: Optimizing customer experiences and personalization strategies. 🛒🤖 📈 4. Challenges and Considerations: Understand the potential challenges of using synthetic data, such as ensuring data quality and addressing ethical concerns, and explore strategies to overcome them. 🌟⚖️ 🔮 5. The Future of Synthetic Data: Get insights into the future trends and developments in synthetic data, including advancements in data generation techniques and its expanding role in AI innovation. 🌟🔮 📈 6. How to Get Started with Synthetic Data: Learn practical steps for integrating synthetic data into your AI projects, from selecting the right tools to setting up your data generation pipeline. 🚀🛠️ 🔗 Read the full article here: https://lnkd.in/gi7g9Wpx 👉 Join the conversation! How is your organization leveraging synthetic data in AI development? Share your experiences and insights below! 💬👇 #SyntheticData #AI #MachineLearning #TechInnovation #DataPrivacy #ArtificialIntelligence #BigData #AITrends #DigitalTransformation #DataScience #Innovation #FutureOfAI

Researchers call this “hitting the data wall.” And they say it’s likely to happen as soon as 2026. That makes creating more AI training data a billion-dollar question — one that an emerging cohort of startups are looking for new ways to answer. One possibility: creating artificial data. That’s five-year-old startup Gretel’s approach to AI’s data problem. It makes what’s known as “synthetic data” — AI-generated data that closely mimics factual information, but isn’t actually real. For years, the startup, now valued at $350 million, has provided synthetic data to companies working with personally identifiable information that needs to be protected for privacy reasons — patient data, for example. But now its CEO Ali Golshan sees an opportunity to supply data-starved AI companies with fake data made from scratch, which they can use to train their AI models. https://lnkd.in/eDjp5DCm

Using low-quality or improperly annotated data when developing AI can pose significant risks to the performance, reliability, and fairness of AI systems. Poor-quality data can introduce biases, errors, and inaccuracies into AI models, leading to suboptimal outcomes and potentially harmful consequences. For example, biased training data can result in AI systems making unfair or discriminatory decisions, such as denying opportunities or services to certain groups based on race, gender, or other protected attributes. Additionally, inaccurate or incomplete data can undermine the effectiveness of AI algorithms, resulting in unreliable predictions, recommendations, or classifications that may lead to costly errors or adverse outcomes. Properly annotated, high-quality data is essential for training AI systems that are accurate, reliable, and fair. High-quality data ensures that AI models learn from diverse and representative examples, enabling them to generalize well to new situations and make informed decisions across a wide range of scenarios. Moreover, properly annotated data provides ground truth labels and context that help AI algorithms understand the semantics and nuances of the data they are processing, improving their ability to interpret and respond to complex inputs accurately. By prioritizing data quality and implementing robust quality control measures, developers can mitigate risks, improve performance, and build AI systems that are trustworthy, transparent, and ethical. In conclusion, the significance of using properly annotated, high-quality data when developing AI cannot be overstated. Quality data forms the foundation of AI systems, influencing their accuracy, reliability, and fairness. By ensuring that AI models are trained on reliable, unbiased data, developers can build more robust and ethical AI solutions that deliver value, promote trust, and enhance the well-being of individuals and society as a whole. Utilizing APTO guarantees access to high-quality data crucial for developing AI systems. APTO's advanced platform ensures fast, cost-efficient, and reliable data annotation services, allowing developers to efficiently label vast datasets with precision. With APTO's expertise and scalable annotation workflows, developers can expedite the AI development process without compromising on data quality. By leveraging APTO's services, developers can confidently train AI models on accurately annotated data, resulting in more reliable and effective AI solutions that meet the highest standards of performance and integrity! Contact us for more! info@apto.co.jp #ai #artificialintelligence #machinelearning #deeplearning #datascience #datasolutions #annotation #bigdata #chatgpt #openai #llm #mlops #generativeai

📢 Navigating the Promise of Synthetic Data in AI Training 📢 For engineering undergraduates and recent graduates exploring the power of AI, understanding synthetic data is crucial. Synthetic data, or AI-generated data, is becoming a popular solution for training models, especially as access to real data tightens. Companies like Meta and Anthropic have adopted synthetic data in model training, citing cost savings and flexibility. The synthetic data industry is projected to be worth $2.34 billion by 2030, making it an exciting field for emerging engineers. But synthetic data isn't perfect. Just like any other dataset, it can carry biases or inaccuracies, amplifying flaws from the original dataset. If used improperly, synthetic data can lead to “model collapse,” where AI’s outputs degrade over time. As engineers, understanding how to curate and validate data, whether synthetic or real, is essential to building robust AI systems. Think of synthetic data as a tool in your toolkit that, with proper usage and oversight, can create endless possibilities. #EngineeringExcellence #AI #DataScience

Synthetic data, generated by AI models, promises a solution to the growing scarcity of real-world data for training AI systems. Major tech companies are embracing synthetic data, citing lower costs and fewer ethical concerns compared to human-annotated datasets. However, synthetic data carries risks of compounding biases and hallucinations present in the original models, potentially leading to degraded performance over time. While synthetic data can supplement real-world examples, experts caution against over-reliance, emphasizing the need for careful curation and pairing with fresh data to mitigate quality issues. The jury is still out on whether synthetic data alone can fuel the next generation of AI without human oversight. Read more here: https://lnkd.in/dHQD-y4X #AI #tech #law #wolftheiss

The High Stakes of AI Training Data #Data is becoming increasingly expensive, posing a challenge for smaller tech companies to access the necessary resources for advanced #AI systems. James Betker from OpenAI emphasized the significance of training data in shaping AI model capabilities, suggesting that it's more crucial than model design or architecture. #GenerativeAI systems rely heavily on training data to make probabilistic guesses, with larger #datasets often resulting in better performance. While training on larger datasets can lead to performance gains, the quality of data is equally important as quantity. Concerns arise regarding the centralization of AI development among tech giants with substantial budgets to acquire high-quality datasets, potentially hindering #innovation and equitable access to resources. Some companies engage in questionable practices to acquire training data, including transcribing copyrighted content without permission and exploiting low-wage labor for #dataannotation. Licensing content for AI training datasets is becoming increasingly expensive, creating barriers for smaller players in the AI research community. Efforts by independent groups to create open training datasets, such as EleutherAI's The #Pilev2 and Hugging Face's #FineWeb, aim to provide accessible resources for AI model development. However, these efforts face challenges such as copyright issues and resource limitations compared to #BigTech's data collection capabilities. For more information: https://lnkd.in/dQY95Bxy #TrainingData #AI #DataChallenges #GenerativeAI #TechEthics #DataAccess #DataLicensing

"Easier said than done" could characterize a lot of data and AI projects, actually. 😅 ‎ But as AI governance grows in importance for companies, it's time to start doing, despite challenges. At BigDATAwire‎, Alex Woodie offers some key insights: 1. AI governance is still in its infancy: Despite advancements in AI technology and investment, there are few concrete rules or regulations. The EU is leading the way with the AI Act, and President Biden has issued guidelines in the U.S., but there's still a lot to learn. 2. Data governance is crucial: Companies are working to ensure that AI and machine learning models don't go off the rails. As AI evolves, customers are demanding more control over how data is consumed in large language models (LLMs) and other AI applications. 3. The challenge of unstructured data: Much data governance technology has been focused on detecting sensitive data in databases. However, providing the same level of governance for unstructured data used in GenAI is a more complex task. Remember, AI governance is a journey, not a destination. How will you create an AI governance structure that works for your organization?  Read more at Datanami: https://lnkd.in/gkfbAGjn

SYNTHETIC DATA On many occasions synthetic data is being discussed as a potential solution. Any sensible conversation about the use of synthetic data starts off by clarifying what we are using that data for: a) Training AI models b) Building Data Pipelines In the case of b) it seems that many do not understand that a hard part of building data pipelines especially on older data sources comes from data not being as expected and that many corner cases must be handled. That makes generating synthetic data for building data pipelines missing a major aspect of what is hard about building data pipelines. Of course it works well for scrambling to ensure privacy and copies to test volume resilience or real-time capabilities. In the case of a) it is a different discussion - still, however, with both pros and cons. There are some obvious invariances which are naturally handled by manipulated data (like rotation, scaling, and translation), while others like representativity is far less obvious - if interested in that part you might like the article linked to below .. #laerdalCPHlife #helpingsavelives #ai https://lnkd.in/dAM5VEUZ

“𝗨𝗻𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝗔𝗜’𝘀 𝗦𝗲𝗰𝗿𝗲𝘁 𝗪𝗲𝗮𝗽𝗼𝗻: 𝗛𝗼𝘄 𝗦𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗶𝘀 𝗦𝗼𝗹𝘃𝗶𝗻𝗴 𝗣𝗿𝗶𝘃𝗮𝗰𝘆, 𝗖𝗼𝘀𝘁, 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗶𝗻 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 🚀” Did you know that AI can generate synthetic data to train other AI models? It’s an emerging but lesser-known innovation in machine learning, especially useful when real-world data is limited, costly, or sensitive. This technique is becoming vital in fields like healthcare, autonomous driving, and financial modeling. 𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗱𝗮𝘁𝗮 𝘄𝗼𝗿𝗸𝘀 𝗮𝗻𝗱 𝘄𝗵𝘆 𝗶𝘁’𝘀 𝘀𝗼 𝘃𝗮𝗹𝘂𝗮𝗯𝗹𝗲: 𝟭. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗦𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗗𝗮𝘁𝗮? Synthetic data is artificially generated data that simulates real-world data but is created using AI models. Unlike random or fabricated data, synthetic data maintains statistical properties and patterns found in the original dataset, making it suitable for training and testing machine learning models without the challenges of obtaining real-world data. 𝟮. 𝗛𝗼𝘄 𝗶𝘀 𝗦𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗖𝗿𝗲𝗮𝘁𝗲𝗱? A common approach to generating synthetic data is through 𝙂𝙚𝙣𝙚𝙧𝙖𝙩𝙞𝙫𝙚 𝘼𝙙𝙫𝙚𝙧𝙨𝙖𝙧𝙞𝙖𝙡 𝙉𝙚𝙩𝙬𝙤𝙧𝙠𝙨 (𝙂𝘼𝙉𝙨) or 𝙑𝙖𝙧𝙞𝙖𝙩𝙞𝙤𝙣𝙖𝙡 𝘼𝙪𝙩𝙤𝙚𝙣𝙘𝙤𝙙𝙚𝙧𝙨 (𝙑𝘼𝙀𝙨). These models learn from real-world data and generate new, similar data. GANs, for instance, consist of two networks: 𝙏𝙝𝙚 𝙂𝙚𝙣𝙚𝙧𝙖𝙩𝙤𝙧: Creates new data that resembles real data. 𝙏𝙝𝙚 𝘿𝙞𝙨𝙘𝙧𝙞𝙢𝙞𝙣𝙖𝙩𝙤𝙧: Evaluates the quality of the generated data, comparing it to the real dataset. Over time, the generator improves its ability to create data that is indistinguishable from the real thing, ensuring the synthetic data is highly realistic and usable for training purposes. 𝟯. 𝗪𝗵𝘆 𝗨𝘀𝗲 𝗦𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗗𝗮𝘁𝗮? 𝙁𝙞𝙡𝙡𝙞𝙣𝙜 𝘿𝙖𝙩𝙖 𝙂𝙖𝙥𝙨: Generate data for rare or missing scenarios, especially useful in fields like autonomous driving where critical events are underrepresented. 𝙋𝙧𝙞𝙫𝙖𝙘𝙮 𝙖𝙣𝙙 𝙀𝙩𝙝𝙞𝙘𝙨: Safely share anonymized data without violating privacy laws (e.g., in healthcare and finance). 𝘿𝙖𝙩𝙖 𝘼𝙪𝙜𝙢𝙚𝙣𝙩𝙖𝙩𝙞𝙤𝙣: Balance unbalanced datasets, improving model accuracy. 𝘾𝙤𝙨𝙩-𝙀𝙛𝙛𝙚𝙘𝙩𝙞𝙫𝙚: Generate large datasets quickly and affordably. 𝙏𝙚𝙨𝙩𝙞𝙣𝙜 𝙀𝙙𝙜𝙚 𝘾𝙖𝙨𝙚𝙨: Simulate rare but essential events for more robust model training. Synthetic data is transforming AI by overcoming limitations in privacy, cost, and data availability. It offers a scalable, ethical solution for training machine learning models more effectively. Have you worked with synthetic data? Let’s talk about how it's shaping AI’s future!👇 #AI #SyntheticData #GANs #DataPrivacy #MachineLearning #TechInnovation #DeepLearning #GenerativeAI

The second post in our #AI #ML for #PropTech series focuses on data preparation for training AI models. Without properly completing this step, even the latest and greatest scientific AI algorithms become entirely ineffective ("garbage in – garbage out," as they say). But jokes aside, why is this step so crucial? Firstly, because approximately 80% of the time and effort in AI/ML projects is devoted to data preparation. Secondly, improperly prepared, low-quality data will result in inaccurate models, making their results unusable. Therefore, before training the model, it's essential to prepare the raw data: clean it, format it correctly, structure it, label it. Importantly, this should be a collaborative effort between Property Industry experts and AI experts. And yes, HiPer it! has both teams in place. So, what exactly needs to be done?  1. A massive amount of data, commonly referred to as #BigData, will be collected.   Data from facility management software like #EMS, #BMS, and #SCADA is insufficient for AI training as it only captures a tiny fraction of the overall context. Data will be collected at high frequencies from carefully chosen key equipment points through the installation of sensors, detectors, and probes to compile a comprehensive Big Data set.   2. To ensure data readiness for AI model training, the initial focus lies in cleanup: rectifying errors, filling gaps, and maintaining consistency.  Both software tools and human expertise to be employed to filter out incorrect data, correct errors, and align timelines. Furthermore, validation of data's relevance to the problem and its accuracy in representing real-world phenomena is required. The avoidance of biased or incomplete data is paramount, as they can significantly skew AI model outcomes.  3.  Once data is well-prepared, the next step involves structuring. Data lake, created at the previous stage, to be transformed into a structured Data Base format. Data is labeled with appropriate tags or categories to facilitate supervised learning.  4. Annotation and enrichment represent a pivotal stage in AI model training. Here, metadata or annotations are integrated, offering vital context and depth to the dataset, thereby enhancing comprehension and performance.  In practice, Property Industry experts meticulously mark and label data segments classifying specific scenarios, such as excessive energy consumption, equipment malfunction, faults, or conversely, normal operation and energy consumption patterns.    Now armed with the appropriate dataset, the AI model training is ready to be kickstarted.. Stay tuned for the next post in this series, where the intricacies of the training process will be delved into. #energyefficiency #hiperware #iot #manufacturing #ESG