Benoit Bergeret (Ben)’s Post

AI leaders need to practice responsible AI communication. The message this article lands is that few technological innovations have captured the public imagination as profoundly as AI. AI has become a battleground for responsible science communication because of discrepancies between popular portrayals and nuanced realities. The problem is that AI is often represented in discourse and media like it is in science fiction. While rich in creative capital, such portrayals are problematic when imported into the technology ecosystem. These narratives perpetuate the idea that the most significant risk associated with AI lies in the potential for human extinction when we really should be focused on other issues like the very immediate risk that AI could deepen current societal inequalities. Besides AI leaders, in our world of sensationalism and click metrics, I would add that journalists and the media also have a critical role to play by sharing those ‘sound bites’ and headlines they craft in the context of the conversation they have had. https://lnkd.in/dDCBffxi #ai #aigovernance #aiethics #responsibleai #trustedai

There is a real prospect that AI models will run out of data to train them. Enterprises must either find more data or shrink the needs of their models. In Technology Decisions, George Dragatsis, ANZ Chief Technology Officer, explains what can organizations do to continue to innovate with AI in the face of training data exhaustion. https://lnkd.in/gcbXSK5M #AI #LLM #SLM #Innovation

Synthetic Data? Synthetic data, or artificially generated data, offers exciting opportunities in AI and machine learning by providing scalable datasets without privacy risks. It can fill gaps in real data, solve bias issues, and enhance model training. However, it's not without risks—low-quality synthetic data can skew models and fail to capture the complexity of real-world scenarios. The key is balance: quality over quantity for better, unbiased outcomes. Interesting read: https://lnkd.in/g5iX8qtA #AI #SyntheticData #MachineLearning #TechInnovation

The rise of synthetic training data has unlocked new possibilities for scaling AI, but it comes with an emerging challenge: Model Collapse—a phenomenon where models trained on AI-generated data degrade in quality over time. Researchers are now questioning whether there's something inherently unique in human-generated data that gives models their edge, something synthetic data might lack. This remains to be seen. To mitigate these risks, AI companies are not abandoning synthetic data. Instead, they’re adopting hybrid approaches—combining synthetic data with meticulous verification and benchmarking against human-curated datasets. The pressing question is no longer whether synthetic data can be useful, but how far it can be trusted when scaled for high-stakes. #ai #syntheticdata #modelcollapse

Can AI Outsmart Itself? As AI models rely heavily on massive amounts of training data, there’s a growing challenge: data is becoming more scarce and expensive to obtain. To bridge this gap, companies are turning to synthetic datasets—data generated by other AI models. But here’s the catch: relying too heavily on synthetic data introduces a “Chinese whispers” effect. Small initial errors can gradually amplify, leading to a decline in quality. This ripple effect could result in models hallucinating or drifting further from reality as they become more complex. While the vision is for AI to someday generate synthetic data sophisticated enough to train itself effectively, that tech isn’t here yet. For now, and in the foreseeable future, using real data remains crucial. For more insight read the interesting article in TechCrunch by Kyle Wiggers. #ArtificialIntelligence #MachineLearning #SyntheticData #DataScience #AIInnovation #AITech #FutureOfAI #TechTrends #ResponsibleAI #HumanInTheLoop #AIEthics https://lnkd.in/dGUn6Q7u

🌐💡 Facing an unprecedented challenge, AI companies are on the verge of exhausting the entire internet's data for training advanced models! From exploring synthetic data to tapping into unorthodox data sources, the quest for innovative solutions is on. But as debates over sustainability and ethical implications intensify, the industry stands at a crossroads. Can we pave a path towards more efficient, responsible AI development, or will we witness a shift in the quest for 'bigger and better'? Dive into the details and join the conversation here https://lnkd.in/gx2n6mzd #AIEthics #DataCrisis #SustainableAI #TechInnovation"

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

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.

𝗧𝗵𝗲 𝗥𝗼𝗹𝗲 𝗼𝗳 𝗦𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗔𝗜 We often think of data as the fuel driving AI innovation, but not all data is created equally. Synthetic data artificially generated data that mimics real-world scenarios is becoming a powerful tool in AI development, especially when access to real-world data is limited due to privacy or availability concerns. From rare medical conditions in healthcare to fraud detection in finance, synthetic data allows AI engineers to train models without compromising sensitive information. It’s more cost-effective, scalable, and even cleaner than some real-world data. However, like all tools, it has its risks. Over-reliance can lead to “model collapse,” where AI systems fail to represent the complexities of real-world scenarios. A balanced approach that combines synthetic data with real-world examples is crucial to ensuring AI systems remain robust and accurate. As AI continues to evolve, synthetic data will play a key role in responsible innovation, but careful validation and diverse data sources will be the key to success. What are your thoughts on the growing use of synthetic data in AI? Do the benefits outweigh the risks? #AI #SyntheticData #MachineLearning #Innovation #GenerativeAI https://lnkd.in/eSJ-nF6n

MIT Technology Review: This Is Where the #Data to Build #AI Comes From New findings show how the sources of data are concentrating power in the hands of the most powerful tech companies. “AI is all about data. Reams and reams of data are needed to train algorithms to do what we want, and what goes into the AI models determines what comes out. But here’s the problem: AI developers and researchers don’t really know much about the sources of the data they are using. AI’s data collection practices are immature compared with the sophistication of AI model development. Massive data sets often lack clear information about what is in them and where it came from. The Data Provenance Initiative, a group of over 50 researchers from both academia and industry, wanted to fix that. They wanted to know, very simply: Where does the data to build AI come from? They audited nearly 4,000 public data sets spanning over 600 languages, 67 countries, and three decades. The data came from 800 unique sources and nearly 700 organizations. Their findings, shared exclusively with MIT Technology Review, show a worrying trend: AI's data practices risk concentrating power overwhelmingly in the hands of a few dominant technology companies. […] The Western focus of these data sets becomes particularly clear with multimodal models. When an AI model is prompted for the sights and sounds of a wedding, for example, it might only be able to represent Western weddings, because that’s all that it has been trained on, Hooker says. This reinforces biases and could lead to AI models that push a certain US-centric worldview, erasing other languages and cultures.” By Melissa Heikkilä, Stephanie Arnett https://lnkd.in/eGKWB3WT #algorithms #AI #ArtificialIntelligence #LLMs #regulations #intellectualproperty #art #artists #creators #justice #equality #bias #health #socialmedia #media #productivity #labor #bigtech #startups #technology #datascience #privacy #security #journalism #democracy #humanity