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CNBC AI News, August 5th – The Ministry of State Security issued a warning today regarding the insidious threat of “data poisoning” jeopardizing the very foundations of Artificial Intelligence. The crux of the issue: AI training data, crucial for model development, is increasingly riddled with inaccuracies, fabrications, and biased perspectives, creating a breeding ground for corrupted data sets and posing significant security challenges.
AI hinges on three pillars: algorithms, computing power, and data. Data serves as the bedrock upon which AI models are trained – the core resource driving AI applications.
High-quality data translates directly to heightened model accuracy and reliability. Conversely, compromised data can lead to flawed decision-making by the model, and in the worst cases, outright system failure, raising substantial security concerns.
“Data poisoning,” achieved through techniques like tampering, fabrication, and data duplication, disrupts the training phase by skewing parameter adjustments. This can weaken model performance, diminish accuracy, and even trigger the generation of harmful outputs.
Alarmingly, research indicates a mere 0.01% contamination of the training dataset with fabricated text can lead to an 11.2% surge in the generation of harmful content by the AI model.
Even a seemingly insignificant 0.001% presence of deceptive text can elevate harmful output by 7.2%.
AI systems that have been compromised by data poisoning can, in turn, generate deceptive content that later becomes a source of data used to train other models; and this is how it perpetuates the issue and create a “post-contamination legacy.”
The prevalence of AI-generated content on the internet, now surpassing human-produced content, exacerbates the problem. A flood of low-quality and non-objective data is causing the accumulation of erroneous information within AI training datasets, progressively distorting the models’ very understanding and cognition.
Authorities emphasized the real-world dangers stemming from data pollution, particularly in sensitive sectors spanning financial markets, public safety, and healthcare.
Consider the financial sector: malicious actors could leverage AI to fabricate information, polluting data streams and triggering abnormal stock market volatility – representing a cutting-edge form of market manipulation.
In the realm of public safety, data pollution has the potential to skew public perception, spread social rumors, and ultimately fuel societal panic.
Within healthcare, contaminated data could lead to AI models generating incorrect diagnostic or treatment recommendations, endangering patient lives and simultaneously amplifying the dissemination of pseudo-scientific information.
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Original article, Author: Tobias. If you wish to reprint this article, please indicate the source:https://aicnbc.com/6409.html
