⚖️ Deepfake Porn Prompts Legal Action

PLUS: Researchers trick ChatGPT into revealing private data.

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Good morning! Today is Monday, December 4th and today we're looking at deepfake privacy concerns spurring US legislative action and new analysis on the energy consumption of AI image generation. New to The Intelligence Age? Sign up here.
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News & Insights

A High School’s Deepfake Porn Scandal Is Pushing US Lawmakers Into Action

In the wake of a deepfake porn scandal involving high school students, lawmakers are being pushed into action. Francesca Mani, a 14-year-old victim, sparked a legislative push against nonconsensual deepfake pornography after her own image was manipulated. With the simplicity of creating such content from a single image, the risks have escalated. Francesca, in response, has reached out to lawmakers, catalyzing bipartisan support for bills aimed at criminalizing the creation and distribution of these images.

The technological sophistication of AI has turned privacy breaches into a pressing concern. Representative Morelle's Preventing Deepfakes of Intimate Images Act proposes to make creators and distributors liable, marking a shift in legal responsibility. With the bill under consideration, and Francesca's continued advocacy, there's a burgeoning drive for accountability. But with the ease of AI manipulation, what's the next step in safeguarding digital identities? technologyreview

Making An Image With Generative AI Uses As Much Energy As Charging Your Phone

Generative AI's carbon footprint is no small matter. A study by Hugging Face and Carnegie Mellon University highlights this, revealing that generating one image equals the energy used to fully charge a smartphone. But the devil's in the details: text generation is lighter on resources, using less energy than a fraction of a phone charge for 1,000 prompts. This insight into AI's environmental impact is crucial, especially when considering the massive scale of daily AI interactions worldwide.

Is it time to recalibrate our use of AI? Sasha Luccioni of Hugging Face urges a more selective approach, advocating for specialized models over energy-hungry generalists. The research suggests a stark contrast in carbon emissions between broad-spectrum generative models and their leaner, task-specific counterparts. It's an important conversation for a tech-driven future, steering us towards more sustainable AI usage patterns. technologyreview

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Practical AI

How AI Is Being Utilised In Supply Chain Risk Management

In a world teeming with data, Overhaul's CPTO, Jonathan Ryan, harnesses AI to transform supply chain risk management. One key innovation? RiskGPT. A digital double-check for risk managers, it boosts confidence under the real-time pressures of cargo monitoring. Overhaul's approach, integrating tools like Azure Synapse and Delta Lake, simplifies data management, carving out a unified data view from the chaos of spreadsheets.

Sustainability isn't just a buzzword at Overhaul; it's actionable intelligence. With billions of data points, Ryan's team advises on routes that hit the sweet spot between fuel economy and trip efficiency. But they're pushing boundaries further, eyeing machine learning to predict and optimize routes, slashing carbon footprints and driving eco-friendly logistics. siliconrepublic

Around The World

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  • IBM advances AI governance by establishing comprehensive principles on fairness, introducing robust processes to ensure equitable artificial intelligence applications across various industries.

  • Meta's Dhruv Batra provides insights into the future of robotics, discussing advances in AI, challenges in the field, and the potential impact on society in a comprehensive Q&A session.

  • OpenAI Purchases $51 Million Worth of AI Chips From Startup Supported by CEO Sam Altman to Boost Its Machine Learning Capabilities.

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Research Review

Hack Tricks ChatGPT Into Spitting Out Private Email Addresses, Phone Numbers

In an unsettling revelation, DeepMind researchers have demonstrated how ChatGPT can inadvertently reveal private data. Their method, disarmingly simple, involved repetitive prompts leading the AI to divulge sensitive details. This flaw underscores a significant concern: the AI's tendency to parrot information from its training material. "It's wild to us that our attack works," the team expressed, pointing to the potential risk of mass data extraction. The implications are profound; the security of generative AI is in question, especially when such breaches are achievable with minimal resources.

While OpenAI acted swiftly to patch the exploit, the incident raises a critical question: Can we ever fully sanitize AI from leaking its training data? As the attack involved a mere $200, it's chilling to consider the damage a more funded adversary could inflict. The researchers' findings serve as a stark reminder that even "aligned" AI can falter, and proprietary models may obscure deep-seated vulnerabilities. "No one has ever noticed that ChatGPT emits training data with such high frequency until this paper," they remarked, exposing a blind spot in AI oversight. futurism

Written by Isaac R. Ward, Casey Clifton, and Alex Brogan.

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