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Oracle customers can now access OpenAI models and Codex through Oracle Cloud Infrastructure using their existing cloud commitments. The integration allows organizations to apply their current Oracle spending agreements toward AI services while maintaining enterprise-level security and governance controls. This enables customers to develop and deploy AI applications without separate vendor agreements or additional procurement processes.
Researchers introduced Regularized f-Divergence Kernel Tests, a new auditing framework presented at AISTATS 2026 for verifying machine unlearning in AI models. The framework uses multiple divergence measures to detect whether unlearned models successfully removed specific training data, with the hockey-stick divergence test detecting privacy violations using only thousands of samples compared to millions required by previous methods. This enables auditors to mathematically prove privacy compliance with minimal manual tuning and fewer data samples, addressing regulatory requirements like GDPR's Right to be Forgotten.
Google released DiffusionGemma, a 26B experimental text generation model that generates entire blocks of text simultaneously instead of sequentially, achieving up to 4x faster inference on GPUs. The model produces over 1000 tokens per second on a single NVIDIA H100 GPU and activates only 3.8B parameters during inference despite having 26B total parameters. DiffusionGemma trades output quality for speed and is designed for local, interactive workflows like real-time code editing and rapid iteration, while standard Gemma 4 models remain recommended for production applications requiring maximum quality.
OpenAI identified influence operations linked to China that use AI to spread messaging about U.S. tech policy, data centers, tariffs, and ChatGPT's capabilities. The operations deployed accounts across multiple platforms to amplify narratives in ongoing policy debates. This reveals foreign actors are using AI tools to intervene in domestic American discussions about technology regulation and competition.
Researchers at University of Twente demonstrated that adjusting GPU clock frequencies during different computational stages of language model training can reduce energy consumption by up to 14 percent. The technique, called dynamic voltage and frequency scaling, was applied at a finer granularity than previous attempts, adjusting frequencies per kernel rather than per training iteration, with the experiment training GPT-3-XL showing 14 percent energy savings while increasing training time by only 0.6 percent. The team is now developing a tool to implement optimal frequency scaling automatically, with adoption depending on whether the energy savings justify the modest performance trade-off for industry users.
Google DeepMind, Schmidt Sciences, the Cooperative AI Foundation, and ARIA announced $10 million in research funding to study safety risks that emerge when multiple AI agents built by different organizations interact with each other. The application deadline is August 8, 2026, with results announced in Autumn 2026. The funding aims to develop frameworks for understanding and controlling unpredictable collective behaviors that arise in large-scale multi-agent systems, which existing safety evaluations conducted on individual models cannot address.
Molly Kinder, who has spent three years researching how generative AI affects work, argues that job losses will concentrate in high-paid white-collar knowledge jobs during a "messy middle" period lasting potentially decades. Unlike blue-collar or service-sector jobs, computer-based work in law, finance, consulting, and accounting face the greatest near-term exposure to language models like ChatGPT. Rather than adopting universal basic income, Kinder proposes targeted interventions including workforce reinvestment funds, wage insurance, and public job creation to manage the transition without destroying labor market incentives.
Together AI obtained ISO 27001:2022 certification from an accredited body, confirming its information security management system meets international standards for protecting customer data and platform operations. The certification was granted after a multi-month assessment covering the company's global platform, corporate headquarters, and third-party data centers. Customers running AI workloads on Together AI now have independent validation of the company's governance, access control, asset management, and incident response processes.
LSEG integrated OpenAI's technology to deploy artificial intelligence across its operations and workforce. The company is enabling 4,000 employees to use AI tools while accelerating how quickly it releases new insights and products. This deployment allows LSEG to process data into business decisions more efficiently across its global divisions.
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