NEWS IN BRIEF: AI/ML FRESH UPDATES

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Open AI vs. Tech Council: Copyright Clash

OpenAI's Chris Lehane declares AI models will be in Australia despite copyright disputes with Tech Council of Australia at SXSW Sydney. ChatGPT's chief global affairs officer discusses geopolitics of AI and Australia's tech future at the event.

Revolutionizing Music Creation with AWS Trainium and SageMaker

Generative AI transforms music creation, as Splash Music collaborates with AWS to develop HummingLM, a cutting-edge model enabling users to create studio-quality tracks effortlessly. Splash Music overcomes challenges in model complexity, industry pace, and infrastructure scaling to revolutionize music production.

Mastering the Art of LLM Selection

Selecting the right large language model (LLM) is crucial but challenging. Moving from subjective vibes to comprehensive metrics is essential for accurate evaluation and model selection.

AI Sora Videos: Legal Experts Sound Alarm

Video app creates realistic deepfakes of Marx and MLK Jr. Some question use of 'historical figures' for legal testing. Night out with Henry VIII, DJing with Princess Diana, and shopping with Karl Marx in engaging scenarios.

Peak Power Rewards: Lowering US Bills

'Demand flexibility policy could ease strain on grid caused by AI datacenters, lower power bills, prevent blackouts, and help climate by rewarding customers for using less power or selling excess energy from solar panels.'

AI Assisted Barrister Caught Using Fake Cases

Immigration barrister Chowdhury Rahman caught using ChatGPT-like AI software for legal research, wasting tribunal's time with fictitious cases. Rahman failed to verify accuracy, raising concerns about reliance on AI in legal profession.

Remembering John Searle

John Searle's 1984 lecture challenged the concept of AI thinking. Researchers continue exploring meaning and learning in AI today.

Personalized Object Detection: A Breakthrough in Generative AI

MIT and MIT-IBM Watson AI Lab researchers introduce a new training method to teach vision-language models to localize personalized objects in a scene, outperforming state-of-the-art systems. This approach could help AI systems track specific objects over time and assist visually impaired users in finding items in a room.