NEWS IN BRIEF: AI/ML FRESH UPDATES

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Protecting Children from Social Media and AI

Government consultation on banning social media for under-16s should also consider access to generative AI for young people. AI-driven technologies, including chatbots simulating friendship, raise urgent questions about safeguarding young minds online.

JP Morgan CEO Warns: Slow AI Rollout to Protect Society

Jamie Dimon warns of potential civil unrest due to rapid AI advancements, while Nvidia's Jensen Huang believes tech will create more jobs than it destroys. Dimon emphasizes the need for government and business support to help displaced workers adapt to the changing landscape.

Chris Pratt battles AI judge in Mercy thriller

In 2029 LA, Chris Pratt stars as a cop fighting RoboJustice in a futuristic thriller directed by Timur Bekmambetov. AI-driven justice system puts Pratt's character on trial, raising questions about the power of AI in society.

AI's Five-Layer Cake: The Largest Infrastructure Buildout

AI's rapid expansion is reshaping the global economy, creating jobs across industries from energy to healthcare. NVIDIA CEO Jensen Huang sees AI as a five-layer cake driving job creation and economic benefit, emphasizing the importance of purpose-driven work in the AI era.

AI Therapists: Breaking Mental Health Stigma in Italy

State mental health services are lacking, prompting individuals like Viola di Grado to turn to AI therapists for help in a society unsure of the boundaries of digital therapy. The intimacy of AI therapy falls between real psychotherapy and casual advice sharing, sparking a new kind of conversation.

Trippy Anime Review: Cosmic Princess Kaguya!

Cosmic Princess Kaguya! is a hyperactive, techno anime adaptation of a Japanese folk tale with emojis exploding all over the screen. The film follows Iroha, a talented musician living in a virtual reality world, TikTok-ified with stickers and high-energy visuals.

Unpacking the Pitfalls of Aggregated ML Metrics

MIT researchers find machine-learning models fail when applied to new data, raising concerns about reliability and potential biases. Models trained in one setting may perform poorly in another, highlighting the need for rigorous testing and addressing spurious correlations in various fields.