Originally Published 2 months ago — by Hacker News
The article discusses concerns about AI and data privacy, highlighting how web browsing sessions are used for training AI models, the potential for increased surveillance and control, and the security risks associated with AI browsers like ChatGPT's Atlas. It also reflects on societal impacts, the evolution of interfaces, and the importance of vigilance in safeguarding privacy and freedom in the digital age.
Nvidia CEO Jensen Huang explained that the company is developing humanoid robots because much of the data used to train them comes from human movement, making them more productive in tasks designed for humans. The company unveiled Project GROOT, a general-purpose foundation model for humanoid robots, at its conference in San Jose, California. Huang also emphasized Nvidia's role as a market maker in technology and its potential to create jobs and increase productivity for companies.
OpenAI's text-to-video generator, Sora, will be available to the public later this year, with plans to eventually incorporate audio and allow user editing. The tool, capable of generating hyperrealistic scenes based on text prompts, is more expensive to power and will have policies similar to DALL-E, including not creating images of public figures and adding watermarks to videos. Concerns about generative AI tools and misinformation persist as the release approaches.
Stack Overflow, an online community for software coders, has experienced a decline in traffic since the release of powerful AI models like GPT-4, which were partly trained on Stack Overflow's freely available data. This trend raises concerns about the future of online forums and the availability of human data for AI training. Stack Overflow's CEO, Prashanth Chandrasekar, is responding by exploring ways to charge tech companies for using their data and engaging in conversations with large companies. The outcome of Stack Overflow's response has broader implications for businesses that rely on posting and hosting free information online, as well as the quality of AI models in the future.
Researchers from Britain and Canada introduce the phenomenon of model collapse, a degenerative learning process where models forget improbable events over time, even when no change has occurred. They provide case studies of model failure in the context of the Gaussian Mixture Model, the Variational Autoencoder, and the Large Language Model. Model collapse can be triggered by training on data from another generative model, leading to a shift in distribution. Long-term learning requires maintaining access to the original data source and keeping other data not produced by LLMs readily available over time.
