This appears to be a newsletter article summarizing recent news and developments in the field of Artificial Intelligence (AI). Here’s a breakdown of the main points:
Bias in AI Data Sources
- A study found that imagery from Google was more likely to reinforce gender stereotypes for certain jobs and words than text mentioning the same thing.
- Images were also found to be more effective at associating roles with one gender, even when people viewed them days later.
Google’s Image Generator Diversity Fiasco
- The article mentions a recent controversy surrounding Google’s image generator, which was criticized for its lack of diversity in generated images.
LLMs in Chemistry and Literature
- Researchers found that Large Language Models (LLMs) can be fine-tuned to help with chemistry tasks after minimal training.
- LLMs were also shown to be able to extrapolate from yes/no question-and-answer data, making them a useful tool for chemists.
Other Developments
- A group of researchers opened sourced a "reasoning" AI model called Sky-T1 that can be trained for under $450.
- Nvidia’s AI empire was highlighted, with the company investing in various startups related to AI research and development.
Concerns about Bias and Stereotypes
- The article notes that these biases have real effects on people and that it’s essential to address them to ensure fairness and accuracy in AI systems.
Overall, this newsletter provides a summary of recent developments in AI, highlighting the importance of addressing bias and stereotypes in AI data sources.
