⭐️ Question: Is there a Spotify-wrapped version of what happened in AI this year?

 Answer: Yes, check out this study from Ezra Eeman!

TBH, it’s not as cool as your personal Spotify wrapped, but it does a great job summarizing and personalizing what happened in AI this year from a 100,000-foot perspective. Let me know what you think!

AI and generative technologies are advancing rapidly, data streams now fuel smart systems everywhere, younger generations are AI-savvy, the pace of change causes concern, and new opportunities as well as ethical challenges abound. Key takeaways:

• A torrent of AI-generated content is flooding platforms, challenging notions of authenticity and originality. Distinguishing human vs AI-made creations is becoming more difficult.

• Generative AI could soon account for 50% of all video game content development within 5-10 years. Tools are emerging to let anyone create games.

• Anyone born after 2010 is considered Generation Alpha. They and Gen Z are AI-fluent, and comfortable creating and engaging with generative tools and content. They are redefining entertainment and self-expression.

• 82% of Gen Z internet users are actively creating and posting online video content in some form. 60% are open to watching AI-generated content from creators. This signals a real blurring of lines between content consumers and producers.

• The exponential pace of technological change is causing "future shock", stoking anxieties globally about societal strains, democratic struggles, climate threats etc.

• Search and social media referral traffic to news sites is declining.

• In 2023 for the first time, over 50% of audio listening time in the US shifted to on-demand platforms rather than radio. After 7 years of steady growth, streaming and podcasts now rival linear listening.

• The creator economy landscape demonstrates a stark divide; a tiny fraction achieves vast success while most struggle financially, highlighting extremes.

• BiasTestGPT is an open-source tool for detecting biases in language models like ChatGPT. It generates test sentence pairs to reveal biases.

• It automatically creates diverse test cases covering complex intersections of gender, race, religion, etc. This gets rid of the need for manual test templates. Yay!

• Researchers (or you) can use it to quantify bias levels in LLMs by running the test cases through a target model and analyzing the problematic responses.

• Plug and play integration with HuggingFace— it’s pretty simple to set up comprehensive bias testing for any open-sourced language model.

TLDR: AI is biased, this tool helps you eliminate outputted biased language through testing.

Definition: Diffusion Model

Using text and image encoding, the diffusion model utilizes text guidance to create new images in the latent space aka these are the ingredients needed for stable diffusion to work.