Training Data AI Digest #2
Welcome to the AI Insights Digest, your primary source for all AI-related news! This week, as always, we are presenting the most recent AI insights, captivating articles, and highlights from the world of AI events.
Today we will delve into the promising new AI tools from LinkedIn and Meta, explore how artists are earning money through generative AI, and discuss the potential impact of the writers' strike on the industry.
Breaking News and Insights
Would you like to learn about what has happened in the field of AI in the past two weeks in just 5 minutes? We have gathered all the key information in this section.
📒 After nearly five months, the Writers Guild of America resolved its strike with Hollywood studios. AI became a significant point of disagreement between the writers and studios during this strike.
💻 The latest additions, spanning from an AI assistant to image editing, leverage the capabilities of generative AI to enhance the appeal of Meta's technology.
🎸Spotify seems to be working on a new way to incorporate AI into its app: AI-generated playlists. Clues found within the app's code suggest that the company might be working on playlists generated by AI, which users could create using prompts. How will they achieve this?
📊 LinkedIn is making significant strides in harnessing the power of OpenAI with a major update that includes enhancements to its Recruiter talent sourcing platform, the introduction of an AI-powered LinkedIn Learning coach, and the unveiling of a new AI-driven tool for marketing campaigns.
Articles You Can’t Miss
Welcome to the weekly highlights – a carefully chosen collection of the most fascinating content related to AI and technology. Here we explore the latest articles, reports, and findings that have piqued our interest.
💰 As tech companies begin to monetize generative AI, the creators on whose work it is trained are asking for their fair share. How much can artists realistically make from generative AI?
💵 Gigasheet managed to achieve a 10x better return on engineering effort with AI. How did they manage to do so, and what approaches did they use?