The Observation Engine

The recent debut of Tilly Norwood, an AI-generated actress created by Eline Van der Velden’s UK-based studio Particle6, has ignited a fierce debate within Hollywood and beyond about the future of acting, creativity, and the role of artificial intelligence in entertainment. The Controversy First “AI Actress” Signing: Tilly Norwood made headlines when Van der Velden revealed at the Zurich Summit (September 27) that a talent agency was in talks to represent the synthetic character for acting roles. Hollywood Reactions: Emily Blunt called the move “terrifying,” urging agencies to stop “taking away our human connection.” Simu Liu responded sarcastically, blasting the idea of movies starring only AI versions of people. Whoopi Goldberg said AI actors have an “unfair advantage,” given the vast digital libraries they draw from. Actors Union Response: SAG-AFTRA (the U.S. actors union) condemned the trend, stating that creativity must “remain human-centered” and arguing ...

Contrary to widespread fear and numerous headlines, a new study from Yale University’s Budget Lab finds that since the release of ChatGPT in November 2022, AI technology has not yet disrupted job numbers or workforce composition in the United States . Key Findings: Stability, Not Disruption No Major Changes in Job Exposure: The study analyzed workforce data over 33 months, grouping workers by their exposure to AI (high, medium, low). If AI were eliminating jobs, the share of high-exposure workers would have gone down, but the data show those shares have barely changed. Labor Force Composition Unchanged: Yale researchers compared recent trends to historical shifts during the computer boom of the 1980s and the internet tech surge of the late 1990s. Unlike those earlier tech revolutions, AI’s impact on America’s labor force so far appears minimal. Young Worker Analysis: Tracking the spread of jobs among recent college graduates (ages 20–24) and comparing them to older cohorts...

A new study published on September 24 in PLoS One has shown that average listeners can no longer reliably tell apart AI-generated “deepfake” voices from real human voices. Study Findings: AI vs. Human Voices Voice Cloning vs. From-Scratch AI: While generic AI voices made from scratch can still be spotted by most listeners (only 41% fooled), AI voice clones trained with real human samples are nearly indistinguishable : 58% of these deepfake voices were rated as genuine, compared to 62% for actual human voices. Minimal Data Needed: Researchers created convincing voice clones using as little as four minutes of audio and basic commercial software, requiring minimal expertise and cost. Security and Ethics Risks: Sophisticated deepfake voices make it easy for criminals to bypass voice authentication systems at banks, impersonate loved ones, or even fabricate public statements from politicians and celebrities. Example: A victim gave $15,000 after being tricked by a “daughter’s...