Timnit Gebru - скачать в хорошем качестве

Timnit Gebru -

In the last few years, the quest to build so-called Artificial General Intelligence (AGI), an undefined system which seemingly can do any task under any circumstance, has captured the public’s imagination. Those whose mission has been to build this system, like the leaders of OpenAI, Deepmind and others, discuss the utopia that will imminently come from building AGI, or the apocalypse that will be caused by it rendering humanity extinct. In this talk, I discuss the history of the AGI movement, and its link to the 20th century eugenics movement, with those who “christened” the term AGI having the goal of replacing humans with a superior race they call “transhuman AGI.” I outline the harms the quest to build AGI has caused, including labor exploitation, centralization of power and the safety issues associated with building an unscoped system. I close by giving examples of various movements to resist this trend, including artists’ fight to preserve their humanity and dignity with the hashtag #TeamHuman. I urge the machine learning community to focus on small, constrained, task specific models, and present some of our work from DAIR showing how this approach outperforms the one size fits all trend to building machine learning based models. Dr. Timnit Gebru is known for her research into bias and inequality in AI, and in particular for a 2018 paper she co authored with Joy Buolamwini that highlighted how poorly commercial facial-recognition software fared when attempting to classify women and people of color. Their work sparked widespread awareness of issues common in AI today, particularly when the technology is tasked with identifying anything about human beings. At Google, Gebru was the co-leader of the company’s ethical AI team, and one of very few Black employees at the company overall (3.7% of Google’s employees are Black according to the company’s 2020 annual diversity report)— let alone in its AI division. The research scientist is also cofounder of the group Black in AI. 0:00 Lecture 49:33 Q&A