#176: The Future of Artificial Intelligence at Scale

I've got to admit it's getting better (Better)

A little better all the time (It can't get no worse)

Paul McCartney and John Lennon, respectively, sum up the two ways to look at the future of artificial intelligence (AI) and machine learning — with optimism over limitless potential or pessimism over increased job loss and income equality. The New Stack's Libby Clark and Jennifer Riggins sat down (via Zoom) with The New York Times's Martin Ford, author of Architects of Intelligence: The truth about AI from the people building it, and Ofer Hermoni, chair of the technical advisory council for The Linux Foundation’s Deep Learning Foundation projects, to talk about the current state of AI, how it will scale, and its consequences. 

The last year alone has seen major advancements in deep learning, machine learning, and neural networks — frameworks for machine learning algorithms to work together and process complex data inputs. However, as Ford points out in this podcast, we are only at the start of the ethical implications of AI, including the implications of reduced privacy, potential weaponization, and the unconscious bias that is feeding much of the data going into these models.