Jul 30, 2021
While it seems like every second conversation over the past year has been about technology enabling remote working, this has not slowed companies’ interest – and investment – into tech like the Internet of Things (IoT), Machine Learning (ML) and Artificial Intelligence (AI).
Unfortunately, too many businesses are not realising the value from these investments, says Richard Firth, CEO of MIP Holdings.
“This is partly because these emerging technologies are part of a long-term strategy and are only expected to start impacting on the financials further down the line, and partly because a lot more work needs to happen before they can start delivering on their promises. AI is a perfect example. In the rush to AI, many companies have neglected to put the right foundation in place,” he points out.
Inflection, the opinion, editorial, and news analysis journal of
AcademicInfluence.com features Larson s first-hand insights from his latest book: I figured that readers should know about the difference between commercial and practical AI as well as the sort of general intelligence that futurists insist will soon emerge, from steady progress in practical and commercial applications all the way up to the development of true, or general, AI, says Larson. The key point is devastating: there isn t a path from one to the other, or what s called narrow (usable) AI to general intelligence. General AI may still be possible, of course, but we can t get there riding a wave of machine learning, or for that matter any of the other traditional methods that we ve retired.
Homo sapiens, now is the dawning of the
Homo Faber era. The idea that I think therefore I am has become quaint in this new age of builders and creators. But has our continued obsession with technology and progress actually managed to instead set back our capacity for humanity?
In his new book,
The Myth of Artificial Intelligence: Why Computers Can t Think the Way We Do, author and pioneering researcher in the field of natural language processing, Erik J Larson, investigates the efforts to build computers that process information like we do and why we re much farther away from having human-equivalent AIs than most futurists would care to admit.