AI has fallen from glorious summers into dismal “winters” before. The temptation to predict another such tumble recurs naturally. So that is the question the BBC posed to AI researchers: Are we on the cusp of an “AI winter”:
The 10s were arguably the hottest AI summer on record with tech giants repeatedly touting AI’s abilities.
AI pioneer Yoshua Bengio, sometimes called one of the “godfathers of AI”, told the BBC that AI’s abilities were somewhat overhyped in the 10s by certain companies with an interest in doing so.
There are signs, however, that the hype might be about to start cooling off.Sam Shead, “Researchers: Are we on the cusp of an ‘AI winter’?” at BBC News
I keep up with this kind of thing. The answer is: Yes, and no. AI did surge past milestones during the 2010s that it had not been expected to cross for many more years:
2011 — IBM’s Watson wins at Jeopardy! IBM Watson: The inside story of how the Jeopardy-winning supercomputer was born, and what it wants to do next (Tech Republic, September 9, 2013)
2012 —Google unveils a “deep learning” systems that recognized images of cats
2015 —Image recognition systems outperformed humans in the ImageNet challenge
2016 — AlphaGo defeats world Go champion Lee Sedol: “In Two Moves, AlphaGo and Lee Sedol Redefined the Future” (Wired, March 16, 2016)
2018 —Self-driving cars hit the road as Google’s Waymo launched (a very limited) self-driving taxi service in Phoenix, Arizona
But other headlines during the period have been less heeded:
Despite High Hopes, Self-Driving Cars Are “Way in the Future” (2019)
“The Next Hot Job: Pretending to Be a Robot” (2019)
Boeing’s Sidelined Fuselage Robots: What Went Wrong? (2019)
Self-driving cars: Hype-filled decade ends on sobering note (2019)
Tesla driver killed in crash with Autopilot active, NHTSA investigating (2016)
Don’t fall for these 3 myths about AI, machine learning (2018)
A Sobering Message About the Future at AI’s Biggest Party (2019)
And so on.
So which is it? AI Winter or Robot Overlords? I suggest neither. And so do active researchers.
Gary Marcus, an AI researcher at New York University, said: ‘By the end of the decade there was a growing realisation that current techniques can only carry us so far.’
He thinks the industry needs some “real innovation” to go further.
‘There is a general feeling of plateau,’ said Verena Rieser, a professor in conversational AI at Edinburgh’[s Heriot Watt University.
One AI researcher who wishes to remain anonymous said we’re entering a period where we are especially sceptical about AGI.Sam Shead, “Researchers: Are we on the cusp of an ‘AI winter’?” at BBC News
Recent AI developments, notably those lumped under the rubric of “Deep Learning” have advanced the state-of-the-art in machine learning. Let’s not forget that prior efforts, such as the poorly named “Expert Systems,” had faded because, well, they weren’t expert at all. Deep Learning systems, as highly flexible pattern matchers, will endure.
What is not coming is the long-predicted AI Overlord, or anything that is even close to surpassing human intelligence. Like any other tool we build, AI has its place when it amplifies and augments our abilities.
Just as tractors and diggers have not led to legions of people who no longer use their arms, the latest advances in AI will not lead to human serfs cowering before beneath an all-intelligent machine. If anything, AI will require more from us, not less, because how we choose to use these tools will make an increasingly stark difference between benefit and ruin.
As Samin Winiger, a former AI research at Google says, “What we called ‘AI’ or ‘machine learning’ during the past 10-20 years, will be seen as just yet another form of ‘computation’”
Machines are tool in the toolbox, not a replacement for minds. An AI winter would only be coming if we forgot that.
Here are some of Brendan Dixon’s earlier musings on the concept of an “AI Winter”:
Just a light frost? Or an AI winter? It’s nice to be right once in a while—check out the evidence for yourself
AI Winter Is Coming: Roughly every decade since the late 1960s has experienced a promising wave of AI that later crashed on real-world problems, leading to collapses in research funding.