At Rise of AI 2018 in Berlin, science-fiction author and futurist Daniel Jeffries argues that despite loving the concept in his own fiction, he does not believe the technological singularity is near, because no one currently knows how to build artificial general intelligence or abstract reasoning. He traces AI's history as decades of failure and "winters," credits Geoffrey Hinton with keeping neural net research alive, and explains that people predict the future badly because they project a technology's current, flawed iteration forward instead of picturing a category maturing over time. He calls sentencing algorithms in US courts an example of the "ugly" side of AI, since black-box systems trained on biased historical data reproduce the same injustices against poor and disadvantaged people. He argues the more realistic future is not artificial general intelligence but narrow, "semi-alien" intelligence augmenting humans, illustrated by the MIT-covered "Rise of the Centaurs" chess result, and applies this to medical monitoring, smart homes and self-driving cars, cautioning that these benefits will roll out more slowly than people expect while automation, as in past eras, will create new jobs rather than eliminate work overall.
Key takeaways
- Jeffries argues no one currently knows how to build artificial general intelligence or abstract reasoning, making the singularity unlikely in the near term.
- He says people predict technology poorly because they extrapolate a flawed current iteration instead of thinking in categories that mature over successive versions.
- He calls US sentencing algorithms an example of AI's ugly side, since systems trained on biased legal history reproduce injustice against poor and disadvantaged people.
- He points to the MIT-covered "Rise of the Centaurs" chess result, where amateur players plus AI beat AI-only teams, as a model for human-AI collaboration.
- He argues automation has repeatedly eliminated most existing jobs throughout history while creating new ones, and expects the same pattern with AI.
Detailed notes
Why the singularity is fantasy
Jeffries says that despite writing the singularity into his own fiction, he thinks Ray Kurzweil and others will be disappointed at the end of their lives, because no one currently knows how to build artificial general intelligence or how to do abstract reasoning. He describes AI's history as roughly 50 years of failure and repeated "AI winters," during which most researchers gave up and Geoffrey Hinton was kept going largely by a single university grant, until GPUs applied to existing techniques drove the deep learning breakthroughs of 2012, such as surpassing humans on ImageNet-style perceptual tasks.
How to actually predict the future
He argues most people are bad at forecasting because they extrapolate a technology's current, flawed iteration (citing early home video and CD-ROM books) instead of thinking in categories that iterate over time, such as VHS to laserdisc to DVD to Blu-ray. He attributes this partly to humans having lived as hunter-gatherers for 1.8 million years versus only 12,000 years of agriculture, leaving minds focused on immediate survival rather than long horizons. He says one can responsibly predict the general characteristics a mature technology needs, such as good screen, portability and battery life for what became the Kindle or iPad, but not unpredictable "black swan" inventions.
Abstraction, automation and the limits of narrow AI
Jeffries frames societal progress as a history of abstraction and automation, from the hammer and nail enabling multi-story buildings onward, and says today's AI systems are precursors to future intelligent machines but still cannot do abstract reasoning or generalize the way humans do, citing failed attempts at hand-coded symbolic rule systems that collapse into a "towering inferno of brittle rules."
Sentencing algorithms and the ugly side
He identifies sentencing algorithms already used in many US court systems, black-box statistical tools that recommend bail or jail decisions, as a clear example of AI's "ugly" side, arguing that a system trained on the history of the legal system will reproduce its history of injustice against poor and disadvantaged people. He blames the humans behind the data and design, not the machines, and says the future is neither purely good nor bad but exists in a gray area.
Rise of the centaurs
Jeffries argues the near-term future is not artificial general intelligence but "semi-alien" narrow intelligence assisting humans at tasks they do poorly, such as politicians setting budgets inefficiently versus an AI analyzing sensor data on failing roads. He cites the MIT-covered "Rise of the Centaurs" story, in which, after Deep Blue beat Garry Kasparov and IBM declined a rematch, a subsequent centaur chess tournament was won not by a grandmaster but by amateur players teamed with AI, beating AI-only entrants. He argues automation has already eliminated roughly 95% of past jobs multiple times in history while creating new ones, pointing to how few people today work in agriculture or as hunter-gatherers, and how women's job options expanded far beyond the three available in the 1950s.
Where AI will help: medicine, smart homes, self-driving cars
He describes plausible near-future benefits: a smartphone that flags a suspicious skin spot to help doctors triage urgency, a wearable tracking blood markers and nudging behavior before a heart attack, and a smart home that identifies a real fire versus a dead alarm battery and can call the fire department with context. He criticizes media coverage of self-driving car crashes as misleading given that 1.25 million people die in car wrecks yearly, but acknowledges real limits, noting Yann LeCun's self-driving car effort could not yet interpret road workers' hand signals. He predicts all of this will unfold more slowly than people expect, that neural nets will hit diminishing returns and possibly another, smaller AI winter, but that society will keep creating new kinds of jobs as it has throughout history.
Quotes
The history is a long litany of failure, actually, for about 50 years.
Daniel Jeffries
I actually worry more about narrow intelligence controlled by dumb, ignorant, violent human beings than I worry about a machine rising up and deciding to kill us all.
Daniel Jeffries
The future is not set in stone. It is also not binary.
Daniel Jeffries
Questions answered in this talk
Why does Daniel Jeffries think the technological singularity is not near?
According to Daniel Jeffries at Rise of AI 2018, no one currently knows how to build artificial general intelligence or how to do abstract reasoning, so despite using the singularity in his own fiction, he expects it to remain unrealized for the foreseeable future.
What does Daniel Jeffries say is dangerous about sentencing algorithms?
According to Daniel Jeffries at Rise of AI 2018, black-box sentencing algorithms already used in US courts are trained on historical legal data, so they reproduce the same injustices against poor and disadvantaged people that the legal system has always produced.
What does the "Rise of the Centaurs" chess story illustrate about AI and human work?
According to Daniel Jeffries at Rise of AI 2018, in a chess tournament the year after Deep Blue beat Garry Kasparov, amateur human players paired with AI beat AI-only entrants, showing that human-AI collaboration can outperform either humans or AI alone.