At Rise of AI 2017 in Berlin, Danko Nikolic argues that the AI field risks repeating past AI winters because it has forgotten the original meaning of "strong AI." He explains that the term, coined by a philosopher in the 1960s or 70s, originally described biological intelligence, such as a dog's, as strongly intelligent because it learns from very few examples with limited resources, in contrast to today's usage of strong AI as synonymous with human-level AGI. Estimating that humans can distinguish between 10 to the power of 20 and 10 to the power of 48 things without new learning, against roughly 100,000 for contemporary AI systems, he argues that scaling AI to human-level intelligence requires resources growing far faster than linearly, making it effectively unaffordable with today's approach. He warns that over-promising based on small-scale demos, as happened with a major Japanese government AI project, is what historically triggers AI winters and could again trigger an economic crisis. He closes by describing his own startup's experiment, in which an evolved agent learned to recognize handwritten digits from only three training examples per character.
Key takeaways
- Nikolic argues the term "strong AI" originally meant biological-style intelligence, not human-level AGI, citing a dog as an example.
- He estimates humans can distinguish between 10^20 and 10^48 things, versus about 100,000 for today's AI systems.
- He argues resource requirements for AI scale exponentially, not linearly, with the range of things it must recognize.
- He cites a major Japanese government AI project that failed to deliver despite heavy funding as a cause of a past AI winter.
- His startup's evolved agent reached about 91 percent test accuracy recognizing handwritten digits from only three training examples per digit.
Detailed notes
What strong AI originally meant
Nikolic says most people today equate strong AI with artificial general intelligence, a usage he traces to Ray Kurzweil, but the term was originally coined by a philosopher in the 1960s or 70s to mean intelligence achieved the way biological systems achieve it, regardless of human-level scope. In that original sense, a dog is strongly intelligent because it excels at tasks like navigating complex environments, recognizing patterns and catching prey, even though it cannot read or speak, while today's AI, despite lacking these biological mechanisms, is called weakly intelligent by comparison.
Why today's AI can't scale to human intelligence
As a cognitive scientist, Nikolic estimates the number of distinguishable things an adult human can recognize without new learning at between 10 to the power of 20 and 10 to the power of 48, derived from behavioral experiments rather than counting neurons or synapses. He contrasts this with roughly 100,000 distinguishable categories for the best contemporary AI systems, a gap of more than 40 orders of magnitude. He argues that resource needs for AI to distinguish more categories grow far faster than linearly, so that reaching human-level performance with current deep learning would require, by his estimate, a computer the size of a planet and more money than exists in the world.
How over-promising causes AI winters
Nikolic recalls a major Japanese government AI project, reportedly around a decade long, that received large funding but failed to deliver on its promises, contributing to a past AI winter that made "AI" an unpopular term for years. He argues winters recur when small-scale demonstrations, such as a chatbot handling a few sentences, are assumed to scale up linearly to human-level performance within a few years. Because required resources actually scale exponentially with the range of things a system must recognize, he warns that promising ever more general intelligence to investors risks not just another AI winter but an economic crisis, and that current narrow, vertical startup business models are themselves a symptom of this scaling problem.
Learning from few examples: a startup demonstration
Nikolic describes leaving academia to co-found two startups, one narrowly vertical and one aimed at technology that learns from small datasets with limited compute, inspired by how a child already carries prior expertise before ever tying a shoe. Using the MNIST handwritten-digit dataset, his team built an agent with pre-evolved expertise on digits zero through six, then trained it to distinguish the digits seven, eight and nine from only three examples each, without using the standard training set. On the 10,000-example test set, accuracy rose from 0 percent to 89 percent after one training round and to about 91 percent after further rounds, a result Nikolic says required significant compute to evolve the agent once, but very little to apply it afterward.
Quotes
The direction in which I would like to see AI develop is that it becomes so smart that we do not need big data, that we do not need big computation.
Danko Nikolic
We would need a computer of the size of a planet to do that.
Danko Nikolic
Questions answered in this talk
What did the term "strong AI" originally mean?
According to Danko Nikolic at Rise of AI 2017, strong AI was originally coined by a philosopher to describe intelligence achieved the way biological systems achieve it, such as a dog's, not human-level general intelligence as the term is commonly used today.
Why does Danko Nikolic think another AI winter is coming?
According to Danko Nikolic at Rise of AI 2017, AI winters occur when small-scale demos are wrongly assumed to scale linearly to human-level performance, while the actual resource requirements grow exponentially, a pattern he says already caused a major failed Japanese government AI project.
How did Nikolic's startup teach an AI to recognize handwritten digits from few examples?
According to Danko Nikolic at Rise of AI 2017, his team evolved an agent with prior expertise on digits zero through six, then trained it on the digits seven, eight and nine using only three examples of each, reaching about 91 percent accuracy on a 10,000-example test set.