At Rise of AI 2016 in Berlin, brain scientist Danko Nikolic argues that current machine learning cannot produce human-level or superhuman AI because it stores knowledge in a single large, uninterruptible box trained by comparatively simple learning rules, whereas human intelligence is distributed across millions of specialized learning rules compressed into our genes by billions of years of evolution. He illustrates the gap with a demonstration of how humans instantly reinterpret an ambiguous image as a car, a train, or a table and chairs, something today's AI cannot do without prior training on each category. Citing the no-free-lunch theorem, he argues no single general algorithm can be both broad and efficient, which is why the brain relies on many specialized learning rules instead of one. Since evolving such a genome from scratch would take billions of years, Nikolic proposes "AI Kindergarten": starting from a simple genome, deploying it to users such as data scientists, and feeding real-world experience back to a central server to gradually evolve better genomes, accumulating knowledge instead of erasing it between tasks. He concludes that AI built this way could approach but never exceed human intelligence, ruling out an intelligence-explosion singularity.
Key takeaways
- Nikolic argues today's AI stores knowledge in one large box trained by comparatively simple learning rules, unlike the brain's millions of specialized learning rules.
- He invokes the no-free-lunch theorem to argue no single algorithm can be both general and efficient across all problems.
- He proposes AI Kindergarten: evolving a machine genome via a central server that cycles feedback from users back into improved genomes.
- He argues this architecture can only ever approach, never exceed, human intelligence, which rules out an intelligence-explosion singularity.
- He expects future AI to require constant human supervision, meaning most people would effectively become supervisors of machines rather than be replaced.
Detailed notes
How a child understands a car differently from an AI
Nikolic opens by contrasting how a child and a machine learn to recognize a car: a child needs to play with one single car to recognize cars far better than any AI of the time, and can even build a car from Lego or create something new based on understanding what a car is, while an AI needs thousands of training images. He demonstrates with an ambiguous image that a person can instantly see as a car, then, once told, instantly reinterpret as a train, then as a table with two chairs, and then as a classroom, without ever having been trained on that specific image before. He argues this rapid reinterpretation of the same input shows a fundamental difference from doing statistics on images.
Why today's AI architecture cannot scale to human intelligence
Nikolic describes today's AI as storing knowledge in one large, uninterruptible box, such as a set of network weights or a probability distribution, produced by learning rules whose code is far smaller than the resulting knowledge store. He argues the real intelligence lies in creating the learning rule itself, comparing a compressed rule like backpropagation to the compactness of Einstein's E=mc². By his calculations, reaching human-level intelligence with this single-box architecture would require pre-programming every possible situation a human could encounter, needing a computer roughly the size of a planet.
Millions of specialized learning rules, and the no-free-lunch theorem
Nikolic argues the brain instead stores knowledge across millions of specialized learning rules rather than one big box, which lets it compress and store far more knowledge. He grounds this in the no-free-lunch theorem, which he summarizes as meaning no single general algorithm can be efficient across all possible problems: an algorithm is either general and slow, or specific and efficient. Humans are born not with knowledge of, say, cars, but with genes containing rules for how to quickly learn new things, which he describes as the true site of intelligence, compressing the experience of billions of years of ancestors, from fish and algae onward, in a way he says cannot be replicated by engineers.
AI Kindergarten: evolving a genome instead of training a model
Since evolving such a genome from scratch would take a billion years, Nikolic proposes transferring and adapting knowledge from human genes to a machine genome instead, through a method he calls AI Kindergarten. A central server evolves an AI's genome, delivers it to users such as data-mining practitioners, who apply and learn from it and feed experience back to the server, which uses that feedback to develop an improved genome in a repeating cycle. He contrasts this with deep learning today, where training a network on a new data type typically erases prior knowledge, saying his architecture would instead accumulate knowledge, giving the example of a system that starts by recognizing simple drawings and gradually gains the ability to draw and recognize cars. He suggests a first, simple genome for time-series analysis could be built within a year or two, already outperforming current tools by learning from smaller datasets.
Implications: no singularity, and everyone becomes a supervisor
Nikolic argues this architecture implies an AI can never exceed human intelligence, since it only receives a partial transfer of humanity's evolved genome, meaning the exploding-intelligence singularity cannot occur, which he calls good news because there is no reason to fear it running out of control. He also argues it should reduce fear of job loss: such AI would take over many tasks but, since it can never fully match human intelligence, would remain more like a very capable but limited system requiring constant human supervision, so that in a future built on such machines, he expects everyone to effectively become a supervisor, or "boss," of a machine.
Q&A highlights
Asked about machines already outperforming humans at specific tasks, such as a calculator doing arithmetic faster than any person, Nikolic says that is not the real question; the question is whether a machine can reach human-level general intelligence, an AGI as smart as a human overall, which he maintains cannot be done in a lab because what makes human intelligence possible is billions of years of accumulated work stored in our genes.
Quotes
We have to build AI in a different way than what we have been doing so far if we want to create machines that somehow approach the level of intelligence that we humans have.
Danko Nikolic
There could never ever exist an AI that's more intelligent, that has a better, smarter genome than us.
Danko Nikolic
Questions answered in this talk
Why does Danko Nikolic think today's AI cannot reach human-level intelligence?
According to Danko Nikolic at Rise of AI 2016, today's AI stores knowledge in one large box built from relatively simple learning rules, whereas human intelligence comes from millions of specialized learning rules compressed into our genes over billions of years of evolution, which cannot be replicated in a lab.
What is AI Kindergarten?
According to Danko Nikolic at Rise of AI 2016, AI Kindergarten is his proposed method for evolving a machine's genome through a central server: the genome is delivered to users, their experience is fed back to the server, and the genome is improved in repeating cycles, accumulating knowledge instead of erasing it between tasks.
Does Danko Nikolic believe the AI singularity, an intelligence explosion, could happen?
According to Danko Nikolic at Rise of AI 2016, no: because his proposed architecture can only approach but never exceed human intelligence, an exploding, self-improving superintelligence cannot occur, which he says is good news.