At Rise of AI 2018 in Berlin, Chris Boos, CEO of Arago, argues that current AI is far from strong AI and pushes back on what he calls AI hype. He states plainly that machines do not understand anything, that comparing neural networks to the human brain is misleading given the gap in scale (about a million nodes versus roughly 84 billion neurons) and missing chemical and quantum layers, and that deep learning is only one part of AI, not AI itself. He distinguishes narrow AI, applied to single problems; science-fiction-style strong AI, which he says no one seriously building AI knows how to start; and generalized AI, applicable to more than one problem with the same engine. He describes three underlying algorithm types, machine learning, natural language processing and machine reasoning, tying each to an evolutionary analogue, and argues companies should first explicitly communicate the knowledge they already have to a machine before relying on costly trial-and-error learning, since data by itself can never fully explain the world.
Key takeaways
- Boos argues comparing neural networks to the brain is misleading: about a million nodes versus roughly 84 billion neurons, missing chemical and quantum layers.
- He distinguishes narrow AI (single-problem), science-fiction strong AI (no credible path yet), and generalized AI (applicable across problems with one engine).
- He estimates natural language processing systems are at about 1 percent of what real human dialogue requires.
- Using Arago's Civilization-playing AI, he shows machine-learned site-selection logic mirrored a real-estate customer's decision process, illustrating unrealized knowledge-transfer potential.
- Boos argues companies should first explicitly communicate existing knowledge to a machine, saying this has roughly a 95 percent energy advantage over pure trial-and-error learning.
Detailed notes
Deep learning is not AI
Boos opens by rejecting several claims common in AI marketing. He states that machines do not understand anything: a machine compares incoming data to a pre-created structure, and expecting machine "understanding" to behave like human understanding leads to wrong decisions. He also rejects the framing that neural networks are "modeled after the human brain," pointing out that a large neural network has about a million nodes against roughly 84 billion neurons in an adult human brain, that a neural network needs about half a nuclear power plant to run versus 20 watts for a brain, and that the brain is also a chemical and, per some neuroscience research, possibly quantum system that current AI does not replicate. Finally, he states deep learning is only part of AI, and warns that treating it as the whole field risks a new AI winter.
Three types of AI
Boos describes three categories. Narrow AI applies one algorithm or small set of algorithms to a single niche problem; his example is a company that trained a neural network to increase tomato yield in greenhouses by 20 percent, which fails if applied to a different crop in the same greenhouse. Science-fiction AI, the "strong" version capable of something like genuine love, has, in his view, no credible development path yet, leaving ethics squarely a human responsibility. Generalized AI sits between the two: applicable to more than one problem using the same type of engine and data, valuable because AI's real purpose, he argues, is not just insight but execution and automation, which industrialization already made possible by breaking work into explainable pieces.
The three core algorithms and their limits
Boos frames machine learning as automated pattern recognition tied to a positive action, comparable to animal instinct, and says it has a natural data limit: there is never enough data to fully describe the present. Natural language processing, which he calls the compressed output of evolution, lets humans transmit experience without trial and error, but he estimates current systems are at about 1 percent of what real dialogue requires. Machine reasoning, including case-based reasoning, lets systems weigh future options the way humans do mentally, but maintaining its rule bases is costly, and he says once maintenance costs exceed the benefit outside a narrow domain, it should not be used.
Knowledge transfer and Moravec's paradox
Boos names three central AGI problems: Moravec's paradox, that tasks easy for humans (evolved over billions of years) are hard for machines and vice versa; "little data," since humans make confident decisions from too little information while AI confidence is mathematically dependent on sufficient data; and knowledge transfer, illustrated by an example where Arago's AI learned city-placement logic playing Civilization that mirrored a real-estate customer's site-selection process, showing transferable patterns exist but current data is not connected enough to transfer automatically.
Why companies should teach machines what they already know
Boos argues data should be owned by whoever produces it because it is a tradable asset used to explain the world to machines, not only for predictive analytics, which he calls a rebranding of decades-old linear regression. He contrasts two ways of teaching a machine: observation and trial and error, which is data-heavy, versus direct communication of existing knowledge. He says he is surprised most companies default to the trial-and-error route first, since companies exist because they already know something; explicitly transferring that known knowledge before learning the rest by observation, he says, has roughly a 95 percent energy advantage over machine learning alone.
Quotes
Machines do not understand anything, like zero. There's no understanding in a machine.
Chris Boos
If you want to build an AI, anyone can do this, you need algorithms, data, you need talent, and you need compute power.
Chris Boos
Questions answered in this talk
Is deep learning the same thing as AI?
According to Chris Boos at Rise of AI 2018, deep learning is only part of AI, not the whole field, and treating it as a single universal solution risks causing a new AI winter.
What are the three types of AI Chris Boos distinguishes?
Chris Boos describes narrow AI, applied to one problem at a time; science-fiction-style strong AI, which he says has no credible development path yet; and generalized AI, applicable to multiple problems with the same engine and data.
Why does Chris Boos say companies should communicate their existing knowledge to a machine instead of relying only on machine learning?
Boos argues at Rise of AI 2018 that companies already possess valuable knowledge, and explicitly transferring it to a machine before relying on trial-and-error learning has roughly a 95 percent energy advantage over machine learning alone.