At Rise of AI 2017 in Berlin, Christoph von der Malsburg argues that neither of the two historical routes to AI, the general-purpose computer and the brain-inspired neural network, can on its own solve general intelligence. The computer, he says, is a universal but empty machine that depends entirely on human programming, while today's neural networks and deep learning depend entirely on annotated data; in both cases, he argues, AI remains, in his words, human thought loaded into machines in a detailed way. He then discusses how AI systems must widen their contextual scope by decomposing a situation into abstract, relatively independent aspects, and draws an analogy to children, who apply innate, abstract behavioral patterns to concrete situations within their first years of life and learn concepts such as a tricycle from a single exposure. He argues general intelligence will come from a conceptual breakthrough rather than decades of engineering, supporting this with an information-theoretic gap between the cortex's wiring complexity and the genome's size. He closes by describing his own model of recognition through switchable, collectively decided "network fragments," illustrated with a face-recognition system and a Raven's matrices reasoning test.
Key takeaways
- Von der Malsburg argues neither classical symbolic AI nor today's neural networks can alone solve general intelligence: one frame is too large, the other too narrow.
- He argues today's AI remains entirely dependent on human input, either through programming or through annotated training data.
- He argues general intelligence will arrive through a conceptual breakthrough rather than decades of incremental engineering.
- He calculates that describing the cortex's wiring would take about a petabyte of information while the genome carries roughly a gigabyte, implying the brain relies on built-in regularity rather than fully learned detail.
- He proposes that recognition arises from switchable network fragments whose decisions are made collectively by the active network rather than by single neurons.
Detailed notes
Two routes to AI, and why both are dependent on humans
Von der Malsburg frames AI's history around two competing routes: the computer, a universal machine that can in principle run any algorithm but starts completely empty, needing everything loaded in by people, tracing back to the 1956 Dartmouth conference and John McCarthy; and the brain, whose mere existence proves general intelligence is possible, which inspired neural networks, machine learning and, today, deep learning. His conclusion is sobering: classical AI got stuck in a combinatorial search space that is too large, while neural networks work within a frame that is too narrow to capture what happens in the brain and depend entirely on annotated data. He summarizes today's AI, in a phrase from one of his blog posts, as nothing but human thoughts in tin cans.
Widening the scope: decomposing situations into abstract aspects
He recalls that a good computer-vision PhD thesis once meant analyzing a single image, with no guarantee the method worked on the next one; systems now handle whole benchmarks of images, but still need much greater contextual scope. He argues scope can be widened by decomposing a situation into aspects, such as motion, texture or depth, that must be abstract and only relatively independent of each other, giving object recognition's need for translation invariance as an example.
Learning from children: instinct, one-shot learning and the case for a breakthrough
Von der Malsburg notes that animals and infants are born with a repertoire of instincts abstract enough to be applied immediately in a natural environment, and asks the audience whether they expect general intelligence to require decades of hard work or a sudden breakthrough, declaring himself in the breakthrough camp. His first argument is that children grasp the reality of their environment within two to three years in a simple, stable nursery setting, and can recognize a new object like a tricycle as something to sit on and steer after a single exposure, which he calls one-shot learning.
The information gap between the cortex and the genome
His second argument is quantitative: describing the wiring pattern of the cerebral cortex would take on the order of a petabyte of information, while the genetic information that builds it is closer to a gigabyte, a gap of about six orders of magnitude. He concludes that the vast majority of the cortex's structure cannot be explained by individually specified connections and must instead come from built-in regularity, a strong structural prejudice tuned to the world we live in, which he believes could in principle be replicated by training in a virtual-reality environment built from a comparatively small amount of information.
Network fragments, the neural code, and a face-recognition system
Von der Malsburg proposes that recognition works through what he calls network fragments: a two-dimensional array of local feature detectors in early visual cortex is linked, through switchable projection connections, to a matching model elsewhere in the brain, with connectivity able to change as fast as the eyes move. He built a face-recognition system on this idea to convince colleagues; he says it failed to convince them but became the basis of two still-active companies, one in Los Angeles and one in Germany. He stresses that recognition decisions are made collectively by an active network, not by a single neuron, and links the idea to the older AI concept of frames associated with Marvin Minsky and Roger Schank. He illustrates abstract, schema-based reasoning with a Raven's matrices intelligence-test item solved by recognizing numerical relationships between figures, and closes by describing his company's goal of integrating separate vision algorithms, for motion, texture and other aspects, into a shared network-fragment language so that ambiguous individual readings resolve into one consistent scene.
Quotes
It is nothing but human thoughts in tin cans.
Christoph von der Malsburg
The brain is obviously totally dominated by regularity.
Christoph von der Malsburg
A decision is taken by the establishment of this whole network. The decision is a collective decision.
Christoph von der Malsburg
Questions answered in this talk
Why does Christoph von der Malsburg think today's AI is limited?
According to Christoph von der Malsburg at Rise of AI 2017, classical computer-based AI works within a search space that is too large, while neural networks and deep learning work within a frame too narrow to capture what happens in the brain and depend entirely on annotated data, so both routes remain dependent on detailed human input.
Does von der Malsburg think general intelligence will come from years of engineering or a sudden breakthrough?
According to Christoph von der Malsburg at Rise of AI 2017, he expects a conceptual breakthrough rather than decades of incremental work, pointing to how quickly children learn to understand their environment and to the large information gap between the cortex's wiring complexity and the genome's size.
What is a "network fragment" in von der Malsburg's model of the brain?
According to Christoph von der Malsburg at Rise of AI 2017, a network fragment is a connectivity pattern, such as a two-dimensional array of feature detectors linked by projection fibers, that can switch on and off as fast as the eyes move, with recognition decided collectively by the active network rather than by any single neuron.