DEEP LEARNINGFOR SYSTEM 2PROCESSINGYOSHUA BENGIOAAAI’2019 Invited TalkFebruary 9th, 2020, New York City

NO-FREE-LUNCH THEOREM, INDUCTIVE BIASES& HUMAN-LEVEL AI• No-free-lunch theorem à there is no completely general
intelligence, some inductive biases / priors are necessary• Generality & discoverability: simpler less specialized priors are
however more likely to be discovered by evolution and applicable
to a broader set of contexts• Deep learning already incorporates human-inspired priors•

Computation as composition of simpler pieces, neurons in layers, layers over layers(Pascanu et al ICLR 2014; Montufar et al NeurIPS 2014)•More powerful priors can bring up to an exponential advantage in sample complexity

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SYSTEM 1 VS. SYSTEM 2 COGNITION
2 systems (and categories of cognitive tasks):

Manipulates high-level /semantic concepts, which canbe recombined
combinatorially

System 1System 2• Intuitive, fast, UNCONSCIOUS,
non-linguistic, habitual• Slow, logical, sequential, CONSCIOUS,linguistic, algorithmic, planning, reasoning• Current DL• Future DL3

MISSING TO EXTEND DEEP LEARNING TO REACH
HUMAN-LEVEL AI• Out-of-distribution generalization & transfer• Higher-level cognition: system 1 → system 2•

High-level semantic representations•Compositionality•Causality• Agent perspective:•

Better world models•

Causality•

Knowledge-seeking• Connections between all 3 above!
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HYPOTHESES FOR CONSCIOUS PROCESSING BY AGENTS,
SYSTEMATIC GENERALIZATION• Sparse factor graph in space of high-level semantic variables• Semantic variables are causal: agents, intentions, controllable objects• Shared ’rules’ across instance tuples (arguments)• Distributional changes due to localized causal interventions (in semantic space)• Meaning (e.g. grounded by an encoder) is stable & robust wrt changes in distribution• Credit assignment is only over short causal chains

5Proposal: what may be the evolutionary advantage of system 2 processing?

DEALING WITH
CHANGES IN
DISTRIBUTION

AGENT LEARNING NEEDSOOD GENERALIZATION
Agents face non-stationarities
Changes in distribution due to• their actions• ESPECIALLY:
actions of other agents• different places, times, sensors,
actuators, goals, policies, etc.
Multi-agent systems: many changes in distribution
Ood generalization needed for continual learning
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SYSTEMATIC
GENERALIZATION• Studied in linguistics• Dynamically recombine existing concepts• Even when new combinations have 0 probability
under training distribution(Lake et al 2015)• E.g. Science fiction scenarios• E.g. Driving in an unknown city
• Not very successful with current DL(Lake & Baroni 2017)(Bahdanau et al & Courville ICLR 2019)
CLOSURE: (Bahdanau et al & Courville arXiv:1912.05783) on CLEVR
Fig. 1. People can learn rich concepts from limited data. (A and B) A single example of a new con
the (i) classification of new examples, (ii) generation of new examples, (iii) parsing an object into p8generation of new concepts from related concepts. [Image credit for (A), iv, bottom: With permissio1332

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CONTRAST WITH THE SYMBOLIC AI PROGRAM
Avoid pitfalls of classical AI rule-based symbol-manipulation• Need efficient large-scale learning• Need semantic grounding in system 1• Need distributed representations for generalization• Need efficient = trained search (also system 1)• Need uncertainty handling
But want• Systematic generalization• Factorizing knowledge in small exchangeable pieces• Manipulating variables, instances, references & indirection
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SYSTEM 2 BASICS:
ATTENTION ANDCONSCIOUS
PROCESSING

CORE INGREDIENT FOR CONSCIOUS PROCESSING:
ATTENTION(Bahdanau et al ICLR 2015)• Focus on a one or a few elements at a time
• Content-based soft attention is convenient,can backprop to learn where to attend• Attention is an internal action, needs alearned attention policy (Egger et al 2019)
Attention•

Operating on unordered SETS of (key, value) pairs•

SOTA in NLP
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FROM ATTENTION TO INDIRECTION• Attention = dynamic connection• Receiver gets the selected value• Value of what? From where?
Attention

à Also send ‘name’ (or key) of sender• Keep track of 'named’ objects: indirection• Manipulate sets of objects (transformers)

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FROM ATTENTION TO CONSCIOUSNESS
C-word not taboo anymore in cognitive neuroscience
Global Workspace Theory(Baars 1988++, Dehaene 2003++)• Bottleneck of conscious processing• WHY A BOTTLENECK?
• Selected item is broadcast, stored in short-term
memory, conditions perception and action• System 2-like sequential processing, conscious
reasoning & planning & imagination

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ML FOR CONSCIOUSNESS & CONSCIOUSNESS FOR ML• Formalize and test specific hypothesized
functionalities of consciousness• Get the magic out of consciousness• Understand evolutionary advantage ofconsciousness: computational and statistical(e.g. systematic generalization)• Provide these advantages to learning agents

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THOUGHTS, CONSCIOUSNESS, LANGUAGE• Consciousness: from humans reporting• High-level representations, language<latexit sha1_base64="vV9KmI3DSqORXXjKQpnPOIDgOOM=">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</latexit>• High-level concepts: meaning anchored in lowlevel perception and action à tie system 1 & 2• Grounded high-level concepts
à better natural language understanding• Grounded language learning
e.g. BabyAI: (Chevalier-Boisvert and al ICLR 2019)15

WHY ACONSCIOUSNESSBOTTLENECK?
THE CONSCIOUSNESSPRIOR= SPARSE FACTOR
GRAPH

CONSCIOUSNESS PRIORè SPARSE FACTOR GRAPH
Bengio 2017, arXiv:1709.08568• Property of high-level variables which wemanipulate with language:
we can predict some given very few others•

E.g. "if I drop the ball, it will fall on the ground”• Disentangled factors != marginally independent,e.g. ball & hand
• Prior: sparse factor graph joint distribution between
high-level variables• Inference involves few variables at a time, selected byattention mechanism and memory retrieval

META-LEARNING: ENDTO-END OOD
GENERALIZATION,SPARSE CHANGE PRIORMETA-LEARNING FOR TRAINING TOWARDS OOD
GENERALIZATION• Meta-learning or learning to learn(Bengio et al 1991; Schmidhuber 1992)•Backprop through inner loop or REINFORCE-like estimators• Bi-level optimization•

Inner loop (may optimize something) → outer loss•

Outer loop: optimizes E[outer loss] (over tasks, environments)• E.g.
••

Evolution ० individual learning

Lifetime learning ० fast adaptation to new environments• Multiple time-scales of learning• End-to-end learning to generalize ood + fast transfer
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WHAT CAUSES CHANGES IN DISTRIBUTION?
Underlying physics: actions are localizedin space and time.

Hypothesis to replace iid assumption:changes = consequence of an intervention on few causes or mechanisms

Extends the hypothesis of (informationally) Independent Mechanisms (Scholkopf et al 2012)è local inference or adaptation in the right model

Change due
to intervention

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COUNTING ARGUMENT:LOCALIZED CHANGE→OOD TRANSFER
Good representation of variables and mechanisms + localized change hypothesis→ few bits need to be accounted for (by inference or adaptation)→ few observations (of modified distribution) are required→ good ood generalization/fast transfer/small ood sample complexity

Change due
to intervention

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META-LEARNING KNOWLEDGE REPRESENTATION FOR
GOOD OOD PERFORMANCE• Use ood generalization as training objective• Good decomposition / knowledge representation è good ood performance• Good ood performance = training signal for factorizing knowledge

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EXAMPLE: DISCOVERING CAUSE AND EFFECT= HOW TO FACTORIZE A JOINT DISTRIBUTION?
A Meta-Transfer Objective for Learning toDisentangle Causal Mechanisms• Learning whether A causes B or vice-versa• Learning to disentangle (A,B) from observed (X,Y)

ABXY• Exploit changes in distribution and speed ofadaptation to guess causal direction
Bengio et al 2019 arXiv:1901.10912• Ongoing work: theory proving when the correctmodel converges faster by online SGD

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EXAMPLE: DISCOVERING CAUSE AND EFFECT= HOW TO FACTORIZE A JOINT DISTRIBUTION?
Learning Neural Causal Models from Unknown
Ke et al 2019 arXiv:1910.01075
Interventions• Learning small causal graphs, avoid exponential
explosion of # of graphs by parametrizing
factorized distribution over graphs• With enough observations of changes indistribution: perfect recovery of the causal graphwithout knowing the intervention; converges
faster on sparser graphs• Inference over the intervention:
faster causal discovery
24Consequence of the consciousness prior (sparse factor graph):

OPERATING ON SETS OF
POINTABLE OBJECTSWITH DYNAMICALLYRECOMBINEDMODULESRIMS: MODULARIZE COMPUTATION AND OPERATE ON
SETS OF NAMED AND TYPED OBJECTS
Recurrent Independent Mechanisms
Goyal et al 2019, arXiv:1909.10893

Multiple recurrent sparsely interacting
modules, each with their own
dynamics, with object (key/value pairs)
input/outputs selected by multi-headattention
Results: better ood generalization
Ongoing work: hierarchy, top-down
broadcasting, spatial layout of modules
Builds on rich recent litterature on object-centric representations (mostly for images)

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RESULTS WITH RECURRENT INDEPENDENTMECHANISMS• RIMs drop-in replacement for LSTMs in PPO baseline over all Atari games.
• Above 0 (horizontal axis) = improvement over LSTM.

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HYPOTHESES FOR CONSCIOUS PROCESSING BY AGENTS,
SYSTEMATIC GENERALIZATION• Sparse factor graph in space of high-level semantic variables• Semantic variables are causal: agents, intentions, controllable objects• Shared ’rules’ across instance tuples (arguments)• Distributional changes due to localized causal interventions (in semantic space)• Meaning (e.g. grounded by an encoder) is stable & robust wrt changes in distribution• Credit assignment is only over short causal chains28

CONCLUSIONS• After cog. neuroscience, time is ripe for ML to explore consciousness• Could bring new priors to help systematic & ood generalization• Could benefit cognitive neuroscience too• Would allow to expand DL from system 1 to system 2

System 1System 229

THANK YOU