Is Artificial Neural Network Intelligent?

Akira Imada

Brest State Technical University (Belarus)

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Is AI Intelligent?

A history of arguments between pro- & anti-AI parties.

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Harsh criticism to AI by Dreyfus

Alchemy and Artificial Intelligence (1965)

⇓

What Computers Can’t Do? (1972)

⇓

What Computers Still Can’t Do? (1992)

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Alchemy and Artificial Intelligence (1965)

The field of AI exhibits a recurrent pattern:

early dramatic success followed by sudden unexpected difficulties. “As if it were the first man climbed a tree claims a progress toward

reaching the moon.”

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Rebuff by Papert

“The Artificial Intelligence of Hubert L. Dreyfus: A Budget of Fallacies.”(1965).

Rebuffs by the other big names in AI

Edward Feigenbaum:

“What does he offer us? Phenomenology! That ball of fluff. That cotton candy!”

Marvin Minsky:

“They misunderstand, and should be ignored.”

(Actually few responded after the 3rd edition.)

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“Machine who thinks” by Pamela McCorduck (1992)

Papert:

“For Dreyfus, all social sciences are as wrong-headed as AI. This is not an attitude widely held in universities.”

McCorduck:

“If Dreyfus is so wrong-headed, why haven’t AI people made more effort to contradict him?”

Rodney Brooks (1991)

“Artificial intelligence started as a field whose goal was to

replicate human level intelligence in a machine.

⇓

Early hopes diminished as the magnitude and difficulty of that goal

was appreciated. ⇓

No one talks about replicating the human intelligence any more.”

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Can computer play chess?

Dreyfus (1960):

“Program written by Newell, Shaw, and Simon played legal chess, but was beaten in 35 moves by a ten-yea-old novice. ”

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’The New Yorker’ (11 January 1966)

The Talk of the Town:

Computer was beaten in chess by a ten-yea-old novice. ... We don’t care what the machine is going to do.

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Yet another topic on machine translation

’Time flies like an arrow,’ in Russian

⇓

’Time flies enjoy eating arrows,’ in English.

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A chess match: Dreyfus vs. Computer

Papert:

“I organized the famous chess match. That was beautiful.”

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In a bulletin board in SIG AI

Papert wrote:

’A ten-year-old can beat the machine’ – Dreyfus: But the machine can beat Dreyfus.

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Can computer play chess? [II]

Garry Kasparov vs. IBM’s Deep Blue. ⇓ (in 6 game match)

1996: Deep Blue Kasparov even even Kasparov Kasparov
1997: Kasparov Deep-Blue even even  even  Deep Blue

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Is Deep Blue intelligent?

Deep Blue’s strategy

⇓

A brute force to evaluate billions of future positions.

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What should intelligence look like?

Dreyfus (1965)

“A little intelligence is not intelligence at all but stupidity.

Any program that does just one thing well is at best more like an idiot savant than like an intelligent man.”

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Should intelligence be perfect?

Brooks (1991):

“It is unfair to claim that an elephant has no intelligence worth studying just because it does not play chess.”

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Contradiction & Intelligence?

Frosini (2009):

Contradiction can be seen as a virtue rather than as a defect.

⇓

Constant presence of inconsistencies in our thoughts.

⇓

Is contradiction accidental or is it the necessary companion of intelligence?

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What about intelligence by NN?

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McClelland (2009)

The author of the book ’Parallel Distributed Processing’ (1986) ”Is a machine realization of truly human-like intelligence

achievable?”

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What is human-like intelligence?

Human-intelligence is spontaneous, flexible , and/or unpredictable,

more or less. Or even erroneous sometimes.

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“I beg your pardon?”

Intelligent people try a different explanation for an easier understanding

while others just repeat the same expression, maybe louder.

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Let’s make an agent be spontaneous!

Behaviors might differ even in a similar situations.

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To see if it’s possible or not

let’s experiment

in a situation as simple as possible for an essential observation.

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Path-planning as a benchmark

An agent plans a shortest route from start to goal in a grid-world.

Examples of path from start to goal

No obstacle like walls, corridors, or dangerous pond.

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Which route to be chosen?

The number of shortest paths from (0,0) to (m,n)

⇓ infinitely large. ⇓

∑_{i=0}^{m+n}_{m}C_{i} ×_{n}C_{m+n−i}.

Can an agent take a different path from run to run?

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What about from (0,0) to (m,0)?

In this case only one unique solution.

⇓

Can an agent still be flexible from run to run?

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Goal is

to make an agent behave differently even when it encounters the same situation as before.

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NN with fixed weights

⇓ never be intelligent

but only repeats exactly the same action in the same situation.

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Floreano’s approach (2000)

With McCulloch & Pitts neurons

⇓

Modification of w_{ij} during actions of agent with either one of the four Hebbian and Hebbian-like rules.

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Let’s repeat his experiment

with spiking neurons expecting to be more biologically plausible.

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Spike Timing Dependent Plasticity

(STDP)

A counterpart of the Hebbian rule for spiking neurons

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What is STDP?

W (Δt) = \{ \begin{matrix}A_{+} exp(−Δt/τ_{+}) & if Δt ≥ 0 \\ −A_{−} exp(−Δt/τ_{−}) & if Δt < 0 \end{matrix}

where Δt = t_{post} − t_{pre}

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In short

potentiation occurs when a pre-synaptic neuron fires

shortly before a post-synaptic neuron firing

and

depression occurs when the post-synaptic neuron fires shortly after.

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Meunier et al. (2005) ⇓

“Up to now, nobody has been able to show how it is possible to learn with STDP...”

Farries et al. (2007)

⇓

“Although synaptic plasticity is widely believed to be a major component of learning, it is unclear how STDP itself could serve as a

mechanism for general purpose learning.”

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Two implicit implementations of STDP

• Di Paolo (2002) – Recurrent neural network with evolved STDP

– Conductance-based integrate-and-fire (I&F) neurons.

• Florian (2005) – Feedforward neural network with reward-modulated STDP – Stochastic leaky I&F neurons.

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Conductance-based I&F model

Membrane voltage v(t) is:

τ_{m} \frac{dv(t)}{dt} = V_{rest} − v + g_{ex}(t)(E_{ex} − v) + g_{in}(t)(E_{in} − v).

and evolves in continuous time t.

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Di Paolo’s implicit STDP

Using two functions P^{+}(t) and P^{−}(t)

• When no firing occurs, they decay exponentially: τ \frac{d}{dt}P (t) = −P (t)

• Every time a spike arrives at the synapse P^{+}(t) is incremented by A^{+} w_{ij}(t) → w_{ij}(t) − w_{max}P^{+}(t)

• Every time the post-synaptic neuron fires P^{−}(t) is decremented by A^{−}

w_{ij}(t) → w_{ij}(t) − w_{max}P^{−}(t) (Note: Weight is not a fixed value but a function of time.)

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Stochastic Leaky Integrate & Fire Model

Membrane voltage of neuron i is

u_{i}(t) = u_{r} + (u_{i}(t − δt) − u_{r}) exp(−δt/τ) + ∑_{j} w_{ij}f_{j}(t − δt)

where δt is discrete time step.

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Reward-modulated STDP Learning

(Florian 2007)

w_{ij}(t + δt) = w_{ij}(t) + γr(t + δt)ζ_{ij}(t)

where

ζ_{ij}(t) = P_{ij}^{+}(t)f_{i}(t) + P_{ij}^{−}(t)f_{i}(t) P_{ij}^{+}(t) = P_{ij}^{+}(t − δt) exp(−δt/τ_{+}) + A_{+}f_{j}(t) P_{ij}^{−}(t) = P_{ij}^{−}(t − δt) exp(−δt/τ_{−}) + A_{−}f_{j}(t)

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Are we happy?

Simple heuristics can do it using a dice

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Papert (1965)

“A very simple algorithm can sometimes obtain the same results as the holistic, intuitive human mind,”

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Not sufficient to be intelligent

A different-action-even-in-an-identical-situation

⇓ just a necessary condition at the best.

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What else we need to be intelligent?

A spontaneous, flexible or unpredictable behavior should be done consciously.

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Izhikevich (2006)

defined consciousness as attention to memory.

⇓

Let’s try to incorporate a memory of previous navigations

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’Science of the conscious mind’

Ascoli et. al (2008)

Cognitive maps in hippocampus are made up of contexts, such as spatial locations mammals employ for their path-finding

Navigation by hippocampus

Muller’s simulation (1996)

• Assume mapping from

points in 2-D field where rat explores to

pyramidal cells in a recurrent network of hippocampus.

• Mapping is one-to-one but points in 2-D are randomly assigned to neurons.

• Distance relation in 2-D space is stored as a weight configuration.

• The shortest path in neural space is also shortest in 2-D space?

Animal’s navigation by hippocampus

• Merriam’s kangaroo rats can learn the distribution of food patches around its nest in three evenings of foraging;

• Marmoset monkeys reliably relocate food sites and do not revisit a place where food was already eaten on that foraging trip;

• Black-capped chickadees hide insects and seeds in numerous, widely spread caches in trees over its home range.

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Are they intelligent behaviors?

Brooks (1990) again:

An elephant could be intelligent even if it cannot play chess.

Still it’s far from intelligence?

Bratman (1967)

The belief-desire-intention (BDI) model to construct a multi-agent system

or Pereira et al. (2005)

a model of emotional BDI agents. ⇓

These might help us design a NN closer to human intelligence.

Why spontaneous?

We sometimes need spontaneous and unpredictable intelligence rather

than efficiency or effectiveness like in case of SONY’s AIBO. It learns excellently and acts differently in different situation but repeats

the same action in a same situation.

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McClelland (2009) again

“Over the next decade, the butterfly will finally emerge from the

chrysalis, and truly parallel computing will take flight.” ⇓ Let’s try to be optimistic.

Mars Land-rover Problem

Can we design a robot such that it navigates flexibly enough to take a different route from run to run, using a memory with some conscious

intention, hopefully with belief and some sort of desire?

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Thanks!

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• Assume you like Chopin then you also like Chopin from CD?

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• Assume you like Chopin then you also like Chopin from CD? · Different action even in a similar situation.

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• Assume you like Chopin then you also like Chopin from CD? · Different action even in a similar situation.

• Is elephant intelligent or idiot?

· Should intelligence be such perfect?

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• Assume you like Chopin then you also like Chopin from CD? · Different action even in a similar situation.

• Is elephant intelligent or idiot?

· Should intelligence be such perfect?

• Expert of just one thing is not intelligent but an idiot savant?

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• Assume you like Chopin then you also like Chopin from CD? · Different action even in a similar situation.

• Is elephant intelligent or idiot?

· Should intelligence be such perfect?

• Expert of just one thing is not intelligent but an idiot savant?

• Can NN with fixed weights never be intelligent?

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• Assume you like Chopin then you also like Chopin from CD? · Different action even in a similar situation.

• Is elephant intelligent or idiot?

· Should intelligence be such perfect?

• Expert of just one thing is not intelligent but an idiot savant?

• Can NN with fixed weights never be intelligent?

• Is spiking neuron more biologically plausible?

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• Assume you like Chopin then you also like Chopin from CD? · Different action even in a similar situation.

• Is elephant intelligent or idiot?

· Should intelligence be such perfect?

• Expert of just one thing is not intelligent but an idiot savant?

• Can NN with fixed weights never be intelligent?

• Is spiking neuron more biologically plausible?

• Is STDP still unclear about how to be applied for learning?