Finding Faults in Autistic and Software Active Inductive Learning Boris Galitsky and Igor Shpitsberg

Knowledge-Trail Inc. and Rehabilitation Center “Our Sunny World” .

Observations

• Both real life autistic and machine cognitive systems lack features allowing them to build and use an adequate model of external world.

• Both autistic and machine learning systems display deficiencies compared to learning of controls

Both machine learning and autistic system have problems understanding intentions of others

• “Do you want to play here?”• “Do you like this?”• “Are you hungry?”

• “Am I hungry?”• “Does she want to play with you?”• “Do I want to know this?”

Irrelevant: do not understand => no

reaction

Relevant: understand and

react adequately

Questions• How can finding commonalities in autistic and

machine learning deficiencies help better understand them?

• How can observing autistic cognition help designing better active machine learning systems?

• How can observing certain problems in designing machine learning systems help better understanding problems of autistic learning?

Both most machines and most children with autism cannot deceive. To teach

them, we need an explicit definition

Objective

• Describe an autistic learning mechanism from the computational learning standpoint

• Design a machine learning system which would display autistic behavior in its auto-development

• Observe which features of autistic learning can be reproduced

• Help correction of autistic cognition and learning

Learning is deterministic, inductive, active and reward-based

• We use deterministic learning model to avoid uncertainty features and maintain as simple model as possible

• We use inductive learning to obey a clear cause-effect structure, following the traditional inductive schema. The commonality in stimuli is assumed to cause an effect (a target feature)

• Learning is active, since the system itself selects the new elements of training set

• Learning is reward-based, so each correct stimulus recognition problem solved is rewarded. Incoming stimulus are selected from the real world, and the learning system itself chooses the best stimuli to recognize

Active learning loop

Sensory perception of children with autism

• It is well known that sensory perception of children with autism is rather peculiar

• A vast number of children with autism successfully ignore one kind of sensory stimulus and totally intolerable to the others

• They can form simple and complex sequences from various subjects and action, but at the same time refuse to reproduce simple schemata suggested by their teachers

Hypersensitivity

• We hypothesize that a root cause of autistic cognition is hypersensitivity to input stimuli

• Many studies of the dis-ontogenesis and the peculiarities of the development of children with autism confirm it at the earlier stages of ontogenesis(Baron-Cohen 2004; Marco 2011)

• It becomes clear that the development of an adequate sensory system at consecutive development steps by an autistic child is impossible

From hyper-sensitivity to failed cognition• Autistic learning system is initially adequate but hyper-

sensitive• It deviates stronger and stronger from both development

of control children and adequate machine learning systems

• Instead of collecting richer and richer stimuli of the real world, it learns to ignore them and substitute with auto stimulation

• Attempting to recognize real stimuli, such learning system receives negative reward

We simulate such behavior computationally and explain how initial hyper-sensitivity leads to a number of limitations of learning system,

inherent to autistic learning.

Hyper-sensitive machine learning system

If an anomaly detection system is hypersensitive, it becomes dysfunctional

If a customer service agent becomeshypersensitive to details, it becomes dysfunctional as well

Learning weak instead of strong stimuli

• In the efforts to protect themselves from stimuli which are too strong, children with autism develop a mechanism to filter them out

• These strong stimuli are mostly more informative than the weak ones

• Autistic child picks up weaker stimuli , less informative, but with a higher similarity with each other.

Avoiding perception of the

real world

• As an example of such stimuli in visual space, let us consider recognition of (1) child’s mother and (2) repetitive TV commercials

• Since the perceived image of mother’s face varies more significantly than the perceived image of repetitive TV commercials, the latter is preferred

• Image of the mother can be filtered out as being too strong due to its higher variability

• It required higher recognition efforts

Image of the mom vs TV commercials

Repetitive stimuli

• A partial case of stimuli with high similarity • All children select to use as highly repetitive stimuli

as possible as the training set• However autistic children only select most repetitive

stimuli and do not proceed beyond them• As a result of this initial problem, children with

autism stop exploring human behavior and do not communicate properly with their mothers and other humans

Simulate autistic development as a choice of perception mode

• a child selects to recognize humans such as parents and relatives

• a child follows an “easier” way of perception, considering only very similar patterns coming as a sequence, such as TV commercials.

which requires multimodal perception, classification of rather distinct images in a single

pattern, and further emotional and mental development.

This child is deprived of mental and emotional development due to his incapability to

perceive humans and their mental attitudes

Learning from data with high similarity

• If a machine learning system is fed with very similar elements of the training set, it will have a problem of recognizing even fairly similar objects to the training ones.

• It will be unable to recognize the ones with significant deviation from the elements of the training set

• To be rewarded, such learning system would need to find input stimuli which are alike to be able to recognize them.

The whole learning capability will be lacking

Visual and tactile multi-modal perception

Movement and perception of space in autistic development

Visual and tactile auto-stimulation

Lets proceed to computational learning

Generalized active

inductive learning

procedure with positive and

negative cases.

Active learning loop

Faulty active learning scenarios in the real world

• Hypersensitivity of the learning system can be viewed as a high number of features which are mutually correlated, and therefore redundant

• The learning algorithm itself can reasonably tackle such situation of overfitting

…but the active learning would be selecting training objects which would not adequately cover the real world

• To keep being awarded for recognition, the system will at some point stop collecting training objects from external world and start using the existing ones

This is essentially an auto-stimulation

and therefore its proper recognition will not occur.

Our previous studies

The Theory of Mind account is extended to reflect the computational experience of “teaching” computers to reason about mental attitudes (Galitsky 2000, Galitsky 2005).

Various forms of autistic reasoning about action, time, space and probabilities are explored, as well as the prevalence of deductive over inductive, abductive, and analogical forms of reasoning (Galitsky & Goldberg 2003).

Training of reasoning about beliefs, desires and intentions is shown to assists the emotional development (Galitsky 2001) A series of interactive rehabilitation software tools is suggested which stimulate various forms of commonsense reasoning, conversation and decision-making in autistic patients (Peterson et al 2004, Galitsky 2002).

ConclusionsWe designed a plausible machine learning system which shows two forms of behavior:• normal mode, where new features from the real

world form the training dataset and form the basis for its proper recognition

• autistic faulty mode, where the active learning evolves to the set of irrelevant features and although the learning sessions occur, the system is not capable of recognizing the real world

Conclusions: features of autistic cognition

Given the operational learning system:once it becomes hyper-sensitive in an active learning mode, it displays the number of features inherent to autistic cognition:

• Avoiding strong and informative stimuli• Broken multi-modal links• Auto-stimulation• Mixing important and unimportant features• Ability to learn only from a training set with very high

similarity / uniformity / repetition