1.
An Inductive
Inference Machine
(1957)
R. J. Solomonoff
1926 - 2009
Aly O. Abdelkareem
University of Calgary

2.
What is
Inductive
Inference ?
• Inductive inference is the process of reaching
a general conclusion from specific examples.
• The general conclusion should apply to unseen
examples.

3.
Problem
• We will feed the machine these examples
• Here, we want the machine to decide upon
the most probable digit to fit into the empty
square
• Then, we present the machine with this problem

4.
Solutions
N-grams and Prediction n-grams
N-tuples and Structures
N-gram Sets, Prediction n-gram
sets, N-tuple sets

5.
N-Grams
Shannon has show how we may predict words or letters in
English by the use of “n-grams”
If we want to predict the next letter in ”Today we ar?”
• Using bi-gram
- Higher frequency of ”ra, rb, rc, re, rd … etc”

6.
1. N-grams and Prediction n-grams
• N-grams: extend it to 2-grams
• Possibilities:
Prediction is consistent with the other examples
Consistency: of a Prediction n-gram to indicate that all of its predictions, as
applied to the examples given to the machine, have been correct

7.
“=” and “0” are so close
What if we want the machine to find the
solution related to the “=“ ?
Is a spacer and it is not a proper part of the
prediction 3-gram

8.
• N-tuples: is an ordered set of n object.
• Structure: set of instruction for taking the members of an N-tuple and
moving them in a certain way.
2- N-tuples and Structures

9.
Applying N-tuple and Structure
• The structure

10.
What if we have new
examples (unseen data) ?
It is unable to solve this problem, since it
cannot yet have learned the prediction 3-gram

11.
3. Sets: N-gram Sets, Prediction n-gram sets,
N-tuple sets
• N-gram sets: unordered set of n-gram
• Prediction N-gram set: unordered set of prediction N grams
• N-tuple set: unordered set of N-tuple

12.
Using sets we
can then
apply
Cartesian
product
Boolean
product
Boolean sum
Occurrence
operation

13.
Occurrence Operation

14.
Applying to
our problem
N-gram set
3-tuple set
Using this structure
Prediction on n-gram set will be

15.
Limitations
High order sets:
Inability to deal with
members are themselves
sets
• Resolved by defining an N
tuple recursively.
Non- determistic:
Cannot work on problem
with several possible
answers ( language
translation, weather
prediction or information
retrieval.
Generalization:
Small error of input
disturb the machine
• Chomsky machine to find
grammatically correct
sentences

16.
The concept
of Utility
• Utility values are assigned to each abstraction
used
• For example:
1. Prediction n grams: Consistent is 1 value –
high utility –
2. Structures or N-tuple: utility values
proportional to the frequency of create
consistent prediction
• Used to get a priori probability of consistency of
a new created prediction

17.
Mode of
Operation of
Machine
Computer starts out with a small sets, along
with apriori utility assigned to all of them
Using set of transformation rules, the machine
creates a new set of abstractions from the old
set
Select combination with high apriori utilities
and apply empirical evaluation.
Keep the new good abstraction and then
Repeat
Goal:
• Find one that fit the problem
• if more than one fit, then find
the one with highest utility
• If the conflicting utility, then
the answer will not be reliable (
not many examples )

18.
Important
problems that
must yet be
solved
SEARCHING FOR CONSISTENT
PREDICTION THAT FIT A
PARTICULAR QUESTION
SET OF RULES FOR THE
MANIPULATION OF THE SETS
NEED TO BE INVESTIGATED
ASSIGNING UTILITY MUST BE
WORKED OUT IN GREATER
DETAILS
THE OPERATING PROGRAM OF
THE STOCHASTIC MACHINE
HAS BEEN INVESTIGATED AT
GREATER LENGTH
METHODS OF GENERATING
NEW ABSTRACTION FROM
USEFUL OLD ONES MAY E
ADEQUATE.
REALIZING PHYSICALLY AN
INDUCTIVE INFERENCE
MACHINE (STORING INPUT
AND ACCESS SPEED )
3.75 MB
25 ms

19.
Conclusion
• A program has been written for
a computer to learn to work
simple arithmetic problems
after being shown a set of
correctly worked examples

20.
Summary
Accuracy of inference depends upon presented data
Machines takes examples that have been usefull in the past and
derive new reasonable examples
Machine to work on the problem of improving itself
given statistical training sequence
and probability distribution
The machine will be able to prove
theorems, play good chess, and answer
questions in English.
Example: Machine probably be able to recognize the difference
between “grammatically correct or incorrect”, providing a training
sequence of grammatically correct sentence

21.
This paper
Implications
Solomonoff's theory of inductive inference
A mathematical formalization of Occam's razor and
Principle of Multiple Explanations.
Assumed the world is generated by an unknown
computer program.
Based on Algorithmic probability (Solomonoff
probability)
Prediction is done using a
completely Bayesian framework.

22.
Background
Algorithms — We’re looking for an algorithm to
determine truth.
Induction — By “determine truth”, we mean
induction.
Occam’s Razor — How we judge between many
inductive hypotheses.
Probability — Probability is what we usually use
in induction.
The Problem of Priors — Probabilities change
with evidence, but where do they start?
The Solution
Binary Sequences — Everything can be encoded
as binary.
All Algorithms — Hypotheses are algorithms.
Turing machines describe these.
Solomonoff's Lightsaber — Putting it all together.
Formalized Science — From intuition to
precision.
Approximations — Ongoing work towards
practicality.
Unresolved Details — Problems, philosophical
and mathematical.

23.
Solomonoff
induction
Algorithm
Make an observation.
Form a hypothesis that explains the observation.
Conduct an experiment that will test the hypothesis.
If the experimental results disconfirm the hypothesis, return to step #2
and form a hypothesis not yet used. If the experimental results confirm
the hypothesis, provisionally accept the hypothesis.
Now we’ve found the truth, as best as it can be found.

24.
More Details
Bayes' rule: Guide
https://arbital.com/p/bayes_rule/?l=1zq
An Intuitive Explanation of Solomonoff
Induction
https://www.lesswrong.com/posts/Kyc5d
FDzBg4WccrbK/an-intuitive-explanation-
of-solomonoff-induction
How Bayes' theorem is consistent with
Solomonoff induction
https://www.lesswrong.com/posts/5pgsb
B5sqC2wLwr4d/how-bayes-theorem-is-
consistent-with-solomonoff-induction

25.
Thank you