MACHINE LEARNING TECHNIQUES - LASA

8
Why and when do we need learning?
We need learning when the solution to a problem may vary. Any machine bound to interact on a
daily basis with humans or the environment is bound to face variations in its data. Humans cope
with the uncertainty of their environment by constantly adapting and/or learning new behaviors.
Indeed, there is not a day that goes by without us adapting to the circumstances. We adapt our
driving or walking habits to the weather conditions and to the passers-by. We, also, constantly
acquire new knowledge, as we meet new persons, get new information from the media, etc…
Learning here is synonymous to the notion of adaptation. Note that the two terms are sometimes
distinguished in the literature. Learning is usually seen as the creation of a new form of
knowledge (e.g. discovering a law of motion in robotics), while adaptation is simply a slight
update of current knowledge (e.g. changing the parameters of the law of motion).
Learning is a proof of autonomy.
It is however not clear whether all machines should be endowed with long-term open-ended
abilities to learn and adapt.
Most importantly, learning can take an unbearably long time, months if not years. Think simply of
humans. It takes a good year for a baby to master the simple action of using a spoon and one
more year to understand the basics of language.
What would be an acceptable duration of learning in any human-machine interactions?
This remains an open question, and we have yet very few examples to draw inference from. At
present, the only adaptive engines we have at our disposal are speech recognition systems and
web-search engines. It takes about 45 minutes to train a speech engine before it reaches an
acceptable level of performance. A very short duration in comparison to the baby’s learning
curve. However, speech engines remain not very popular.
Probably what is most important is not so much the time it takes for the machine to learn, but,
rather the fact that the learning curve is perceptible to the teacher. While you train your speech
engine, you have no feedback on how well the engine learns. It is only at the end of the 45
minutes that you get to test the machine. If the level remains very unsatisfactory, it is unlikely that
you will be willing to spend yet another three quarters of an hour to retrain it. Teaching and
learning is not a unidirectional process. It is inherently bi-directional. When interacting with a
baby, the caretaker continuously adapts his/her speech and behavior to the baby.
Why are we so dissatisfied with current learning machines (such as speech engines)?
Possibly because learning machines neither reach a level of adaptability similar to the human one
nor achieve a level of perfection associated only with machines. Caretakers draw the motivation
to fetch back the spoon that the baby threw on the floor over and over again because they “care”.
We cannot expect the end-users to “care” about machines the same way they do about humans.
In order to draw the same motivation for the end-user of your learning machine, it is, therefore,
fundamental to think of the reward the user/teacher can find in teaching the machine.
Who says reward thinks also of measure of performance:
Progress in a machine’s performance must be measurable and must be significant. A machine
must eventually reach a minimal level of performance (“good enough”) within an acceptable time
frame.
© A.G.Billard 2004 – Last Update March 2011