“Today, professionals get trained in using tools… there’s a lack of education of fundamentals
like modeling, architecture, methods, or concepts... Getting value out of data
needs professionalization based on education and practical experience.”
In my opinion, he’s spot
on.
My post from March mentions why
new AI languages aren’t exactly heavies of a CV in a mainstream business; in
April, a figure (at the end of the post) also touched on Forest structures in
ML and eXplainable AI.
After a free-wind sail that
took us from detail to architecture, we now go into some structural “forestry”.
It’s about tackling the same domain from multiple viewpoints, instead of
clinging on to one.
1. Multiple trees in UML: Generalization sets
This post from 2015 (in Swedish)
discusses the sets in more detail, so let’s just recap the diagrams, in English,
and add «powertype» on the
fourth one (a power set’s instances are subsets, so by the same token, a UML2 powertype’s
instances are “subtypes” of a general construct).
2. Multiple trees in Machine Learning: random Decision Forests
Here, a designer is not
the one who maps out subclasses. Rather, an ML algorithm generates in training
time, from (labeled) data, an ability to perform classification (i.e accurate
“mapping-out” of “classes”). The decision nodes of a (classification) tree
gradually subdivide the data into more and more fine-grained classes.
Why bother about decision
trees when Deep Neural Networks are booming? Because explainability opens the door
to acceptance in mission-critical apps. ML-generated logic has to be auditable.
User enterprises are pushing for graphicness, conceptualization, traceability, V&V.
Those are the strengths of decision trees, and weaknesses of Deep NNs (we know
those work, but hardly how); same thing with learning time required, size of
training data sets, execution speed, or partitionability (a hint for IT
architects: a tree works independently, whereas a neuron relies on many other
ones). Atop of that, decision trees offer a structural backbone of hybrid AI
systems (see also the last paragraphs in this post).
You might remember that trees
in woods sometimes fuse their
roots and exchange materials. Unsurprisingly, we find some synergy in virtual
forests too. Firstly, our trees are
“grown” on a random sample each
(hence some “biodiversity” too), from one training-data set (hence fewer
terabytes of training data). Secondly, on each sample, its tree’s decision
nodes use a random subset of all available attributes.
This gives architects and other roles some room to tune the mix of efficiency
and explainability; in forests, it’s is near the level of genetic algorithms (GAs
too have possible “mix-tuning points” in “biodiversity steps” Crossover and
Mutation).
The more trees and “biodiversity”
our ML algorithm grows, the more accurate and robust the generated logic
becomes, because the final step is vote counting. A forest’s output (a classification
like here, or a forecast) is an aggregated value of the outputs of all trees (a
statistical mode in classification, or a mean in regression). It prevents the random
decision forest from getting stuck in local optima, that is, we minimize error
rates and overfitting to a given training-data set (which may be both
incomplete and biased).
Trainer at Informator, senior modeling and
architecture consultant at Kiseldalens,
main author : UML Extra Light (Cambridge
University Press) and Growing Modular (Springer), Advanced UML2 Professional (OCUP cert level 3/3).
Milan and Informator collaborate since 1996 on
architecture, modelling, UML, requirements, rules, and design. You can
meet him in September at public courses (in English or Swedish) on AI, Architecture, and ML (T1913),
Architecture (T1101, T1430)
or Modeling (T2715, T2716).
