Intelligible Intelligence: Deep XAI still more R&D than toolbox

Most architectural tradeoffs are hard. So is the one between the accuracy of Deep Machine Learning (ML) and explainability/transparency of explainable AI (XAI). Therefore, DARPA’s initial XAI program is expected to run through 2021.

Few explainability mechanisms have been extensively tested on humans, but current R&D indicates at least some (hybrid) tech to come in a couple of years.
Several well-tried ML technologies work their way through (in steps) from a first random solution to satisfactory ones, and where possible, to an optimal one:
·         Genetic Algorithms, by creating additional generations
·         Forests, by creating additional trees from the same data
·         Deep Neural Networks, by a (cost) function applied (in training) to outputs, based on how they differ from labeled data, and propagated back across all neurons/synapses, to adjust weights
·         Hybrids, by combination (the “new kid on the block”).

NASA Space Technology 5 antenna created by GA (source:

GA and Forests are more than semi-intelligible, by their nature. However, now that deep learning feeds big data through NN that consist of multiple hidden layers of neurons (DNN), it arrives at very accurate solutions to complex multidimensional problems, but at the same time at an inherent black box.
A part of my post from August is about random forests, which offer both more transparency than DNN do plus quite a degree of accuracy. Moreover, trees and NN are even cross-fertilized, to offer explainability without impeding accuracy, and there are already several flavors of this; to pick a handful:
·         adding extraction of simplified explainable models (e.g. trees) onto black-box DNN
·         local (instance-based) explanation of one use case at a time, with its input values, for example animating & explaining its path layer-by layer (like most test tools do).
·         soft trees, with NN-based leaf nodes, that perform better than trees induced directly from the same data
·         adaptive neural trees and deep neural decision forests that create trees (edges, splits, and leafs) , to outperform “standalone” NN as well as trees/forests that skip the combination.
Explainability, transparency, and V&V are absolutely essential to users’ reliance/confidence in mission-critical AI. Therefore, whichever path or paths take us to up-and-running products, XAI is welcome.

Trainer at Informator, senior modeling and architecture consultant at Kiseldalen’s, 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/AI, and design. You can meet him at public courses (in English or Swedish) on AI, Architecture, and ML (T1913 , in December or February), Architecture (T1101, T1430) or Modeling (T2715T2716).

What’s wrong with repairs?

That’s a question, let go just a rhetoric one. Copy-pasting most of what I wrote in this Swedish article in Ny Teknik, 7½ years back, would still work: since then, rather few business-to-consumer industries have replaced the disposable-product mentality, poor quality, and short product lifecycles. But, it’s finally turning.  
Currently, several global companies (including Ikea or Electrolux) are reshaping their business models toward not only recycling but also easy-to-replace parts - to extend both configurability/variability/customizability and lifecycles. For product architects, systems, and processes, this means increased priority of design-to-configure, and of swap-out/swap in parts.
Hopefully, most enterprise architects understand it’s high time. Especially now that demand is losing momentum world-wide, it’ll pay off: an increased proportion of after-sales, parts, services, and customer guidance (on component swaps and similar simple repairs) can compensate for some drop in demand for new complete products.
Many, if not most, business-to-business industries started this journey decades ago: trucks & busses and other transportation equipment, industrial automation, or telecom infrastructure, to name just a handful.

In software architecture, we entered more recently: components, SOA, microservices, variability mechanisms (not least configurability and postponed variant-binding time)… Therefore, most product architects in a number of other industries might find their SW architectures only “almost ready” for the imminent quick transition toward flexible product architecture and configure-to-order. Their points of similarity and mutual influence are covered in Informator´s course. If you’ve attended the Agile Architecture course (T1101 ) recently, you might remember it’s called Modular-Product Line Architecture (T1430 – welcome on November 11).
Trainer at Informator, senior modeling and architecture consultant at Kiseldalens, main author: Growing Modular (Springer) and UML Extra Light (Cambridge University Press). An Advanced UML2 Professional (OCUP cert level 3/3).
Milan and Informator collaborate since 1996 on architecture, modelling/UML, requirements, and design. You can meet him in November and December at public courses (in English or Swedish) on AI/ML for architects (T1913), Architecture, or Modeling (T2715T2716).

Utnyttja dina SA-vouchers innan det är försent

Sen, vad som känns som, tidernas begynnelse
har Microsoft erbjudit sina kunder möjlighet att skaffa sig utbildning inom Microsoft-produkter genom sitt Software Assurance-program. Med så kallade SA-vouchers har du som IT-proffs eller systemutvecklare kunnat betala för officiella Microsoftkurser hos Learning Partners, som t ex Informator. Men, nu blåser förändringens vindar. Framöver kommer Microsoft fortsätta investera kraftigt i sin Microsoft Learn-plattform som erbjuder gratis självstudiekurser online. Samtidigt fasas SA-voucherprogrammet ut.
Från februari 2020 kommer du inte kunna betala för Azure-utbildning med SA-vouchers.  Du kan emellertid fortfarande betala för andra Microsoftkurser, inklusive Office 365, Windows Server, SQL, SharePoint men endast till slutet av 2021. Och du kommer inte kunna skapa nya SA-vouchers efter juni 2021…
Trots stundande förändringar har du alltså chansen att fortfarande utnyttja voucherprogrammet till fullo, och det är värt.
Hör av dig till oss på Informator idag Vi hjälper dig gärna med att planera användningen av dina befintliga SA-vouchers och samarbetar med dig för att ta fram en utbildningsplan som stärker dina medarbetare, dig själv och din verksamhet.

Anna Sahinoja & Åsa Berglund

Vill du läsa mer om SA-Vouchers och hur man utnyttjar dem?

Conscious Leadership tip of the week: Self-esteem

Your self-esteem will define how you as a leader deal with challenges, would it be conflict, performance or other work-related issues. When we lack self-esteem, our responses to challenges tend to increase our disposition to blame other people’s mistakes rather than rising to the challenge with enhanced accountability. We also tend to shield ourselves from being blamed if our self-esteem is threatened. Our tendency of being right then need to be transformed into a solution focus, which takes awareness. How can I as a leader deal with challenges if my self-esteem gets in the way? Here are a few tips: • Instead of being right, stay open to the views of others. Reach out to co-workers and embrace their knowledge and capabilities • Focus on the factors of your influence and see to that you respond to external circumstances with a contributor mindset • Realize that there will be occasions and situations out of your control, but trust that your experience, knowledge and wisdom will help you deal with them appropriately • Make responsibility a day-to-day activity, and your capacity to respond will increase, making sure that your chances of success will escalate rapidly If you would like to learn more, please come and join us at our “Conscious Leader” training at Informator: Skriven av: EPM Thérese Sandberg & Aila Kekkonen hashtagconsciousleadership

Conscious Leadership tip of the week: Selflessness

There’s a paradigm shift happening in the business world. The traditional top-down carrot and stick management mentality is being replaced by a more human value-oriented leadership style. But, why? Studies show that it is not efficient anymore. Only in Sweden there’s an 86% engagement deficit among employees. Although, nearly 77% of the leaders consider themselves doing a good job engaging their people. And the engagement gap doesn’t serve goals, mission, vision and strategy very well. In fact, it’s one of the main causes for poor economical results. So how is selflessness a part of the equation? Selflessness serves as a catalyst, allowing the best to unleash in the people we lead. It’s about getting out of the way, letting other people to shine alongside you. For the good of the company. A few tips: • Be a role model. Make a difference regarding what your company and the market-place need • Create a work culture where everyone wins • Initiate a feedback system from inside your company. Listen to your customers • Empower your people, so they can succeed If you would like to learn more, please come join us at our “Conscious Leader” training at Informator. See: link in the first comment below. Av: Aila Kekkonen & Therése Sandberg Kopplat till Informators kurs:

Pyttestyrning eller coacha till självstyre?

Du är chef. Du har en gruppering under dig som vill styra mer av sin verksamhet själv. Du låter dem.

Men efter en tid märker du att grupperingen nog lider av problem. Du har inte sett riktigt det resultatet från dem du velat se, och när du betraktar hur gruppen arbetar ser du hur de är lite för splittrade. De skulle nog må bra av lite struktur. Så du överväger att genomföra följande förändringar...
Det spelar ingen roll om det du tänker göra är bra eller inte. Bara det att du intervenerar utan föregående varning förstör precis den förmåga till självstyre du och grupperingen försöker bygga upp.
Gruppen har bestämt sig för ett visst arbetssätt. Det är möjligt att det inte är det bästa sättet, men gruppen upplevde sig ha mandat att bestämma det när de bestämde det.
Varje plötslig inblandning i andra människors ansvarsområden gör att deras upplevelse av att ha mandat förstörs. Det spelar ingen roll om du tycker att interventionen är en engångshändelse på grund av en särskild situation, det alla andra ser är ett slumpmässigt bortryckande av befogenheten att leda och fördela sitt eget arbete.
Och när något sker slumpmässigt finns det ju inga garantier att det inte kan hända igen, eller hur? Känslan av kontrollförlust, förlorad autonomi, skakar folk inte av sig så lätt. Du har just brutit ett förtroende.
Ett bättre sätt att närma sig frågan hade varit att...
  • ...möta gruppen (inte enskilda) med observationen och fråga om gruppens syn på saken.
  • ...erbjuda hjälp att tillsammans med gruppen lösa de utmaningar de ser. Din intervention blir då en av många möjliga lösningar.
  • Och om gruppens resultat eller arbetssätt fortfarande är oacceptabla  (men inte förr) köra över dem och bestämma en ny ram för deras arbete.
Det sistnämnda är viktigt. I en organisation som andra än gruppen äger arbetar alla med andras tillgångar. Som chef är du ansvarig för vad som händer med dessa. Gruppens självorganisation är, precis som ditt chefsjobb, ett förvaltarskap. När som helst kan både din och deras rätt att bestämma försvinna.
Men bara för att du har ansvaret, mandatet och möjligheten att intervenera efter eget godtycke är det inte en bra idé om du vill se självorganisation och bemyndigade medarbetare.
Självstyre inom en maktstruktur där chefer får lov att agera efter eget godtycke är bräckliga saker ur ett psykologiskt perspektiv. Själva möjligheten till chefens godtycke utgör ett stort hot, även om du som chef inte upplever dig som speciellt hotfull.
Om du använder dig av din rätt till vad som kan uppfattas som plötsliga godtyckliga interventioner gentemot självstyrande grupperingar så kommer det att få stora konsekvenser mycket snabbt.
Även om du gör det sällan. Även om du agerar i välvilja. Även om din intervention i grunden är bra och smart.

Text skriven av Ola Berg från hans egen blogg

Conscious Leader Tip: Accountability

It is a misconception that conscious leaders would only be nice, wherefore average financial results are to be expected. The fact is that conscious companies have superior financial performance, since a lot of value creation is done towards all stakeholders - and it pays back. Conscious leaders create cultures combining a high level of trust with strong emphasis on accountability. Accountability is mutual: leaders holding their co-workers accountable and the other way around. Accountability also represents having strong “response-ability”, meaning having the ability to respond to a situation; being proactive, asking “What can I do?” It also means, that one does not give the first automatic response that comes to mind. Rather, choosing a more empowered response. One where afterthought and context are embraced. When we become aware of our reaction patterns, we give ourselves the opportunity to become more conscious, thus increasing our ability to respond to different circumstances. We have the power to choose our response to be accountable. Would you like to learn more, please read about our “Conscious Leader” training at Informator.

Therése M. Sandberg &; Aila Kekkonen

Twin examples of multiple trees: 1. UML models, 2. Machine Learning

“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.”                                                                 
Andreas Buckenhofer, Daimler TSS, in an interview by OODBMS on Big Data, July 2019 
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 (T2715T2716).

Leadership Tip - Week 23

Trusting relationships are a prerequisite developing an organization characterized by teamwork, cooperation and success. To build trust you need to be perceived as trustworthy before you can release human potential and empower your co-workers. But trust is a two-way street, and organizations thrive when co-workers not only trust their leaders, but also feel trusted by their leaders. In high-trust organizations, leaders make the effort to care for their employees, customers, stakeholders and the society in which they operate. And it’s conscious leaders make shared purpose an integral part, enabling people to get a feeling of meaningfulness from their work. Organizations driven by conscious leaders benefit from greater synergy among stakeholders. Collectively, it enables them to achieve far more than would it be possible individually. Other benefits include increased respect, cooperation, communication, relations, engaged co-workers, and a greater reputation beneficial for a sustainable successful future. Want to learn more about becoming a Conscious Leader? Please follow the link to our training at Informator in the first comment below. All the best, Therése & Aila Enterprise Performance Management Länk till kursen:

6 Things AI and Machine Learning Reshape in SW

In my recent post, I mentioned that AI and ML challenge architecture, but also offer tools to tackle the challenges. Needless to say, automation of repetitive tasks will change our job descriptions, just like those of our end users.

The SW development lifecycle is changing too. Architects with experience from an environment with external content prosumers or open-source developers, will find some lifecycle changes similar to those two environments.

1. Crowd management
Much of the “crowd” involved is outside the IT organization (e.g. experts in domains other than IT), or even outside the enterprise - not least in Data as a Service offering ML from data sources such as digital twins (of customer-owned equipment, e.g. railroads and trains at Siemens, networks at Ericsson, or farming machinery at John Deere). Architects or CIOs have influence rather than full control, unlike in internal projects.

2. Crowdsourcing
Both the data and some ML-generated logic come from external sources, not least when some ML and computing runs locally on “edge” devices that produce the input data.

3. Adaptive planning
Distinct project phases tend to disappear, partly because of the “crowd” out there, partly because of the explorative nature of ML (“think more like a researcher, less like a programmer”). For example, a partial result of an ML project can hint about additional key domains to drill into, thus widening the scope and postponing the deadline.

4. Incomplete requirements
Customers may have a rather sketchy idea of what they want. “More bang for the buck” wouldn’t hint on “increased harvest, better soils, 80 percent less herbicides due to spraying individual weed plants only”, but ML with fast pattern recognition (in RT field images) does exactly that.

5. Widened job roles
Apart from the SW (development) lifecycle and the nature of new apps, AI reshapes the way they’re developed and thus our roles too. Architects and some devs become even curators of training data sets, co-analysts of ML results, and a guide for experts from non-IT domains, to enable them to apply ML in their specific tasks.

6. A hard core (platform)
The core (e.g. an automated data/ML platform) has to be secure, robust, modular, reliable (fault-tolerant, even on external error), documented and teachable to teams within the enterprise. The ML-generated system has to interoperate with other, programmer-made, systems (pre-ML AI, and other SW). The ML-generated logic has to be auditable and verifiable; indeed, explainability is the door to acceptance in mission-critical apps.

Figure from course AI, Architecture, and Machine Learning (T1913)

Trainer at Informator, senior modeling and architecture consultant at, 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 this Spring at public courses (
in English or Swedish) on AI, Architecture, and Machine Learning  (T1913),  Architecture (T1101, T1430) or Modeling  (T2715T2716).

Yet another AI language you miss in your CV? 4 reasons why it will matter less and less.

It never hurts, but it varies how helpful a (fairly) new programming or script language is. From more or less a prerequisite in R&D and platform-vendor firms, to a nice-to-have CV footnote in mainstream businesses that rather emphasize extended SQL, analytics , data architecture, and automated ML platform/s.

Here are 4 reasonswhy you can live with it (ascending order by weight)

1. The fate of LISP, Prolog, Smalltalk, KQLM etc. (use comment form below to fill in what’s missing : ))
To drive the history of applied “AI 1.0” to the extremes, an enterprise in the 1980-ies was expected to achieve superpowers as soon as the CIO (and preferably, CEO : )) learned at least one exotic-enough AI language. The subsequent AI winter after 1990 happened supposedly because most CIO’s refused to so; best case: they got lost somewhere inside their fifth pair of nested parentheses in LISP (like most of us devs did too, including myself)…

The rise of expert-system development shells in business (near 1990)
Programming languages are versatile. You can get anything you need, given a generous timeframe. Development tools encapsulate a lot of technical detail. You can get more or less what you need, even under tight time constraints. No wonder it’s more appealing to CIOs than nested parentheses.

3. Success of those who used then-mainstream industry languages
Books on configurators, or on LISP, drill down into Digital (HP) XCON/R1, but in books on management and modular manufacturing, you read about Scania Trucks & Buses (reporting profits for 80 consecutive years). Scania’s smart proprietary configurator (an age fellow of XCON) became a backbone of the enterprise, and grew to several times the size of XCON. Unlike XCON, Scania did cope in maintenance and upgrades. For decades. Offering complex customized vehicles assembled from a cluster of common component types. Language: unglamorous then-mainstream Cobol and DL/1.

4. A trend toward frameworks, component libraries, automated analytics & ML platforms
It’s a two-way street. On one hand, “AI 2.0” and ML challenge current architectures, not least data architectures: big-data ingestion, parallelism, fast access aid for non-sequential access because learning (in both human and artificial neural networks) is essentially non-sequential (parallel).
On the other hand, ML offers a toolbox to tackle these challenges, and also, enables quite some automation of the entire data pipeline and of an architect’s (or dev’s) repetitive tasks. I won’t be surprised if automated-ML platforms for big data, using extended SQL instead of script languages, spark success stories of Scania’s magnitude. It’s about the augmentation (or automation) itself, and about architecture fit for business, rather than about the detail.
So from now on, Informator’s new one-day course is called AI, Architecture, and Machine Learning. Neither just AI for Architecture, nor just Architecture for AI. It’s a two-way street.

Rapid progress in the middle
Figure from course AI, Architecture, and Machine Learning (T1913)

Trainer at Informator, senior modeling and architecture consultant at, 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 this Spring at public Architecture courses in English or Swedish (T1913, T1101, T1430) or Modeling courses ( T2715T2716)