Every once in a while we get to experience Murphy’s (dreaded) Law. This time around that had to do with stability issues with a media webcasting platform. We are now working on rescheduling NOKIA HFE18 under a different format. In parallel, we are also kicking off planning for HFE19… and we will take full advantage of lessons learned.
We regret any inconvenience that this eleventh hour change in plans might cause, and remain extremely grateful to both speakers and volunteers who have already invested time and efforts, which should not go to waste.
In the meantime, I’d like to volunteer just a handful of insights on the session that I was scheduled for and, therefore, keep the discussion going. The objective is to further improve what’s already available and allow for an even better session when we get to reconvene. Here is my session’s abstract to begin with.
THE SOFT & HARD NATURE OF ANYTHING DIGITAL
“Our quest to deliver productivity tools yielding operational excellence for DSPs, Digital Service Providers leads to the design of signature experiences by innovating in the process.”
“The Studio at Nokia Software’s Solutions Engineering is set to work with deceptively simple techniques and elegant sophistication… because neither oversimplification nor self-defeating complexity allow end-to-end systems to efficiently operate at digital speed and global scale.”
“This discussion intersects the soft and hard natures of dynamic systems by modeling Human Machine Systems (HMS) and the design of cybernetics. This practice focuses on critical success factors for the early acceptance and broader adoption of emerging technologies.”
“The work at the Studio embraces a renewed approach to QbD, Quality by Design, which is set to left-shift and unveil instrumental considerations at early design stages. The result is Nokia Studio’s QXbD, Quality Experiences by Design, optimizing for customer delight rather than table-stakes customer satisfaction.”
NI – WHAT IS NATURAL INTELLIGENCE? At the time of writing this, we humans possess NI, Natural Intelligence. NI involves naturally developed cognitive functions and models leveraged by the sort of biological beings, which humans happen to be. Intelligence (a) captures, (b) generates, (c) applies and (d) evolves knowledge. Our individual and collective brainpower can be gauged in terms of (e) skills and (f) talent levels, jointly with an understanding of (g) the underlying decisioning process and (h) our perceived experiences in context.
AI – WHAT IS ARTIFICIAL INTELLIGENCE? Intelligence that is not naturally occurring, simulated knowledge in other words. This is generated by programmable artifacts consuming, processing and producing data under closed loop models. Whether working with individual or networked machine intelligence, there is neither information derived from mindfulness nor the type of general purpose sense making that match those of the human experience. The year is 2018… and that’s where state of the art is today.
GI – WHAT IS GENUINE INTELLIGENCE? Earlier in the year I introduced this topic at Design Thinking 2018 (plenary session) and at IEEE Emerging Technologies Roundtable (invitation only workshop.) Coincidentally, both were held in Austin, TX, back in May. I proposed thinking about GI as the outcome of NI powered by AI.
By the way, “genuine” means acting in bonafide. To be clearer: with honesty and without the intention to deceive. Given the trade-offs (pros and cons) that NI and AI bring to the table, GI gets us a step closer to productive bonafide systems.
GI is, therefore, the outcome of purposely crafting optimal technology solutions that augment human possibilities. This is addressed by Human Factors Engineering interdisciplinary science given HFE’s holistic approach and focus on value driven Human-Machine-Systems, HMS.
Quick side note: those of you into Lean and Lean Six Sigma can approach this topic with Jikoda (autonomation.) Ditto for anyone working on Human-in-the-Loop Computing, Affective Computing, RecSys (Recommender Systems,) Human Dynamics and Process Mining with Machine Learning or, better yet, xAI, Explainable Artificial Intelligence.
DDESS – The most tangible design work entails the delivery of DDESS, Digital Decision & Execution Support Systems. This is where GI gets interesting because we need to apply new optics to take a fresh look at what Operational Excellence is (and is not) moving forward.
In a nutshell DDESS’ purpose is to reveal and inform decisions and to make decisions, all in context. But, I will pause here as this topic will be better covered in subsequent posts… just one more thought: DDESS addresses decision support for (NI) humans, (AI) machines, and (GI) human-machine systems. Coming to terms with that one insight alone becomes a critical success factor.
Some other thought… it turns out that, in today’s day and age, projects that are techno-centric heavy only succeed a fraction of the time, 10% or so by some estimates. Selective memories tend to focus and celebrate the 10% that make it… but that is a terrible ROI, Return on Investment, which inflicts (1) severe technical debt, (2) latency costs in systems engineering and (3) a huge opportunity cost as funding and good efforts could have been put to work for more productive endeavours.
By many other well documented and more recent accounts, HCD, Human Centered Design, happens to flip that ratio as designers are obsessed with optimizing for user acceptance and frictionless adoption from day one. HFE takes painstaking work on purposeful and value driven technological solutions where a smart combination of Outside-IN-innovation and Inside-OUT-ingenuity happens to make all the difference.
IEEE CQR-ETR 2018: “Discuss and identify the RAS (Reliability, Availability and Serviceability) challenges, requirements and methodologies in the emerging technology areas like the Cloud Computing, Wireless/Mobility (with focus on 5G technologies), NFV (Network Functions Virtualization), SDN (Software Defined Networking), or similar large-scale distributed and virtualization systems.”
“Discuss the RAS requirements and technologies for mission-critical industries (e.g., airborne systems, railway communication systems, the banking and financial communication systems, etc.), with the goal to promote the interindustry
sharing of related ideas and experiences. Identify potential directions for resolving identified issues and propose possible solution.”
Session Title: A Programmatic Approach for an Artificial Intelligence Code of Conduct.
Today’s DX, Digital Transformation, programs are all the rage, but it takes a fair amount of double clicking and inquisitive questioning to separate facts from vaporware. DX typically involves a wide variety of game changing initiatives intersecting analytics, automation, programmability, software-defined systems, end-to-end integration, service-level composition and controls… all coming together to optimize for Quality as a differentiated and value-based Human Experience. Therefore, Customer Delight metrics (rather than outmoded customer satisfaction ones) are set to redefine the “Q” in CQR, Communications Quality & Reliability in 5G.
While the Telecoms industry rallies toward a zero-touch automation paradigms, which some happen to position as a Human-“OFF”-the-Loop panacea, this session will expose the need for considering, and possibly pivoting, to the kind of Operational Excellence that can only be delivered by adaptive HMS, Human-Machine-Systems instead.
Note the rise of Dataviz (Data and Science Visualization,) ML’s (Machine Learning’s) Collaborative Filtering, AI’s (Artificial Intelligence’s) RecSys (Recommender Systems) and a renewed take on Cybernetics are driving innovation in HILT and HOTL (Human-“IN”-The-Loop and Human-“ON”-the-Loop, Computing,) as well as delivering effective mass-personalization with Affective Computing powered by Human Dynamics’ analytics.
Telecoms’ pioneered HFE, Human Factors Engineering: a holistic systems engineering discipline addressing people (culture, workstyle, skills,) processes (procedures, methods, practices,) and technologies (crafts, tools, systems) so that we can best humanize technology and make a compelling difference across the value chain at all levels. We are now embarked on a new journey.
The sought after outcome of any Digital Service Provider, DSP, is to be instrumental to our Citizens’ Quality Experiences with new service experimentation, transactions and growth models. This takes agility and dynamic system-wide (horizontal and vertical) behaviors, which prompt effortless operability at unprecedented speed, scale and scope. Our work permeates design, development, delivery and serviceability, and continuous intertwined lifecycles instead of lock-step waterfalls.
In this context, AI, Artificial Intelligence, enables us, humans, to envision and implement capabilities beyond the reach of legacy systems’ last gasps. By the same token, practices that might have appeared to serve us well in the past, are exposing their limitations when becoming latency-prone barriers. A successful path forward takes augmented Human-Machine Intelligence. A programmatic approach for an AI’s Code of Conduct would enable us to best model AI’s behavior, design better human-network interactions and collaborate on standardization.