Platt Perspective on Business and Technology

Rethinking the dynamics of software development and its economics in businesses 6

Posted in business and convergent technologies by Timothy Platt on September 9, 2019

This is my 6th installment to a thought piece that at least attempts to shed some light on the economics and efficiencies of software development as an industry and as a source of marketable products, in this period of explosively disruptive change (see Ubiquitous Computing and Communications – everywhere all the time 3, postings 402 and loosely following for Parts 1-5.)

I have been at least somewhat systematically been discussing a series of historically grounded benchmark development steps in both the software that is deployed and used, and by extension the hardware that it is run on, since Part 2 of this series:

1. Machine language programming
2. And its more human-readable and codeable upgrade: assembly language programming,
3. Early generation higher level programming languages (here, considering FORTRAN and COBOL as working examples),
4. Structured programming as a programming language defining and a programming style defining paradigm,
5. Object-oriented programming,
6. Language-oriented programming,
7. Artificial Intelligence programming, and
8. Quantum computing.

And I have successively delved into and discussed all of the first six of those development steps since then, noting how each successive step in that progression has simultaneously sought to resolve challenges and issues that had arisen in prior steps of that list, while opening up new positive possibilities in its own right (and with that also including their creating new potential problems for a next development step beyond it, to similarly address too.)

My goal beyond that has, and continues to include an intention to similarly discuss Points 7 and 8 of the above-repeated list. But in anticipation of doing so, and in preparation for that too, I switched directions in Part 5 and began to at least lay a foundation for explicitly discussing the business model and economic issues that comprise the basic topics goal of this series as a whole. And I focused on the above Points 1-6 for that, as Points 1-5 are all solidly historically grounded for their development and implementation, and Point 6 is likely to continue to develop along more stable, non-disruptive evolutionary lines. That is a presumption that could not realistically be made when considering Points 7 and 8.

I focused in Part 5 of this series on issues of what might be called anticipatory consistency, where systems: hardware and software in nature, are determined and designed in detail before they are built and run, and as largely standardized, risk-consistent forms. And in that, I include tightly parameterized flexibility in what is offered and used, as would be found for example, in a hardware setting where purchasing customers and end users can select among pre-set component options (e.g. for which specific pre-designed and built graphics card they get or for the amount of RAM their computer comes with.)

This anticipatory consistency can only be expected to create and enforce basic assumptions, and both for the businesses that would develop and offer these technologies, and for both their hardware and software, and for how this would shape their business models. And this would be expected to create matching, all but axiomatic assumptions when considering their microeconomics too, and both within specific manufacturing and selling businesses and across their business sectors in general.

I included Point 6: language-oriented programming there, as offering a transition step that would lead me from considering the more historically set issues of Points 1-5, to a discussion of the still very actively emerging Points 7 and 8. And I begin this posting’s main line of discussion here by noting a very important detail. I outlined something of language-oriented programming as it has more traditionally been conceived, when raising and first discussing it in Part 4 of this series. And I kept to that understanding of this software development step in Part 5, insofar as it came up there. But that is not the only way to view this technology, and developments to come in it are likely to diverge very significantly from what I offered there.

Traditionally, and at least as a matter of concept and possibility, language-oriented programming has been seen as an approach for developing computing and information management, problem-specific computer languages that would be developed and alpha tested and at least early beta tested, and otherwise vetted prior to their being offered publically and prior to their being used on real-world, client-sourced problems as marketable-product tools. The nature of this approach, as a more dynamic methodology for resolving problems that do not readily fit the designs and the coding grammars of already-available computer languages, at least for the speed and efficiency that they would offer, is such that this vetting would have to be streamlined and fast if the language-oriented programming protocols involved are to offer competitive value and in fact become more widely used. But the basic paradigm, going back to 1994 as noted in Part 4, fits the same pattern outlined in Part 4 and when considering development steps 1-5.

And with that, I offer what could be called a 2.0 version of that technology and its basic paradigm:

6.0 Prime: In principle, a new, problem type-specific computer language, with a novel syntax and grammar that are selected and designed in order to optimize computational efficiency for resolving that problem, or class of them, might start out as a “factory standard” offering. But there is no reason why a self-learning and a within-implementation capacity for further ontological self-development and change, could not be built into that.

Let’s reconsider some of the basic and even axiomatic assumptions that are in effect built into Points 1-5 as product offerings, as initially touched upon in Part 5 here, with this Point 6 Prime possibility in mind. And I will frame that reconsideration in terms of a basic biological systems evolutionary development model: the adaptive peaks, or fitness landscape model.

Let’s consider a computational challenge that would in fact likely arise in circumstances where more standard computer languages would not cleanly, efficiently code the problem at hand. A first take approach to developing a better language for this for coding it, with a more efficient grammar for that purpose, might in fact be well crafted and prove to be very efficient for that purpose. But if this is a genuinely novel problem, or one that current and existing computer languages are not well suited for, it is possible that this first, directly human coder crafted version will not be anywhere near as efficient as a genuinely fully optimized draft language would be. It might, when considered in comparison to a large number of alternative possible new languages, fit onto the slope of a fitness (e.g. performance efficiency) peak that at its best, most developed possibility would still offer much less that would be possible, overall when considering that performance landscape as a whole. Or it might in fact fit best into a lower level position in a valley there, where self-directed ontological change in a given instantiation of this language could conceivably lead it towards any of several possible peaks, each leading to improved efficiency but all carrying their own maximum efficiency potential. And instantiation A as purchased by one customer for their use self-learns and ontologically develops as a march up what in fact is a lower possible peak in that landscape and the overall efficiency of that, plateaus out with a still relatively inefficient tool. Instantiation B on the other hand, finds and climbs a higher peak and that leads to much better performance. And instantiation C manages to find and climb a veritable Mount Everest for that fitness landscape. And the company that bought that, publishes this fact by publically announcing how effectively its version of this computer language, as started in a same and standardized form, has evolved in their hands and simply from its own performance and from its own self-directed changes.

• What will the people who run and own the client businesses that purchased instantiations A and B think when they learn of this, and particularly if they see their having acquired this new computer language as having represented a significant up-front costs expenditure for them?

I am going to leave that as an open question here, and will begin to address it in my next series installment. In anticipation of that discussion to come, I will discuss both the business model of the enterprise that develops and markets this tool, and how it would offer this product to market, selling or in some manner leasing its use. And that means I will of necessity discuss the possible role that an acquiring business’ own proprietary data, as processed through this new software, might have helped shape its ontological development in their hands. Then after delving into those and related issues, I will begin to more formally discuss development step 7: artificial intelligence and the software that will come to contain it, and certainly as it is being fundamentally reshaped with the emergence of current and rapidly arriving artificial intelligence agents. Meanwhile, you can find this and related material at Ubiquitous Computing and Communications – everywhere all the time 3, and also see Page 1 and Page 2 of that directory.

Meshing innovation, product development and production, marketing and sales as a virtuous cycle 20

Posted in business and convergent technologies, strategy and planning by Timothy Platt on September 3, 2019

This is my 20th installment to a series in which I reconsider cosmetic and innovative change as they impact upon and even fundamentally shape product design and development, manufacturing, marketing, distribution and the sales cycle, and from both the producer and consumer perspectives (see Ubiquitous Computing and Communications – everywhere all the time 2 and its Page 3 continuation, postings 342 and loosely following for Parts 1-19.)

I have been discussing the issues of what innovation is, and how a change would be identified as being cosmetic or more significant in nature, since Part 16 of this series. And as a core element of that narrative I have been discussing both acceptance of new and of innovation, and pushback and resistance to it, and certainly as members of differing readily definable demographics would make their own determinations here and take their own actions. On the resistance side of this, that has meant by discussing two fundamentally distinctive sources of pushback that can in fact arise and play out either independently and separately from each other or concurrently and for members of the same communities and the same at least potential markets:

• The standard innovation acceptance diffusion curve that runs from pioneer and early adaptors on to include eventual late and last adaptors, and
• Patterns of global flatting and its global wrinkling, pushback alternative.

And I begin this continuation of that discussion thread by briefly repeating two points that I laid out and at least briefly began to develop in Part 19 that will prove of significance here too:

• The standard innovation acceptance diffusion curve and a given marketplace participant’s position on it tends to be more determined by how consumers and potential consumers see New and Different as impacting upon themselves and their immediate families, insofar as they look beyond themselves there to making their purchase and use decisions.
• But global flattening (here viewable as being analogous to pioneer and early acceptance in the above), and global wrinkling (that can be seen as a rough counterpart to late and last adaptors and their responses and actions), are more overall-community and societally based and certainly as buy-in or reject decisions are made.

And social networking can play a role in both, and both in framing how individuals would see and understand an innovation change that confronts them, and in how members of larger communities would see it where that perspective would hold defining sway.

I stated at the end of Part 19 that I would focus here, more on global flattening and wrinkling than on standard innovation diffusion curve dynamics and I will, though more fully addressing the first of those dynamics will of necessity require at least commenting on the second of them too. And with that in mind, and with a flattening versus wrinkling possibility in more specific focus here, I repeat one more organizing point that I will build from in this posting:

• Change and innovation per se, can be disruptive and for both the perceived positives and negatives that that can bring with it. And when a sufficiently high percentage of an overall population primarily see positive, or at worst neutral there, flattening is at least going to be more possible and certainly as a “natural” path forward. But if a tipping point level of overall negative impact-perceived response arises, then the acceptance or resistance pressures that arise will favor wrinkling and that will become a societally significant force and it will represent a significant part of the overall voice for those peoples too.

Unless it is imposed from above, as for example through government embargo and nationalism-based threat, global wrinkling is a product of social networking. And so is the perception of threat of New that it can engender, mainstream and even bring to de facto axiomatic stature across communities. And this brings me directly to the issues and questions of Who and of agendas, that I said at the end of Part 19, that I would at least begin to address here.

Let’s begin by considering the players, and possible players in this, starting with the networking strategies and networker types (as determined by what strategies they use there), that I initially offered in my posting: Social Network Taxonomy and Social Networking Strategy.

I began that posting by classifying basic responses to networking opportunities as fitting into four categories:

• Active networkers – people who are seeking to expand their connections reach and really connect with their contacts to exchange value.
• Passive networkers – people who may or may not be looking to expand their networkers and who primarily wait for others to reach out to connect with them.
• Selective networkers – people who are resistant to networking online with anyone who they are not already actively connected with and networking with by other means.
• Inactive networks – people who may very well lean towards selective networking as defined above or tend to be passive networkers when working on their networks but who are not doing so, at least now.

For purposes of this discussion, I set aside the fourth of those groups: inactive networkers as people who are not likely to be strongly influenced by community sourced pressures towards either global flattening and buying into New, or global wrinkling and messages favoring resistance to New – and certainly if those messages come from strangers. Selective networkers who do in fact connect with and more actively communicate with people who they already know and respect, might be significantly influenced one way or the other if their current contacts bring them into this. And the same holds for passive networkers. And with all of this noted, I would argue that it is the active networkers in a community who drive change, and both for its acceptance or rejection.

Looking to those active networkers, I divided the more actively engaged among them into a further set of groups depending on their particular networking strategies followed:

• Hub networkers – people who are well known and connected at the hub of a specific community with its demographics and its ongoing voice and activities.
• Boundary networkers or demographic connectors – people who may or may not be hub networkers but who are actively involved in two or more distinct communities and who can help people connect across the boundaries to join new communities.
• Boundaryless networkers (sometimes called promiscuous networkers) – people who network far and wide, and without regard to community boundaries. These are the people who can seemingly always help you find and connect with someone who has unusual or unique skills, knowledge, experience or perspective and even on the most obscure issues and in the most arcane areas. And for purposes of this line of discussion, these are the people who can and do bring otherwise outliers into larger community discussions and in ways that can spread emerging shared opinions too.

The important point here, is that people have to widely connect to influence, and if not through one directional message broadcasting, then through more two and multi-directional conversation. That does not mean that any and every hub, boundary, or boundaryless networker is widely influential and opinion shaping: only that those who are more widely influential are also usually widely connected in one way or other too. And this is where agendas enter this narrative. Who is so connected? And what if anything are their agendas that would lead them to seek to shape public opinion, and certainly on matters of community response to change and its possibilities?

• Pushback and resistance and the global wrinkling that it would promote, more generally come from those who seek to preserve a status quo, or to restore what to them is a lost but not forgotten, perhaps idealized order.
• Open acceptance and the global flattening that it would promote, come from those who see greater promise in moving past the current and former realities that they see evidence of around themselves. And this is driven by a desire to join in and to move beyond the perhaps marginalizing impact of separation and even parochial isolation that wrinkling can lead to, and a desire to not be left behind as others advance into New and into new opportunities around them.

I offer this as a cartoonishly oversimplified stereotypical representation, but ultimate all join-and-engage moves towards global flattening and all reactive pushback to that, are driven by readily shared simplifications: all such movements become encapsulated in and bounded by slogans and related simplifying and even simplistic shorthand.

To add one more crucially important factor into this narrative here:

• Pushback and resistance to change and to New and certainly to foreign-sourced new, come for the most part from those who face pressures to change and adapt in the face of that New, where their judgments on this are driven by their fears of the foreign and of the different.
• But pressures towards global flattening can and generally do come from multiple directions, with the communities that face this New only serving as one source of that. Equally large and impactful pressures can come from the original sources of the New that is in play there, and that they might be very actively seeking new markets for. And the active networkers and the engaged broadcast format information sharing channels that they use in their promotion of open markets and global flattening can be very important here too.

I am going to continue this line of discussion in a next series installment, where I will more directly and fully discuss business stakeholders as just touched on there, as change accepting and even change embracing advocates. And I will discuss the roles of reputation and history in all of that. Meanwhile, you can find this and related postings and series at Business Strategy and Operations – 5, and also at Page 1, Page 2, Page 3 and Page 4 of that directory. And see also Ubiquitous Computing and Communications – everywhere all the time and its Page 2 and Page 3 continuations.

Reconsidering Information Systems Infrastructure 11

This is the 11th posting to a series that I am developing, with a goal of analyzing and discussing how artificial intelligence and the emergence of artificial intelligent agents will transform the electronic and online-enabled information management systems that we have and use. See Ubiquitous Computing and Communications – everywhere all the time 2 and its Page 3 continuation, postings 374 and loosely following for Parts 1-10. And also see two benchmark postings that I initially wrote just over six years apart but that together provided much of the specific impetus for my writing this series: Assumption 6 – The fallacy of the Singularity and the Fallacy of Simple Linear Progression – finding a middle ground and a late 2017 follow-up to that posting.

I conceptually divide artificial intelligence tasks and goals into three loosely defined categories in this series. And I have been discussing artificial intelligence agents and their systems requirements in a goals and requirements-oriented manner that is consistent with that, since Part 9 with those categorical types partitioned out from each other as follows:

• Fully specified systems goals and their tasks (e.g. chess with its fully specified rules defining a win and a loss, etc. for it),
• Open-ended systems goals and their tasks (e.g. natural conversational ability with its lack of corresponding fully characterized performance end points or similar parameter-defined success constraints), and
• Partly specified systems goals and their tasks (as in self-driving cars where they can be programmed with the legal rules of the road, but not with a correspondingly detailed algorithmically definable understanding of how real people in their vicinity actually drive and sometimes in spite of those rules: driving according to or contrary to the traffic laws in place.)

And I have focused up to here in this developing narrative on the first two of those task and goals categories, only noting the third of them as a transition category, where success in resolving tasks there would serve as a bridge from developing effective artificial specialized intelligence agents (that can carry out fully specified tasks and that have become increasingly well understood and both in principle and in practice) to the development of true artificial general intelligence agents (that can carry out open-ended tasks and that are still only partly understood for how they would be developed.)

And to bring this orienting starting note for this posting, up to date for what I have offered regarding that middle ground category, I add that I further partitioned that general category for its included degrees of task performance difficulty, in Part 10, according to what I identify as a swimming pool model:

• With its simpler, shallow end tasks that might arguably in fact belong in the fully specified systems goals and tasks category, as difficult entries there, and
• Deep end tasks that might arguably belong in the above-repeated open-ended systems goals and tasks category.

I chose self-driving vehicles and their artificial intelligence agent drivers as an intermediate, partly specified systems goal because it at least appears to belong in this category and with a degree of difficulty that would position it at least closer to the shallow end than the deep end there, and probably much closer.

Current self-driving cars have performed successfully (reaching their intended destinations and without accidents) and both in controlled settings and on the open road and in the presence of actual real-world drivers and their driving. And their guiding algorithms do seek to at least partly control for and account for what might be erratic circumambient driving on the part of others on the road around them, by for example allowing extra spacing between their vehicles and others ahead of them on the road. But even there, an “aggressive” human driver might suddenly squeeze into that space, and without signaling that they would change lanes, suddenly leaving a self-driving vehicle following too closely too. So this represents a task that might be encoded into a single if complex overarching algorithm, as supplemented by a priori sourced expert systems data and insight, based on real-world human driving behavior. But it is one that would also require ongoing self-learning and improvement on the part of the artificial intelligence agent drivers involved too, and both within these specific vehicles and between them as well.

• If all cars and trucks on the road were self-driving and all of them were actively sharing action and intention information with at least nearby vehicles in that system, all the time and real-time, self-driving would qualify as a fully specified systems task, and for all of the vehicles on the road. As soon as the wild card of human driving enters this narrative, that ceases to hold true. And the larger the percentage of human drivers actively on the road, the more statistically likely it becomes that one or more in the immediate vicinity of any given self-driving vehicle will drive erratically, making this a distinctly partly specified task challenge.

Let’s consider what that means in at least some detail. And I address that challenge by posing some risk management questions that this type of concurrent driving would raise, where the added risk that those drivers bring with them, move this out of a fully specified task category:

• What “non-standard” actions do real world drivers make?

This would include illegal lane changes, running red lights and stop signs, illegal turns, speeding and more. But more subtly perhaps, this would also include driving at, for example, a posted speed limit but under road conditions (e.g. in fog or during heavy rain) where that would not be safe.

• Are there circumstances where such behavior might arguably be more predictably likely to occur, and if so what are they and for what specific types of problematical driving?
• Are there times of the day, or other identifiable markers for when and where specific forms of problematical driving would be more likely?
• Are there markers that would identify problem drivers approaching, and from the front, the back or the side? Are there risk-predictive behaviors that can be identified before a possible accident, that a self-driving car and its artificial intelligence agent can look for and prepare for?
• What proactive accommodations could a self-driving car or truck make to lessen the risk of accident if, for example its sensors detect a car that is speeding and weaving erratically from lane to lane in the traffic flow, and without creating new vulnerabilities from how it would respond to that?

Consider, in that “new vulnerabilities” category, the example that I have already offered in passing above, when noting that increasing the distance between a self-driving car and a vehicle that is directly ahead of it, might in effect invite a third driver to squeeze in between them, and even if that meant it was now tailgating that leading vehicle and the self driving car that would now be behind it was tailgating it. A traffic light ahead, suddenly changing to red, or any other driving circumstance that would force the lead car in all of this to suddenly hit their brakes could cause a chain reaction accident.

What I am leading up to here in this discussion is a point that is simple to explain and justify in principle, even as it remains difficult to operationally resolve as a challenge in practice:

• With the difficulty in these less easily rules-defined challenges increasing, as the tasks that they would arise in it fit into deeper and deeper areas of that swimming pool in my above-cited analogy.

Fully specified systems goals and their tasks might be largely or even entirely deterministic in nature and rules determinable, where condition A always calls for action and response B, or at least a selection from among a specifiable set of particular such actions that would be chosen from, to meet the goals-oriented needs of the agent taking them. But partly specified systems goals and their tasks are of necessity significantly stochastic in nature, and with probabilistic evaluations of changing task context becoming more and more important as the tasks involved fit more and more into the deep end of that pool. And they become more open-endedly flexible in their response and action requirements too, no longer fitting cleanly into any given set of a priori if A then B rules.

Airplanes have had autopilot systems for years and even for human generations now, with the first of them dating back as far as 1912: more than a hundred years ago. But these systems have essentially always had human pilot back-up if nothing else, and have for the most part been limited to carrying out specific tasks, and under circumstances where the planes involved were in open air and without other aircraft coming too close. Self-driving cars have to be able to function in crowded roads and without human back-up – and even when a person is sitting behind the wheel, where it has to be assumed that they are not always going to be attentive to what the car or truck is doing, taking its self-driving capabilities for granted.

And with that noted, I add here that this is a goal that many are actively working to perfect, at least to a level of safe efficiency that matches the driving capabilities of an average safe driver on the road today. See, for example:

• The DARPA autonomous vehicle Grand Challenge, and
• Burns, L.D. and C Shulgan (2018) Autonomy: the quest to build the driverless car and how that will reshape the world. HarperCollins.

I am going to continue this discussion in a next series installment where I will turn back to reconsider open-ended goals and their agents again, and more from a perspective of general principles. Meanwhile, you can find this and related postings and series at Ubiquitous Computing and Communications – everywhere all the time and its Page 2 and Page 3 continuations. And you can also find a link to this posting, appended to the end of Section I of Reexamining the Fundamentals as a supplemental entry there.

Moore’s law, software design lock-in, and the constraints faced when evolving artificial intelligence 8

Posted in business and convergent technologies, reexamining the fundamentals by Timothy Platt on August 22, 2019

This is my 8th posting to a short series on the growth potential and constraints inherent in innovation, as realized as a practical matter (see Reexamining the Fundamentals 2, Section VIII for Parts 1-7.) And this is also my fifth posting to this series, to explicitly discuss emerging and still forming artificial intelligence technologies as they are and will be impacted upon by software lock-in and its imperatives, and by shared but more arbitrarily determined constraints such as Moore’s law (see Parts 4-7.)

I focused, for the most part in Part 7 of this series, on offering what amount to analogs to the simplified assumption Thévenin circuits of electronic circuit design. Thévenin’s theorem and the simplified and even detail-free circuits that they specify, serve to calculate and mimic the overall voltage and resistance parameters for what are construed to be entirely black-box electronic systems with their more complex circuitry, the detailed nature of which are not of importance in that type of analysis. There, the question is not one of what that circuitry specifically does or how, but rather of how it would or would not be able to function with what overall voltage and resistance requirements and specifications in larger systems.

My simplified assumption representations of Part 7 treated both brain systems and artificial intelligence agent systems as black box entities and looked at general timing and scale parameters to both determine their overall maximum possible size, and therefore their maximum overall complexity, given the fact that any and all functional elements within them would have larger than zero minimum volumes, and well as minimal time-to-task-completion requirements for what they would do. And I offered my Part 7 analyses there as first step evaluations of these issues, that of necessity would require refinement and added detail to offer anything like actionable value. Returning briefly to consider the Thévenin equivalents that I just cited above, by way of analogous comparison here, the details of the actual circuits that would be simplistically modeled there might not be important to or even germane to the end-result Thévenin circuits arrived at, but those simplest voltage and resistance matching equivalents would of necessity include within them, the cumulative voltage and resistance parameters of all of that detail in those circuit black boxes, even if as they would be rolled into overall requirement summaries for those circuits as a whole.

My goal for this posting is to at least begin to identify and discuss some of the complexity that would be rolled into my simplified assumptions models, and in a way that matches how a Thévenin theorem calculation would account for internal complexity in its modeled circuits’ overall electrical activity and requirements calculations, but without specifying their precise details either. And I begin by considering the functional and structural nodes that I made note of in Part 7 and in both brain and artificial intelligence agent contexts, and the issues of single processor versus parallel processing systems and subsystems. And this, of necessity means considering the nature of the information processing problems to be addressed by these systems too.

Let’s start this by considering the basic single processor paradigm and Moore’s law, and how riding that steady pattern of increased circuit complexity in any given overall integrated circuit chip size, has led to capacity to perform more complex information processing tasks and to do so with faster and faster clock speeds. I wrote in Part 7 of the maximum theoretical radius of a putative intelligent agent or system: biological and brain base, or artificial and electronic in nature, there assuming that a task could be completed, as a simplest possibility just by successfully sending a single signal at the maximum rate achievable in that system, in a straight line and for a period of time that is nominally assumed necessary to complete a task there. Think of increased chip/node clock speed here, as an equivalent of adding allowance for increased functional complexity into what would actually be sent in that test case signal, or in any more realistic functional test counterparts to it. The more that a processor added into this as an initial signal source, can do in a shorter period of time, in creating meaningful and actionable signal content to be so transmitted, the more functionally capable the agent, or system that includes it can be and still maintain a set maximum overall physical size.

Parallelism there, can be seen as a performance multiplier: as an efficiency and speed multiplier there, and particularly when that can be carried out within a set, here effectively standardized volume of space so as to limit the impact of maximum signal speeds in that initial processing as a performance degrader. Note that I just modified my original simplest and presumably fastest and farthest physically reaching, maximum size allowing example from Part 7 by adding in a signal processor and generator at its starting point. And I also at least allow for a matching node at the distant signal receiving end of this too, where capacity to do more and to add in more signal processing capacity at one or both ends of this transmission, and without increase in timing requirements for that added processing overhead, would not reduce the effective maximum physical size of such a system, in and of itself.

Parallel computing is a design approach that specifically, explicitly allows for such increased signal processing capacity, and at least in principle without necessarily adding in new timing delays and scale limitations – and certainly if it can be carried out within a single chip, that fits within the scale footprint of whatever single processor chip that it might be benchmark compared to.

I just added some assumptions into this narrative that demand acknowledging. And I begin doing so here by considering two types of tasks that are routinely carried out by biological brains, and certainly by higher functioning ones as would be found in vertebrate species: vision and the central nervous system processing that enters into that, and the information processing that would enter into carrying out tasks that cannot simply be handled by reflex and that would as such, call for more novel and even at least in-part, one-off information processing and learning.

Vision, as a flow of central nervous system and brain functions, is an incredibly complex process flow that involves pattern recognition and identification and a great deal more. And it can be seen as a quintessential example of a made for parallel processing problem, where an entire visual field can be divided into what amounts to a grid pattern that maps input data arriving at an eye to various points on an observer’s retina, and where essentially the same at least initial processing steps would be called for, for each of those data reception areas and their input data.

I simplify this example by leaving specialized retinal areas such as the fovea out of consideration, with its more sharply focused, detail-rich visual data reception and the more focused brain-level processing that would connect to that. The more basic, standardized model of vision that I am offering here, applies to the data reception and processing for essentially all of the rest of the visual field of a vertebrate eye and for its brain-level processing. (For a non-vision comparable computer systems example of a parallel computing-ready problem, consider the analysis of seismic data as collected from arrays of ground-based vibration sensors, as would be used to map out anything from the deep geological features and structures associated with potential petrochemical deposits, or the mapping details of underground fault lines that would hold importance in a potential earthquake context, or that might be used to distinguish between a possible naturally occurring earthquake and a below-ground nuclear weapons test.)

My more one-off experience example and its information processing might involve parallel processing and certainly when comparing apparent new with what is already known of and held in memory, as a speed-enhancing approach, to cite one possible area for such involvement. But the core of this type of information processing task and its resolution is likely to be more specialized, non-parallel processor or equivalent-driven.

And this brings me specifically and directly to the question of problem types faced: of data processing and analysis types and how they can best be functionally partitioned algorithmically. I have in a way already said in what I just wrote here, what I will more explicitly make note of now when addressing that question. But I will risk repeating the points that I made on this by way of special case examples, as more general principles, for purposes of increased clarity and focus and even if that means my being overly repetitious:

• The perfect parallel processing-ready problem is one that can be partitioned into a large and even vastly large set of what are essentially identical, individually simpler processing problems, where an overall solution to the original problem as a whole calls for carrying out all of those smaller t standardized sub-problems and stitching their resolutions together into a single organized whole. This might at times mean fully resolving the sub-problems and then combining them into a single organized whole, but more commonly this means developing successive rounds of preliminary solutions for them and repeatedly bringing them together, where adjacent parallel processing cells in this, serve as boundary value input for their neighbors in this type of system (see cellular automation for a more extreme example of how that need and its resolution can arise.)
• Single processor, and particularly computationally powerful single processor approaches become more effective, and even fundamentally essential as soon as problems arise that need comprehensive information processing that cannot readily be divided up into arrays of similarly structured simpler sub-problems that the individual smaller central processing units, or their biological equivalents, could separately address in parallel with each other, as is the case in my vision example or one of my non-vision computer systems examples as just given.

And this leads me to two open questions:

• What areas and aspects of artificial intelligence, or of intelligence per se, can be parsed into sub-problems that would make parallel processing both possible, and more efficient than single processor computing might allow?
• And how algorithmically, can problems in general be defined and specified, so as to effectively or even more optimally make this type of determination, so that they can be passed onto the right types and combinations of central processor or equivalent circuitry for resolution? (Here, I am assuming generally capable computer architectures that can address more open-ended ranges of information processing problems: another topic area that will need further discussion in what follows.)

And I will of course, discuss all of these issues from the perspective of Moore’s law and its equivalents and in terms of lock-in and its limiting restrictions, at least starting all of this in my nest installment to this series.

The maximum possible physical size test of possible or putative intelligence-supporting systems, as already touched upon in this series, is only one way to parse such systems at a general outer-range parameter defining level. As part of the discussion to follow from here, I will at least briefly consider a second such approach, that is specifically grounded in the basic assumptions underlying Moore’s law itself: that increasing the number of computationally significant elements (e.g. the number of transistor elements in an integrated circuit chip), can and will increase the scale of a computational or other information processing problem that that physical system can resolve within any single set period of time. And that, among other things will mean discussing a brain’s counterparts to the transistors and other functional elements of an electronic circuit. And in anticipation of that discussion to come, this will mean discussing how logic gates and arrays of them can be assembled from simpler elements, and both statically and dynamically.

Meanwhile, you can find this and related material at Ubiquitous Computing and Communications – everywhere all the time 3 and also see Page 1 and Page 2 of that directory. And I also include this in my Reexamining the Fundamentals 2 directory as topics Section VIII. And also see its Page 1.

Innovation, disruptive innovation and market volatility 48: innovative business development and the tools that drive it 18

Posted in business and convergent technologies, macroeconomics by Timothy Platt on August 13, 2019

This is my 48th posting to a series on the economics of innovation, and on how change and innovation can be defined and analyzed in economic and related risk management terms (see Macroeconomics and Business and its Page 2 continuation, postings 173 and loosely following for its Parts 1-47.)

I have been discussing two-organization based, innovation discovery and development scenarios here since Part 43 when I began outlining and analyzing:

• University research labs and the university-as-business systems that they function in, as original sources of new innovation,
• And invention acquiring, for-profit businesses that would buy access to these new and emerging opportunities for development and sale

as organizations of these two types come to work together through technology transfer agreements. And my goal here is to expand that line of discussion to include a wider range of participating organizations. I raised a possibility for that in Part 47, when I made note of how larger companies can and do at least occasionally divest themselves of patents that they hold, but that do not supportively fit into their current or planned business model and its needs. IBM is known for having sold off tens of thousands of patents in that way as they have, as a business, redefined themselves to remain competitively effective in the face of overriding change and its challenges. (See, for example IBM Has Sold Over 15,000 Patents Since 1991; Google is its biggest customer, where this news piece only addresses part of a still larger and longer-term patent divestiture story for this business.)

For purposes of this line of discussion and this posting in it, it does not matter as much what types of businesses are involved in these transactions. It only matters that one of them holds effective control if not direct outright ownership of at least one trade secret, patent protected or otherwise access-limited innovation that another would want to be able to benefit from, and that second organization is both willing and able enough to secure control of this for itself to make a transaction for managing that transfer, a viable option. Under these circumstances, the precise business models and business plans of the enterprises involved, do not particularly matter except insofar as that type of information would add insight into the nature and details of whatever those businesses could negotiate an agreement upon in this specific context. And that means relevant information concerning these businesses that fall into four fairly specific question-defined categories, that are all fundamentally grounded in a same set of operational and strategic concerns:

• What value would the initially owning business gain, or retain if it were simply to maintain tightly controlled, access limited hold over the innovation or innovations in question here?
• What value could it develop and secure from divesting at least contractually specified control over the use or ownership of this innovative potential?
• And from the acquiring business’ perspective, what costs, or loss of positive value creating potential would it face, if it did not gain access to this innovation and on terms that would meet its needs?
• And what positive value would it gain if it did gain access to this, and with sufficient ownership control and exclusivity of use so as to meet its needs?

I posit this entirely in cost and benefits terms, and in terms of risk and benefits where the more disruptively novel the innovations under consideration are in this, the less firm data will be available to calculate a priori, what the actual costs and benefits would be, as efforts are made to answer the above four questions. That places this type of analysis at least significantly in a risk management arena.

Timeframes enter this narrative here, as even the most dramatically new and novel innovation as initially conceived, is going to have a time limited shelf life. And this can be expected to hold true with particular force if an innovation in question offers dramatic new sources, forms or levels of value that would not have been possible before it. As soon as word of its existence gets out, efforts will be made to bypass any ownership or licensure-based access restrictions to what it can do, with duplication of initial discovery and innovation pursued by others and either by directly copying it with knock-offs or through efforts to create similar if analogous product offerings that would capture similar forms and levels of new value, or both. And with this, I cite my above-noted IBM example again. On the whole, the thousands of patents that that company has sold off, have still held value for at least select business-to-business markets and sectors, and for specific types of potentially acquiring businesses. But it is likely that many of them were worth less on an open market of this sort when finally sold off, than they were initially worth. And some of them have undoubtedly fit into a “cut your losses” pattern where it had in fact cost more to initially develop them and secure patent protection over them, than could be fully recouped from their ultimate sale.

I am going to continue this discussion in a next series installment where I will add in the complexities of scale, and for both the divesting, or licensing business and for the acquiring one. And I will also discuss the issues of direct and indirect competition, and how carefully planned and executed transfer transactions here, can in fact create or enhance business-to-business collaboration opportunities too, where that may or may not create monopoly law risks in the process.

Meanwhile, you can find this and related postings at Macroeconomics and Business and its Page 2 continuation. And also see Ubiquitous Computing and Communications – everywhere all the time 3 and that directory’s Page 1 and Page 2.

Reconsidering the varying faces of infrastructure and their sometimes competing imperatives 8: the New Deal and infrastructure development as recovery 2

Posted in business and convergent technologies, strategy and planning, UN-GAID by Timothy Platt on August 4, 2019

This is my 9th installment to a series on infrastructure as work on it, and as possible work on it are variously prioritized and carried through upon, or set aside for future consideration (see United Nations Global Alliance for ICT and Development (UN-GAID), postings 46 and following for Parts 1-7 with its supplemental posting Part 4.5.)

I have, up to here, successively addressed and discussed each of a set of four large scale infrastructure development and redevelopment initiatives in this series, with a goal of distilling out of them, a set of guiding principles that might offer planning and execution value when moving forward on other such programs. And as a core foundational element to this narrative, I began discussing a fifth such case study example in Part 7, that I will continue elaborating upon in at least selective detail here:

• US president Franklin Delano Roosevelt’s New Deal and related efforts to help bring his country out of a then actively unfolding Great Depression.

I focused in Part 7 on some of the underlying causes of the Great Depression, and both for clarifying for purposes of this discussion as to how that historically transformative event arose, and for more clearly stating how and why Roosevelt was challenged as he sought to orchestrate a real recovery from it. And at the end of that posting, and in anticipation of this one to come, I said that I would turn here to quantify the bank failures and their timeline to more fully present the economic challenges faced, as a completion at least for here and now of an underlying cause-oriented discussion as to what motivated a need for the types of infrastructure and related changes that consequentially took place. And I said that I would then discuss Roosevelt’s New Deal as a massive recovery effort, and one that had within it a massive infrastructure redevelopment effort too.

I will in fact delve into those issues as outlined there, continuing my discussion of relevant overall economic background issues in the process, as part of that. But before doing so and to put what follows into clearer perspective, I am going to step back from the specifics of this particular case study to make note of a fundamental aspect of large scale infrastructure development projects and their underlying driving needs in general, that this case study in fact can be seen as highlighting, for its core importance:

• Most people: with a significant proportion included there of the people who would plan out, approve and fund, and carry out large scale infrastructure projects, focus on what is physically built from scratch or replaced with new, and on the visible end-results of infrastructure development per se. This makes sense insofar as these are the aspects of essentially any such large scale initiative that are lastingly visible and that are going to be directly used and long-term.
• But just as importantly, and certainly during early planning and support building stages for realizing such accomplishments, and for when this work is being carried out, are the behind the scenes economic and financing-based considerations and all of the rest of the politically driven decision making processes that are required to make any such development project happen. They shape the What and the When and the How and the By Whom of what in fact can be done, and consideration of them must be included too, in any inventory of what is included in such work, and in any understanding of what is accomplished from it.
• So, for example, in my Great Depression example, I could cite Roosevelt era initiatives such as the Civilian Conservation Corp (CCC) with its explicit physical infrastructure building efforts, and I will in fact do so as I proceed with this case study. That is important here in this case study narrative. But the context and context-building effort that made programs such as the CCC possible, and that made even attempting it challenging, have to be considered and included here too.

In anticipation of my more general comments to come here in this series, regarding infrastructure development as a whole and in general, and with the above points in mind, I offer the following at least preliminary overarching comments:

Infrastructure development, and certainly large scale development and redevelopment projects that would fit into such a rubric, are – or at least should be driven by what can at least categorically be divided into two sometimes competing, sometimes aligned considerations:

• Human needs and meeting them more fully than would be possible absent some given infrastructure effort, and
• Economic and other wherewithal and sustainability factors and how they would be worked within, or adjusted to accommodate new needs and priorities.

The issues that I raised in Part 7 as leading up to and causing the Great Depression, and that made it a true depression and not just a recession, all fit into the second of those bullet pointed considerations. And actually carrying out the programs of Roosevelt’s New Deal and related initiatives, as he conceived them and strove to achieve them as his response to that challenge, was intended to address significant widespread unmet human needs as the Great Depression brought them about.

And with that noted, I turn back to my largely-economics oriented outline of how the Great Depression arose and as a depression: not just as yet another recession, and with a goal of laying a more complete foundation in preparation for a discussion of the “what would be done” side of this, by more fully outlining the societal context that would make that a realistically considered possibility.

I made explicit note of three dates in Part 7 that I would cite again here, as benchmarks for what is to follow. The stock market as tracked on the New York Stock Exchange was seriously challenged on Thursday, October 24, 1929 when it faced what became a catastrophic collapse in value and in underlying investor confidence. And it fell into what amounted to freefall the following Tuesday, October 29: Black Tuesday. And when the US Congress reacted to this and to what immediately followed it from public response, they did so as a reactionary pulling back with the enactment of the Smoot-Hawley Tariff Act on March 13, 1930 – setting off a trade barrier erecting conflict that ultimately largely shut down international trade and not just for the United States.

And to complete this timeline-based set of benchmarks, I add two more dates that should at least be kept in mind for what follows in this posting, including them here to put what I will say in what follows into fuller perspective. And I will make explicit reference to them and to the reality they benchmark, in my next installment to this too. The dates themselves are March 4, 1929 – March 4, 1933: the dates when Herbert Hoover served as the 31st president of the United States. And to round out this dual benchmark entry and at least briefly explain the relevance of it for this narrative, Hoover was elected at a time when most everyone, and both among the general public and among the nation’s leading economists, tacitly assumed that prosperity was there to stay, as an essentially immutable, reliable reality. Then half a year later the United States economy and in fact much of the overall world economy began to collapse. Hoover tried course correcting from this through presidential policy, and he tried leading a recovery from it, in an effort that lasted until late winter, 1933 when Roosevelt was sworn into office and this became his problem.

What, in at least selective numerical detail, was the challenge that Hoover faced and that Roosevelt inherited? Let’s start addressing that question with consideration of the banking system in the United States and on what happened there. Starting from the two October 1929 benchmark dates just cited, and looking out through the first ten months of 1930, a total of 744 US banks failed in the United States: 10 times as many as did in the corresponding 1928-29 period as a closest point of pre-depression comparison. And when trade barrier walls really began to spread, as more and more erstwhile national trading partners took retaliation against the new tariffs that they suddenly faced, by imposing tariffs of their own, this pace accelerated. In all, 9,000 US banks failed during the decade of the 1930’s, and close to 4,000 of them did so in one year alone: 1933. By the start of 1933, depositors had already seen approximately $140 billion of their invested wealth: their life savings disappear through bank failures, independently of any loss faced through failures of the stock market, or from lost income as more and more businesses that they had worked for, scaled back and let employees go, or failed outright themselves.

To put those numbers and bank and business failures in perspective, and to add an impact indicating scale to that, consider the following (with data drawn from U.S. GDP by Year Compared to Recessions and Events:

Year Reported Nominal GDP
($trillions)
GDP ($trillions) GDP Growth Rate % Benchmark Events
1929 0.105 1.109 NA Depression began
1930 0.092 1.015 -8.5 Smoot-Hawley
1931 0.077 0.950 -6.4 Dust Bowl began
1932 0.060 0.828 -12.9 Hoover tax hikes
1933 0.057 0.817 -1.2 New Deal
1934 0.067 0.906 10.8 U.S. debt rose
1935 0.074 0.986 8.9 Social Security started
1936 0.085 1.113 12.9 FDR tax hikes
1937 0.093 1.170 5.1 Depression returned
1938 0.087 1.132 -3.3 Depression ended
1939 0.093 1.222 8.0 WWII began; Dust Bowl ended

Notes, clarifying the events listed in this table for their meaning and relevance here (focusing on entries not already at least briefly discussed:

• The Dust Bowl was an environmental disaster brought about by prolonged drought in the heart of one of the primary agricultural regions in the United States, at a time when the banking system in place was so stressed and limited that it could not offer financial support to farmers, and at a time when the US federal government was unable, unwilling or both, to provide any significant relief. This was all exacerbated by, and in several respects even caused by widespread use of farming practices that did not and could not sustainably work, and certainly under the ongoing drought conditions faced.
• President Herbert Hoover attempted to help pull the United States out of a recession, turned Great Depression, by imposing with Congressional support, a tax increase bill that if anything worsened matters.
• President Roosevelt, pushing back against the concern and even fear of raising taxes and of even attempting tax reform during the Great Depression, and certainly given the outcomes of Hoover’s 1932 effort, decided to reframe and reattempt this basic economy-impacting, government financing approach with his own tax reform: his Revenue Act of 1936.

To put those numbers and their impact into more individually human terms, during the Great Depression the United States as a whole was hit with extremely high unemployment rates. By 1933, the overall national unemployment rate had climbed from 3% (as measured just prior to the October 1929 stock market collapse), up to to 25%. By 1932, over 13 million Americans had lost their jobs. And between late 1929 and late 1932, average incomes were reduced by 40% (see The Great Depression Facts, Timeline, Causes, Pictures.)

And Franklin Delano Roosevelt was sworn into office as the 32nd president of the United States on March 4, 1933. And one of his first acts in office was to formally start a 100 day collaboration with the US Congress that has become known as the 100 Days Congress, for the amount of and far reaching variety of legislation that was debated, voted upon and passed during that fast start to Roosevelt’s first term in office (see First 100 Days of Franklin D. Roosevelt’s Presidency.) And that is where the “what was done” in his response to the Great Depression really began. And I will continue this narrative in a next series installment with an at least selective discussion of that.

Meanwhile, you can find this and related postings and series at Business Strategy and Operations – 5, and also at Page 1, Page 2, Page 3 and Page 4 of that directory. I also include this in Ubiquitous Computing and Communications – everywhere all the time 3, and also see Page 1 and Page 2 of that directory. And I include this in my United Nations Global Alliance for ICT and Development (UN-GAID) directory too for its relevance there.

Rethinking national security in a post-2016 US presidential election context: conflict and cyber-conflict in an age of social media 16

Posted in business and convergent technologies, social networking and business by Timothy Platt on July 20, 2019

This is my 16th installment to a series on cyber risk and cyber conflict in a still emerging 21st century interactive online context, and in a ubiquitously social media connected context and when faced with a rapidly interconnecting internet of things among other disruptively new online innovations (see Ubiquitous Computing and Communications – everywhere all the time 2 and its Page 3 continuation, postings 354 and loosely following for Parts 1-15.)

I have been developing this series as an ongoing connected narrative, building next installments on prior ones. And this posting, as such is a direct continuation of the immediately preceding one offered here (see Part 15.) But this is also a direct continuation of the two installments that immediately preceded that too: Part 13 and Part 14. So to put this posting into clearer perspective, I would at least briefly note what I offered in those three postings, and how they relate to each other.

I have recently been at least selectively exploring Russian history, as a basis for understanding their current and at least near-future anticipatable national defense activities, and certainly as they would arise in a cyber-context (as Parts 13 and 14.) And my goal for that was to offer a longer-term perspective on the concerns and the fears of the Russian people and of their succession of governments as have held power over the centuries, in the face on an ongoing succession of invasions from the outside and of threats of their happening.

I began that narrative with the 12th century Mongol invasions of what is now a part of Russia in Part 13, and continued that up to the beginning of the post-World War II, Cold War between the Soviet Union and its allies of the Warsaw Pact, and the West as led by the United States and Western Europe with their NATO alliance (in Part 14.) And my initial thought for Part 15 was to simply continue that historical narrative up to the present, as a foundation piece for more fully understanding Russia’s current behavior as that nation acts both defensively and offensively in cyber-threat and cyber-warfare contexts. But to put that continuation of narrative into clearer perspective, I decided to step back first, offering a wider perspective framework for considering essentially any specific case in point examples that I might offer here, as to how cyber-weapons might be developed and used. I decided to offer a more general approach to thinking about challenges of the type that Russia creates, as it reactively responds and proactively acts given its history and the assumptions that would create for its leadership, as they at least consider risking “politics by other means” to quote von Clausewitz.

More specifically, I decided to in effect, jump ahead in Part 15 to more directly consider how the type of historical narrative that I have been developing here can offer actionable insight into how perceived national threats are understood, prepared for and responded to in a more current here-and-now setting. And then I would complete my Russian based case in point example, with this commonly held basic understanding and its related defense doctrines in mind. So I briefly outlined what I would argue to be the commonest and even an essentially universally followed cyber-doctrine as at least appears to be followed in practice, and very widely so, and regardless of any collateral side-commentary that might be offered as it is pursued, as to its limitations in practice. And I proposed offering a more proactive alternative to that approach there too, which I will outline in detail in upcoming installments after completing my Russian case study example.

And with that noted, I would suggest at least briefly reviewing Parts 13 and 14 for the historical narrative that I will continue building from here, to put this posting into a fuller timeline perspective if nothing else. And I suggest that you at least briefly review Part 15 for its approach to thinking about, planning for and reacting to cyber-threats and even overt cyber offensives, where the essentially-doctrine level approach that I outline there reflects the realities created by and faced by all involved parties, and certainly as if this writing – and where expectations of that have come to shape today’s Russia’s cyber-doctrine as they have developed one for a more militarized context.

I begin this posting’s main line of discussion here by briefly summarizing a few points from the end of Part 14 and building from there, beginning in the 1940’s. Russia: the then Soviet Union, suffered wide-ranging devastation from Nazi Germany’s invasion of their lands during World War II. Up until then, the most impactfully wide-spread invasion that the country had faced, was quite arguably Napoleon’s invasion of Mother Russia in 1812. All out relentless warfare on the part of the Nazis, coupled with the Soviet Union’s own scorched earth policy to deny those invaders any resources that they might capture and use against Mother Russia, led to an essential annihilation of all infrastructure, including agricultural capabilities and food supplies, for most all of Western Russia, and all the way east to the outskirts of Moscow itself. Villages, towns and even entire large cities were all but razed to the ground from the thoroughness of their destruction. Critically important agricultural lands were overrun and food supplies destroyed. And in the course of this war and as a direct consequence of it, and as a result of failed government policy and actions in Russia leading up to this war, from Stalin’s effort to essentially recreate the nation in a new communist image, tens of millions of Russia’s people died.

No one in fact actually knows the real numbers involved there, and even from the records kept in Moscow by the Soviet government itself, for its own directly carried out activities prior to the war. But best estimates tend to conclude that some 20 million people died in Russia in the years immediately leading up to World War II, and particularly from their government’s failures in their attempts to move virtually all of their food production to communist party controlled collective farms, with the annihilation of Russia’s small landholder Kulak farmers carried out in the process. Russia killed the people who had raised their food and who knew what to plant where and when if they were to plant and harvest successfully. And they put Party apparatchiks in managerial oversight for those critical decisions, who were mostly giving their orders from Moscow, up to thousands of miles away and who knew nothing about actual farming. And then the war happened, and Moscow and Berlin signed a non-aggression treaty – and Berlin’s Nazi government then invaded. And current estimates would suggest that at least 27 million more Russians died from that war and from the starvation and other massively scaled challenges that it brought with it.

According to official Soviet figures, in 1939, the total population of the Soviet Union was 109.3 million people. This would put their total from before Stalin’s farm collectivization and related failures at something closer to 127 million, allowing for an overestimation of up to 2.3 million lives lost during those lean and challenging years (which would be an overstatement here.) But using that number as a here-conservative baseline, and assuming an actual loss of 27 million more from the war, Stalin’s government in Moscow faced the end of World War II with a largely devastated nation and with a population that had been reduced in a few short decades by some 35%! And while the nations of Western Europe were heavily damaged by the war, the United States was left intact from that, and all geared up for war-time level military production and action. And the United States now had the atomic bomb too.

• The Russian government had signed a mutual support treaty with the West as allies in conflict with a shared enemy: Germany’s Nazi government and their armed forces. But the same people in the same Russian government had signed a treaty with Hitler’s government: the Molotov–Ribbentrop Pact, and consider how that was violated.
• So Russia took advantage of the post-war agreement with their Western allies as to which victorious national partner in that treaty would oversee what lands and what nations of Europe: East and West. And they actively sought to bring the nations of Eastern Europe under their control as vassal states, under the organizing structure of what quickly formally became their Warsaw Pact. And the West, alarmed by that, began responding almost as quickly to develop a mutual defense against any possible further expansion of the Soviet sphere of influence and control, by initiating their NATO alliance. And this led to increased suspicion and fear on both sides, and the positive feedback of fear and reaction to it and still more increased fear, that quickly became the Cold War and with anti-Soviet, anti-communist rhetoric dominating the public image of one side and anti-western anti-imperialist rhetoric dominating the public image on the other – and with all presuming the worst from all of this.
• And that was the world that Vladimir Vladimirovich Putin was born into. This was the world context and his country’s context within that, that set the basic axiomatic assumptions that he was born into and that have underlied his thoughts and his decision making since then.

Putin was born on October 7, 1952 in the city of Leningrad: a city that was essentially annihilated – leveled to the ground from siege attacks in the war that had only so recently ended. He studied law at the Leningrad State University, graduating in 1975, and according to the official narrative as offered by his government he was recruited into the Russian KGB soon after graduating. More realistically, he was probably in fact initially approached by the KGB for this, before he graduated. College and university faculty were paid by that agency to scout out potential candidates for recruitment and young Putin showed the political reliability, the intelligence, and yes – the ruthlessness and the ambition needed to succeed as an agent of the state, and as a working member of their principle security organization.

Putin joined the KGB in and trained at their 401st KGB school in Okhta, Leningrad (which has a Facebook page now, as of this writing.) After completing his training there, he was assigned to work at the Second Chief Directorate (which was primarily responsible for internal security matters), to carry out counter-intelligence activities. Then after an initial period there he was transferred to work at the First Chief Directorate, for whom he monitored the activities of foreigners and consular officials in Leningrad. Then in September 1984, he was sent to Moscow for further training at the Yuri Andropov Red Banner Institute: their premier training school for spy craft and espionage, and the active side of their national intelligence system as that faces outward to the world at large.

He graduated from that training facility in 1985 and was assigned to East Germany from then until 1990, where he carried identification papers as being an officer in the East German Ministry for State Security (Ministerium für Staatssicherheit, MfS) or State Security Service (Staatssicherheitsdienst, SSD), commonly known as the Stasi. And this is where this briefly and selectively stated biographical note concerning Russia’s current leader, specifically connects with this historical to current events case study example as developed here for purposes of this series.

Putin served as a KGB agent in Dresden, East Germany 1985 to 1990, using a cover identity as a translator. He is fluent in Russian, German and Swedish so this was a realistic cover for him to use. I have read that he and his colleagues there, were reduced to “mainly to collecting press clippings, thus contributing to the mountains of useless information produced by the KGB.” And that would make sense if he and those colleagues could not find any direct new sources of confidential information from any informants or others, that would not be publically known. But a great many of the KGB and at least some critical parts of the Stasi records of that period were in fact destroyed. And according to Putin’s official biography, he himself burned KGB files during the fall of the Berlin Wall (starting November 9, 1989) to keep them out of the hands of demonstrators. For a fragment of what is left of the Putin record from this time, see this PDF file of relevant surviving documents: Stasi Documents about Vladimir Putin.)

Vladimir Putin served in his nation’s KGB for six years and finally ended that part of his professional life by resigning from the service – officially on August 20, 1991 on the second day of the uprising that overthrew the presidency of Mikhail Gorbachev and ended the communist led Union of Soviet Socialist Republics. He resigned from service with the rank of Lieutenant Colonel (Подполко́вник – Podpolkóvnik.) So it is unlikely that he only collected and forwarded newspaper clippings while serving for his government in East Germany: the Warsaw Pact nation member that Russia feared more than any other, and that they most actively sought to control and dominate, and certainly since their recent Nazi Germany history with its peoples.

• Six years and a few months: less than a career, but a duration and intensity of experience that could shape a life for all that follows.

And what did Putin learn from this? What of this in effect entered his DNA for its depth of subsequent influence upon him? The motto of the KGB was Loyalty to the Party – Loyalty to Motherland (Верность партии – Верность Родине.) And the KGB that he served in blended defense and offense, and proactive offense where that might create advantage, as its basic modus operandi. Threats and both internal within Russia and external, facing inwards toward the Motherland are real and implacable and must be dealt with and as forcefully as needed. And part of what the Vladimir Putin of today, seeks to do is to reestablish the old protective buffer zone, or at least part of it, as that reached its greatest scope under the Warsaw Pact and certainly when considering Western threats. And as a continuation of old approaches of developing such protective buffer zones, the Putin Policy as it has emerged, also calls for the creation of what amount to cyber buffer zones too: areas of Russian dominating cyber influence and control.

Russia’s recent expansion into neighboring territory in the Ukraine at the very least, to reestablish physical world territorial buffer zones there, exemplified the more traditional side to this Putin policy. But his intelligence services, as supported by armies of non-Russian agents in fact, have also carried out extensive defensive and overtly offensive cyber-warfare actions too, and both to disorganize and weaken foreign opposition and potential foreign adversaries, and to build a cyber buffer zone to protect their own interests. And as their incursion into the Ukraine, with its mixed, traditional military plus “local” militia, plus cyber-warfare elements illustrates, Russia is actively developing and testing hybrid defensive and offensive strategies and doctrines for planning and carrying them out.

I am going to at least briefly and selectively discuss this Putin Defense Policy (as I would explicitly name it for purposes of this series) and its implementation in hybrid, traditional military plus cyber, defensive and offensive strategies and doctrines in the next installment to this series. Then I will reconsider the traditional reactive de facto cyber doctrine that seems to dominate planning and action today and certainly in the West, and I will at least begin to discuss a more proactive alternative to that, that I have cited as coming in this series. And I will continue from there as outlined at the end of Part 15. Meanwhile, you can find this and related postings and series at Ubiquitous Computing and Communications – everywhere all the time 3, and at Page 1 and Page 2 of that directory. And you can also find this and related material at Social Networking and Business 3 and also see that directory’s Page 1 and Page 2.

Rethinking the dynamics of software development and its economics in businesses 5

Posted in business and convergent technologies by Timothy Platt on July 2, 2019

This is my 5th installment to a thought piece that at least attempts to shed some light on the economics and efficiencies of software development as an industry and as a source of marketable products, in this period of explosively disruptive change (see Ubiquitous Computing and Communications – everywhere all the time 3, postings 402 and loosely following for Parts 1-4.)

I have been working my way through a brief simplified history of computer programming in this series, as a foundation-building framework for exploring that complex of issues, starting in Part 2. And I repeat it here, at least for its key identifying points and in its current form, as I have updated this list since then:

1. Machine language programming
2. And its more human-readable and codeable upgrade: assembly language programming,
3. Early generation higher level programming languages (here, considering FORTRAN and COBOL as working examples),
4. Structured programming as a programming language defining and a programming style defining paradigm,
5. Object-oriented programming,
6. Language-oriented programming,
7. Artificial Intelligence programming, and
8. Quantum computing.

I have in fact already offered at least preliminary orienting discussions in this series, of the first six entries there and how they relate to each other, with each successive step in that progression simultaneously seeking to resolve challenges and issues that had arisen in prior steps there, while opening up new possibilities in its own right.

I will also discuss steps seven and eight of that list as I proceed in this series too. But before I do that and in preparation for doing so, I will step back from this historical narrative to at least briefly start an overall discussion of the economics and efficiencies of software development as they have arisen and developed, and particularly through the first six of those development steps.

I begin that by putting all eight of the technology development step entries of that list into perspective with each other, as they are now perceived, with a goal of at least initially focusing on the first six of them:

• Topic Points 1-5 of the above list all represent mature technology steps at the very least, and Point 6 has deep historical roots, at least as a matter of long-considered principle. And while it is still to be more fully developed and implemented in a directly practical sense, at least current thinking about it would suggest that that will take a more step-by-step evolutionary route that is at least fundamentally consistent with what has come up to now, when and as it is brought into active ongoing use.
• Point 7: artificial intelligence programming has been undergoing a succession of dramatically disruptively novel changes and the scope and reach of that overall effort is certain to expand in the coming years. That noted, it also has old and even relatively ancient roots and certainly by the standards and time frames of electronic computing per se. But it is heading into a period of unpredictably disruptively new. And my discussion of this step in my above-listed progression will reflect that.
• And Point 8: quantum computing, is still, as of this writing, at most just in its early embryonic stage of actual realization as a practical, working source of new computer systems technologies and at both the software and even just the fundamental proof of principle hardware level. So its future is certain to be essentially entirely grounded in what as of this writing would be an emerging disruptively innovative flow of new and of change.

My goal for this installment is to at least briefly discuss something of the economics and efficiencies of software development as they have arisen and developed through the first six of those development steps, where they collectively can be seen as representing a largely historically grounded starting point and frame of reference, for more fully considering the changes that will arise as artificial intelligence agents and their underlying technologies, and as quantum computing and its, come into fuller realization.

And I begin considering that historic, grounding framework and its economics and efficiencies, by setting aside what for purposes of this discussion would qualify as disruptively innovative cosmetics as they have arisen in its development progression to date. And yes, I am referring with that label to the steady flow of near-miraculous technological development that has taken place since the initial advent of the first electronic computers, that within a span of years that is essentially unparalleled in human history for its fast-paced brevity, has led from early vacuum tube computers to single transistor per chip computers to early integrated circuit technology to the chip technology of today that can routinely and inexpensively pack billions of transistor gates onto a single small integrated circuit, and with all but flawless manufacturing quality control perfection.

• What fundamental features or constraints reside in both the earliest ENIAC and similar vacuum tube computers and even in their earlier electronic computer precursors, and also in the most powerful supercomputers of today that can surpass petaflop performance speeds (meaning they’re being able to perform over one thousand million million floating point operations per second), that would lead to fundamental commonalities in the business models and the economics of how they are made?
• What fundamental features or constraints underlie at least most of the various and diverse computer languages and programming paradigms that have been developed for and used on these increasingly diverse and powerful machines, that would lead to fundamental commonalities in the business models and the economics of how they are used?

I would begin approaching questions of economics and efficiencies here, for these widely diverse systems, by offering an at least brief and admittedly selective answer to those questions – noting that I will explicitly refer back to what I offer here when considering artificial intelligence programming and certainly its modern and still-developing manifestations, and when discussing quantum computing too. My response to this set of questions in this context will, in fact service as a baseline starting point, for discussing new issues and challenges that Points 7 and 8 and their emerging technologies raise and will continue to raise.

Computer circuit design and in fact overall computer design have traditionally been largely fixed at least within the design and construction of any given device or system, for computers developed according to the technologies and the assumptions of all of these first six steps. Circuit design and architecture, for example, have always been explicitly developed and built towards, as fixed product development goals that would be finalized before any given hardware that employs it would be built and used. And even in the most actively mutable Point 6: language-oriented programming scenario per se as currently envisioned, a customized programming language and any supportive operating system and other software that would be deployed and used with it, is essentially always going to have been finalized and settled for form and functionality prior to its use, in addressing any given computational or other information processing tasks that it would be developed and used for.

I am, of course, discounting hardware customization here, that in usually comprised of swapping different version, also-predesigned and finalized modules into a standardized hardware framework. Yes, it has been possible to add in faster central processing unit chips out of a suite of different price and different capability offerings that would fit into some single same name-branded computer design. And the same type and level of flexibility, and of purchaser and user choice has allowed for standardized, step-wise increased amounts of RAM memory and cache memory, and of hard drive and other forms of non-volatile storage. And considering this from a computer systems perspective this has meant buyers and users having the option of incorporating in alternative peripherals, specialty chips and even entire add-on circuit boards for specialized functions such as improved graphics and more, and certainly since the advent of the personal computer. But these add-on and upgrade features and options only add expanded functionalities to what are essentially pre-established computer designs with for them, settled overall architectures. The basic circuitry of these computers has never had to capability of ontological change based simply upon how it is used. And that change: a true capability for programming structure-level machine learning and adaptation, are going to become among the expected and even automatically assumed features of Point 7 and Point 8 systems.

My focus in this series is on the software side of this, even if it can be all but impossible to cleanly and clearly discuss that without simultaneously considering its hardware implementation context, so I stress here that computer languages and the code that they convey in specific software instances have been fundamentally set and in similar ways and to similar degrees by the programmers who have developed them, to any hardware lock-in that is built in at least by the assembly floor, a priori to their being loaded into any given hardware platform and executed there, and certainly prior to their being actively used – and even in more dynamically mutable scenarios as envisioned in a Point 6 context.

This fundamental underlying standardization led to and sustained a series of fundamental assumptions, and practices that have collectively gone a long way to shaping both these systems themselves and their underlying economics and their cost-efficiencies:

This has led to the development and implementation of a standardized, paradigmatic approach that has led from initial concept to product design and refinement, prototyping as appropriate, and alpha and beta testing and certainly in any realized software context and its implementations, and with every step of this following what have become well understood and expected cost and returns based financial models. I am not saying here that problems cannot or do not arise, as specific New is and has been developed in this type of system. What I am saying here is that there is a business process and accounting-level microeconomic system that has arisen and that can be followed according to scalable, understandable risk management and due diligence terms. And a big part of that stability comes from the simple fact what when a business, hardware or software in focus, has developed a new product and brings it to market, they are bringing what amounts to a set and settled finalized product to market that they can calculate all costs paid and returns expected to be received from.

The basic steps and performance benchmarks that arise in these business and economic models and process flows, and that are followed in developing these products can and do vary in detail of course, and certainly when considering computer technologies as drawn from different steps in my first five points, above. And the complexity of those steps has gone up, and of necessity as computer systems under consideration have become more complex. But at least categorically, the basic types of business and supportive due diligence steps that I refer to here have become more settled and even in the face of the ongoing technological change that they would manage.

But looking ahead for a moment, consider one step in that process flow, and from a software perspective. What happens to beta testing (as touched upon above) when any given computer system: any given artificial intelligence agent can and most likely will continue to change and evolve and on its own and starting the instant that it is first turned on and running, and with every one of a perhaps very large number of at least initially identical agents, coming to individuate in its own potentially unique ontological development direction? How would this type of change impact upon economic modeling: microeconomic or macroeconomic that might have to be determined for this type of New?

I am going to continue this discussion in my next installment to this series, with at least a brief discussion of the balancing that has to be built for, when managing both in-house business model and financial management requirements for the companies that produce these technologies, and the pressures that they face if they are to be, and if they are remain effective when operating in demanding competitive markets. Then after that I will at least begin to discuss Point 7: artificial intelligence programming, with a goal of addressing the types of questions that I have begun raising here as to business process and its economics. And in anticipation of that, I will add more such questions to complement the basic one that I have just started that line of discussion with. Then I will turn to and similarly address the above Point 8: quantum computing and its complex of issues.

Meanwhile, you can find this and related material at Ubiquitous Computing and Communications – everywhere all the time 3, and also see Page 1 and Page 2 of that directory.

Meshing innovation, product development and production, marketing and sales as a virtuous cycle 19

Posted in business and convergent technologies, strategy and planning by Timothy Platt on June 29, 2019

This is my 19th installment to a series in which I reconsider cosmetic and innovative change as they impact upon and even fundamentally shape product design and development, manufacturing, marketing, distribution and the sales cycle, and from both the producer and consumer perspectives (see Ubiquitous Computing and Communications – everywhere all the time 2 and its Page 3 continuation, postings 342 and loosely following for Parts 1-18.)

I initially offered a set of to-address topics points in Part 16 that I have been discussing since then. And I repeat that list here as I continue doing so, noting in advance that I have in effect been simultaneously addressing its first three points up to here, due to their overlaps:

1. What does and does not qualify as a true innovation, and to whom in this overall set of contexts?
2. And where, at least in general terms could this New be expected to engender resistance and push-back, and of a type that would not simply fit categorically into the initial resistance patterns expected from a more standard cross-demographic innovation acceptance diffusion curve and its acceptance and resistance patterns?
3. How in fact would explicit push-back against globalization per se even be identified, and certainly in any real case-in-point, detail-of-impact example, where the co-occurrence of a pattern of acceptance and resistance that might arise from that might concurrently appear in combination with the types and distributions of acceptance and resistance that would be expected from marketplace adherence to a more standard innovation acceptance diffusion curve? To clarify the need to address this issue here, and the complexities of actually doing so in any specific-instance case, I note that the more genuinely disruptively new an innovation is, the larger the percentage of potential marketplace participants there would be that would be expected to hold off on accepting it and at least for significant periods of time, and with their failure to buy and use it lasting throughout their latency-to-accept periods. But that failure to buy in on the part of these involved demographics and their members does not in and of itself indicate anything as to their underlying motivation for doing so, longer term and as they become more individually comfortable with its particular form of New. Their marketplace activity, or rather their lack of it would qualify more as noise in this system, and certainly when anything like a real-time analysis is attempted to determine underlying causal mechanisms in the market activity and marketplace behavior in play. As such, any meaningful analysis and understanding of the dynamics of the marketplace in this can become highly reactive and after the fact, and particularly for those truly disruptive innovations that would only be expected to appeal at first to just a small percentage of early and pioneer adaptor marketplace participants.
4. This leads to a core question of who drives resistance to globalization and its open markets, and how. And I will address that in social networking terms.
5. And it leads to a second, equally important question here too: how would globalization resistance-based failure to buy in on innovation peak and then drop off if it were tracked along an innovation disruptiveness scale over time?

My primary goal for this series installment is to focus on Points 3 and 4 of that list, but once again, given the overlaps implicit in this set of issues as a whole, I will also return to Part 1 again to add further to my discussion of that as well.

To more formally outline where this discussion is headed, I ended Part 18 with this anticipatory note as to what would follow, at least beginning here:

• I am going to continue this discussion in a next series installment where I will make use of an approach to social network taxonomy and social networking strategy that explicitly addresses the issues of who networks with and communicates with whom, and that also can be used to map out patterns of influence as well: important to both the basic innovation diffusion model and to understanding the forces and the dynamics of global flattening and wrinkling too. In anticipation of that discussion to come, that is where issues of agendas enter this narrative. Then after discussing that, I will explicitly turn to the above-repeated Point 3: a complex of issues that has been hanging over this entire discussion since I first offered the above topics list at the end of Part 16 of this series. And I will address a very closely related Point 4 and its issues too, as already briefly touched upon here.

I will in fact address all of that in what follows in this series. But to set the stage for that, I step back to add another layer of nuance if not outright complexity to the questions and possible answers of what innovation is in this context, and to whom. And I will very specifically use the points that I will make there, in what follows in addressing the issues of the above-added bullet point.

• As a first point that I raise here, a change might arise in its significance to be seen as an innovation because “at least someone might realistically be expected to see it as creating at least some new source or level of value or challenge, however small, at least by their standards and criteria” (with “…value or challenge” offered with their alternative valances because such change can be positively or negatively perceived.)
• But it is an oversimplifying mistake to only consider such changes individually and as if they only arose as in a context-free vacuum. More specifically, a sufficient number of individually small changes: small and even more cosmetic-in-nature innovations, all arriving in a short period of time and all affecting a same individual or group, can have as great an impact upon them and their thinking as a single, stand alone disruptively new innovation would have on them. And when those people are confronted with what they would come to see as an ongoing and even essentially ceaseless flood of New, and even if that just arrives as an accumulating mass of individually small forms of new, they can come to feel all but overwhelmed by it. Context can in fact be essentially everything here.
• Timing and cumulative impact are important here, and disruptive is in the eye of the beholder.

Let’s consider those points, at least to start, for how they impact upon and even shape the standard innovation acceptance diffusion curve as empirically arises when studying the emergence and spread of acceptance of New, starting with pioneer and early adaptors and continuing on through late and last adaptors.

• Pioneer and early adaptors are both more tolerant of and accepting of new and the disruptively new, and more tolerant of and accepting of a faster pace of their arrival.
• Or to put this slightly differently, late and last adaptors can be as bothered by too rapid a pace of new and of change, as they would be bothered by pressure to adapt to and use any particular new innovation too quickly to be comfortable for them, and even just any new more minor one (more minor as viewed by others.)
• Just considering earlier adaptors again here, these details of acceptance or caution, or of acceptance and even outright rejection and resistance stem from how more new-tolerant and new-accepting individuals and the demographics they represent, have a higher novelty threshold for even defining a change in their own thinking as actually being significant enough to qualify as being more than just cosmetic. And they have a similarly higher threshold level for qualifying a change that they do see as being a significant innovation, as being a disruptively new and novel one too.
• What is seen as smaller to the earlier adaptors represented in an innovation acceptance diffusion curve, is essentially certain to appear much larger for later adaptors and for whatever individual innovative changes, or combinations and flows of them that might be considered.

And with that continuation of my Point 1 (and by extension, Point 2) discussions, I turn to consider how a flow of new innovations would impact upon a global flattening versus global wrinkling dynamic.

While most if not all of the basic points that I have just raised here in my standard innovation acceptance curve discussion apply here too, at least insofar as its details can be mapped to corresponding features there too, there is one very significant difference that emerges in the flattening versus wrinkling context:

• Push-back and resistance, as exemplified by late and last adaptors in the standard acceptance curve pattern, is driven by questions such as “how would I use this?” or “why would I need this?”, as would arise at a more individual level. But resistance to acceptance as it arises in a wrinkling context, is driven more by “what would adapting this new, challenge and even destroy in my society and its culture?” It is more a response to perceived societal-level threat.

This is a challenge that is defined at, and that plays out at a higher, more societally based organizational level than would apply to a standard innovation acceptance curve context. And this brings me very specifically and directly to the heart of Point 4 of the above list and the question of who drives resistance to globalization and its open markets, and how. And I begin addressing that by noting a fundamentally important point of distinction:

• Both acceptance of change and resistance of it, in a global flattening and wrinkling context, can and do arise from two sometimes competing, sometimes aligned directions. They can arise from the bottom up and from the cumulative influence of local individuals, or they can arise from the top down.
• And to clarify what I mean there, local and bottom up, and (perhaps) more centralized for source and top down can mean any combination of two things too, as to the nature of the voice and the power of influence involved. This can mean societally shaped and society shaping political authority and message coming from or going to voices of power and influence there. Or this can mean the power of social media and of social networking reach. And that is where I will cite and discuss social networking taxonomies and networking reach and networking strategies as a part of this discussion.

I am going to continue this discussion in a next series installment where I will focus explicitly on the issues and challenges of even mapping out and understanding global flattening and its reactive counterpoint: global wrinkling. And as a final thought for here that I offer in anticipation of that line of discussion to come, I at least briefly summarize a core point that I made earlier here, regarding innovation and responses to it per se:

• Change and innovation per se, can be disruptive and for both the perceived positives and negatives that that can bring with it. And when a sufficiently high percentage of an overall population primarily see positive, or at worst neutral there, flattening is at least going to be more possible and certainly as a “natural” path forward. But if a tipping point level of overall negative impact-perceived response arises, then the acceptance or resistance pressures that arise will favor wrinkling and that will become a societally significant force and it will represent a significant part of the overall voice for those peoples too.

I will discuss the Who of this and both for who leads and for who follows in the next installment to this narrative. Meanwhile, you can find this and related postings and series at Business Strategy and Operations – 5, and also at Page 1, Page 2, Page 3 and Page 4 of that directory. And see also Ubiquitous Computing and Communications – everywhere all the time and its Page 2 and Page 3 continuations.

Reconsidering Information Systems Infrastructure 10

Posted in business and convergent technologies, reexamining the fundamentals by Timothy Platt on June 20, 2019

This is the 10th posting to a series that I am developing, with a goal of analyzing and discussing how artificial intelligence and the emergence of artificial intelligent agents will transform the electronic and online-enabled information management systems that we have and use. See Ubiquitous Computing and Communications – everywhere all the time 2 and its Page 3 continuation, postings 374 and loosely following for Parts 1-9. And also see two benchmark postings that I initially wrote just over six years apart but that together provided much of the specific impetus for my writing this series: Assumption 6 – The fallacy of the Singularity and the Fallacy of Simple Linear Progression – finding a middle ground and a late 2017 follow-up to that posting.

I have been discussing artificial intelligence agents from a variety of perspectives in this series, turning in Part 9 for example, to at least briefly begin a discussion of neural network and related systems architecture approaches to hardware and software development in that arena. And my goal in that has been to present a consistently, logically organized discussion of a very large and still largely amorphous complex of issues, that in their simplest case implementations are coming to be more fully understood, but that are still open and largely undefined when moving significantly beyond that.

We now have a fairly good idea as to what artificial specialized intelligence is and certainly when that can be encapsulated into rigorously defined starter algorithms and with tightly constrained self-learning capabilities added in, that would primarily just help an agent to “random walk” its way towards greater efficiency in carrying out its specifically defined end-goal tasks. But in a fundamental sense, we are still in the position of standing as if at the edge of an abyss of yet to acquire knowledge and insight, when it comes to dealing with genuinely open-ended tasks such as natural conversation, and the development of artificial agents that can master them.

I begin this posting by reiterating a basic paradigmatic approach that I have offered in other information technology development contexts, and both in this blog and as a consultant, that explicitly applies here too.

• Start with the problem that you seek to solve, and not with the tools that you might use in accomplishing that.

Start with the here-artificial intelligence problem itself that you seek to effectively solve or resolve: the information management and processing task that you seek to accomplish, and plan and design and develop from there. In a standard if perhaps at least somewhat complex-problem context and as a simple case ideal, this means developing an algorithm that would encapsulate and solve a specific, clearly stated problem in detail, and then asking necessary questions as they arise at the software level and then the hardware level, to see what would be needed to carry that out. And ultimately that will mean selecting, designing and building at the hardware level for data storage and accessibility, and for raw computational power requirements and related capabilities that would be needed for this work. And at the software level this would mean selecting programming languages and related information encoding resources that are capable of encoding the algorithm in place and that can manage its requisite data flows as it is carried out. And it means actually encoding all of the functionalities required in that algorithm, in those software tools so as to actually perform the task that it specifies. (Here, I presume in how I state this, as a simplest case scenario, a problem that can in fact be algorithmically defined up-front and without any need for machine learning and algorithm adjustment as better and best solutions are iteratively found for the problem at hand. And I arbitrarily represent the work to be done there as fitting into what might in fact be a very large and complex “single overall task”, and even if carrying it out might lead to very different outcomes depending on what decision points have to be included and addressed there and certainly at a software level. I will, of course, set aside these and several other similar more-simplistic assumptions as this overall narrative proceeds and as I consider the possibilities of more complex artificial intelligence challenges. But I offer this simplified developmental model approach here, as an initial starting point for that further discussion to come.)

• Stepping back to consider the design and development approach that I have just offered here, if just in a simplest application form, this basic task-first and hardware detail-last approach can be applied to essentially any task, problem or challenge that I might address here in this series. I present that point of judgment on my part as an axiomatic given and even when ontological and even evolutionary development, as self-organized and carried out by and within artificial agents carrying out this work, is added into the basic design capabilities developed. There, How details might change but overall Towards What goals would not necessarily do so, unless the overall problem to be addressed in changed or replaced.

So I start with the basic problem-to-software-to-hardware progression that I began this line of discussion with, and continue building from there with it, though with a twist and certainly for artificial intelligence oriented tasks that are of necessity going to be less settled up-front as to their precise algorithms as would ultimately be required. I step back from my more firmly stated a priori assumptions as explicitly outlined above in my simpler case problem solving scenario, that I would continue to assume and pursue as-is in more standard computational or data processing task-to-software-to-hardware computational systems analyses, and certainly where off the shelf resources would not suffice, to add another level of detail there.

• And more specifically here, I argue a case for building flexibility into these overall systems and with the particular requirements that that adds to the above development approach.
• And I argue a case for designing and developing and building overall systems – and explicitly conceived artificial intelligence agents in particular, with an awareness of a need for such flexibility in scale and in design from their initial task specifications step in this development process, and with more and more room for adjustment and systems growth added in, and for self-adjustment within these systems added in for each successive development step as carried out from there too.

I focused in Part 9 on hardware, and on neural network designs and their architecture, at least as might be viewed from a higher conceptual perspective. And I then began this posting by positing in effect, that starting with the hardware and its considerations might be compared to looking through a telescope – but backwards. And I now say that a prospective awareness of increasing resource needs, with next systems-development steps is essential. And that understanding needs to enter into any systems development effort as envisioned here, and from the dawn of any Day 1 in developing and building towards it. This flexibility and its requisite scope and scale change requirements, I add, cannot necessarily be anticipated in advance of its actually being needed, and at any software or hardware level, and certainly not in any detail. So I write here of what might be called flexible flexibility: flexibility that itself can be adjusted and updated for type and scope as changing needs and new forms of need arise. So on the face of things, this sounds like I have now reversed course here and that I am arguing a case for hardware then software then problem as an orienting direction of focused consideration, or at the very least hardware plus software plus problem as a simultaneously addressed challenge. There is in fact an element of truth to that final assertion, but I am still primarily just adding flexibility and capacity to change directions of development as needed, into what is still basically a same settled paradigmatic approach. Ultimately, the underlying problem to be resolved has to take center stage and the lead here.

And with that all noted and for purposes of narrative continuity from earlier installments to this series if nothing else, I add that I ended Part 9 by raising a tripartite point of artificial intelligence task characterizing distinction, that I will at least begin to flesh out and discuss here:

• Fully specified systems goals (e.g. chess rules as touched upon in Part 8 for an at least somewhat complex example, but with fully specified rules defining a win and a loss, etc. for it.),
• Open-ended systems goals (e.g. natural conversational ability as more widely discussed in this series and certainly in its more recent installments with its lack of corresponding fully characterized performance end points or similar parameter-defined success constraints), and
• Partly specified systems goals (as in self-driving cars where they can be programmed with the legal rules of the road, but not with a correspondingly detailed algorithmically definable understanding of how real people in their vicinity actually drive and sometimes in spite of those rules: driving according to or contrary to the traffic laws in place.)

My goal here as noted in Part 9, is to at least lay a more detailed foundation for focusing on that third, gray area middle-ground task category in what follows, and I will do so. But to explain why I would focus on that and to put this step in this overall series narrative into clearer perspective, I will at least start with the first two, as benchmarking points of comparison. And I begin that with fully specified systems and with the very definite areas of information processing flexibility that they still can require – and with the artificial agent chess grand master problem.

• Chess is a rigorously definable game as considered at an algorithm level. All games as properly defined involve two players. All involve functionally identical sets of game pieces and both for numbers and types of pieces that those players would start out with. All chess games are played on a completely standardized game board with opposing side pieces positioned to start in a single standard accepted pattern. And opposing players take turns moving pieces on that playing board, with rules in place that would determine who is to make the first move, going first in any given game played.
• The chess pieces that are employed in this all have specific rules associated with them as to how they can be moved on a board, and for how pieces can be captured and removed by an opposing player. And chess games proceed until a player sees that they are one move away from being able to win in which case they declare “check.” Winning by definition for chess always means capturing an opposing player’s king piece. And when they win and with the determination of a valid win fully specified, they declare “checkmate.” And if a situation arises in which both players realize that a definitive formal win cannot be achieved in a finite number of moves from how the pieces that remain in play are laid out in the board, preventing one player from being able to capture their opponent’s king piece and winning, a draw is called.
• I have simplified this description for a few of the rules possibilities that enter into this game when correctly played, omitting a variety of at least circumstantially important details. But bottom line, the basic How of playing chess is fully and readily amenable to being specified within a single highly precise algorithm that can be in place and in use a priori to the actual play of any given chess game.
• Similar algorithmically defined specificity could be offered in explaining a much simpler game: tic-tac toe with its simple and limited range of moves and move combinations. Chess rises to the level of complexity and the level of interest that would qualify it for consideration here because of the combinatorial explosion in the number of possible distinct games of chess that can be played, each carrying out an at least somewhat distinct combination of moves when compared with any other of the overall set. All games start out the same with all pieces identically positioned. After the first set of moves with each player moving once, there are 400 distinct board setups possible with 20 possible white piece moves and 20 possible black piece moves. After two rounds of moves there are 197,742 possible board layouts and after three, that number expands out further to over 121 million. This range of possibilities arises at the very beginning of any actual game with the numbers of moves and of board layouts continuing to expand from there, and with the overall number of moves and move combinations growing to exceed and even vastly exceed the number of board position combinations possible, as differing move patterns can converge on same realized board layouts. And this is where strategy and tactics enter chess and in ways that would be meaningless for a game such as tic-tac toe. And this is where the drive to develop progressively more effective chess playing algorithm-driven artificial agents enters this too, where those algorithms would just begin with the set rules of chess and extend out from there to include tactical and strategic chess playing capabilities as well – so agents employing them can play strongly competitive games and not just by-the-rules, “correct” games.

So when I offer fully specified systems goals as a task category above, I assume as an implicit part of its definition that the problems that it would include all involve enough complexity so as to prove interesting, and that they be challenging to implement and certainly if best possible execution of the specific instance implementations involved in them (e.g. of the specific chess games played) is important. And with that noted I stress that for all of this complexity, the game itself is constrainable within a single and unequivocal rules-based algorithm, and even when effective strategically and tactically developed game play would be included.

That last point is going to prove important and certainly as a point of comparison when considering both open-ended systems goals and their so-defined tasks, and partly specified systems goals and their tasks. And with the above offered I turn to the second basic benchmark that I would address here: open-ended systems goals. And I will continue my discussion of natural conversation in that regard.

I begin with what might be considered simple, scale of needed activity-based complexity and the numbers of chess pieces on a board, and on one side of it in particular, when compared to the number of words as commonly used in wide-ranging conversation, in real-world natural conversation. Players start out with 16 chess pieces each and with fewer functionally identical game piece types than that; if you turn to resources such as the Oxford English Dictionary to benchmark English for its scale as a widely used language, it lists some 175,000 currently used words and another roughly 50,000 that are listed as obsolete but that are still at least occasionally used too. And this leaves out a great many specialized terms that would only arise when conversing about very specific and generally very technical issues. Assuming that an average person might in fact only actively use a fraction of this: let’s assume some 20,000 words on a more ongoing basis, that still adds tremendous new levels of complexity to any task that would involve manipulating and using them.

• Simple complexity of the type addressed there, can perhaps best be seen as an extraneous complication here. The basic algorithm-level processing of a larger scale piece-in-play set, as found in active vocabularies would not necessarily be fundamentally affected by that increase in scale beyond a requirement for better and more actively engaged sorting and filtering and related software as what would most probably be more ancillary support functions. And most of the additional workload that all of this would bring with it would be carried out by scaling up the hardware and related infrastructure that would carry out the conversational tasks involved and certainly if a normal rate of conversational give and take is going to be required.
• Qualitatively distinctive, emergently new requirements for actually specifying and carrying out natural conversation would come from a very different direction, that I would refer to here as emergent complexity. And that arises in the fundamental nature of the goal to be achieved itself.

Let’s think about conversation and the actual real-world conversations that we ourselves enter into and every day. Many are simple and direct and focus on the sharing of specific information between or concerning involved parties. “Remember to pick up a loaf of bread and some organic lettuce at the store, on the way home today.” “Will do, … but I may be a little late today because I have a meeting that might run late at work that I can’t get out of. I’ll let you know if it looks like I am going to be really delayed from that. Bread and lettuce are on the way so that shouldn’t add anything to any delays there.”

But even there, and even with a brief and apparently focused conversation like this, a lot of what was said and even more of what was meant and implied, depended on what might be a rich and complex background story, and with added complexities there coming from both of the two people speaking. And they might individually be hearing and thinking through this conversation in terms of at least somewhat differing background stories at that. What, for example, does “… be a little late today” mean? Is the second speaker’s boss, or whoever is calling this meeting known for doing this, and disruptively so for the end of workday schedules of all involved? Does “a little” here mean an actual just-brief delay or could this mean everyone in the room feeling stressed for being held late for so long, and with that simply adding to an ongoing pattern? The first half of this conversation was about getting more bread and lettuce, but the second half of it, while acknowledging that and agreeing to it, was in fact very different and much more open-ended for its potential implied side-messages. And this was in fact a very simple and very brief conversation.

Chess pieces can make very specific and easily characterized moves that fit into specific patterns and types of them. Words as used in natural conversations cannot be so simply characterized, and conversations – and even short and simple ones, often fit into larger ongoing contexts, and into contexts that different participants or observers might see very differently. And this is true even if none of the words involved have multiple possible dictionary definition meanings, if none of them can be readily or routinely used in slang or other non-standard ways, and if none of them have matching homophones – if there is not confusion as to precisely which word was being used, because two or more that differ by definition sound the same (e.g. knight or night, and to, too or two.)

And this, for all of its added complexities, does not even begin to address issues of euphemism, or agendas that a speaker might have with all of the implicit content and context that would bring to any conversation, or any of a wide range of other possible issues. It does not even address the issues of accent and its accurate comprehension. But more to the point, people can and do converse about any and every of a seemingly entirely open-ended range of topics and issues, and certainly when the more specific details addressed are considered. Jut consider the conversation that would take place if the shopper of the above-cited chat were to arrive home with a nice jar of mayonnaise and some carrots instead of bread and lettuce, after assuring that they knew what was needed and saying they would pick it up at the store. Did I raise slang here, or dialect differences? No, and adding them in here still does not fully address the special combinatorial explosions of meaning at least potentially expressed and at least potentially understood that actual wide-ranging open ended natural conversation brings with it.

And all of this brings me back to the point that I finished my above-offered discussion of chess with, and winning games in it as an example of a fully specified systems goal. Either one of the two players in a game of chess wins and the other loses, or they find themselves having to declare a draw for being unable to reach a specifically, clearly, rules-defined win/lose outcome. So barring draws that might call of another try that would at least potentially reach a win and loss, all chess games if completed, lead to a single defined outcome. But there is no single conversational outcome that would meaningfully apply to all situations and contexts, all conversing participants and all natural conversation – unless you were to attempt to arrive at some overall principle that would of necessity be so vague and general as to be devoid of any real value. Open-ended systems goals, as the name implies, are open-ended. And a big part of developing and carrying through a realistic sounding natural conversational capability in an artificial agent has to be that of keeping it in focus in a way that is both meaningful and acceptable to all involved parties, where that would mean knowing when a conversation should be concluded and how, and in a way that would not lead to confusion or worse.

And this leads me – finally, to my gray area category: partly specified systems goals and the tasks and the task performing agents that would carry them out and on a specific instance by specific instance basis and in general. My goal for what is to follow now, is to start out by more fully considering my self-driving car example, then turning to consider partly specified systems goals and the agents that would carry out tasks related to them, in general. And I begin that by making note of a crucially important detail here:

• Partly specified systems goals can be seen as gateway and transitional challenges, and while solving them at a practical matter can be important in and of itself,
• Achieving effective problem resolutions there can perhaps best be seen as a best practices route for developing the tools and technologies that would be needed for better resolving open-ended systems challenges too.

Focusing on the learning curve potential of these challenge goals, think of the collective range of problems that would fit into this mid-range task set as taking the overall form of a swimming pool with a shallow and a deep end, and where deep can become profoundly so. On the shallow end of this continuum-of-challenge degree, partly specified systems merge into the perhaps more challenging end of fully specified systems goals and their designated tasks. So as a starting point, let’s address low-end, or shallow end partly specified artificial intelligence challenges. At the deeper end of this continuum, it would become difficult to fully determine if a proposed problem should best be considered partly specified or open-ended in nature, and it might in fact start out designated one way to evolve into the other.

I am going to continue this narrative in my next installment to this series, starting with a more detailed discussion of partly specified systems goals and their agents as might be exemplified by my self-driving car problem/example. I will begin with a focus on that particular case in point challenge and will continue from there to consider these gray area goals and their resolution in more general terms, and both in their own right and as evolutionary benchmark and validation steps that would lead to carrying out those more challenging open-ended tasks.

In anticipation of that line of discussion to come and as an opening orienting note for what is to come in Part 11 of this series, I note a basic assumption that is axiomatically built into the basic standard understanding of what an algorithm is: that all step by step process flows as carried out in it, would ultimately lead to or at least towards some specific at least conceptually defined goal. (I add “towards” there to include algorithms that for example seek to calculate the value of the number pi (π) to an arbitrarily large number of significant digits where complete task resolution is by definition going to be impossible for that. And for a second type of ongoing example, consider an agent that would manage and maintain environmental conditions such as atmospheric temperature and quality within set limits in the face of complex ongoing perturbing forces, where an ultimate, final “achieve and done” cannot apply.)

Fully specified systems goals can in fact often be encapsulated within endpoint determinable algorithms that meet the definitional requirements of that axiomatic assumption. Open-ended goals as discussed here would arguably not always fit any single algorithm in that way. There, ongoing benchmarking and performance metrics that fit into agreed to parameters might provide a best alternative to any final goals specification as presumed there.

In a natural conversation, this might mean for example, people engaged in a conversation not finding themselves confused as to how their chat seems to have become derailed from a loss of focus on what is actually supposedly being discussed. But even that type and level of understanding can be complex, as perhaps illustrated with my “shopping plus” conversational example of above.

So I will turn to consider middle ground, partly specified systems goals and agents that might carry out tasks that would realize them in my next installment here. And after completing that line of discussion, at least for purposes of this series, I will turn back to reconsider open-ended goals and their agents again, and more from a perspective of general principles.

Meanwhile, you can find this and related postings and series at Ubiquitous Computing and Communications – everywhere all the time and its Page 2 and Page 3 continuations. And you can also find a link to this posting, appended to the end of Section I of Reexamining the Fundamentals as a supplemental entry there.

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