A New Framework for Knowledge Management

Several weeks ago I wrote two posts on the topic of Knowledge Management (KM):

1. Whatever happened to Knowledge Management
2. Redefining Knowledge Management

This post represents the third in this series and tackles what a new framework for Knowledge Management might look like.

At the highest level, the Framework that I’m proposing for KM acknowledges several new technologies that weren’t readily available back when the notion of KM was first promoted within IT. The Framework also clearly acknowledges that KM is part of a larger ecosystem of related but separate technologies that cooperate to achieve a variety of Knowledge-related goals. Those goals could be characterized in the following manner:

• The capture of insights on multiple levels, e.g. Individual, Organizational and Community. In this context, Community represents a community of practice (probably global in nature) but could also be a market of some sort.
• The ability to both define knowledge expectations as well to discover hidden knowledge.
• Support the assimilation of source information within “levels of context” (e.g. personal, organizational and community).
• The ability to capture and reuse Knowledge Relationships & Learning Paths (I’ll describe these in more detail in a bit). This particular goal goes to the heart of one of the original value propositions behind KM in the old days – the idea that knowledge capital ought to be captured in a fashion that allows it to be reused such that if individual knowledge holders were to leave the organization it wouldn’t suffer a true “Brain Drain.”

I wanted to briefly explain what I meant by “Learning Paths” and “Knowledge Relationships.” Learning Paths are more or less Dynamic Curricula, in other words self-defining paths that traverse specific learning topics in the context of both individual and organizational learning. Let’s say you have access to 1,000 learning resources and choose to build your own learning program using say 20 of them to learn (for the sake of argument) Node.js. The chosen topics and their sequence can become a learning path which could be reused by other individuals or the organization as a whole. A Knowledge Relationship on the other hand is a little more complicated because it can be manifested in more than one way.  A Knowledge Relationship could be as simple as terms combined through metadata, or terms listed within a shared taxonomy or as complex as a SQL query or defined relationships using RDF. Knowledge Relationships is an area where the initial promise of Semantic technology has fallen a bit short of expectations but will likely continue to improve in the coming years

The most immediate realization when considering both the proposed framework and its potential goals is the fact that there isn’t now nor is there likely to be one tool that accomplishes all of what we consider to be part of a larger KM process. Maybe someday this will change, but for the foreseeable future, establishing and taking advantage of KM within an organization will require a mix of tools (many of which are likely already in place). Here is a conceptual view of the proposed framework:

There are several principles associated with this proposed KM Framework:

1. The idea that learning drives knowledge assimilation
2. The idea that analytics drive discovery and that discovery also drives knowledge assimilation
3. The idea that AI or AT can make both Learning & Discovery more effective
4. The Idea that AI or AT can empower Search capabilities in new ways and that Search drives both Analytics and knowledge discovery
5. The idea that knowledge can be layered up from the individual to the community level, thus supporting a variety of collaborative knowledge capture and assimilation capabilities  
6. The idea that underlying all these knowledge layers can reside a defined identity – this can take the shape of shared semantics, business rules and more
7. The idea that the source information (content, databases etc.) can be acted upon simultaneously from several related processes to produce meaningful insights which in turn can be captured and built upon

In many ways, I think this type of a framework is actually preferable to dependence on single class of tools in that it has a certain flexibility more or less built in. As we’ve witnessed over the past decade, there have been not one but many disruptive new technologies which can be applied to Knowledge Management including but not limited to Mobile technology, AI and Big Data. There are likely several more trends waiting on the wings that could enhance or otherwise contribute to KM in the future. This type of framework can easily accommodate any such advances.  

I will write at least one more follow-up post on this theme and in that article will explore what a real-world next generation KM process and architecture might look like within a typical enterprise and how it might be exploited in a variety of real-world scenarios.

Copyright 2017,  Stephen Lahanas

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Whatever Happened to Knowledge Management?

Information Technology is a dynamic field, one often driven by buzzwords and fleeting trends. Sometimes these trends continue for decades, other times they fade somewhat quickly. One trend that experienced that fate seems to be Knowledge Management (KM). I recall first hearing about it in the early 2000’s and at that time it seemed to encompass several classes of subordinate technologies including but not limited to the following:

• Document Management
• Content Management
• Search technology (various)
• Business Intelligence (A.K.A. Decision Support or Analytics)
• Metadata Management
• Learning Management

It also tended to include more specialized tools such as knowledgebases or FAQ generators and it potentially seemed to include more integrative content-focused technologies such as Wikis. For a time KM seemed poised to also include a wide range of Semantic technologies as well. However, in the last two years or so in particular, the term Knowledge Management has seemingly dropped off of the map. I was wondering to myself why this may have happened and whether anything in particular had replaced it.

To be sure, there are a couple of related trends that have dominated the IT landscape in the past two or three years; the most significant of those being Data Science & Big Data. However, neither of these seems to fulfill the role KM was being groomed for over the previous decade. In fact, in some ways these more recent trends have become much less specific in regards to their expectations or scope (which has actually become a problem for both of them). This situation may actually help to explain what happened to Knowledge Management - perhaps the original scope was too expansive? But was it just a scope issue?

I want return again to the core or implied premise associated with Knowledge Management, that there ought to be some sort of enterprise-wide ability to help unify all of these knowledge related functions or processes and resources. The problem with this premise is related to the scope, in that there isn’t one product group of technologies associated with it, but rather a fairly large set of technologies – some of them not closely related at all other than in a philosophical sense – in other words that they could be construed as part of a larger knowledge ecosystem. So, we seem to be missing an industry impetus, but we also seem to be lacking any sort of agreed upon knowledge process or framework that would necessarily help to tie all of these diverse technologies together. This latter problem takes us deep into the heart of the larger philosophical question which KM seemed to be begging – e.g., what is the difference between information and knowledge? That isn’t an easy question to answer – and in some sense parallels my recent discussion on Artificial Intelligence versus Artificial Thought. In fact, AI could even be considered as part of KM depending on how you look at it.

So back to the tough question, what makes data or information become knowledge? Is that dependent on adding value to the data or information though specific types of processes or is it merely in the integration and analysis of such source data that the source transcends itself to become knowledge? Or is knowledge only something we can consider in a collective sense; with the sum total of all data assets being knowledge potential of some sort? These types of questions may have been raised during the years that KM was discussed actively but I think never properly answered.

You might have noticed from my initial list of related technologies that I seemed to have left out Data Management or Information Management. Either of those could potentially be considered to be part of a knowledge management framework – however the reason I left them out is that for those fields, there is a much better operational understanding of how those type of technologies work. In fact, the view from with one of these areas, Data Management overlaps quite a bit with some of what I’ve attributed to Knowledge Management (one need only look at the DMBOK to see this illustrated). Thus Data Management as a trend has continued (for decades now), primarily concerned with operational maintenance of a number of unrelated technologies without the implied necessity to integrate it all into something transcendent across the enterprise.

What if we did wish to settle the deeper question though, regarding the differing expectations between operational management of source data and knowledge exploitation? It is now 2017, are we in a position to define an integrative knowledge framework and if so what would the philosophical foundation consist of? Moreover, would coming up with this type of framework help to redeem the dimming trends of Big Data and Data Science? I think it’s worth trying to answer the question and also worth taking a shot at defining the missing knowledge framework. I will tackle both parts of this problem in two upcoming posts…

Copyright 2016, Stephen Lahanas

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A Framework for Evolutionary Artificial Thought

This past week, I was making the long commute between Dayton and Columbus, Ohio and trying to amuse myself the best I could – in this case by listening to a college course on the fundamentals of Particle Physics. One might not think there is an obvious connection between Particle Physics and Artificial Intelligence, but it turns out there is at least one. The connection, in my mind at least, was the framework used in Physics to help organize the field of subatomic particles – it’s known as the Standard Model of Particle Physics.

In the Physics world in the early 20th century, as in the field of Artificial Intelligence now, there was a tremendous amount of information which was obtained from various sources and very little ability to place it all into a unified context. We discovered particles, defined new mathematics and reinvented philosophy through Relativism and Uncertainty, yet without a guiding framework it all must have seemed terribly chaotic and random for those working in the field. Out of that chaos emerged a framework though; half invented, half discovered – one that helped to focus technology, philosophy and application of the science in question. I thought to myself, this is exactly what we’re missing with Artificial Intelligence. But as I posited in my last post, the framework that’s needed might also require a philosophical adjustment – one that illustrates Intelligence in the context of a combination of evolutionary capabilities which might best be described as Artificial Thought. In that article, I tried to make the philosophical case for something like that might make sense, in this post, I’m going to get a bit more specific and examine the more pragmatic aspects of a Framework for Artificial Thought.  

Let’s start at the beginning – what does this or any such “Framework” buy us? In the case of The Standard Model of Particle Physics, the framework provided the following benefits:

1. The ability to place a number of potentially divergent concepts and discoveries within a unified and coherent context
2. The ability to explain the nature of something in a manner consistent with empirical data
3. The ability to support a variety of predictions, like for example the discovery of specific types of new particles, the most recent and famous of those being the Higgs Boson

For Artificial Thought, the benefits might be a little different, but perhaps not as much as one would think. The high-level value proposition associated with a framework for Artificial Thought might include the following benefits:

1. The ability to align a diverse set of AI theories, techniques and technologies within a coherent, unified context
2. The ability to define a clear evolutionary path within that context whereby component capabilities can be combined to achieve ever greater orders of Thought and eventually Intelligence
3. The ability to better chart and predict success in achieving Thought or AI milestones

I suppose the biggest difference between Particle Physics and Artificial Thought is that we’re bypassing the need to discover the functions of natural intelligence on the biological level. In other words, there are no CERN-like labs available to discover Thought in progress the way we discover subatomic particles. This could make our efforts harder to achieve, but perhaps only if our goal was to recreate natural intelligence as opposed to generating capabilities which are logically similar if not actually organic in nature. This brings us back to the central premise in the previous article, that recreating the most complex and comprehensive capability is a hell of a tough goal and we should instead worry mostly about intermediate steps rather than the end game (and I might add without getting lost in the growing tangle of 100’s of immediate, lower level details or approaches and opportunities)

Let’s take a look at what a Framework for Artificial Thought might look like…

The framework resembles an IT architecture because in a sense that’s what it is. Imagine a situation in a few years where we have a galaxy of AI-related capabilities, how might they work together and to what end? This view gives us a hint as to what that might look like. It does more than that though, it also shows how we move from lower-level Thought to higher-level Thought and it also begins to illustrate the potential for orders of Artificial Intelligence through integration of Thought capabilities (both within and across Tiers). The Tiers themselves mimic to some extent the natural intelligence we’ve referred to (both human and otherwise) by illustrating how basic capabilities might evolve into something more.

Tier 1 is Awareness and I think it’s safe to say this is the area where traditional AI has made the most progress thusfar and that only stands to reason. As we discussed before, Awareness in this context is nothing at all like Self Awareness. We can imagine a rover crawling along the rocky, barren landscape of Mars, avoiding obstacles by becoming aware of them through sensory apparatus and that fits this tier just fine. Is the rover Intelligent? Not really, yet some of the rovers we’ve built or are building can potentially operate on their own without explicit direction or intervention from human operators. This is a good starting place…

Tier 2 might witness our rover becoming more sophisticated, perhaps learning from its environment and building upon its experiences yet still basically reacting to environment. As you might have noticed from the diagram, it’s clear that Artificial Thought will occur both individually and collectively – something perhaps not fully anticipated by the founders of AI back in the 50’s and 60’s. This has critical implications to the applications of Artificial Thought, for example in the case of the rover, the implication might be that it distributes some of its higher function elsewhere. If maintaining higher Thought from Earth presents difficulties due to the 8 minute travel time for instructions, then perhaps there might be cognitive capability in another part of the lander or on an orbiting satellite. The key idea here though is that the rover itself doesn’t need all of the cognitive capability itself, which would become even more important if for some reason the rover was instead some type of Martian UAV and needed to operate for long periods of time with minimal fuel. The bottom line, though, is that Collective Thought is still Thought, regardless of how it might be distributed or otherwise combined. This is one area where our current view of individualistic human-mimicked Thought really diverges from where we’re headed.

The question begins to arise in relation to Tier 2 capability as to whether if we combined all of these functions and integrated them somehow, would we in fact achieve a level of Intelligence? For the sake of argument, let’s say yes, that it would. If we combine all capabilities that fit with Tier 1 and Tier 2 Thought, we might say we have achieved a “level 0” order of intelligence. I don’t think Tier 1 by itself would justify that assignment, yet when we look at what Tier 1 represents it does seem to mimic much of what might be required for a lower order of intelligent life to survive.

With Tier 3, things get more interesting. This is where Watson and some of the other more ambitious AI projects have been focused with limited success, but still some progress has been made. The distinction between reactive and proactive, between simple and complex is a big leap and one that not all Artificial Thought has to make. It’s important to keep in mind here also that the Tiers are not actually separate from one another, rather the higher tiers build from the lower. This model is evolutionary on several levels, both in terms of building capabilities up but also in our ability to mimic the progression of Thought metaphorically from its beginnings to somewhere at least close to how we view it. Beyond those considerations, it also represents a real-time integration architecture as well – with lower information feeding higher level capabilities. If enough integration (across Tier 3 capabilities) occurs then we might say that we’ve reached a “level 1” intelligence.

Of course, I haven’t defined what level 0 or 1 orders of intelligence represent, but the taxonomy might look something like this:

• Level 0 – An order of intelligence mimicking primitive life
• Level 1 – An order of intelligence mimicking intermediate forms of life, but not humans
• Level 2 – An order of intelligence that truly mimics human intelligence
• Level 3 – An order of intelligence beyond human intelligence

Each of these orders or levels of Intelligence involve a multitude of complex Thinking behaviors (I’ve abstracted the view to a great extent for this dialog). The framework I’ve outlined above isn’t focused on level 2 or 3 Intelligence, we can leave that for science fiction for now. But, the next ten years could see some remarkable breakthroughs on the lower levels.  “It thinks, therefore it works,” might be a good corollary to Descartes’ original premise.

Copyright 2016, Stephen Lahanas

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How Artificial Thought Can Save AI

Over the past several decades, I’ve seen an endless stream of predictions and articles in regards to Artificial Intelligence and it occurred to me recently that we may have missed an important point relating to this topic. One reason the expectations and the reality of AI have diverged so greatly may be due entirely to our obsession with the notion that in creating it we ought to be somehow be mimicking ourselves through some sort of human intelligence without perhaps truly understanding what that represents. This seems to be simultaneously our greatest goal and our worst nightmare relating to AI. But then I got to wondering, what’s the difference between Artificial Intelligence and Artificial Thought and if we viewed the question from the latter perspective are we in fact actually making some real progress?

Before we can dive into that question it is worthwhile to try to define what we mean by Intelligence and Thought. Here’s a good definition of Intelligence (signed by 52 scientists in the field):

A very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings—"catching on," "making sense" of things, or "figuring out" what to do.

I like this definition as opposed to some others I’ve seen because I think it carries within it a larger scope that we tend to include with the notion of what Intelligence is or what an intelligent being / entity has to be able to accomplish. This type of definition serves us for example when considering what extraterrestrial intelligence might be. Thought, at first glance, might be considered a subset of Intelligence – it is the act of demonstrating one’s Intelligence per se but also the product of that demonstration. The definitions of Thought are somewhat less concise and often seem a bit recursive though – take this one for example: “a single act or product of thinking; idea or notion.” Thought can be both a verb and a noun. Perhaps the reason that it is so difficult to nail down the definition for Thought is because we do tend to view it as a subset of the superset Intelligence and describing a component of that process or capability without fully explaining or understanding it is challenging. One definition that I think fits our topic a little better comes from the Merriam Webster dictionary; “reasoning power or a developed intention or plan.” This definition of Thought doesn’t try to explain it as much as it tends to highlight how or what it represents. Interestingly, this more compact definition also closely mirrors some of the first definitions for Artificial Intelligence, but we’ll return to that in a minute.

So, at the highest level, “Intelligence” might be considered a higher reasoning power, one that also tends to imply self-awareness and continuity of thought and a sort of assimilation of knowledge into a Self as time passes. Intelligence may be more than that as well in that there is an integrative aspect to it that often isn’t included the definitions – sometimes we view that integrative aspect as Self but not always. If we return to the Ontology of the subject, we might be able to state that you can’t have Intelligence without the thought process or individual thoughts but perhaps you can have thoughts without Intelligence per se. In other words, Intelligence at least on the face of it, seems to represent a higher order than Thought, although as we know from the real world that there are also various orders or levels of Intelligence too.

This is more of a philosophical question then a technical one, but let me follow it a bit further. Let’s say that there can be Thought without Intelligence and the key difference between the two in the context of Human Intelligence (which seems to be what many have tried to emulate) may be self-awareness and the ability to integrate reality within a unique perspective and context. At its most fundamental, Thought can be disconnected from other Thought as well as from any experience or capability resembling self-awareness and / or complex integrative interpretation. By this definition, there are likely a wide variety of animals that have mental activity that might be described as Thought, but certainly not Intelligence in the sense we tend to attribute to humans. There can be lower levels of Intelligence, but the difference there between Thought has less to with self and more to do with Integrative interpretation. If we view any organism as a complex system or system of systems, then some level of integrative coordination is always occurring. This coordination is often automatic, even for humans, but sometimes it is deliberate. Deliberate integration may be classified as Thought, but it doesn’t necessarily require Self-Awareness.

All of this begs the question, what is Self-Awareness and why is it so important in the distinction between Thought and Intelligence?

Self-Awareness implies an understanding or expectation of identity. There can, of course, be Awareness without any indication that a Self exists. Take away Self from the equation of Intelligence and what you have left are a lot of the same capabilities; such as Learning (to a point), Memory (from an objective rather than a subjective context), and Thought which can accomplish many of the same goals that intelligent Thought can, but not all of them. Take Self away and you can also potentially discard the requirement for complex integration (the level and type of integration can become much more selective). We might refer to this Selfless state as “Targeted Thought” and this is a little more interesting in that it represents areas within which Thought can be developed, specialized and reinforced without higher-level expectations. The difference between that and Thought in the traditional sense (as a subset of Intelligence) is that there are likely to be clear boundaries that constrain the operation of Targeted Thought. A Targeted Thought “Boundary” for example might operate solely within the context of airline routes and all of the processes directly associated with flight routing. Within that boundary, reasoning power based on planning and guided toward specific intentions could take place to help solve questions of efficiency or profit. This type of Thought can still be considered “novel” thought as long as it isn’t fully determined in advance, but it is unlikely that something artificially constrained to a single purpose ought to be considered intelligent in the way we tend to view Intelligence.

So, Artificial Thought in the abstract sense might be considered as the ability to derive novel outputs from similar inputs based on real-world situations and unique (yet mostly static) rulesets and perhaps various Targeted boundaries. This definition provides a framework in which Artificial cognition or Thought might obtain near-term success – a much narrower view to be sure and one already potentially aligned with nearly every practical AI effort yet undertaken. This definition is perhaps not too far off from what the initial definitions for Artificial Intelligence were, yet the types of predictions that we can make about what Artificial Thought can or can’t do will likely become much better defined within this narrower confine.

Let’s go from the abstract to the real world, Nature. In Nature, lower level organisms likely have many potential applications for Thought but don’t build that type of Thought around language or complex symbols but rather through some sort of connection to various sensory capability and stimuli. It wouldn’t be expected for a squirrel to memorize a complex path between several dozen trees bearing acorns, yet in order for the squirrel to succeed and survive through the Winter, he must have a well-defined ruleset informed by recent and current experience that facilitates such navigation. The squirrel’s journey is a problem-solving exercise, one that he may or may not be able to learn from or remember but one that he has to be able to repeat successfully under dynamic conditions. The squirrel might be viewed as a system which demonstrates a limited level of awareness, and employs novel thought in an integrative manner so it might be considered a lower-level intelligence (when all of those capabilities are combined).

My point with the squirrel analogy is this; if we were to attempt to create & program an Intelligent squirrel (using the more expansive of scope of the definition for Intelligence) to think through all of this we might be missing the point or be conducting a certain level of overkill. We could instead take any portion of the capability the squirrel possesses and use that to solve various types of problems or perform tasks. In other words, in deconstructing even a lower order of Intelligence, we can extract some Artificial Thinking capability that might prove useful – capability that likely far outstrips what we’re currently able to do. 

And even if we were to view all those capabilities combined in the case of the Squirrel’s survival, he simply doesn’t require a general purpose Intelligence the way we’ve been defining it in AI, but he does require a certain level or type of Thought (novel action resulting from dynamic inputs). This is not to downplay the complexity of the squirrel’s mission, which is in fact fairly daunting. Any successful squirrel must regularly evade predators, find food, find shelter and navigate its environment in a dynamic 24/7 environment. The navigation problem alone is challenging enough, and we use such problems all the time to test the efficacy of AI programs. However, while the squirrel needs to be able to jump from one branch to another without falling, it doesn’t require a complex understanding of Physics to do that successfully. A squirrel utilizes sensory information to determine speed, distance and other factors and makes a decision as to whether he should or shouldn’t jump. If we can achieve any sort of novel decision making, even in areas much less complex than the squirrel experiences, we will have made serious progress.

Let’s return to the concept of Thought again for a moment. What we’ve just described as Artificial Thought in essence represents a combination of Data Fusion (sensory data) and logical problem solving. The rulesets can be hardwired directly into the thinking machine, without it ever having to learn or improve upon them, although some limited form of learning may be a possibility. The requirement for natural or machine learning is not an absolute necessity for Artificial Thought whereas it might be for AI. Nature for example, endows its various creatures with a minimal number of rulesets – just what’s needed and not much more and only very basic learning abilities. This primitive level of Thought and corresponding lack of self-awareness is perfectly acceptable for a multitude of tasks (and keep in mind that a lack of self-awareness does not imply lack of awareness). This scenario, or even pieces of it, still more or less surpasses what Artificial Intelligence can achieve today, yet it represents a much more realistic target if we reconsider how we might go about achieving it. If we were to combine a number of diverse Thought processes we might be said to be building a lower order of Intelligence, but not yet perhaps the general purpose, human inspired intelligence most associated with AI. And that is perfectly ok as it still represents real progress.

There’s another consideration and area of confusion as well when it comes to the struggle to create Artificial Intelligence; it has to do with the obsession of creating architectures inspired by what we believe to be the structures underlying human intelligence (e.g. the human brain). Neural networks as a metaphor is perhaps the best example of this but not the only one. Sometimes, I think this is like designing a mission to Mars based on the understanding of how a bottle-rocket works; while there are bound to be some similarities – our understanding of human physiology in regards to Intelligence is still relatively primitive. More importantly perhaps, is the realization that there are a massive set of applications available for machines that think but aren’t necessarily intelligent. This means that even if we did understand how to recreate human or even a general intelligence, the overhead for doing so might not really be necessary or at least not yet. There’s a lot we could do using mere Thought in a more selective sense.

And why is selective or Targeted Thought of any value? Well, if we look at IT for example and the amount of effort directed towards explicitly defining behaviors in code the value should become readily apparent – Thought – even targeted and selective Thought – could make the operation of any type of machine or system infinitely more efficient. And just because the machines in question cannot appreciate the Thoughts they’re having, doesn’t mean that can’t build new behaviors or learn from an initial set of foundational rules (either individually or collectively). We don’t have to worry about recreating nature per se, we simply have to keep in mind the pragmatic motivations behind the value proposition that nature has illustrated so convincingly to us. If we do that, and work towards simpler goals in an evolutionary fashion, we can grow Artificial Thought into a powerful part of most industries. This approach also builds upon areas where success has already occurred and has the potential to accelerate those successes but it also tempers expectations in regards to what can or even what should be done. Artificial Thought (as opposed to Artificial Intelligence) can become much more focused and specialized in terms of its architectural objectives.

It’s time to come full-circle and consider again why there is such confusion and disappointment in the field of AI. It starts with the definition for Artificial Intelligence:

Artificial intelligence (AI) is the intelligence exhibited by machines. In computer science, an ideal "intelligent" machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at an arbitrary goal. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving." Here’s another definition:

A branch of computer science dealing with the simulation of intelligent behavior in computers; the capability of a machine to imitate intelligent human behavior.

When I refer to Thought versus Intelligence, I’m not implying that Thought can or should resemble human thought, rather what I’m positing is that Thought is any cognitive ‘processing’ that isn’t explicitly programmed up front (e.g. it is semi-random in nature based upon inputs fed into it). We’re not talking about simulating human intelligence or behavior and this type of thought is limited in terms of the Intuitive or contextual capability – it is certainly not creative Thought. The distinctions I’m making are important. In our industry (IT), we’re perhaps too enthusiastic in pronouncing this or the other technology as being “Intelligent” in some regard. The reality is that none of them truly are, which is also why after pursuing Artificial Intelligence for more than 50 years, few are willing to say anyone has actually achieved it. But that’s not to say we’ve achieved nothing – we have in fact built a wide variety of foundational technologies which are coming close to filling effective roles as Cognitive Aids. These technologies don’t simulate human thought, but rather expand or supplement it with Artificial Thought which we can choose to apply intelligently or otherwise. The type of thinking that we should be focused on is discrete, focused and targeted.

I supposed this dialog risks the possibility of replacing one vague and hard to realize concept with another, but there does need to be a way to classify intermediary cognitive capability that goes beyond standard computing but falls short of human cognition. We don’t need to speculate too much on the quality of thought in various animals that clearly have the ability to think on some level and we might extend the same courtesy to machines or systems. We don’t have to consider either as Intelligent to appreciate some value in what they do – and many successful organisms obviously don’t think at all – but the ones that do can become role models so to speak for the near-term goals associated with artificial cognition. Understanding or recreating biological neural processes aren’t necessary here either – the models we’re aiming for are pragmatic and logical with animals simply providing a useful analogy if nothing more (which means we’d use them as models in a way somewhat different than we might for Robotics). 

Descartes once said, “I think, Therefore I am.” Someday, I’m sure there will be a machine that becomes Intelligent in this context, by becoming self-aware. It’s time to recognize that the path towards machine intelligence ought to follow a more rigorous evolution of less lofty goals. In my next article in this series, I’m going to provide a framework for classifying types of Artificial Thought and discuss how that can be allied with architectural objectives as well as current or near-term technologies and applications.

Copyright 2016, Stephen Lahanas

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The Art in Artificial Intelligence - Part 2

Part of the reason we decided to explore this topic on Technovation Talks was the claim made earlier this Summer that an AI had finally passed the Turing Test. So what's the Turing Test? It was a very basically described metric by which any sort of true machine intelligence might be assessed or otherwise verified. Here's the basic premise of the test - if an AI can engage in normal conversation with multiple human participants without the humans realizing that they were conversing with a machine (obviously it would be a remote conversation of some sort) - then the machine could be considered intelligent.

Alan Turing - BTW: a movie about him will be hitting theaters soon...

According to Turing's predictions in 1950, we should have already achieved this level of machine intelligence (by the end of the last century). Yet if you look at the story about this Summer's supposed triumph (which might be considered the first time it has in fact been achieved, there are nothing but problems and doubts):

• First off, the answers are screwy and it's clear that much of what the computer heard it misinterpreted. 
• Then they presented the AI as if it were an adolescent from war-torn Ukraine.
• And they also used the lowest possible threshold to gauge success - this threshold which represented a part of Turing's paper on the subject - suggested that success be declared if on average at least 30% of humans judging the AI would be fooled into thinking it was a human. So, the AI named Eugene, scored a 33% - but that is only because judges lowered the bar thinking he was a semi-illiterate teen. 

More important than all of this of course is the central question as to whether or not the metric or test is actually an accurate way to assess machine intelligence anyway? In a way, every system that has ever tried to compete in one of these tests to date has been purpose-built to pass the test. But does that make it intelligent (if it were actually to pass it)? The technology necessary for a machine to "think" through a conversation the way a human does simply does not exist - nor are we even close to understanding what that model would even look like. The systems trying to pass the Turing Test are simply conversational "hacks," in other words they include built-in tricks like responding to a question with a question or trying to work off of keyword cues. What's missing of course is any continuity of thought - any consciousness - and even the most simplistic conversation requires that. None of these systems can think and none of them can really learn. 

Now it may be that conversation hacking may become sophisticated enough in coming years so that many of these systems may actually pass the Turing Test threshold of 30% on a regular basis. But that test as it is now defined will never provide us with an accurate assessment as to whether a machine has in fact achieved some innate level of intelligence. There is no way to determine through the conversation if the system has "added value" to the topic rather than simply replied phrase by phrase in rather one-sided dialectics. It will be difficult to assess or acknowledge any growth or change. There is no expectation in a simple conversation to determine if you are in fact conversing with a self-aware entity.

In the movie Her, this guy falls in love with his operating system (and it didn't come from the Apple store!)

The first thing we need to do before we tackle how we might achieve AI is to determine what the appropriate assessment or validation for human-like intelligence really needs to be. We are going to suggest one and explain the rationale for it...

The Technovation AI Test -

AI Test Prerequisites / Expectations

• The Test is not meant to assess acquired knowledge per se, it is meant to assess cognitive ability. In other words, it is not about preparation or repetition of learned information, but is concerned with potential and / or application of any particular knowledge set.
• The Test does not have to occur in one sitting, but take place over any duration (within reason).
• The Test isn't merely concerned with correct answers or maturity in a point of time, but can also assess the ability to grow over time based upon responses to various aspects of the test (or other stimuli encountered within the time-frame of the test).
• The Test is not merely a linguistic exercise - the machine must not merely demonstrate the ability to communicate like a human, it must also demonstrate it can learn. 
• Foremost above all else though, the machine must demonstrate the one trait most closely associated human intelligence (as opposed to raw computing power) - it must demonstrate intuition. In this context, Intuition represents shorthand problem-solving (which we will discuss in much more depth in a future post). 
• On last aspect of the test that must be included is a review of the code to ensure that "conversational snippets" are not allowed to be prep-programmed. This implies that the majority of dialog is generated 'real time' by the machine. Now, that would not prevent the machine from reviewing logs of previously generated dialog (in some database), but that review could not lead to verbatim quoting -  rather must paraphrase or other restate previous points. 

The AI Test 

In a series of panel interviews, the AI must convince the judges or reviewers that it should be hired to perform a complex human role. The type of job and foundational knowledge can cover any number of topics but must be sufficiently complex to avoid "lowering the bar." (so, any job that requires a degree). Also, the interview style must be open (similar to essay tests in written assessments) - the answers must not just be correct, they must demonstrate value added insight from the intelligence conveying them. And the answers may be entirely subjective... (even better as long as the machine can rationalize them)

This test necessarily implies a very high threshold - perhaps in excess of a 90% rating for a very complex set of conversations. Why raise the bar this high? Simple - this is the one way we can force the development of a system that can both learn and apply that knowledge to problem solving and do it on the fly. To have human like intelligence, machines must have the ability to understand nuances of human communication and psychology - thus it must not only be able to interact, it must be able to convince us as well.  

Now that we have a more concrete target to aim for - how do we get there. In our next post, we'll delve into Learning - what works and what doesn't and how human and machine intelligence differ today.

Copyright 2014, Stephen Lahanas

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The "Art" in Artificial Intelligence - Part 1

Today, we are going to launch a problem-solving exercise on what might be the single most complex topic in Information Science - Artificial Intelligence. The goal here is not to provide any sort of comprehensive survey of current theory or practice; rather our journey begins with the stark realization of how little we've achieved in the field since the term was first coined 58 years ago. This is a problem statement and problem resolution exercise and an excellent case study in technology focused innovation. Let's begin...

We'll start at the beginning with some definitions and a review of key assumptions.

Artificial Intelligence, Defined 
The ability for a machine to consistently demonstrate core cognitive skills generally associated with human intelligence; including, learning, problem solving, intuitive reasoning and contextual memory retention and extraction. These skills generally imply the need to achieve some level of self-awareness.

What We Haven't Achieved, Yet
I said that this problem statement is focused around a lack of success in the AI field to date; let's try to quantify that first.

• No computer can learn like a human.
• No computer can speak like a human (this is deceptive, tools like Siri will provide responses back to you, but is that in fact anything like human speech? The processing that goes on within Siri is a relatively primitive form of pattern recognition as opposed to what even the least capable human mind can produce).
• No computer can handle complexity in the same manner a human can (this warrants much more explanation and we'll come back to it).
• No computer can problem-solve the same way humans can. (there are types of problem solving where computers are of course far superior to humans, yet even with all that power they still fail to solve relatively simple questions that humans can handle naturally).
• No computer has achieved anything coming close to consciousness or self awareness (despite the endless slew of sci-fi stories where this is a common fixture). 

Anybody trying to solve the AI problem is a mad scientist, right? 

Now, there's another ethical or moral side to this topic which we won't jump into until the end - the question as to whether we should even try to endow a machine with these traits - but then again it is likely that someone will do this regardless of the ethical objections. Part of the human learning process seems to require learning through our mistakes - a trait we may eventually end up passing to artificial entities, someday. But back to the problem.

Challenging Assumptions

As with most problem spaces, the set of initial assumptions associated with it tends to drive all else until or unless those assumptions evolve in some fashion. For Artificial Intelligence, there have been a number of assumptions that have helped define what's its become to date and also help to explain its limited success - they include the following:

• The notion that brute force computing power will eventually resolve many issues and push through various AI barriers. This is partially true, but then again we sent Apollo to the Moon with the computing power of a standard calculator (by today's standards), how much computing power do we really need to mimic human thought? Nature got us here through elegance, not waste.
• The notion that we fully understand how the human mind creates consciousness or exercises cognitive capability. We don't, yet.
• The very flawed notion that machine learning can or should have any connection to the current methods we use to teach each other. 
• The lack of sensory input associated with most AI paradigms. AI is IO dependent and that usually means keyboard and mouse although images and video (and audio) have now begun to play an important role. We'll get to this in more detail later.
• The notion that simulation can perform like the real thing; building to the simulation ensures a thing always remains a simulation (thus never achieving actual reproduction). This has some interesting implications which will eventually get us into a discussion of genetic engineering. 
• A lack of focus on natural language. This at first proved too difficult and now natural language does factor into much of the AI research going on. However, natural language hasn't been looked at as the core logic for an AI system - but it should be - instead we tend to view in terms of how an AI system (or other type of system for that matter) can interact with humans (or just capture human speech accurately). 

Watson actually showed us what he/she/it was thinking - if only we could
have seen into the minds of the humans, then we could have seen whether they think the same way...

A Brief Chronology of AI

• Ancient Greece - those happy go lucky philosophers in togas considered the notion of thinking machines. 
• About 150 years ago or longer - Artificial Intelligence (although not explicitly identified as such) becomes a popular topic in Science Fiction.
• 1930s - The golden age of Science Fiction novels includes more than a few stories about artificial brains and super robots.
• 1955 - The Term "Artificial Intelligence" is invented by John McCarthy and within a year the first conferences on the topic are held.
• 1968 - The Arthur C. Clarke novel 2001 becomes a box office hit and HAL, the emotionally unstable AI computer on board the Jupiter, becomes a celebrity. Take your stress pill, Dave.
• 1980 - Matthew Broderick makes it big playing games with an AI pentagon computer - the game is Thermonuclear War but of course you can't win it, right? 
• Early 80's - LISP is introduced.
• Mid-1980's - Expert Systems become popular.
• 1984 - Skynet becomes 'self aware' and Terminators start trying to kill John Conner.
• 1987 - The pasty-faced Commander Data steals the show on Star Trek the Next Generation.
• 1990's - An IBM computer wipes the floor with the world's chess masters.
• 2001 - Stanley Kubrick's last movie becomes Stephen Spielberg's tribute to him as Joel Haley Osment becomes one of the last humans playing a robot (as opposed to CGI animations with voice actors playing humans, robots and everything else).
• 2010 - Stanford opens up an AI online course to the general public, several hundred thousand sign up - few finish the course. 
• 2011 - Siri shows us that smart phones can indeed become somewhat intelligent.
• 2012 - IBM's Watson wipes the floor with the pantheon of Jeopardy champions.

Don't imprint unless you're serious...

The expectations for AI have thusfar radically outstripped the progress made. Now in the same 50 or so years we've taken the Star Trek communicator & tricorder fictions and made them reality in the form of smart phones (some of which you can now load with apps that when used with sensors can measure one's vital functions much like a tricorder).

A lot of smart people have tried for decades to make AI a reality and across the globe billions or hundreds of billions of dollars have been spent on related research. What's going wrong here? It can be only one of two possibilities:

1. Human like intelligence cannot be artificially created and maintained or
2. We've approached the problem wrong

I content that the second possibility is in fact what has happened. As we will progress with the series, it will become clear that the "Art" I referred to in the title is the ability to pick the right path for problem resolution.  In part two of this series, we will examine the question; How can machines learn?

Copyright 2014, Stephen Lahanas

#Semantech
#StephenLahanas
#TechnovationTalks

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