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 5 Principles of Cyber Warfare

This week we got a partial glimpse into the types of action that the United States might consider to be acts of Cyber Warfare. I had written about this topic 2 weeks ago in regards to Voting Integrity in the face of Russian cyber attacks, but the story has escalated since then – culminating this week in direct accusations against the Russian government. The CIA and even President Obama have directly implicated Putin as being personally involved with the deliberate aim of swaying the 2016 election. In a year of big stories, this may have been the most far reaching in its implications. One of those implications, which has already been alluded to by many in Washington, is that this act may in fact represent a form of Cyber Warfare.

So, what exactly does Cyber Warfare mean and how does it differ – if at all – from Cyber Terrorism? That’s a tough question, one that I’ve not seen answered clearly before. Cyber Terrorism can come from nation states, such as China, North Korea, Iran and so forth, but one might expect that actions perpetrated by nation-states are less like terrorism per se and more like warfare. It is worthwhile at this point to step back into the not too distant past and bring up a similar question that also still applies here – what’s the difference between a “Cold” and a “Hot” war? The Cold War, as you might remember, involved a whole host activities from espionage to proxy wars. The Hot or real war between the super-powers never occurred and it didn’t happen primarily because of the concept of Mutual Assured Destruction through use of our nuclear arsenals. In that case, the distinction between the terms also involved both the nature of the participants as well as the types of activities involved which is similar to the current question.

None of this really helps though to clear up the confusion regarding what is or what isn’t Cyber Warfare. Here are a few reasons why:

• Cyber Warfare can be both covert and overt – depending on the nature and intent of the attacks as well on the determination as to whether they should be publicized in any way.
• Cyber Warfare could be conducted by both Nation States and Terrorist organizations. The key distinction here though would be that we wouldn’t necessarily classify acts committed by smaller unknown groups or even individuals as Cyber Warfare. In those instances, the term Cyber Terrorism might be more applicable. However, it is also clear that in Cyber Warfare, as in traditional warfare, non-nation state organizations can and have conducted offensive operations.
• Cyber Warfare can be a standalone or blended activity (e.g. coordinated with other traditional war-fighting activities). It’s conceivable that an entire conflict could be fought solely within the Cyber Domain. Cyber “Domain” here refers to the notion that Cyber represents one of several potential war-fighting domains such as Land, Sea, Air and Space. The US military formally acknowledged Cyber as such a domain with its creation of US Cyber Command several years ago. Of course the reality of this statement is more complicated than it sounds as Cyber also infiltrates all other warfare domains through the technology implied by it – it is cross-cutting domain and even if an attack were completely limited to Cyber actions it is highly likely that physical capabilities (such war-fighting assets as ships, planes etc.) might be impacted.
• Cyber Warfare can be directed at the Government or the Industrial Base or both. We can’t say for example, that all attacks against businesses must be considered Terrorism per se – the intent is what’s important. If the intent of an attack is to cripple the country that’s been targeted, then a Cyber attack like that is no different in principle from the types of bombing raids we conducted against Germany in WW2 in order to cripple its industrial base. Today though, the sectors that are perhaps more vulnerable might be Energy and Finance as opposed to Manufacturing. The results might be the same though if the goal is hobble an economy or otherwise disrupt a nation state.

Now, we are ready to consider what the distinctions between Cyber Warfare and Cyber Terrorism really are. They would likely involve the following considerations:

1. Cyber Warfare must necessarily consist of a sustained campaign of Cyber activities, designed to disrupt any mission critical functions of an enemy at a national level. This doesn’t mean the activities have to occur in many places to effect a national impact, it merely has to be designed to impact an opponent that way (and would also likely encompass more than one attack or incident).
2. Cyber warfare must necessarily occur between substantial Cyber combatants. The nature of what constitutes a ‘substantial’ combatant lies in what resources they have to bring to bear in any given conflict. A well-established terrorist or rebel group may have the money and personnel to manage sustained attacks. However smaller groups with few resources may only be able to sustain limited operations or a single attack. While there is always the possibility that an individual or a small group might be able to do harm at the national level, it is unlikely that they could sustain this over months or years and it would be more akin to one-off terrorism than warfare in the context of sustained operations and likely outcomes.
3. Cyber warfare, in general, involves more specific objectives in contrast to Terrorism which is often random in nature and may only be focused on making a statement rather than effecting some desired outcome.

By these definitions, I’d have to say that the Russian hacking of the DNC computers and related activities designed to impact the 2016 election falls under the category of Cyber Warfare rather than Terrorism. And this begs the question, why does all of this matter and why do we need more specific definitions? The bottom line is, that if we don’t have a clear idea of what represents acts of Cyber warfare (either covert or overt), it’s highly likely we won’t be able measure our response properly. Deciding how to respond is obviously a very big deal – as any such decisions could quickly escalate from the Cyber domain into all the others. Perhaps our government does have all of this worked out, and maybe it’s just too secret for any of us to know about. However, from our vantage point now it’s all bit fuzzy. When the President says “we will retaliate in a manner and time of our own choosing” we basically don’t have a clue to what that really means.

Rather than spend a lot of time speculating as to what our response might be, we can instead highlight some principles that may apply to any such situation. The following principles represent a potential framework that might be used to help deal with Cyber warfare as it continues to evolve.

1. Proactive Awareness – In order to survive or win any Cyber conflict, the nation needs to know when in fact it is under attack. Some attacks are more obvious than others and as the recent election shows, our response can be slow or too late to avoid impacts. Proactive Cyber Awareness is not about hacking into everyone’s cell phones, but rather it is about being able to identify unusual behavior in key systems and sectors across the country (or wherever our interests may be). This means we need more selective and actionable intelligence then we seem to be getting now.
2. Measured Response – This has been mentioned in the news, but as I noted it’s not been explained by anyone (at least publicly) yet. For this to actually work, someone needs to define the measured responses up front rather than assessing each event as if it were the first time it had been considered. The landscape is fairly complicated so this involves a lot of work and some automation. However, it shouldn’t fully automatic any more than our current traditional war-fighting capabilities are – the human in the loop must always be present.
3. Defined Escalation Approach – This is a process and it ought to be built atop the measured responses defined previously, the idea being that whenever or wherever Cyber activities begin crossing over to other areas there needs to be another level of safeguards built in to avoid any type of cascading escalation that could lead to something like a nuclear conflict.
4. Maintain a Consistent Policy - In theory, our management of Cyber war shouldn’t be unique in each potential scenario – there ought to be a consistent expectation as to what will happen if enemies launch attacks against the US. This is a key point in the recent debate over Russia as the situation has also become embroiled in US political differences, confusing the matter. While there will always need to be specific considerations given to certain situations, we should never give an indication to any opponent that Cyber attacks may be permitted without any response coming from the US. This would be an extremely dangerous precedent and helps to explain why the President and CIA made statements this week to the effect that election interference would not go unpunished. Better late than never and like all of warfare, if we're in the game we should build policy around what's necessary to win - as opposed to settling for mere survival. There may such as a thing as a Cyber Maginot Line...
5. Continuous Innovation – This may be the most important point, given the stark reality that it is easier and more cost effective to mount a Cyber attack than it is to defend against one. Despite the billions spent each year in the US across government and the private sectors, Cyber Security breaches and attacks have only become more prevalent and severe. More focus needs to be given to pushing the envelope on innovation to help reduce the current advantages enjoyed by our Cyber opponents. Today, much if not the majority of innovation has come from the attackers and we’ve been playing catch-up. As in every other realm of warfare, the side with the greatest technological advantage tends to win.

It’s anyone’s guess as to whether the current Russian hacking crisis will boil over into something more, but one thing is certain, the age of Cyber Warfare has most definitely dawned.

Copyright 2016, Stephen Lahanas

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Technology & Election 2016 part 3 – The Failure of Data Science?

The first reaction on election night, November 8th, 2016 was – what, how did that happen? The entire country and in fact the whole world was more or less shocked at the unexpected outcome. But why was it so unexpected? The top level answer to that is simply that nearly every major poll or projection turned out to be wrong. What kind of numbers are we talking about? For example, Nate Silver’s FiveThirtyEight blog projected Clinton to win by about 5% in the popular vote (with 71% certainty). If we drill down to the state polls we see that the projections showed all 3 key turnover states, Wisconsin, Michigan and Pennsylvania going to Clinton by 3 to 5% in each contest. This is significant because many of these projections (and blogs like FiveThirtyEight) were using aggregates of dozens or hundreds of polls, not just one or a handful and the differences exceeded the margin of error.

Let’s step back for a moment and talk about the typical role that Data Science plays in the election process today. This role encompasses several well-known and some lessor known functions, including:

• Predictive polling
• Exit polling
• Predictive modeling
• Vote targeting (which facilitates a sort of CRM for campaign marketing as well as Get Out the Vote efforts)

There’s nothing new about polling, it’s been around for a long time. In fact, the last time there was a collective shock like this year’s outcome was in 1948, when the polls had predicted Dewey would win (by 50 to 45% - but Truman won by 50% to 45%). Polls have improved since then and of course, now we have the benefit of the latest data technology as well as 70 years of added experience, so how did nearly every major poll get it wrong this year? There are some theories; they include the following:

1. A lot of people changed their minds at the last moment and weren’t particularly firm in their previous opinions.
2. The Russians did it.
3. Many polls were not properly targeting prospective voters for their models.
4. Many people who had said they were voting for Clinton didn’t turn out to vote at all (e.g. the lack of enthusiasm)
5. The poll numbers for the 3rd party candidates may have been inflated, and when it came to election day these candidates received far fewer votes that had been predicted (the implication being they went ahead and voted for one of the main candidates with Trump being the main beneficiary).

To be honest, we may never have a fully satisfactory answer for what happened in the 2016 election. It is likely that we’ve never had a race where both of the main candidates were universally unpopular and that kind of situation might never happen again (and there’s no telling how that may have impacted the polling results). How do we move forward then? Did technology, did Data Science fail us in 2016? Maybe, but probably not. What we witnessed however is that technology is only as good as our ability to apply it. If situations become more dynamic, complex within a relatively short window, do we stick with what we know or do we adjust our models or practices?

I’d like to step back for a moment here and ask a larger question. Do we really want ironclad predictions before elections in the first place? Before the big upset on election night, many pundits were talking about the lessons learned from the 1980 election where results from the East Coast encouraged West Coast voters to stay home thinking their votes didn’t really count. Because of that, the FEC passed a rule prohibiting networks from announcing winners before certain polls close. Don’t polls predicting a sure outcome before an election have a similar chilling effect? This year for example, how many voters may have stayed home because while they weren’t terribly enthused about Clinton, thought she would win? That’s a hard question to ask because people who don’t show up to vote can’t be interviewed in exit polls (or at least typically aren’t interviewed as part of the election post mortem).

What can we do in Future to avoid getting surprised?

I think it is important that in coming up with suggestions here, we need to weigh the relative value of using a particular solution with the potential impacts of using it. In other words, if we view predictive polling as a relativistic activity (e.g. a bit like Heisenberg’s principle in that taking a measure can influence the outcome), then we might conclude that the highest value of such predictive polls relative to possible harm might end several weeks before the election. How then could we be assured that elections are honest, that public sentiment is in fact aligned with election results? Well, that can still come through exit polling – polls taken of actual voters on election day and in addition, sentiment polls taken the day after the election of potential voters who didn’t vote (something that doesn’t typically happen now).  

Suggestion – Place a moratorium on predictive polls at least 2 weeks prior to election day and preferably 4 weeks prior. Why would this work? Here are few potential benefits of doing it:

• This has the immediate effect of not making the election as much of a horse race and more of a contest of ideas.
• It has at least the potential of driving up voter participation – a lot can happen in 4 weeks, people can vote based on their opinions and do a little less hedging in making their decisions.
• It helps to combat Group-think (people swarming to the anticipated victor or becoming despondent about their own preferred candidates)
• It certainly eliminates the main source of any potential surprise.
• It may encourage candidates to take a more expansive view towards courting voters, recent trends have focused way too much attention on potential swing states and districts.

As you can tell, this and potentially other suggestions may have relatively little to do with Data Science itself, but have everything to do with how we apply it to given situations. I don’t believe the technology failed us here, I think we failed to recognize how much it already influences election outcomes. In my next post in this series, I’ll talk a bit more about Get Out the Vote (GOTV), politics as demographics and how technology has been and will be used to manage campaigns.

Copyright 2016, Stephen Lahanas 

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How Technology Defined the 2016 Election

There have been many fascinating stories, themes and memes that have emerged from the 2016 Election, but interestingly there is a common undercurrent running through them all that’s not being publicized so well. We’ve collectively focused almost entirely on the personalities behind the election with some minimal examination of processes, demographics, strategies and perhaps even less attention paid to policy & issues. But the real story may not be the “who” but the “how.” The actual game changer in 2016 wasn’t Donald Trump the candidate – it was technology as change facilitator.

Now, many of you might remember similar assertions being made after one or both of President Obama’s victories in 2008 or 2012. Social media and Big Data were credited with helping him to generate momentum and target voters. While it is true that those technologies made a difference for Obama in those elections, they weren’t exactly game changers in the overall scheme of things. Traditional media buys still happened, conventional wisdom on polling prevailed, Get out the Vote (GOTV) still functioned as usual and of course there weren’t any concerns that Russians were trying to pull a Watergate. This year though, all of that was turned on its head; the polls failed completely, Russians were launching cyber attacks, traditional media budgets and buys didn’t determine outcomes and GOTV was abandoned by one side entirely and yet they still won. Why did all of this happen?

Technology has finally caught up with the election process in almost all aspects of that process in 2016. One side understood the implications, the other didn’t and the rest is history.

At this point, I need to provide the obligatory disclaimer that like most of you, I wasn’t pleased with either choice presented in the November election. I’m not trying to compliment or side with one group or another here. But I do think it is important for both parties as well as the American Electorate to understand what mechanisms helped to shape outcomes in this year’s election. So, let’s take a look at each of these elements in turn. We’ll start in this post with Social Media.

Social Media – A lot was happening here and in truth it is still happening as Twitter has become the President Elect’s de facto Press Secretary. Some of us in the technology business had been positing for quite awhile that Social Media could and likely would begin to replace traditional media in terms of overall impact on the electorate so it wasn’t a complete surprise. However, when it finally happened this year it played out a little differently than we may have expected. For example, relatively few of us had taken into account the rapid and overwhelming influence of “Fake News,” on social media sites. However, to refer to all of this as fake news is perhaps a stretch, as there has been quite a lot of Advocacy journalism out there for awhile. But a certain percentage this year did fall into the totally fake news category and one might wonder if this in fact represents a form of Cyber attack or cyber warfare given the intent to change election outcomes. The reason I make the inference is due to the determination that much of this fake news was coming from Eastern Europe and may have been linked to the same Russian agenda that led to the confirmed hacks on Democratic Party servers, email accounts etc. But this is only part of the story behind the rise of Political Social Media.

Why is it that Social Media eclipsed traditional media in this year’s election? Here are some of the factors involved:

• Social Media is now more used and trusted than it was even 4 years ago. Combined with smartphone mobile technology it is everywhere, all the time.
• Traditional Media as we know it is beginning to submerge into a much larger and richer world of content which is being driven primarily by – you guessed it – technology. How soon will it be before Netflix or Amazon add news or information channels to the hundreds of cable & Roku channels already available. Once upon a time, we had 3 TV networks and perhaps 2 newspapers in each major city. Those days are gone forever.
• Social Media is interactive and participatory – it isn’t just information consumption. While people can comment on traditional media sites, on social media ordinary people can actually drive the dialog. This is enormously powerful and it does in many ways represent a sort of technological populism. (and despite all of the recent news equating Populism with the Alt Right, Populism is not inherently Right Wing at all – in fact our entire form of government is predicated on what was in essence a Populist experiment).
• People are tired of the art of negative advertising, at least in its 30 second format. Also, at a deeper level, when one advertises on traditional television or radio he or she is “interrupting” something (e.g. providing content we don’t want) whereas on Social Media the politics can be the entertainment in that people are directed to or served content based on their interests. The medium is the message here for real – in ways Marshall McLuhan never could have predicted.
• Thus, people can tailor their media experience using Social Media based on their own comfort levels. This is perhaps the area that concerns most traditional journalists and social scientists who have complained ever since the first Mosaic browser came out that Internet content could not be trusted nor could the people viewing it. However, this does give people a feeling of empowerment they wouldn’t otherwise have.  
• Social media is a much more cost effective proposition in terms of production of content and the ability to reach interested audience. A traditional political campaign may have spent ½ or more of their budget on ad production and ad buys in the past. This is no longer required and in fact may be the biggest single blow to the flood of dark money in politics that’s ever happened – we just haven’t recognized that yet given that the person who exploited this is a billionaire supported by a lot of dark money donors. Regardless though, the door has been opened.
• And perhaps most importantly in this election, Social Media’s focus on advocacy and dialog allowed for a much more intense debate without some of the controls that would have been in place under traditional media. People enjoy spectacle, sensation and those who are willing to focus on that over ideas or facts tend to get a lot of free attention on Social Media. While there have always been Tabloid publications out there, they were never accepted into the mainstream the way Social Media has. We see all of these factors coming together to become game changing – fake news, comfort zone filtering of content, sensationalism and the ability to not only join, but to really influence the national dialog. While we might not like some of this, it may be problematic trying to impose limits on it, so for better or worse, we need to deal with it.

I think it may be a safe prediction at this point to say that any campaign for any office going forward that doesn’t view Social Media as its primary media channel, will likely risk losing their election and will certainly experience a diminishing return on investment for dollars spent on traditional media buys. This could, at least near-term, tend to even the playing field for many candidates.

In my next post in this series, I’ll discuss the Russians, Cyber threats and voting integrity.  

copyright 2016 - Stephen Lahanas

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Building Effective IT Strategy - part 3

In our last two post on IT Strategy, we highlighted how Strategy can be structured, how it differs fro Tactics (although we will explore that in more depth in this post) and how to employ a consistent process towards developing strategy. The first two steps involved determining what the core strategic approach might be; the second focused on goal-setting. The third and most difficult part is assigning actions to goals...

So, using our Big Data case study, how would begin to translate the higher level goals into definitive actions and then what types of tactics might be used to carry out those actions?

This illustration highlights where Big Data fits within a larger set of Strategic elements in an overall Transformation initiative. This type of representation helps to define relationships, dependencies and quantifies where work needs to happen once the higher level goal-setting has been defined.  

The types of actions that might be involved with actualizing a Big Data Strategy might include the following:

• Creation of a team or center of excellence to manage the technology / project
• Definition and Deployment of a proof of concept 
• Acquisition of the raw data intended for use in the Big Data solution (so let's say this is for an energy company it might include SmartGrid sensor information).
• Acquisition and / or development of the Big Data Platform

All of these possible actions of course imply a number of key decisions that must be made; the following are a few examples of those;

• Determination of Big Data technology to use (triple store, key value etc.)
• Determination / selection of a Big Data solution hardware platform
• Determination of modeling or data profiling approach
• Choice of BI platform for data visualization

All of this information is going to be necessary in order to complete detailed roadmaps and ensure accurate estimates for those who manage the IT portfolio process in any given organization. Actions can then begin to be translated into milestones with traceable costs. Those Action-Milestones are then mapped specifically to goals/objectives previously identified in the higher level strategy.

Now, how does action to goal alignment involve Tactics? In the case we've introduced and in most others, the tactics involve the core tools for decision-making. So, for all of the decisions listed above, individual analyses of alternatives might be conducted. For product decisions, run-offs / competitions / evaluations and source selection processes are applied. For design considerations, an architecture approach is applied. All of these activities can also fit within a lifecycle process - all of this represents Tactics.  Why? because, we could use roughly the same lifecycle approaches for any type of technology - whether it is UAV development, Quantum Computing or building a SharePoint portal. It is the interchangeable actualization toolset for all strategy.

The hardest part of aligning Strategy, sub-strategies and tactics is when you find yourself in a very large transformation effort (one perhaps dealing with 100's of systems, dozens of technologies and perhaps thousands of people). There is no single solution, tool or approach for managing that - it represents what mathematicians often refer to as a unsolvable problem (NP Hard). We will look at IT Transformation and intense complexity in an upcoming post.


Copyright 2014, Stephen Lahanas


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What is Digital Transformation?

It sounds a bit misleading perhaps - maybe one gets the impression that this is something akin to rendering special effects for movies. In mainstream IT however, Digital Transformation has come to mean something quite different.

The shorthand definition for it is this; "the coordinated integration of all customer-facing digital capabilities." This includes mobile, web and social media. There is perhaps an implied level of underlying integration supporting of all it, but that isn't always required - at least not at first anyway.

We use the word "customer" here a bit loosely, as Customer can refer to any users or members of a certain organization. So, a government entity might consider citizens as customers and the military could consider its rank and file 'customers' in this sense also. Digital Transformation can occur in just about any type of organization specifically because the set of technologies involved is common across industries.

Ten years ago a 'Digital Transformation' would have likely been referred to as a "Portal Strategy." At the time, it was thought that the portal metaphor (and associated software products) would suffice to meet all customer-facing needs. A typical portal might include the following elements in 2005:

• Extranet
• Intranet
• Collaboration tools
• Content Management tools

What happened since then? Well, at least two and possibly three revolutions in technologies with more on the cusp; the key revolutions include:

• Mobile - (not laptops of course, but the smartphone and mobile)
• Social Media - some re-branding of collaborative capabilities merged onto smartphones as well
• Cloud Computing - re-branding of visualization - and importantly opening that up to customer facing apps (web and mobile).
• with Big Data coming on strong (although to be honest, most places still don't know how to use it so it's an outlyer with respect to customer facing technology).

On the backend of course are all the systems that actually make the organization go - but for the customer this is like the engine within the chassis. Most customers don't care what's under the hood until or unless it fails to perform.

Digital Transformation is a lot like enterprise integration but on the customer facing side of things. It is the alignment, coordination and update in tandem of the core mission apps and interfaces. And it can potentially include the Web of Things as well. So for example, if you have a grocery store then perhaps there are kiosks inside and gas pumps outside and nifty new cooler video displays etc. All of that technology is customer-facing so for a true transformation it all must be managed within a shared context.  The transformation has one goal - to go from a heterogeneous mix of random technologies to a holistic solution designed to facilitate strategic goals.

So, how does an organization undergo a Digital Transformation? We will look at this in more detail in one of our upcoming posts.

Some organizations (like larger retailers) face dual Digital Transformations; in so much as they must serve both traditional customers and the needs of many thousands of employees (with separate capabilities) such as in the example above targeted to employees...


Copyright 2014, Stephen Lahanas


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#StephenLahanas
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Outsourcing Innovation

With the recent focus on H1B reform; this is a good time to step back and consider the implications of current policy on our nation’s future. Anyone working in IT today cannot help but notice the flood of foreign workers that now populate every IT department and company in the US. Over the past 10 years the internal demographics of the typical IT group has changed radically; and this doesn't necessarily reflect the offshore workforce either. Taken together, it is probably safe to assume that many companies now source half of their IT workforce from talent who aren't US citizens. This demographic shift has been occurring at the same time that career opportunities and income growth have been shrinking across the US economy. Somewhere between 400,000 to 500,000 work visas were handed out during the Great Recession – yet remarkably lawmakers bought into the myth perpetrated by a handful a major IT companies that there was a shortage of skilled technology workers.

http://news.dice.com/2013/05/14/the-facts-behind-the-h-1b-debate/

Will these graduate have a future in the IT job market?

This situation is not only disingenuous and unfair though, it is dangerous and threatens the future of our economy and standing in the world. The reason for the flood of foreign workers is clear – larger employers are trying to commoditize IT labor to drive costs down as much as possible. The easiest way to do that is to either outsource work internationally or import international workers and pay them far less than American citizens. For the record, there is no shortage of skilled IT workers in the US and never has been. The current policies have driven thousands and perhaps millions of Americans out of work or at least out of the IT industry.

So why does this matter? Is it is any different than Manufacturers shipping their factories to China or other industries sending their customer support overseas?

Yes, it is different. And it matters because Information Technology is where most of this country’s emerging industry is or at least is dependent upon. Our leaders will talk endlessly about innovation and how it drives economic growth – but what happens when we outsource the most critical portions of innovation? Here are some of the more obvious implications:

1. Our current model places intellectual property at extreme risk and more or less ensures that key innovations will be released globally before the US has a chance to fully exploit new capability developed / invented here.
2. We will more or less permanently hamstring our internal ability to support all aspects of our own R&D. Unrealistically low labor cost expectations will make us dependent on cheaper and cheaper labor and will drive talented people out of IT and into other emerging fields.
3. We will kill the emerging STEM focus in secondary and post-secondary education and seriously damage the value proposition for pursuing any advanced education as it becomes clearer and clearer that American Citizens will be discriminated against when it comes to IT hiring.
4. The talent who truly drive innovation will leave this country and head to Europe and elsewhere where labor commoditization doesn't drive IT hiring. The focus and centers of innovation will move to places where talent rather than cost is valued.

We are setting ourselves up for a crisis that doesn't need to occur. The net result of that crisis will be a continuing slide of the standard of living of most Americans as technology leadership migrates elsewhere. The short term greed of a handful of major employers is reshaping our entire economic landscape for the next 50 years. It’s time we recognized that risk and built a policy that encourages development of the American IT workforce rather than its replacement. This country was built on the promise of opportunity – instead we've developed a new and semi-feudalistic labor class system for the most important segment of our workforce. Any foreign labor that we do allow in should be brought in with Green Cards and paid the prevailing rates rather than artificially low ones, and the numbers of those visas approved should be pegged to unemployment rather than to claims of phantom shortages. American Innovation is built on the foundation of a fair, just and open society; those principles should not be outsourced. If Americans can no longer be trusted to manage our own innovation – what is there for us do in our economy – what future can we truly call our own?

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