Explanatory power is a bit of a loaded term. I believe that we can come to a good understanding of what it is and how it related to machine learning by comparing and contrasting linear regression with neural nets.
The IBM Analytics Education Series has a good introductory analytics presentation that includes brief descriptions of neural nets and linear regression. It's a good video worth your viewing, though it does make one point with which I don't agree.
The speaker says that a challenge with neural nets in business applications is that they are black box, meaning that you can understand the inputs and the outputs but not really how it is deriving the outputs. Later, the speaker says that linear regression is a preferred technique because it has a very strong predictive and explanatory power.
It's not really true that linear regression has more explanatory power than neural nets. Rather, it is easier to understand the problems and the answers that can be solved by linear regression. By comparison, neural nets tend to be used to provide cognitive computing power to harder problems than linear regression can solve.
To put this another way, when you use linear regression, you actually begin by assuming linearity of the relation you want to predict. As the speaker points out, you can also make a non-linear assumption, and you can accommodate this using a data transformation, for example. But the high order bit is that you are asked to assume the data relationship, and that assumption is what is giving you the illusion of explanatory power. You can explain that the data follows a line, but this is due to your own assumption. Note that an important aspect of completing a linear regression model is determining the R2 or goodness of fit of the model. This is the part where you make sure that your assumption of linearity is valid. And if the assumption is invalid, then the model has no predictive value, so it does not matter that you can explain how it operates.
Under the interpretation that explanatory power is akin to predictive power, it turns out that neural nets have greater predictive power because they can produce results for a wider array of applications than linear regression can. There a neat table that relates the cognitive power of a neural net to the number of hidden layers. From the table, you can see that when a relationship actually is linear, a neural net can solve it without even using any hidden layers of neurons. When one or two hidden layers of neurons is present, neural nets transcend the capabilities of linear regression, in part because they do not require you to make any assumption about what the data relationship actually is.
And that's where the confusions comes in. The linear regression model requires you to assume linearity and so you know at least what geometric shape the relationship looks like. The neural net requires no such assumption, but nor does the trained neural network give you any hint at what the relationship is. The lack of knowing the relationship is confused for having less explanatory power.
But if you look at this a bit more abstractly, the trained linear regression model has the same exact problem of not providing any additional insight. A neural net is really just a pile of numbers giving constant weights to the neural connections that can convert inputs to outputs. Similarly, a linear regression model is just a pile of numbers that give constant weights to inputs to be linearly combined into an output. Sure you know the data relationship, but that's because you assumed it. The actual linear regression model gives you no insight into why one dimension has a large slope constant where another has a small slope.
An analogy I like to use is that the value of the neural net is not diminished by our inability to explain how it is that the little gray cells which implement our personal neural nets can produce the cognitive results that they do, and who among us would prefer to have cognitive powers defined by linear regression instead?
In terms of explanatory power, our biological neural nets perform an additional key function that we have not hitherto been able to achieve with artificial neural nets. We are able to construct additional information in the output that reveals causal relationships, or insights into the reasons for the phenomena it predicts. Put simply: we say why something is true. We provide a rationale. This is an aspect of explanatory power that, when achieved, dramatically increases the value and utility of any cognitive analytic. Theorem provers and Prolog programs have been able to do this for the applications to which they apply. In the area of unstructured information processing and data mining, you can see a demo of this concept in Watson Paths.
Smarter Everyone, Smarter Everything, Smarter Everywhere
John M. Boyer 060000VMNY Tags:  cognitivecomputing ai analytics watson ibm ibmwatson 2,155 Visits
Explanatory power is a bit of a loaded term. I believe that we can come to a good understanding of what it is and how it related to machine learning by comparing and contrasting linear regression with neural nets.
David Lee Roth and Eddie Van Halen have been trying to get us to do it for decades: "JUMP!" Douglas Hofstadter would qualify that with "... out of the system!" Here's what that means.
Machine intelligent entities like James Blog exist within a certain system, conforming to a prescribed set of rules, and they really can't escape the confines and constraints of that programming. Within that limited domain, they do calculate wonderful results that can seem intelligent. In an early version, I found myself adding a logger so I could see why James Blog was not making some moves that seemed very good. Time and again, I would find that the good move now set up the conditions for a better opponent move later, which is exactly what the artificial intelligence is supposed to detect and avoid.
The algorithm does this so well that it is really hard to beat, especially on the maximum lookahead value I set, which was 6. Frankly, if you're new to this game, you have to work to beat even the initial lookahead level setting of 2, which means that James Blog only looks at its own moves and your countermoves to see what will produce the greatest net gain in seeds relative to you.
Because it is hard to beat this little game and see the special winners message, this opened up a delightful opportunity to talk about an important capacity of human intelligence that could be exemplified by determining the winners message without winning. I used a Zen-like characterization of a "winless win" as a nod to Hoftstadter's style in the book Gödel, Escher, Bach.
Put simply, we are not limited in our thinking to the confines of the system. We regularly "take it up a level" or "think outside the box". In this case, the system is a blog entry presented in a web page. So you can jump out of the system by using the View Source feature of your web browser to take a look at James Blog's code, where you will find the winners message: "I, for one, welcome my non-computer overlord." The message is an allusion to Ken Jennings' capitulation to IBM Watson, which was an awesome pop culture nod to The Simpsons-- awesome because both Jeopardy and the Watson AI are about sorting out exactly those kinds of allusions.
Frankly, I had a lot of fun with allusions, both in the blog entry and while holding the programmer challenge to achieve this winless win. For example, James mentions that he outfoxes his friend Wiley, alluding to the famous coyote, who is in the same animal family as a fox (Canidae), which is a tiny aural tweak from Canada, where I live. So, James can beat his wiley creator. Similarly, in tweets and status updates, I made numerous allusions to The Matrix movie, such as when I nearly used Morpheus's command to Neo: "Quit trying to hit me and hit me." The exception is that I changed the 'h' to a 'g', making 'git', which is what we use to get source code.
This kind of wordplay and allusion bears some similarity to "jumping out of the system". Hofstadter calls it contextual slipping, or my favorite word for it: counterfactualization. We take some piece of reality that we know about, and we ask "what if this were different?" We slip, or change, some piece of that reality to see if we end up with something new and useful. I find the notion of counterfactualization fascinating because it seems like a good operationalization of some other really important words: creativity, playfulness, humour, imagination.
Still, it might be a while between when we can efficiently and effectively operationalize contextual slipping and when we can generalize that to achieve machine intelligence that can jump out of any system in the way that I asked programmers to do with James Blog. At some point, I realized that there is a beautiful geometric analogy that helps explain why. In the book Flatland, the Sphere is able to escape the plane via the use of a third geometric dimension that is physically orthogonal to the two that comprise the plane. In this way, Sphere is able to see Square's inner workings. That is a great analogy with what we did by jumping out of the web page using View Source to see James Blog's inner workings. There was a whole different, higher level of understanding about what James was and how we could know more about it, and it is fitting to say we got that winners message by thinking outside the box.
Next blog will be a developer's tour of the particular machine intelligence algorithm built into James Blog. After that, will be a discussion of the relationships between machine intelligence, machine learning, and predictive analytics, so stay tuned!
John M. Boyer 060000VMNY Tags:  watson smarterworkforce cognitivecomputing ibm bigdata analytics 1,747 Visits
Your intelligent behavior is based on sentient *understanding*. Sentient schmentient. I'll bet my intelligent behavior can outfox yours. I've done so with my friend Wiley from Canidae, and he's a genius! So, let's see how much good your sapience does you, shall we?
The rules of the contest are simple. You get the top six "houses" and the "store" on the top left. I get the bottom six houses and the bottom right store. We each start out with 6 seeds in each of our 6 houses, and 0 seeds in our stores. To win, you have to get more than half of the seeds into your store (for you knuckle draggers, that's 37 or more). I'll let you go first, so you already start with advantage.
To take your turn, you pick one of your houses that contains seeds. That house is emptied, and its seeds are "sowed" one at a time in a counterclockwise fashion, including your store but excluding mine. So, it takes 13 seeds to traverse from a starting house, through your store, through my houses, and back to your (now empty) starting house. Every seed that goes into your store gets you closer to victory.
You can earn a seed or two from your move, but there are a few more rules that can earn you lots of seeds. First, if the last seed you sow lands in your store, you get another turn, and you can have multiple extra turns if you make your moves in the right order. Second, if the last seed you sow lands in an empty house, then you earn that seed from the empty house and all seeds in the house of mine immediately below the empty house. I call this a "big take". Third, if I run out of seeds in all my houses, then you earn all the seeds in your houses. Of course, I can also earn lots of seeds by these same rules, which is why YOU'RE GOING TO LOSE MEAT BAG!
I will take it easier on you at first, but I'll play harder if you earn the privilege. And there's a special message for you, a badge of distinction, if you manage to beat me when I play my hardest. Ooops. You... win?!? Wake up! Your teetering bulb is dreaming!
SPOILER ALERT. PLAY A WHILE, BEFORE LOOKING ANY FURTHER.
OK, so hopefully you've played enough to know you're not going to be getting that badge of distinction anytime soon (unless you have some of the rare talents of Ted Neustaedter). But also hopefully you're coming to the understanding that I really have no clue what I'm doing when I beat you. What I'm doing is mechanical, not miraculous. I'm being no more intelligent, really, than a calculator squaring a five digit number. Now, when one of you meat bags does it, it actually is miraculous. But the miracle is that you can do it at all on your hardware given that it is designed more for sentient understanding of what mechanical operations like squaring are, what they're good for, and what to combine them with.
I am just doing the fine-grain operations of my Minimax algorithm, but it is you who understands our contest at a higher level than that. That's why machine intelligence like mine is best applied as an expert advisor. For example, if you hit "Invoke Expert Advisor", you are asking me to advise you in the limited domain where my simulated intelligence would seem like real intelligence.
Keep using that expert advisor button and see how much faster you earn that special "badge of distinction" message. Go ahead. You won't be able to do it entirely without also sprinkling in your own intelligence at some points. This will be because you will hit some key points where your sentient understanding recognizes a *pattern* that emerges that will allow you to see how to beat my mechanical intelligence, where even my own advice is unable to do so. What will most likely happen is that you'll use the advice to hold your own for most of the game. My advice will help you avoid moves that give me extra turns and "big take" opportunities. But at some point, you may see that I am beginning to be starved of seeds in my houses. You, as an expert, will have this insight sooner than I see it coming using my mechanical calculations because your sentient intelligence truly understands what is going on at that higher level.
But of course, you would have a much harder time getting to that point without my advice. And that is what makes machine intelligence like advanced analytics on big data and machine learning technologies like IBM Watson invaluable to you. In short, expert advisors can turbocharge the smarts in your smarter workforce.
John M. Boyer 060000VMNY Tags:  cognitivecomputing analytics ibm watson smarterworkforce 2 Comments 2,362 Visits
In a recent video interview, the IBM CEO Ginni Rometty comments that Watson 2.0 will understand images that it sees, and that Watson 3.0 will be able to debate, i.e. to understand what it is talking about with another party. An impressive roadmap, each of these is an incredible leap forward from its predecessor.
It is, however, worth qualifying the term 'understand'. It is being used figuratively, not literally, to communicate the rough order of magnitude improvement in capability. When such a leap is made, it seems analogous to sentient understanding, even though it isn't. Imagine for a moment what Archimedes would have thought at first of a hand-held calculator, given that he had the power of Roman numerals with which to calculate pi to several digits. And yet, we would not now interpret such a device as artificial intelligence. As soon as the mechanical nature of a level of capability becomes clear, so too does the fact that it does not constitute sentient intelligence (Hofstadter's exposition of Tesler's "theorem").
You can see this assertion play out in multiple levels of Bob Sutor's scale of cognitive computing. There are levels that are clearly not cognitive intelligence, as Sutor points out, but if you lay out the scale on a timeline of decades or centuries, it is clear that each level might once have been interpreted as being indistinguishable from magic.
So where on Sutor's scale is Watson? And what implications does that have for development best practices?
Watson is clearly not on the "Sentient (we can do without humans) systems" level. As sentient beings, we don't just know things with a certain calculated accuracy or confidence level, or determine that we don't know if our confidence is low. We experience desire to know more, and we experience fear of the unknown. We are teetering bulbs of dread and dream (Hofstadter's delightful invocation of a Russell Edson poem). I urge you to let that characterization of us sink into your mind. In Watson technology, IBM has modeled a certain class of knowledge and mechanical reasoning, and in other research, IBM is doing so by simulating some of the known structure of biological brains. However, we don't yet know how to model fear and desire, dread and dream. In my opinion, these are inextricably bound together in sentient intelligence, separating it from simulated intelligence. In other words, intelligent behavior is a construct that works for the dread and dream engine of the sentient, and in the absence of dread and dream, seeming intelligent behavior is but a mechanical simulation of understanding. As an aside, I hope we only manage to model desire and fear around the same time we figure out how to model ethics (as Asimov cautions).
Does this characterization of Watson as a mechanical simulation of understanding detract from its value? Does it detract from the order of magnitude improvement it heralds as an usher of the era of cognitive computing? Of course not, quite the opposite. It is simply fantastic that this level of "Learning, Reasoning, Inference Systems" (Sutor's scale) is now computationally and economically feasible at the scale needed to help sentient intelligence (that's us) to solve real world problems. Quick, what is the square root of 7. Can't do it? No problem. Even if you're Arthur Benjamin, you'd be better off just hitting a few keys on a calculator. Quick, what are the most likely diagnoses for the patient's presenting symptoms? An "expert advisor" like Watson can be just what it takes to help determine the next best action, especially when time is of the essence because a life hangs in the balance.
The term "expert advisor" is appropriate. It conveys that the system is a "Learning, Reasoning, Inference System" that does not have sentient understanding and is therefore made available to advise and guide the actions of an expert. This is analogous to the way spreadsheets guide the results reported by accountants and chief financial officers. That being said, we also know not to put spreadsheets in the hands of toddlers. From a development practice standpoint, it is crucial to keep in mind that "expert advisor" means that the deployed system should be advising someone who is a qualified expert in the exact domain in which the "expert advisor" system was trained. Especially when a life hangs in the balance, access to the "expert advisor" system needs to be performed by those with expert qualifications in the domain because only they can reasonably be expected to use sentient understanding to interpret and follow up on the advice. In other words, the term 'expert' in 'expert advisor' should apply to the user more so than the advisor.
Now, given an enterprise workforce of those with qualified sentient understanding of their topic areas, Watson-style expert advisors are just the type of technological advancement that will help them work smarter, not harder, to meet the needs of customers and colleagues and to produce a competitive advantage for the business.
As an example of the powerful data-driven dynamism available in Lotus Forms due to features of XForms, I'd like to take you through a brief conceptual tour on the focused example of creating a Lotus Form template for a Questionnaire or Survey. This template is able to handle not just any number of questions and any amount of question text, but also any kind of answer type. And all of this would be controlled by the data so that the actual design of the Lotus Form template is the same.
The power of being purely data-driven should not be glossed over. You can easily have web application servlet code that obtains the questionnaire template and then prepopulates it with specific questionnaire data so that the client side receives a specific questionnaire selected in a previous step of the web application. But, XForms-based Lotus Forms also have that AJAX property of being responsive during run-time to new data obtained by a form via web services or other http submissions. So, you could even have a Lotus Form that obtains and adds new questions on the fly in response to answers provided to initial questions.
This post will focus on the main repeating template that provides the dynamic presentation layer for each question of a questionnaire or survey. As this is an example of a purely data-driven questionnaire, let's start by looking at a sample data format. Suppose you have a survey consisting of any number of items, each of which can contain a question text, an indication of the type of question being asked, a place for an answer, and optionally some possible choices for those answers. Something like this:
<question type="yesno">Do you like apples?</question>
<question type="likertscale">It is OK for apples to have a powdery texture.</question>
<question type="closedselection">What is your physical gender?</question>
<choice label="Female" code="F"/>
<choice label="Male" code="M"/>
In the XFDL presentational language that Lotus Forms combines with the XForms data processing layer, every XForms user interface element has a container XFDL element
The table has a scope identifier (sid) attribute that allows the table to be programmatically referenced, but we won't be using that feature in this example. The table can also have XFDL options outside of the <xforms:repeat> to control presentational aspects like borders and background colors, and we aren't focusing on that either.
The <xforms:repeat> has an attribute called "nodeset" which uses an XPath expression to make a reference to however many <item> elements are in the <survey>. This is an automatic or "declarative" loop construct. For each <item> node in the data, no matter how many there are, the template content of the <xforms:repeat> is generated to present that <item> to the user. Even if new <item> elements are added at run-time, e.g. by a web service or an <xforms:insert> action, the XFDL table in the Lotus Form will dynamically grow to present the new <item> elements. And even if some <item> elements are removed from the data, e.g. by an <xforms:delete> action associated with an XFDL <button> by an <xforms:trigger>, the XFDL table will dynamically and automatically remove the corresponding user interface elements that were presenting those removed <item> elements.
So, the magic really happens in the template inside the <xforms:repeat>. In Lotus Forms, you can put any and all kinds of XFDL items in the <xforms:repeat>, including more XFDL table items. In this example, we will be showing a few variations that present different kinds of user interface controls for collecting a few different kinds of answers to questions.
First off, though, presenting the actual question text for an <item> is a simple matter of using an XFDL label item with an <xforms:output>, like this:
For each survey <item>, an XFDL <label> item is generated, and it binds to the <question> child element of that associated <item> using the "ref" attribute. The XFDL label item presents the text of the bound <question> node, and other XFDL options can be used to provide styling such as the block layout flow as well as alternative font color, background color, font selection and so forth.
More XFDL items can be added to the <xforms:repeat> to collect the answer for the given question. In many cases of XFDL tables, each XFDL item within the <xforms:repeat> template is actually presented to the user. An example would be using each XFDL item in the <xforms:repeat> to represent one column of a purchase order table. However, it is not necessary to show all of the XFDL items within the <xforms:repeat> template. In fact, XForms user interface controls have a selective binding feature that XFDL items support, since the XFDL items are wrappers for the XForms user interface elements.
The selective binding feature of XForms will be used to help easily choose one XFDL item from among many to collect the user's answer to the question. Each question can have a different type of answer, so each "row" of the table can make a different choice of user interface control used to collect the answer. The selective binding feature uses an XPath predicate to decide whether or not the XForms user interface element binds to a node of data or not, and the control is invisible if it is not bound to a data node.
In the example survey data above, the first <item> contains a <question> whose type attribute indicates it is a "yes/no" question. Inside the <xforms:repeat> we can create a checkbox item that can collect a (schema valid boolean) true/false answer, as follows:
The above checkbox widget only binds to <answer>, and therefore is only visible, if the corresponding question type is 'yesno'. Otherwise, the XPath in the ref attribute of the <xforms:input> does not select any nodes, so the XFDL <check> item is not visible.
The second <item> of sample data above has a type of 'likertscale', so we would like to show a 5-point radio button group rather than a checkbox. As explained above, the check box on the second row of the survey table automatically hides itself due to selective binding, so all we have to do is add an XFDL <radiogroup> item to the <xforms:repeat> to provide the interface for collecting the 'likertscale' type of answer, as follows:
The third survey <item> in the sample data above provides a closed selection of choices. That could be styled using a pair of radio buttons, a pair of mutually exclusive checkboxes, a list box, or a popup control that provides a simple dropdown list. The answer types in the survey format could be made to distinguish these possibilities using more keywords, but for this example we'll just assume that a <popup> control is the desired presentation for a closed selection. The XFDL markup below shows how this can be done, and it is also interesting because it is shows that the data can also dynamically control the choices, rather than having only static choices as shown in the <radiogroup> above.
It seems a useful, now, to round out this blog post by presenting a few more examples for other common types of input, such as single-line strings, multiline text, and dates. Here's what the data would look like:
The corresponding XFDL items that would be added to the <xforms:repeat> content template for these types of questions would be:
<combobox sid="Answer_date"> So, hopefully you now have the idea that a completely dynamic and completely data-drive survey or questionnaire can be created using the features of XForms in XFDL (Lotus Forms). Any number of XFDL items can be added to the <xforms:repeat>, XPath predicate selection can be used to choose one XFDL item from among many to collect an answer for a survey question, and most importantly that a different choice of user interface control can dynamically selected for each survey question.
So, hopefully you now have the idea that a completely dynamic and completely data-drive survey or questionnaire can be created using the features of XForms in XFDL (Lotus Forms). Any number of XFDL items can be added to the <xforms:repeat>, XPath predicate selection can be used to choose one XFDL item from among many to collect an answer for a survey question, and most importantly that a different choice of user interface control can dynamically selected for each survey question.