To illustrate the Abstraction issue I raised in Learning....Slowly, here are a couple of examples from my study of probability. To lay the groundwork, there are three basic operations OR, AND, and GIVEN and for the most part they are defined in terms of each other in a tight little tautology -- see my terminology summary here. Every time I tried to figure out what they _really_ did I ended up in some kind of sink-hole-loop. This was exacerbated by the only two fully worked problems in the AIMA textbook where the behavior of OR and AND distinctly diverged from the definitions.
So I tortured myself for about a week with: "What are they trying to tell me?" Then I took a couple of showers...
The first problem was OR. The definition summed up a set of values and subtracted the AND of those values, but the book example just summed a buncha things and was done with it. After the first shower I realized that the AND part was there to eliminate double-counting of certain values and that "they" has silently elided it because "they" had also silently elided the actual double count that would have been subtracted out. A little note to that effect would have saved me a week's worry....maybe.
The second problem was AND. The definition shows a product of values, i.e., multiplying them all. The book example showed a sum... Well, WTF?! I went around and around on that and complained to anyone who showed any semblance of interest -- where such interest died fairly quickly with no positive results. During the second shower it occurred to me that I had only seen addition in one other place in this whole mess, and that was in calculating the Total Probability of a set of variables. Since probabilities were usually specified as Conditionals -- the probability that X is true GIVEN that Y is known to be true -- this involved multiplying a buncha values (one for each variable of interest) which were "conditioned" on Y being true, then multiplying a buncha different values, conditioned on Y being false, and then SUMMING the results... Eureka! That's what the (fkrs) were doing in the book: The values they were working with came from a table where all the multiplying had already been done, so all they had to do was add them up. Jeez, maybe just another little note would have been in order? Or maybe I wasn't supposed to be looking at it so closely?
So...The point is, my (slightly) Intelligent Behavior was the result of hot water....no, no, it was the result of having a higher level view of the problem and seeing patterns that were not apparent in the details. This is what I'm trying to call Abstraction. Of course this "ability" is probably the result of billions-and-billions of mindless iterations in some very low level neural processing, just like looking at a map integrates a huge amount of visual information with a huge amount of "common sense" information to come up with a route. And this is what Krakauer was trying to get at in his talks: What we really want to call Intelligence is so far and above what our poor little machines are doing these days that the scales need to be re-calibrated all the way down.
Monday, October 31, 2011
Saturday, October 29, 2011
Local Color III -- only in the SouthWest...
Police responded to a stabbing call at 1:21 a.m. Wednesday in ... southwest
Santa Fe.
Deputy Kurt Whyte arrived at the apartment where he says he found the 48-year-old male stabbing victim, "bleeding heavily from his head and right wrist area."
Chavez, who police say admitted stabbing the man with a kitchen knife, was arrested and charged with aggravated battery on a household member with a deadly weapon, battery upon a peace officer, assault upon a peace officer and resisting or evading a police officer. She remained jailed late Wednesday in lieu of a $5,000 surety bond... Her boyfriend, meanwhile, remained hospitalized late Wednesday but was in stable condition...
Santa Fe County Sheriff's deputies took the 60-year-old [Chavez] directly to jail after they say she repeatedly stabbed her boyfriend Wednesday after arguing during a game of Monopoly.
Police say both Chavez and her boyfriend appeared to be intoxicated.
Deputy Kurt Whyte arrived at the apartment where he says he found the 48-year-old male stabbing victim, "bleeding heavily from his head and right wrist area."
Chavez, who police say admitted stabbing the man with a kitchen knife, was arrested and charged with aggravated battery on a household member with a deadly weapon, battery upon a peace officer, assault upon a peace officer and resisting or evading a police officer. She remained jailed late Wednesday in lieu of a $5,000 surety bond... Her boyfriend, meanwhile, remained hospitalized late Wednesday but was in stable condition...
Santa Fe County Sheriff's deputies took the 60-year-old [Chavez] directly to jail after they say she repeatedly stabbed her boyfriend Wednesday after arguing during a game of Monopoly.
Police say both Chavez and her boyfriend appeared to be intoxicated.
Friday, October 28, 2011
AI Class 3, Learning...Slowly
Well. I survived last week's class and got 100% on the homework!! Part of this was due to a sudden realization that the demo code I had been so assiduously analyzing actually contained the skeleton of a system for answering three of the questions. And part was dumb luck, tempered with reason of course. The realization part happened after I had worked out the problems on my own, but I used the software to validate my answers -- which were, amazingly but truly, correct.
The original estimate for time to be spent on the class was a glib 1-10 hours a week -- it's not clear if that included watching all the video lessons which are at least 2 hours a shot -- and maybe some go-getter StanfooFrosh with all their god-given brain cells still intact could do it. Me? I'd say 50 hours last week trying to intuit the inner workings of Probability...
This week -- Machine Learning -- I got off easy. After only three days I believe I'm done. Or else I've missed something really important. Those days include time spent shuttling around finding working internet connections -- because my usually-fairly-almost reliable LCWireless coop took a big poop right after the videos were posted on Tuesday -- and summarizing the lessons for an online study group which meets Thursday evenings. In the course of the summary I discovered that I had developed a simplified method for working the hard parts of the homework (which I _might_ reveal after entries are closed next week). In keeping with standard practice only the first of the two lessons had any relevance to the homework. So now I'm living with the sneaking fear that the exams will cover the missing lessons.
As has been pointed out a number of times: Why do I care about my grade? I dunno. Knee jerk reaction to jerks I guess.
Moving on to the philosophy portion of our time here together....One thing I've noticed about the class so far is that it makes heavy use of exactly what computers are good at: Mindless Iteration.
First we had Search which is just opening doors and walking down hallways until you stumble upon that which you were seeking. Admittedly there are some shortcuts. And even some automated ways to discover the shortcuts. But it's really just wandering around in a big field without your glasses.
Then there was my bugaboo, Probability. This boils down to multiplying and adding big lists of small numbers. Over and over. It's something that Professor Sebastian seems to pride himself on being able to do, but god help me, that's why we have computers isn't it? Of course one does need to be able to set up the problem and understand the necessary transformations -- and the results, which are in many cases "not obvious" -- but that's Systems Analysis.
And this week, Machine Learning. Many of the problems presented make big use of Probability so it goes without saying that there's a lot of repeated number crunching. Moving on to Regression and Clustering, to para-quote: "Often there are no closed form solutions so you have to use iteration." All manner of try-try-again-until-you-succeed perseverationist algorithms are put to use. Gradient Descent is just bumbling-around-in-a-field search with a proviso that one always bumbles down hill. And we haven't even addressed non-local minima yet.
So my question: Is this Intelligent behavior? In one respect, once a computer finds a way to do something we used to pride ourselves on, we always diss it by saying, "Well, that's not _really_ intelligent after all now is it?" But in another respect I think number-crunching may be going about it wrongly. In the map problem used to introduce different types of searching the question was how to get from Arad to Bucharest -- which is probably easier if you are in Romania A human would look at the map, squint their eyes for a couple seconds, and then go, "Yah shure, we gotta go through Rimnicu." The computer however tries all the possibilities...in the "less intelligent" versions it even goes the wrong direction, just to, you know, see...and then finally pretends that it has discovered a route.
What the computer does is wander around in the field until it trips on the solution, but what the human does is some kind of integration and abstraction of the data. I think this ability to Abstract is at the core of intelligence. We may get to some bits of that in this class but it's gonna be some rough iterations.
The original estimate for time to be spent on the class was a glib 1-10 hours a week -- it's not clear if that included watching all the video lessons which are at least 2 hours a shot -- and maybe some go-getter StanfooFrosh with all their god-given brain cells still intact could do it. Me? I'd say 50 hours last week trying to intuit the inner workings of Probability...
This week -- Machine Learning -- I got off easy. After only three days I believe I'm done. Or else I've missed something really important. Those days include time spent shuttling around finding working internet connections -- because my usually-fairly-almost reliable LCWireless coop took a big poop right after the videos were posted on Tuesday -- and summarizing the lessons for an online study group which meets Thursday evenings. In the course of the summary I discovered that I had developed a simplified method for working the hard parts of the homework (which I _might_ reveal after entries are closed next week). In keeping with standard practice only the first of the two lessons had any relevance to the homework. So now I'm living with the sneaking fear that the exams will cover the missing lessons.
As has been pointed out a number of times: Why do I care about my grade? I dunno. Knee jerk reaction to jerks I guess.
Moving on to the philosophy portion of our time here together....One thing I've noticed about the class so far is that it makes heavy use of exactly what computers are good at: Mindless Iteration.
First we had Search which is just opening doors and walking down hallways until you stumble upon that which you were seeking. Admittedly there are some shortcuts. And even some automated ways to discover the shortcuts. But it's really just wandering around in a big field without your glasses.
Then there was my bugaboo, Probability. This boils down to multiplying and adding big lists of small numbers. Over and over. It's something that Professor Sebastian seems to pride himself on being able to do, but god help me, that's why we have computers isn't it? Of course one does need to be able to set up the problem and understand the necessary transformations -- and the results, which are in many cases "not obvious" -- but that's Systems Analysis.
And this week, Machine Learning. Many of the problems presented make big use of Probability so it goes without saying that there's a lot of repeated number crunching. Moving on to Regression and Clustering, to para-quote: "Often there are no closed form solutions so you have to use iteration." All manner of try-try-again-until-you-succeed perseverationist algorithms are put to use. Gradient Descent is just bumbling-around-in-a-field search with a proviso that one always bumbles down hill. And we haven't even addressed non-local minima yet.
So my question: Is this Intelligent behavior? In one respect, once a computer finds a way to do something we used to pride ourselves on, we always diss it by saying, "Well, that's not _really_ intelligent after all now is it?" But in another respect I think number-crunching may be going about it wrongly. In the map problem used to introduce different types of searching the question was how to get from Arad to Bucharest -- which is probably easier if you are in Romania A human would look at the map, squint their eyes for a couple seconds, and then go, "Yah shure, we gotta go through Rimnicu." The computer however tries all the possibilities...in the "less intelligent" versions it even goes the wrong direction, just to, you know, see...and then finally pretends that it has discovered a route.
What the computer does is wander around in the field until it trips on the solution, but what the human does is some kind of integration and abstraction of the data. I think this ability to Abstract is at the core of intelligence. We may get to some bits of that in this class but it's gonna be some rough iterations.
Friday, October 21, 2011
AI Class 2, Probably
(I realized my numbering scheme for these posts is bad, so I'm going to use AI Class N, where N is the week.)
Anyway... Week 2 and counting with 10% of the class under my belt.
I missed one question on the first homework -- after receiving actual clarifications-from-on-high vis the ambiguities, some of which were significantly different from the assumptions I was making. What I missed is the idea that, for an environment to be "Completely Observable" requires that the agent use no memory of previous states. Which, IMHO, is a little strange, since it was pointed out that chess and checkers need one bit of memory to determine who's turn it is next...But there's no point in arguing with two-dimensional-video-professors, so I guess I will Just Let It Go.
Now well into Week 2: Probability: Our two new video-Lessons were posted a day late due to crashes in the homework system from the previous week AND after three mostly full time days I'm still only half finished. So I am calculating the priority of my posterior, i.e.: it is kicking my asp. I suspect that my chance of completing the class is 1/N as weeks progress. Fortunately it seems that the homework only requires the absorption of the first of the two Lessons, and that only one of the six questions requires the full-press-calculations that the prof -- Sebastian Thrun -- so gleefully dragged us all through. Repeatedly.
As per established process the first Lesson was a (long) set of short videos each explaining a concept, followed by a quick quiz question. And in keeping with tradition, most of the quizzes were used to introduce the next section rather than reviewing and using what was covered in the current one. Sometimes a quiz introduced new notation and concepts with no explanation. I finally realized that entering random answers would move things along so I could get to the point. Occasionally a question could be answered by grinding through the material at hand, so that does make the occasional correct answer a thrilling Thank God! moment.
We now return to our original programming: Unit 4 -- Probabilistic Inference. Oy.
Anyway... Week 2 and counting with 10% of the class under my belt.
I missed one question on the first homework -- after receiving actual clarifications-from-on-high vis the ambiguities, some of which were significantly different from the assumptions I was making. What I missed is the idea that, for an environment to be "Completely Observable" requires that the agent use no memory of previous states. Which, IMHO, is a little strange, since it was pointed out that chess and checkers need one bit of memory to determine who's turn it is next...But there's no point in arguing with two-dimensional-video-professors, so I guess I will Just Let It Go.
Now well into Week 2: Probability: Our two new video-Lessons were posted a day late due to crashes in the homework system from the previous week AND after three mostly full time days I'm still only half finished. So I am calculating the priority of my posterior, i.e.: it is kicking my asp. I suspect that my chance of completing the class is 1/N as weeks progress. Fortunately it seems that the homework only requires the absorption of the first of the two Lessons, and that only one of the six questions requires the full-press-calculations that the prof -- Sebastian Thrun -- so gleefully dragged us all through. Repeatedly.
As per established process the first Lesson was a (long) set of short videos each explaining a concept, followed by a quick quiz question. And in keeping with tradition, most of the quizzes were used to introduce the next section rather than reviewing and using what was covered in the current one. Sometimes a quiz introduced new notation and concepts with no explanation. I finally realized that entering random answers would move things along so I could get to the point. Occasionally a question could be answered by grinding through the material at hand, so that does make the occasional correct answer a thrilling Thank God! moment.
We now return to our original programming: Unit 4 -- Probabilistic Inference. Oy.
Sunday, October 16, 2011
Local Color II
Form the Eldorado Police Blotter....
"Victim reported that while on vacation an unknown person has twice made entry into the residence by unknown means and stole $5000.00 in cash and placed erotic items on a bed. No damage was reported to the residence and nothing else appeared to be disturbed."
"Victim reported that while on vacation an unknown person has twice made entry into the residence by unknown means and stole $5000.00 in cash and placed erotic items on a bed. No damage was reported to the residence and nothing else appeared to be disturbed."
Wednesday, October 12, 2011
AI Class III, Homework I
Well, we finally got it, and it's not so scary, just a little vague... They posted a set of seven short videos each posing a question with a multi-choice or small numeric answer. Unfortunately there are some ambiguities about the constraints and exact definitions in some of the problems. There are a couple useful discussion threads on the Reddit AICLASS site which are wrangling about the specifics:
I have distilled and posted the Week 1 Homework questions and options, along with comments about the ambiguities encountered (mine and others from the reddit threads). Stay tuned for answers next week...
To get to the bottom of it we're really gonna just have to wait until we get clarification from on high. We hope we do anyway. As someone in those threads pointed out, this is the kind of stuff one would ask the TA or Prof if one were having a two-way class like experience.
<IMHO mode="I could be wrong about this">
One thing that has come up in general is how to deal with the basic Environment class definitions:
For Instance: one homework question asks if coin flips are Partially Observable and if they are Stochastic or not. There seems to be some confusion about the scope of Observability, e.g., if you don't know the future is the system Fully Observable? Or from a different tack, "If you don't know how your Adversary is going to respond, is it Partially Observable or even Stochastic?"
Because I think that being Fully Observable covers just the current system state and doesn't preclude being uncertain about future states, in this context I would say: "Do we know the entire result after each action is performed, or is there still ambiguity in the current state of the system?"
There's also confusion about Discrete vs Continuous. The questions are more philosophical than practical, such as "Can one even have a Continuous representation of a system?" or "Since the result of a coin flip is dependent on exactly how it is flipped, isn't that Continuous?" I say, lighten up a bit...If you've got something that can take any real-number value, it's Continuous. But if it can only take a sub-set of values, it's Discrete. So the result of a coin flip is ??? -- maybe I'll answer next week, eh?
And there was a funny mis-apprehension on the Unit-1 quiz that asked if a robot car driving in a "real" environment was Adversarial. The given answer was No -- admittedly with a little joshing around. I think this is because the instructors live in Palo Alto and only have to deal with Volvo-Soccer-Mom's passive aggressive driving, rather than in New Mexico where every drunk wants to be in your lane.
</IMHO>
I have distilled and posted the Week 1 Homework questions and options, along with comments about the ambiguities encountered (mine and others from the reddit threads). Stay tuned for answers next week...
To get to the bottom of it we're really gonna just have to wait until we get clarification from on high. We hope we do anyway. As someone in those threads pointed out, this is the kind of stuff one would ask the TA or Prof if one were having a two-way class like experience.
<IMHO mode="I could be wrong about this">
One thing that has come up in general is how to deal with the basic Environment class definitions:
- Fully vs Partially Observable
- Deterministic vs Stochastic
- Discrete vs Continuous
- Benign vs Adversarial
For Instance: one homework question asks if coin flips are Partially Observable and if they are Stochastic or not. There seems to be some confusion about the scope of Observability, e.g., if you don't know the future is the system Fully Observable? Or from a different tack, "If you don't know how your Adversary is going to respond, is it Partially Observable or even Stochastic?"
Because I think that being Fully Observable covers just the current system state and doesn't preclude being uncertain about future states, in this context I would say: "Do we know the entire result after each action is performed, or is there still ambiguity in the current state of the system?"
There's also confusion about Discrete vs Continuous. The questions are more philosophical than practical, such as "Can one even have a Continuous representation of a system?" or "Since the result of a coin flip is dependent on exactly how it is flipped, isn't that Continuous?" I say, lighten up a bit...If you've got something that can take any real-number value, it's Continuous. But if it can only take a sub-set of values, it's Discrete. So the result of a coin flip is ??? -- maybe I'll answer next week, eh?
And there was a funny mis-apprehension on the Unit-1 quiz that asked if a robot car driving in a "real" environment was Adversarial. The given answer was No -- admittedly with a little joshing around. I think this is because the instructors live in Palo Alto and only have to deal with Volvo-Soccer-Mom's passive aggressive driving, rather than in New Mexico where every drunk wants to be in your lane.
</IMHO>
Tuesday, October 11, 2011
AI Class II
Ok then. They got the Search lessons up. And are promising to post a homework assignment by about 4 hours ago... Also the quiz-post-refusal thing seems to have been a server loading problem and I didn't have any trouble posting answers today. So still a bit behind the curve here, but moving in the right direction.
There are some slips, probably mostly on my part. Like the quiz question about whether a Depth First Search is guaranteed to find a goal and be complete. I forgot to remember that the lecturer mentioned that we were dealing with an infinite depth search tree for this particular incident. So, more minus-quiz points for me. Gotta hang onto every word apparently.
<Edit mode="stew">
Overnight I realized that there were two (my count) examples of lapses in pedagogical technique in the first week's videos -- three if you count not defining Rationality but then including it in the summary slide for Unit 1.
First is the Depth First Search question above. I replayed the lesson -- unit 2.20 -- and he does say "...lets move .... to infinite trees..." 20 seconds before completion of the quiz question presentation. So I should have remembered it. But, if he had repeated the infinite tree condition at the end of the question I might have caught on to what he was getting at.
The second was in unit 2.31. He describes the simple "Vacuum World" environment and calculates the number of states in a two position system by writing 2 x 2 x 2 = 8. This is the correct number but not the right calculation, and -- my excuse for failing the quiz at the end of the next unit -- when the system is scaled up with more positions one needs to use the right calculation, which is: 2 x 2^2 (notice that 2 is one of two values where this is equivalent to the previous multiplication, ?maybe three if zero^zero is a number?). This is because there are X possible conditions for N positions -- every position can be either clean or dirty -- so the total number of environmental states is X^N, not X*N. I merrily went along with the multiplication paradigm when it came to scaling up to 10 positions and multiplied 2 times 10 instead of raising 2 to the 10th power. Again I might have caught on, and had a better understanding of the issue, if it had been treated more rigorously in the introductory case.
</Edit>
In a different example they present the idea that you can use an estimated-cost-to-goal value to guide a search in fruitful directions. This is called an "Heuristic". However they never defined the word but just started using it in the middle of describing some algorithms. Lucky me, I'd already read the Book so I knew. Just like the "Rationality" thing in Unit 1...
Have an online study group meeting tomorrow (Weds) night in which we are supposed to discuss homework confusions (among others). So I hope we get the homework in time...
There are some slips, probably mostly on my part. Like the quiz question about whether a Depth First Search is guaranteed to find a goal and be complete. I forgot to remember that the lecturer mentioned that we were dealing with an infinite depth search tree for this particular incident. So, more minus-quiz points for me. Gotta hang onto every word apparently.
<Edit mode="stew">
Overnight I realized that there were two (my count) examples of lapses in pedagogical technique in the first week's videos -- three if you count not defining Rationality but then including it in the summary slide for Unit 1.
First is the Depth First Search question above. I replayed the lesson -- unit 2.20 -- and he does say "...lets move .... to infinite trees..." 20 seconds before completion of the quiz question presentation. So I should have remembered it. But, if he had repeated the infinite tree condition at the end of the question I might have caught on to what he was getting at.
The second was in unit 2.31. He describes the simple "Vacuum World" environment and calculates the number of states in a two position system by writing 2 x 2 x 2 = 8. This is the correct number but not the right calculation, and -- my excuse for failing the quiz at the end of the next unit -- when the system is scaled up with more positions one needs to use the right calculation, which is: 2 x 2^2 (notice that 2 is one of two values where this is equivalent to the previous multiplication, ?maybe three if zero^zero is a number?). This is because there are X possible conditions for N positions -- every position can be either clean or dirty -- so the total number of environmental states is X^N, not X*N. I merrily went along with the multiplication paradigm when it came to scaling up to 10 positions and multiplied 2 times 10 instead of raising 2 to the 10th power. Again I might have caught on, and had a better understanding of the issue, if it had been treated more rigorously in the introductory case.
</Edit>
In a different example they present the idea that you can use an estimated-cost-to-goal value to guide a search in fruitful directions. This is called an "Heuristic". However they never defined the word but just started using it in the middle of describing some algorithms. Lucky me, I'd already read the Book so I knew. Just like the "Rationality" thing in Unit 1...
Have an online study group meeting tomorrow (Weds) night in which we are supposed to discuss homework confusions (among others). So I hope we get the homework in time...
Subscribe to:
Posts (Atom)