I presently attend New York University where I study Computer Science, and when I have time I also visit University of Maryland, College Park, in the Math and Philosophy departments, and conduct research with their Human-Computer Interaction Lab.

As time permits I shall publish research material and works in progress. This may include topics in Cognition (Organic and Machine Learning), Model Theory, and Computational Complexity, but I may also share some anecdotes about methodology as my lab work progresses. Additionally, my previous and forth-coming publications/collaborations shall reside here.

Tutorial: Machine Learning Data Set Preparation, Part 3

To see the entire Machine Learning Tutorial, go here.

Remember that boring data?

Eliza Santiago Guatemala Yes No
Fred Winchester Canada No Yes
Marvin Ngoma Ghana Yes No
Xiong Mao USA Yes ???

This example may remind some of the battered adage that “correlation does not imply causality.” This cautionary statement is often the only thing people remember from their brief exposure to statistics. While it is certainly useful, it is not entirely the case.

Let’s remove the extraneous details such as the name and country, replacing them with a generic, indexed tag. After all, we aren’t really (at this point), interested in whether certain details such as the number of vowels in a name, or what part of the world we find a country, have any impact on car ownership or ice-cream preference. To take those into account would be to introduce overfitting, which is the phenomenon of having so much information in a model that it becomes burdensome to separate the data that has a causal effect from that which we ought to consider arbitrary.

To take it a step further, let’s generalize car ownership and ice-cream preference to “A” and “B.” We obtain something very similar to a truth table in deductive logic.

n1 c1 A NOT-B
n2 c2 NOT-A B
n3 c3 A NOT-B
n4 c4 A ???

One of the early promises of inductive and probabilistic models is that by putting data into a sophisticated enough machine, hidden rules will emerge. From this it can become really tempting to treat these hidden rules as having deductive weight, in the same way that statements such as “all birds have wings” and “all creatures with wings can fly” allow one to deduce “all winged creatures can fly.” But there are massive problems with this beyond mere arrogance. The biggest problem is that with data obtained in the wild may not have been generated from a deductive rule (if A then B). With my personal methodology, chaos typically reigns supreme.

One is a threshold problem. As I see it, what is the ideal threshold between underfitting (too little data to gleam any decent insight) and overfitting (too much data to get a reliable model that can provide accurate results in a reasonable amount of time)?

Consider this model:

Animal Feathers? Wings? Can Fly?
Merlin Yes Yes Yes
Kiwi Yes No No
Dolphin No No No
Vampire Bat No Yes ???
Penguin Yes Yes ???

Here, we give our inductive engine (i.e. a machine learning agent) a lot of details from which to issue decisions. We could assume that this engine is intelligent enough not to take “the animal’s name ends with –in” as a criteria, but that is a bold assumption. Sure, if we are doing supervised machine learning, then we should train our machine to answer whether a given animal can fly, based off of a combination of the most relevant information. But just how this machine agent knows what information is relevant and which should be considered a coincidence lays squarely on the humans training that machine.

In unsupervised models, we can’t make the assumption that machines won’t learn from superfluous details such as whether an animal’s name ends with –in or not. Adding the generic, indexed tags as animal names, similar to the tags in the second table of this lesson, can sidestep this and lessen the risk of overfitting.

Given the data on Merlins, Kiwis, Dolphins, Vampire Bats, and Penguins, what answer should we expect regarding bats’ and penguins’ ability to fly?

Tutorial: Machine Learning Data Set Preparation, Part 2

To see the entire Machine Learning Tutorial, go here.

Let’s start with the data.

Data just means “that which is given” in Latin. If I had you a muffin, that’s data. But that’s not the data we’re going to go into. Data in this case means a bunch of information, and we need to extract it from a source, put it into a format a machine can read, and then parse it in order to gleam something that isn’t immediately obvious from first examination.

What we’re given can be a picture, or a string of letters. The task of machine learning is to infer from what is given, things that are not given. In some cases, humans will assist in a training role. That is, a person will be asked to identify what object(s) are in a picture (is it a fire hydrant? is it a car?) or what a sentence means. In other cases, there will be no training, and the machine will learn from just the data.

This concept translates fairly well over both visual and linguistic cases, but the linguistic case is a bit easier to get into.

So let’s consider some really boring data.

Eliza Santiago Guatemala Yes No
Fred Winchester Canada No Yes
Marvin Ngoma Ghana Yes No
Xiong Mao USA Yes ???

We have some made-up people: Fred Winchester, Eliza Santiago, Marvin Ngoma, and Xiong Mao. And we have their phone numbers, the type of ice-cream they like, and if they drive a car. From this, we must ask part of the data we actually need and what is extraneous. Phone numbers are assigned in a largely arbitrary (but not necessarily random) fashion.

For the sake of reducing details (and thereby lessening the possibility of overfitting), we ought to either discard the phone number entirely, or at least reduce it to just the country code (say, +44 for the UK) and area code prefix. Let’s assume we had their name, their number, and the answer to at least one of the questions about their ice cream preferences and whether they drive.

So the easy question is, provided with this information, do we have sufficient data to know Xiong Mao’s ice cream preference?

The harder question is why this information is or is not sufficient. In subsequent installments of this tutorial I will go into the arguments for and against whether we can make a deductive claim off of incomplete information (this being one of the core premises and promises of machine learning).

Tutorial: Machine Learning Data Set Preparation, Part 1

To see the entire Machine Learning Tutorial, go here.

In this multi-part tutorial, I shall go over the basics of taking “live” human data and putting into a suitable format to feed into one’s machine learning platform of choice. This series will go from general to specific and offer insight on methodology before going into gathering data, putting this data into a machine-readable format, and then feeding this into machine learning platforms such as TensorFlow and Weka.

To start, I have to think of the data set in at least two lights.

One is theme-oriented: the data has to have a thematic character that is neither too broad as to capture spurious correlations, and not so narrow that it confirms the obvious. This is the story told by the data.

The other is feature-oriented. A good data set needs to have its raw data converted into a workable ontology — which is just a fancy word to refer to the objects and the landscape they inhabit. These are the nouns of story told by the data.

These are methodological, but reflect a deeper concern: doxology. What human belief do these reinforce? Will these reinforce a prevailing status quo, something people take as a given, or will the data be such that new insights can be gleamed? This goes beyond merely trying to avoid overfitting (when data is so finely grained that it has too many details and thereby has too much extraneous information to report to be of much use). This means making sure that if your data set is going to be free from racial bias, care has been taken to remove proxies for racial demographics, such as zip code.

Summary: Machine Learning on the Rosanne-ABC Firing Incident Dataset

Summary of results: which methodology/modality “wins?”

Vanilla Merged
Algorithm Speed CCI % ROC AUC RMSE F-1 CCI % ROC AUC F-1 RMSE
ZeroR Instant 37.9333 0.4990 0.4144 NULL 47.1000 0.4990 NULL 0.4536
OneR Instant 43.0000 0.5420 0.5339 NULL 52.9833 0.5660 NULL 0.5599
NaiveBayes Fast 63.8500 0.8160 0.3808 0.6410 63.9667 0.8000 0.6430 0.4374
IBK Fast 56.5333 0.6910 0.4386 0.5230 59.5833 0.6510 0.5470 0.4972
RandomTree Fast 59.5833 0.6800 0.4474 0.5920 62.7167 0.6700 0.6210 0.4954
SimpleLogistic Moderate 73.6500 0.8850 0.3065 0.7320 73.6500 0.8730 0.7300 0.3502
DecisionTable Slow too slow for viable computation on consumer-grade hardware
MultilayerPerceptron Slow
RandomForest Slow
Vanilla Merged
Meta-Classifier Speed CCI % ROC AUC RMSE F-1 CCI % ROC AUC F-1 RMSE
Stack (ZR, NB) Moderate 37.9333 0.4990 0.4144 NULL vacuous results, omitted
Stack (NB. RT) Moderate 63.7000 0.8230 0.3795 0.6350 61.9833 0.6980 0.6130 0.4523
Vote (ZR, NB, RT) Moderate 62.0833 0.8430 0.3414 0.6110 64.0500 0.8330 0.6260 0.3830
CostSensitive (ZR) Instant 37.9333 0.4990 0.4144 NULL 36.6667 0.4990 NULL 0.4623
CostSensitive (OR) Instant 42.7000 0.5400 0.5353 NULL 39.6167 0.5170 NULL 0.6345
CostSensitive (NB) Fast 63.8500 0.8160 0.3808 0.6410 64.0833 0.8010 0.6450 0.4365
CostSensitive (IBK) Fast 56.5333 0.6910 0.4386 0.5230 59.5833 0.6510 0.5470 0.4972
CostSensitive (RT) Fast 59.5833 0.6800 0.4474 0.5920 63.3833 0.7050 0.6350 0.4728
CostSensitive (SL) Moderate 73.6500 0.8850 0.3065 0.7320 74.7833 0.8780 0.7450 0.3478

My results are contained in a separate text file in lab journal format. Salient results consisted of:

Continue reading “Summary: Machine Learning on the Rosanne-ABC Firing Incident Dataset”

Lab Journal: Machine Learning on tweets related to the Roseanne-ABC firing incident

This is my lab journal for the analysis of a data set composed from tweets related to the 2018 firing of Rosanne from ABC over a racist statement.

A summary of these results with methodological comments can be found here.

1) Preparation/Parsing the data set

2) Running the data-set as-is (“vanilla”)

3) Merged data set (Pro, Anti, UncNeut)

4) Meta classifications – Voting and Stacking on the vanilla and merged data sets

5) Introduction of Penalties via CostSensitiveClassifier

Continue reading “Lab Journal: Machine Learning on tweets related to the Roseanne-ABC firing incident”

Turing Tests (Chess)

One of these sets of Chess moves represents a match between two human agents; the other has at least one machine agent as a player.

a) 1. e4 e5 2. Nf3 Nc6 3. Bb5 Nf6 4. d3 Bc5 5.Bxc6 dxc6 6. Nbd2 Bg4 7. h3 Bh5 8. Nf1 Nd7 9. Ng3 Bxf3 10. Qxf3 g6 11. Be3 Qe7 12. 0-0-0 0-0-0 13. Ne2 Rhe8 14. Kb1 b6 15. h4 Kb7 16. h5 Bxe3 17. Qxe3 Nc5 18. hxg6 hxg6 19. g3 a5 20. Rh7 Rh8 21. Rdh1 Rxh7 22. Rxh7 Qf6 23. f4 Rh8 24. Rxh8 Qxh8 25. fxe5 Qxe5 26. Qf3 f5 27. exf5 gxf5 28. c3 Ne6 29. Kc2 (diagram) Ng5 30. Qf2 Ne6 31. Qf3 Ng5 32. Qf2 Ne6 ½–½

b) 1. Nf3 Nf6 2. d4 e6 3. c4 b6 4. g3 Bb7 5. Bg2 Be7 6. O-O O-O 7. d5 exd5 8. Nh4 c6 9. cxd5 Nxd5 10. Nf5 Nc7 11. e4 d5 12. exd5 Nxd5 13. Nc3 Nxc3 14. Qg4 g6 15. Nh6+ Kg7 16. bxc3 Bc8 17. Qf4 Qd6 18. Qa4 g5 19. Re1 Kxh6 20. h4 f6 21. Be3 Bf5 22. Rad1 Qa3 23. Qc4 b5 24. hxg5+ fxg5 25. Qh4+ Kg6 26. Qh1 Kg7 27. Be4 Bg6 28. Bxg6 hxg6 29. Qh3 Bf6 30. Kg2 Qxa2 31. Rh1 Qg8 32. c4 Re8 33. Bd4 Bxd4 34. Rxd4 Rd8 35. Rxd8 Qxd8 36. Qe6 Nd7 37. Rd1 Nc5 38. Rxd8 Nxe6 39. Rxa8 Kf6 40. cxb5 cxb5 41. Kf3 Nd4+ 42. Ke4 Nc6 43. Rc8 Ne7 44. Rb8 Nf5 45. g4 Nh6 46. f3 Nf7 47. Ra8 Nd6+ 48. Kd5 Nc4 49. Rxa7 Ne3+ 50. Ke4 Nc4 51. Ra6+ Kg7 52. Rc6 Kf7 53. Rc5 Ke6 54. Rxg5 Kf6 55. Rc5 g5 56. Kd4 1-0

The answer is below.

Continue reading “Turing Tests (Chess)”

Talk: Evolution of Memory

I presented the following at University of Maryland, College Park, 30 Oct 2017. It summarises three papers with constructive feedback on where to improve their methodology.

The bottom line is simple: we know memory is fallible and that we evolved this sort of memory mechanism rather than just a purely rigidly veridical mechanism — the question is why evolve a seemingly imperfect mechanism?

Abstract: The following three approaches show that updating information in novel situations (rather than a well-defined niche) differentiates the distinctly human form of memory from that which non-human agents possess: we need to update information as time passes and as social arrangements change (not so much the environment in which we must survive and reproduce, but rather, in the uniquely human terrain or social landscape, ie. regarding what is “due” others as well as, or, more importantly, what is “due” us in particular). Rigid memory serves us well (and we seem to possess this just as non-human animals do, in cases such as locating resources ); but it breaks down in social interactions when we must perform so-called moral book-keeping to disentangle our ever-changing social obligations (and, more saliently, what others owe us — as human memory has an ego-driven, self-knowing, meta-representational character).

Continue reading “Talk: Evolution of Memory”

The Generality Constraint [Draft]

Gareth Evans, in The Varieties of Reference, described “thought” in functional and structural terms. He called this the generality constraint. This small article shall gather some formulations of the generality constraint that offer some suggestions on where it can be used elsewhere in and outside of Philosophy.

  • Evans’ definition.
  • Carruthers’ weak&strong formulations.
  • Some considerations such as Camp’s claim that categorial restrictions are not allowed; and my own counter-claim that a basic notion of extensibility does away with the need for such restrictions.
  • Lastly, how to actually use the generality constraint beyond argue over what Evans meant. This includes future directions and applying generality constraint elsewhere outside of Philosophy: type theory for computation, natural language processing, X-Phil tests, cross-cultural associations, cog sci (autism, developmental psych) and so on.

From The Varieties of Reference:
Generality Constraint
(Unrestricted): If an agent can think the thought “A is an F,” and the agent can think the thought “B is a G,” then the agent can think the thoughts “B is an F” and “A is a G.”

Two interpretations of the constraint, via Peter Carruthers in “Invertebrate Concepts Confront the Generality Constraint (and Win)” are as follows:
Strong Generality Constraint: If an agent possesses the concepts A and F (and is capable of thinking “A is an F”), then for all (or almost all) other concepts B and G that the agent could possess, it is metaphysically possible for the agent to think “A is a G,” and in the same sense possible for it to think “B is an F.”
Weak Generality Constraint: If an agent possesses the concepts A and F (and is capable of thinking “A is an F”), then for some other concepts B and G that the agent could possess, it is metaphysically possible for the agent to think “A is a G,” and in the same sense possible for it to think “B is an F.”

(as a work in progress, this will be updated as time permits)