brendan_oconnor_ai brendan_oconnor_ai-2009 brendan_oconnor_ai-2009-134 knowledge-graph by maker-knowledge-mining
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Introduction: Article: Fannie Mae Logic Bomb Would Have Caused Weeklong Shutdown | Threat Level from Wired.com . I love the term “logic bomb”. Can you pair it with a statistics bomb? Data-driven bomb? Or maybe the point is a connectionist bomb.
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same-blog 1 1.0 134 brendan oconnor ai-2009-01-30-“Logic Bomb”
Introduction: Article: Fannie Mae Logic Bomb Would Have Caused Weeklong Shutdown | Threat Level from Wired.com . I love the term “logic bomb”. Can you pair it with a statistics bomb? Data-driven bomb? Or maybe the point is a connectionist bomb.
2 0.18667321 126 brendan oconnor ai-2008-11-21-The Wire: Mr. Nugget
Introduction: One of my favorite scenes of wisdom from The Wire : D: Nigga please. The man who invented them things, just some sad ass down at the basement of McDonald’s, thinkin’ of some shit to make some money for the real playas. POOT: Nah, man, that ain’t right. D: Fuck right. It ain’t about right, it’s about money. Now you think Ronald McDonald go down to that basement and say “Hey Mr. Nugget, you da bomb, we sellin’ chicken faster than you can tear the bone out, so I’m gonna write my clowney-ass name on this fat-ass check for you?”
3 0.18481296 54 brendan oconnor ai-2007-03-21-Statistics is big-N logic?
Introduction: I think I believe one of these things, but I’m not quite sure. Statistics is just like logic, except with uncertainty. This would be true if statistics is Bayesian statistics and you buy the Bayesian inductive logic story — add induction to propositional logic, via a conditional credibility operator, and the Cox axioms imply standard probability theory as a consequence. (That is, probability theory is logic with uncertainty. And then a good Bayesian thinks probability theory and statistics are the same.) Links: Jaynes’ explanation ; SEP article ; also Fitelson’s article . (Though there are negative results; all I can think of right now is a Halpern article on Cox; and also interesting is Halpern and Koller .) Secondly, here is another statement. Statistics is just like logic, except with a big N. This is a more data-driven view — the world is full of things and they need to be described. Logical rules can help you describe things, but you also have to deal wit
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Introduction: Like everyone, I’ve been just starting to look at the new, tentative, proof that P != NP from Vinay Deolalikar. After reading the intro, what’s most striking is that probabilistic graphical models and mathematical logic are at the core of the proof. This feels like a machine learning and artificial intelligence-centric approach to me — very different from what you usually see in mainstream CS theory. (Maybe I should feel good that in my undergrad I basically stopped studying normal math and spent all my time with this weird stuff instead!) He devotes several chapters to an introduction to graphical models — Ising models, conditional independence, MRF’s, Hammersley-Clifford, and all that other stuff you see in Koller and Friedman or something — and then logic and model theory! I’m impressed.
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Introduction: It’s a human political belief model — based on Cyc! I’m not sure logic represents how people think all that well, but seeing the formalization of ideology is fascinating. And besides, the methodology of cognitive modelling is awesome. The link: Modeling How People Think About Sustainability David C. James, M. P. Aff LBJ School of Public Affairs The University of Texas at Austin May 2005 First Reader: Lodis Rhodes Second Reader: Chandler Stolp How effectively can a computer model represent the belief systems of different people? How would one go about representing a belief system using formal logic? How would that ideology react to different scenarios related to sustainable development? The author constructs the Cyc Agent-Scenario (CAS) model as a way to investigate these questions. The CAS model is built on top of ResearchCyc, a knowledge base (KB) and logical inference engine. The model consists of two agents (Libertarian and Green) and two scenarios. The model simula
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same-blog 1 0.99637282 134 brendan oconnor ai-2009-01-30-“Logic Bomb”
Introduction: Article: Fannie Mae Logic Bomb Would Have Caused Weeklong Shutdown | Threat Level from Wired.com . I love the term “logic bomb”. Can you pair it with a statistics bomb? Data-driven bomb? Or maybe the point is a connectionist bomb.
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Introduction: One of my favorite scenes of wisdom from The Wire : D: Nigga please. The man who invented them things, just some sad ass down at the basement of McDonald’s, thinkin’ of some shit to make some money for the real playas. POOT: Nah, man, that ain’t right. D: Fuck right. It ain’t about right, it’s about money. Now you think Ronald McDonald go down to that basement and say “Hey Mr. Nugget, you da bomb, we sellin’ chicken faster than you can tear the bone out, so I’m gonna write my clowney-ass name on this fat-ass check for you?”
3 0.67163324 54 brendan oconnor ai-2007-03-21-Statistics is big-N logic?
Introduction: I think I believe one of these things, but I’m not quite sure. Statistics is just like logic, except with uncertainty. This would be true if statistics is Bayesian statistics and you buy the Bayesian inductive logic story — add induction to propositional logic, via a conditional credibility operator, and the Cox axioms imply standard probability theory as a consequence. (That is, probability theory is logic with uncertainty. And then a good Bayesian thinks probability theory and statistics are the same.) Links: Jaynes’ explanation ; SEP article ; also Fitelson’s article . (Though there are negative results; all I can think of right now is a Halpern article on Cox; and also interesting is Halpern and Koller .) Secondly, here is another statement. Statistics is just like logic, except with a big N. This is a more data-driven view — the world is full of things and they need to be described. Logical rules can help you describe things, but you also have to deal wit
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Introduction: Like everyone, I’ve been just starting to look at the new, tentative, proof that P != NP from Vinay Deolalikar. After reading the intro, what’s most striking is that probabilistic graphical models and mathematical logic are at the core of the proof. This feels like a machine learning and artificial intelligence-centric approach to me — very different from what you usually see in mainstream CS theory. (Maybe I should feel good that in my undergrad I basically stopped studying normal math and spent all my time with this weird stuff instead!) He devotes several chapters to an introduction to graphical models — Ising models, conditional independence, MRF’s, Hammersley-Clifford, and all that other stuff you see in Koller and Friedman or something — and then logic and model theory! I’m impressed.
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Introduction: It’s a human political belief model — based on Cyc! I’m not sure logic represents how people think all that well, but seeing the formalization of ideology is fascinating. And besides, the methodology of cognitive modelling is awesome. The link: Modeling How People Think About Sustainability David C. James, M. P. Aff LBJ School of Public Affairs The University of Texas at Austin May 2005 First Reader: Lodis Rhodes Second Reader: Chandler Stolp How effectively can a computer model represent the belief systems of different people? How would one go about representing a belief system using formal logic? How would that ideology react to different scenarios related to sustainable development? The author constructs the Cyc Agent-Scenario (CAS) model as a way to investigate these questions. The CAS model is built on top of ResearchCyc, a knowledge base (KB) and logical inference engine. The model consists of two agents (Libertarian and Green) and two scenarios. The model simula
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same-blog 1 0.97096306 134 brendan oconnor ai-2009-01-30-“Logic Bomb”
Introduction: Article: Fannie Mae Logic Bomb Would Have Caused Weeklong Shutdown | Threat Level from Wired.com . I love the term “logic bomb”. Can you pair it with a statistics bomb? Data-driven bomb? Or maybe the point is a connectionist bomb.
2 0.80583692 20 brendan oconnor ai-2005-07-11-guns, germs, & steel pbs show?!
Introduction: Looks like it’s become a mini-series: Jared Diamond’s Guns, Germs, and Steel has hit PBS! Great book, if repetitive and a little too ambitious — he has a great environmental/technology explanation of the differences in societal development between Europe and the Americas, but he’s pretty weak when trying to tackle Asia vs. Europe, or a number of other situations talked about near the end of the book. But anyway, it’s fantastic social science. Wish I had a TV around… it would be nice if the show made it onto the web. For some time, I remember hearing that all of Commanding Heights was available for free on the web, but it looks like they’ve taken it down. We shall see.
3 0.38237035 66 brendan oconnor ai-2007-06-29-Evangelicals vs. Aquarians
Introduction: Just read an interesting analysis on the the simultaneous rise of the cultural left and right (“hippies and evangelicals”) through the 50′s and 60′s. Brink Lindsey argues here that they were both reactions to post-war material prosperity: On the left gathered those who were most alive to the new possibilities created by the unprecedented mass affluence of the postwar years but at the same time were hostile to the social institutions — namely, the market and the middle-class work ethic — that created those possibilities. On the right rallied those who staunchly supported the institutions that created prosperity but who shrank from the social dynamism they were unleashing. One side denounced capitalism but gobbled its fruits; the other cursed the fruits while defending the system that bore them. Both causes were quixotic, and consequently neither fully realized its ambitions. I love neat sweeping theories of history ; I can’t take it overly seriously but it is so fun. Lindsey
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Introduction: Here’s Gibbs sampling for a Dirichlet process 1-d mixture of Gaussians . On 1000 data points that look like this. I gave it fixed variance and a concentration and over MCMC iterations, and it looks like this. The top is the number of points in a cluster. The bottom are the cluster means. Every cluster has a unique color. During MCMC, clusters are created and destroyed. Every cluster has a unique color; when a cluster dies, its color is never reused. I’m showing clusters every 100 iterations. If there is a single point, that cluster was at that iteration but not before or after. If there is a line, the cluster lived for at least 100 iterations. Some clusters live long, some live short, but all eventually die. Usually the model likes to think there are about two clusters, occupying positions at the two modes in the data distribution. It also entertains the existence of several much more minor ones. Usually these are shortlived clusters that die away. But
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Introduction: I was cleaning my office and found a back-of-envelope diagram Shay drew me once, so I’m writing it up to not forget. The definitions of the logistic-normal and log-normal distributions are a little confusing with regard to their relationship to the normal distribution. If you draw samples from one, the arrows below show the transformation to make it such you have samples from another. For example, if x ~ Normal , then transforming as y=exp(x) implies y ~ LogNormal . The adjective terminology is inverted: the logistic function goes from normal to logistic-normal, but the log function goes from log-normal to normal (other way!). The log of the log-normal is normal, but it’s the logit of the logistic normal that’s normal. Here are densities of these different distributions via transformations from a standard normal. In R: x=rnorm(1e6); hist(x); hist(exp(x)/(1+exp(x)); hist(exp(x)) Just to make things more confusing, note the logistic-normal distributi
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