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wkvong
When applied to linguistic input, even general statistical tools can recover some aspects of linguistic structure across a variety of domains (Redington, Crater, and Finch 1998; Frank, Bod, and Christiansen 2012; Elman et al. 1996).1!
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While promising, these proposals have as their primary challenge that children show evidence of abstractions that encode key aspects of linguistic competence, even at an early age and in the absence of substantial input. For example, young children show systematic word orders even in the absence of structured input (Goldin-Meadow and Mylander 1983). Further, typically developing children interpret the arguments of novel verbs (Gertner, Fisher, and Eisengart 2006) and show evidence of category structure for syntactic categories such as determiners (Yang 2013) and structures such as the...1!
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The type of proposals that are on offer within this space often include “principles and parameters”-type theories, in which languages share a set of syntactic principles that govern syntactic combination but vary on a relatively small set of parameters that control how these structures vary (Baker 2005; Yang 2002).1!
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Statistical thinking is a way of understanding a complex world by describing it in relatively simple terms that nonetheless capture essential aspects of its structure, and that also provide us some idea of how uncertain we are about our knowledge.1!
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We can distinguish statistical thinking from other forms of thinking that are less likely to describe the world accurately.1!
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There are three major things that we can do with statistics: ¶ Describe: The world is complex and we often need to describe it in a simplified way that we can understand. ¶ Decide: We often need to make decisions based on data, usually in the face of uncertainty. ¶ Predict: We often wish to make predictions about new situations based on our knowledge of previous situations.1!
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My answer is that I think that the best way to learn statistical tools is to work directly with data, and that working with graphical packages insulates one from the data and methods in way that impedes true understanding.1!
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In addition, for many of the students in my class this may be the only course in which they are exposed to programming; given that programming is an essential ability in a growing number of academic fields, I think that providing these students with basic programming literacy is critical to their future success, and will hopefully inspire at least a few of them to learn more.1!
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Scientific discovery is the process or product of successful scientific inquiry. Objects of discovery can be things, events, processes, causes, and properties as well as theories and hypotheses and their features (their explanatory power, for example).1!
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Most philosophical discussions of scientific discoveries focus on the generation of new hypotheses that fit or explain given data sets or allow for the derivation of testable consequences.1!
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Because the concept of discovery did not have a specified meaning and was used in a very broad sense, almost all seventeenth- and eighteenth-century treatises on scientific method could potentially be considered as early contributions to reflections on scientific discovery.1!
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Abduction is normally thought of as being one of three major types of inference, the other two being deduction and induction.1!
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The mere fact that an inference is based on statistical data is not enough to classify it as an inductive one. You may have observed many gray elephants and no non-gray ones, and infer from this that all elephants are gray, because that would provide the best explanation for why you have observed so many gray elephants and no non-gray ones. This would be an instance of an abductive inference. It suggests that the best way to distinguish between induction and abduction is this: both are ampliative, meaning that the conclusion goes beyond what is (logically) contained in the premises (which...1!
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A noteworthy feature of abduction, which it shares with induction but not with deduction, is that it violates monotonicity, meaning that it may be possible to infer abductively certain conclusions from a subset of a set S of premises which cannot be inferred abductively from S as a whole.1!
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It is a common complaint that no coherent picture emerges from Peirce’s writings on abduction. (Though perhaps this is not surprising, given that he worked on abduction throughout his career, which spanned a period of more than fifty years. For a concise yet thorough account of the development of Peirce’s thoughts about abduction, see Fann 1970.)1!
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One main difference between his conception and the modern one is that, whereas according to the latter, abduction belongs to what the logical empiricists called the “context of justification”—the stage of scientific inquiry in which we are concerned with the assessment of theories—for Peirce abduction had its proper place in the context of discovery, the stage of inquiry in which we try to generate theories which may then later be assessed.1!
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Abduction is supposed to be part of the logic of science, but what exactly is logical about inventing explanatory hypotheses?1!
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Something has changed though, just in the last few weeks. I find myself able to follow the basic mathematics that are used to describe and evaluate algorithms: set notation makes sense to me. I'm working my way through an algorithm's book with my sister - exercises in it that even a few weeks ago might have been a struggle to really understand are now decipherable.1!
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It is the most unsettling and yet riveting experience that I have ever had. The only thing that comes close to this is the moment that I realized I was fluent in Portuguese. Real, true fluency is an unforgettable experience. It's as if a veil is pulled away and you're suddenly swimming in a wide, expansive, boundless ocean. You open your mouth, and where once there was stumbling and a stunted desire for expression, suddenly there are only words. Full, coherent words.1!
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The power I feel from this newfound technical fluency is intoxicating and terrifying. There are books that I want to read, so many code libraries I can just look at and understand. There's so many new places to go, so many new things to learn. I want to learn about cryptography and physics. I want to learn more about set theory notation. Most terrifying of all, spending all of my time learning and exercising my fluency feels like the most natural and right thing I could ever imagine. Computer science, despite my best intentions, has found me.1!
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Lately, I’ve noticed that every time something awful happens on the internet, we start blaming infrastructure providers. I understand the intention behind this. Like my twelve year-old self reading about mistreated animals on the internet, we’re horrified by the bad things that happen in this world. These stories make us feel a profound loss of control, and we process them by finding a way to believe that we could prevent anything like it from happening again. ¶ Translating this theory to practice, however, is a Sisyphean endeavor. It starts with cloud hosting and payment providers. What...1!
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Technology itself may be amoral, but what we do with technology is absolutely up for moral debate.4!
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My main idea is that we need to teach C in a way that helps students understand why a very large fraction of critical software infrastructure, including almost all operating systems and embedded systems, is written in C, while also acknowledging the disastrously central role that it has played in our ongoing computer security nightmare.1!
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We’d like students to be able to answer the question: Is C an appropriate choice for solving this problem?1!
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Gifted politicians have long known that their profession is often more about hope than empirical judgment. Maybe it’s time for policy analysts to recognize the same.1!
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