metamitya
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https://www.nasa.gov/sites/default/files/files/earth_night_rotate_1280x720.mp4
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metamitya
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22:00 7
blackbox timing for chrome
metamitya
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9:20 7
sniffing browser history
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America’s new tribalism can be seen most distinctly in its politics. Nowadays the members of one tribe (calling themselves liberals, progressives, and Democrats) hold sharply different views and values than the members of the other (conservatives, Tea Partiers, and Republicans). Each tribe has contrasting ideas about rights and freedoms (for liberals, reproductive rights and equal marriage rights; for conservatives, the right to own a gun and do what you want with your property). Each has its own totems (social insurance versus smaller government) and taboos (cutting entitlements or raising taxes). Each, its own demons (the Tea Party and Ted Cruz; the Affordable Care Act and Barack Obama); its own version of truth (one believes in climate change and evolution; the other doesn’t); and its own media that confirm its beliefs. The tribes even look different. One is becoming blacker, browner, and more feminine. The other, whiter and more male. (Only 2 percent of Mitt Romney’s voters were African-American, for example.)
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word2vec
metamitya
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https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf
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what is king - man + woman? This is similar to an SAT-style analogy (man is to woman as king is to what?). And a computer solved this equation and answered: queen. Under the hood, the machine gets that the biggest difference between the words for man and woman is gender. Add that gender difference to king, and you get queen.
metamitya
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word2vec and solr
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After the query is issued to the index, the best results from that query are passed into the model, and re-ordered before being returned to the user
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With the Learning To Rank (or LTR for short) contrib module you can configure and run machine learned ranking models in Solr. The module also supports feature extraction inside Solr. The only thing you need to do outside Solr is train your own ranking model.
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Learning to rank[1] or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems.[2] Training data consists of lists of items with some partial order specified between items in each list. This order is typically induced by giving a numerical or ordinal score or a binary judgment (e.g. "relevant" or "not relevant") for each item. The ranking model's purpose is to rank, i.e. produce a permutation of items in new, unseen lists in a way which is "similar" to rankings in the training data in some sense.
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This tutorial introduces the concept of pairwise preference used in most ranking problems. I'll use scikit-learn and for learning and matplotlib for visualization.
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Every now and then we’ll come across a search problem that can’t simply be solved with plain Solr relevancy. This usually means a customer knows exactly how documents should be scored. They may have little tolerance for close approximations of this scoring through Solr boosts, function queries, etc. They want a Lucene-based technology for text analysis and performant data structures, but they need to be extremely specific in how documents should be scored relative to each other.
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Semantic similarity is a metric defined over a set of documents or terms, where the idea of distance between them is based on the likeness of their meaning or semantic content as opposed to similarity which can be estimated regarding their syntactical representation (e.g. their string format). These are mathematical tools used to estimate the strength of the semantic relationship between units of language, concepts or instances, through a numerical description obtained according to the comparison of information supporting their meaning or describing their nature.[1] The term semantic similarity is often confused with semantic relatedness. Semantic relatedness includes any relation between two terms, while semantic similarity only includes "is a" relations.[2] For example, "car" is similar to "bus", but is also related to "road" and "driving". Computationally, semantic similarity can be estimated by defining a topological similarity, by using ontologies to define the distance between terms/concepts. For example, a naive metric for the comparison of concepts ordered in a partially ordered set and represented as nodes of a directed acyclic graph (e.g., a taxonomy), would be the shortest-path linking the two concept nodes. Based on text analyses, semantic relatedness between units of language (e.g., words, sentences) can also be estimated using statistical means such as a vector space model to correlate words and textual contexts from a suitable text corpus.
metamitya
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word2vec comes to mind.
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metamitya
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36:37 7
we're in the dark ages of search relevancy, now entering the age of enlightenment
metamitya
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26:39 7
lists features we can use for learning to rank, including comparative signals which i believe is a feature that describes eg. similarity between a query and a document.
metamitya
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25:03 7
building a relevance ml model. reminds me of the learning to rank video. mentions semantic cohesion, reminded me of word2vec. use model via a lucene custom operator.
metamitya
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22:26 7
predictive analytics: search engine rankings not based on math or statistics. tf/idf is a heuristic based ranking.
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19:57 7
graph of iterative engine score improvement, with blog mentioned: http://www.searchtechnologies.com/blog/search-engine-relevance-scoring
metamitya
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39:13 7
pulling scores out of solr like tfidf
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29:27 7
demo
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27:38 7
thresholding. meaning if the probability of relevancy is low you can retry the query before returning results to users!
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18:35 7
offline engine analysis. the search algorithm becomes the independent variable passed to a function which runs a static set of queries against a static index and outputs a score.
metamitya
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16:03 7
score each query from the perspective of each user
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psychologists recently conducted a comprehensive review of the extent to which Nobel Prize winners in the sciences, members of the Royal Society and US National Academy of Sciences, and members of the US public reported engaging in arts and crafts-based pursuits. They found that members of the Royal Society and National Academy of Sciences were almost twice as likely to report engaging in arts and crafts pursuits as the general public. Eminent Nobel laureate scientists were almost three times more likely to report such activities.
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This map, made with Gephi, represents the patterns of VVC extracted from obituaries in Eugene, Oregon. Family and friends are central values here.
metamitya
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Some nice visualizations here.
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It might be that human values will forever remain somewhat mysterious. But to the extent that our values are revealed in our behavior, you would hope to be able to prove that the machine will be able to “get” most of it. There might be some bits and pieces left in the corners that the machine doesn’t understand or that we disagree on among ourselves. But as long as the machine has got the basics right, you should be able to show that it cannot be very harmful.
metamitya
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nice summary
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“What you get from dance and singing on its own is a sense of belonging. It happens very quickly. What happens, I suspect, is that it can trigger very easily trance states,” Dunbar said. He theorizes that these spiritual experiences matter much more than dance and song alone. “Once you’ve triggered that, you’re in, I think, a different ballgame. It ramps up massively. That’s what’s triggered. There’s something there.”
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From the viewpoint of increasing fitness, that is, passing on one’s genes, being able to make war or to repel aggression required larger and unified tribes. A need to bind people together in larger aggregates then drove the evolution of traits that promoted group loyalty. Religion, especially early religions, served not only to bring people together but to give them assurance that they and their families would survive, either in this life or in one to come. A belief that one’s God(s) had chosen one’s own tribe gave members the strength to face overwhelming odds, with the result that they sometimes turned back less dedicated opponents. Moreover, religion and its constant demands facilitated the identification of potential free riders, strengthening group solidarity.
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The emergence of high-moralizing gods is an important example of this. In small-scale hunter-gatherer religions, the gods are typically whimsical. They're amoral. They're not concerned with your sexual behavior or your social behavior. Often you'll make bargains with them, but as we begin to move to the religions in more complex societies, we find that the gods are increasingly moralizing. They're concerned about exactly the kinds of things that are going to be a problem for running a large-scale society, like how you treat other members of your religious group or your ethnic group. Experiments run at UBC and elsewhere have shown that when you remind atheists, it doesn't matter, but if you remind believers of their god, believers cheat less, and they're more pro social or fair in exchange tasks, and the kinds of exchange tasks that they're more pro social in are the ones with anonymous others, or strangers.
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Psychohistory depends on the idea that, while one cannot foresee the actions of a particular individual, the laws of statistics as applied to large groups of people could predict the general flow of future events. Asimov used the analogy of a gas: an observer has great difficulty in predicting the motion of a single molecule in a gas, but can predict the mass action of the gas to a high level of accuracy. (Physicists know this as the Kinetic theory). Asimov applied this concept to the population of his fictional Galactic Empire, which numbered a quintillion. The character responsible for the science's creation, Hari Seldon, established two axioms: that the population whose behaviour was modeled should be sufficiently large that the population should remain in ignorance of the results of the application of psychohistorical analyses There is a third underlying axiom of Psychohistory, which is trivial and thus not stated by Seldon in his Plan: that Human Beings are the only sentient intelligence in the Galaxy.
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Hoffman has spent the past three decades studying perception, artificial intelligence, evolutionary game theory and the brain, and his conclusion is a dramatic one: The world presented to us by our perceptions is nothing like reality. What’s more, he says, we have evolution itself to thank for this magnificent illusion, as it maximizes evolutionary fitness by driving truth to extinction.