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Neuromorphic Computing at Tennessee. This is a classic application from control theory.

Neuromorphic Computing at Tennessee

A pole is to be balanced on a cart that can move horizontally within a fixed area. The pole has a mass on its top. The pole starts in some imbalanced starting state, at some angle from vertical, rising or falling at some velocity. The goal of the system is to apply periodic forces to move the cart left or right, to keep the pole from falling, and to keep the cart from moving beyond its boundaries. Our goal is for our neuromorphic models to "solve" instances of the pole balancing problem. Our simulation is set up so that every 1/50s, one can apply a force to the cart to move it either to the left or to the right. The following video shows another example of the pole balancer. Now, to have a neuromorphic implementation solve the pole balancing problem, we need to translate instances of the problem into charge events that are input to the neuromorphic implementation. Our solution works as follows. The picture below summarizes:

Lumiverse. Algorithmist. Ваш персональный курс по Big Data. Привет, хабр!

Ваш персональный курс по Big Data

После публикации нескольких статей по Big Data и Машинному обучению, ко мне пришло немало писем от читателей с вопросами. За последние несколько месяцев мне удалось помочь многим людям сделать быстрый старт, некоторые из них — уже решают прикладные задачи и делают успехи. А кто-то уже устроился на работу и занимается решением реальных задач. Моя цель — чтобы вокруг меня были умные люди, с которыми в том числе и я смогу работать в дальнейшем. Поэтому я хочу помочь тем, кто действительно хочет научиться решать настоящие задачи на практике. Станьте отличным математиком Да, это наверное самое важное — математическое мышление, которое надо развивать в себе постоянно с младших лет.

После того, как ваш мозг научился оперировать с дисретными обьектами, а также развилось алгоритмическое мышление вам необходимо научиться мыслить в терминах теории вероятности. Google Develops AI that is Entirely Self Learning. Google scientists and engineers have created the first ever computer program that is capable of learning a wide variety of tasks completely independently, in what is a giant leap towards true general artificial intelligence.

Google Develops AI that is Entirely Self Learning

The AI, or as Google refers to it the“agent”, has learnt to play almost 50 different retro computer games, and came up with its own strategies for winning completely without human input. The same approach could be used to control self-driving cars or personal assistants in smartphones. This research was conducted at a British company the Google acquired a few years ago called DeepMind Demis Hassabis, who founded DeepMind said: “This is the first significant rung of the ladder towards proving a general learning system can work. And continued to draw comparisons with IBM’s DeepBlue chess computer.

“With Deep Blue, it was team of programmers and grand masters that distilled the knowledge into a program. (source Guardian) Рушим капчу SilkRoad. Silk Road, знаменитый черный рынок, был закрыт около года назад.

Рушим капчу SilkRoad

До недавнего времени я думал, что он мертв. Это бы облегчило написание статьи, но не все так хорошо. Я только что прочитал о его возвращении в сеть. Сейчас я хочу углубиться в код, который был написан мною несколько лет назад, чтобы разобрать механизм чтения капчи «старой» версии сайта. Bloom Filters. Everyone is always raving about bloom filters.

Bloom Filters

But what exactly are they, and what are they useful for? Operations The basic bloom filter supports two operations: test and add. Test is used to check whether a given element is in the set or not. If it returns: false then the element is definitely not in the set. true then the element is probably in the set. Add simply adds an element to the set. Applications The classic example is using bloom filters to reduce expensive disk (or network) lookups for non-existent keys.

If the element is not in the bloom filter, then we know for sure we don't need to perform the expensive lookup. Bloomfilter.js I wrote a very fast bloom filter implementation in JavaScript called bloomfilter.js. The implementation also uses JavaScript typed arrays if possible, as these are faster when performing low-level bitwise operations. Interactive Demonstration. The Science of Crawl (Part 1): Deduplication of Web Content — URX Blog. Machine Learning and related area blogs. Ds.algorithms - Core algorithms deployed - Theoretical Computer Science Stack Exchange. Maybe all the major/preferred algorithms of interest to this audience have been mentioned at this point. however, a few more deserve mention for completeness.

ds.algorithms - Core algorithms deployed - Theoretical Computer Science Stack Exchange

& some analysis of what is considered a significant algorithm is relevant here. in CS & IT fields there seems to be a phenomenon noticed long ago in AI called "moving the goalposts". this is a psychological phenomenon where the field advances relatively quickly but people quickly mentally adjust to "the new normal" and take real or even breakthrough advances as mundane or unremarkable in retrospect, after accomplished, ie downplayed or minimized. this is highly captured in this question in the way that algorithms move from R&D into "deployment". quoting the author of the question in later comments: In fact, a negligible fraction of all the code that gets written is implementing anything that is interesting from an algorithmic point of view.

Big-O Algorithm Complexity Cheat Sheet.