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Cross Validated 2020-10-31 15:06 UTC Score 7.0 AI-113-20201031-social-media-2b66bfb1

Parameters of LDA

I was learning from Elements of statistics p.109 under the topic LINEAR DISCRIMINANT ANALYSIS and I saw the function below for linear discriminant function $\delta_k = X^T\sum^{-1}\mu_k -\frac{1}{2}\mu_k^T\mu_k+ \log\pi_k$ where $\pi_k = N_k/N$ where $N_k$ is the number of class-k observations; $\mu_k$ = $\sum_{g_i = k} x_i/N_k$ $\sum = \sum_{k=1}^k\sum_{g_i = k}(x_i - \mu_k)(x_i - \mu_k)^T/(N - K)$ Please I want know the parameters for this function (I am thinking is $\mu_k$ ) and how come it has (K-1) x (p + 1) parameters

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Cross Validated 2020-10-28 18:41 UTC Score 15.0 AI-113-20201028-social-media-4efffe16

How to test multicollinearity in Fixed Effects Model in R?

I was using plm package in R and run some pooling and fixed effects model. For pooling models I was able to use vif() for getting Variance Inflation Factor, but when I run it for fixed effect model, it showed me the below error: > > vif(modelFE.1.i) > > Error in R[subs, subs] : subscript out of bounds In > addition: Warning message: In vif.default(modelFE.1.i) : No intercept: > vifs may not be sensible. So, I was wondering if there is some way to find multicollinearity under Fixed Effects settings? The error says that VIF cannot be computed without intercept, I understand that. But, what can be the other tests that I can do for testing multicollinearity?

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Inria AI 2020-10-21 12:40 UTC Score 29.0 USR-0036-20201021-research-aca-33f3db61 Full article

Masterclass Pharo : l’expertise informatique open source d’Inria rayonne à l’international

Masterclass Pharo : l’expertise informatique open source d’Inria rayonne à l’international decarpig mer, 10/21/2020 - 14:40 Des chercheurs en informatique Inria Lille s’impliquent dans la stratégie de diffusion des logiciels libres à destination des entreprises du numérique. Le langage de programmation Pharo a reçu l’attention d’une large communauté scientifique à l’occasion d’une masterclass animée par Stéphane Ducasse, expert internationalement reconnu en informatique, et organisée par Inria Academy et Inria au Chili. © Inria / Photo C. Morel Communiquer et échanger des informations ou des objets plus rapidement, produire et distribuer de l’énergie plus efficacement, proposer de nouveaux services ciblés sur les besoins de chacun : le numérique bouleverse de nombreux secteurs d’activité et leur offre de prometteuses perspectives d’innovation et de développement. Cachés derrière les algorithmes et les lignes de code que l’informatique et le numérique mettent en œuvre, les logiciels et les langages de programmation sont les éléments incontournables de cette transformation numérique. C’est le cas de Pharo , présenté en juillet dernier à plus de 80 participants lors d’une masterclass organisée conjointement par Inria Academy et Inria Chile (voir encadré). Animée par Stéphane Ducasse , directeur de recherche en informatique et responsable de l’équipe Rmod, elle a mis en lumière les qualités de l’outil, illustrées par des usages concrets. Pharo , un langage évolutif et polyvalent…

Open Source AI
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Cross Validated 2020-10-01 03:02 UTC Score 12.0 AI-113-20201001-social-media-2b12c627

Latent Profile Analysis - standardizing the variables and interpreting it

This is the first time I'm running an LPA and prior to running it, I standardized all variables because I read that would help interpret the profile structure later. And now I'm confused in the interpretations; for example, is it right that in the solution below, both groups are lower than average on the purple item...? how does that make sense? Really appreciate any help!

RAG
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GlobalPolicy.AI 2020-09-17 09:17 UTC Score 28.0 USR-0163-20200917-ai-specialis-337503b0 Full article

Inter-American Development Bank

Achieving impact through intergovernmental co-operation on artificial intelligence About Key focus areas Events AI events calendar Events on Globalpolicy.AI Partners Reports FAQ Contact Search English Français Inter-American Development Bank (IDB) The Inter-American Development Bank (IDB) has organised activities on AI in Latin America and the Caribbean, ranging from generation of knowledge, webinars and workshops on […]

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GlobalPolicy.AI 2020-09-17 09:14 UTC Score 33.0 USR-0163-20200917-ai-specialis-b25607f3 Full article

OECD and OECD.AI

Achieving impact through intergovernmental co-operation on artificial intelligence About Key focus areas Events AI events calendar Events on Globalpolicy.AI Partners Reports FAQ Search English Français Organisation for Economic Co-operation and Development (OECD) OECD AI Policy Observatory The OECD AI Policy Observatory (OECD.AI) is an online platform that provides a valuable reference for international dialogue and collaboration on AI public policy […]

AI Regulation & Policy
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GlobalPolicy.AI 2020-09-14 09:23 UTC Score 28.0 USR-0163-20200914-ai-specialis-1cd27f5e Full article

United Nations

Achieving impact through intergovernmental co-operation on artificial intelligence About Key focus areas Events AI events calendar Events on Globalpolicy.AI Partners Reports FAQ Contact Search English Français United Nations (UN) Roadmap for Digital Cooperation The United Nations presented the Secretary-General’s Roadmap for Digital Cooperation in June 2020, to address a range of issues related to AI […]

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GlobalPolicy.AI 2020-09-14 09:14 UTC Score 28.0 USR-0163-20200914-ai-specialis-8da04c86 Full article

UNESCO

Achieving impact through intergovernmental co-operation on artificial intelligence About Key focus areas Events AI events calendar Events on Globalpolicy.AI Partners Reports FAQ Search English Français The United Nations Educational, Scientific and Cultural Organization (UNESCO) Artificial Intelligence (AI) applications continue to expand opportunities for the achievement of the Sustainable Development Goals. UNESCO is working to harness […]

AI Research & Papers
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GlobalPolicy.AI 2020-09-13 09:38 UTC Score 33.0 USR-0163-20200913-ai-specialis-6ce40407 Full article

World Bank Group

Achieving impact through intergovernmental co-operation on artificial intelligence About Key focus areas Events AI events calendar Events on Globalpolicy.AI Partners Reports FAQ Search English Français The World Bank Group (WBG) Tools for Identifying the Human Rights Impact and Algorithmic Accountability of Artificial Intelligence in World Bank Operations Initiated in 2022, this ongoing project is focused […]

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Cross Validated 2020-09-13 00:01 UTC Score 9.0 AI-113-20200913-social-media-bb8c1259

Probability of standard normal greater than another standard normal conditional on truncation

$X,Y \sim N(0,1)$ independently. Find $P(Y > 3X | Y > 0)$ . My attempt: $$\begin{eqnarray*} P(Y > 3X | Y > 0) &=& P(X 0) \\ &=& E(1(X 0) \\ &=& E\big(E(1(X 0\big) \\ &=& E\big(P(X 0 \big) \quad \because X \perp Y \\ &=& E(\Phi(Y/3) | Y>0) \end{eqnarray*}$$ Is there any way to get a closed form expression for this? I know how to get $E(\Phi(aY + b))$ but getting the expectation over a truncated normal distribution seems elusive.

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Distill Archive 2020-09-11 20:00 UTC Score 12.0 AI-038-20200911-ai-specialis-dc00cb08 Full article

Communicating with Interactive Articles

Examining the design of interactive articles by synthesizing theory from disciplines such as education, journalism, and visualization.

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Creating a biased sample with two variables matching distributions from another dataset
Cross Validated 2020-09-04 22:13 UTC Score 17.0 AI-113-20200904-social-media-e0abce43 Full article

Creating a biased sample with two variables matching distributions from another dataset

I have a biased sample from a user activity dataset with known distributions of two variables, the amount of times a user logged in during last week (Poisson distribution) and user's weekly revenue (this one looks normally distributed). I need to create another biased sample from the same dataset and keep the same distributions for both of these variables. Here's my drawing of the situation: So far I only managed to think of something like splitting all observations into bins, figuring out the number of observations I need in each bin in order for the distribution of the new sample to look like the distribution from the first one (for both variables) and then going through the dataset looking at each user like: this one is bin 3 for logins but bin 1 for revenue, check this one is bin 2 for logins and bin 1 for revenue, check this one is bin 4 for logins and bin 1 for revenue, but bin 1 for revenue is full so it goes nowhere... until all the bins are full. But this seems tedious, naïve and possibly incorrect. What would be the correct way to do what I want?

AI Ethics AI Research & Papers
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Distill Archive 2020-08-27 20:00 UTC Score 16.0 AI-038-20200827-ai-specialis-a3f33e76 Full article

Self-classifying MNIST Digits

Training an end-to-end differentiable, self-organising cellular automata for classifying MNIST digits.

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Distill Archive 2020-08-27 20:00 UTC Score 12.0 AI-038-20200827-ai-specialis-daf318da Full article

Thread: Differentiable Self-organizing Systems

A collection of articles and comments with the goal of understanding how to design robust and general purpose self-organizing systems.

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What is the difference between Permutation Importance and Drop Column Importance?
Cross Validated 2020-08-11 16:11 UTC Score 23.0 AI-113-20200811-social-media-b3fa8983 Full article

What is the difference between Permutation Importance and Drop Column Importance?

I would like to understand what is the difference between Permutation Importance (as outlined by Breiman in his original paper on Random Forests) and Drop Column Importance. From a cursory look at both methods, they seem to be doing almost the same thing: Calculate a baseline score by training a model and obtaining some kind of metric (in this case R2), do something to one of the features and then calculate the score again and record the difference between the baseline and updated scores, thus generating a ranking of how much each feature influences the goodness of the model by whatever metric we are using. The do something part is where the two methods diverge, where for permutation importance we permute one of the features considering independence with respect to the target variable and other predictor features, while instead for drop column we remove the feature entirely and retrain the model before computing the score again. To me these two operations should give almost the same result, with the disadvantage in drop column of having to train the model P times (no. of features). Of course this is not the case, as per this blog post (emphasis mine): Permutation importance does not require the retraining of the underlying model [...], this is a big performance win. The risk is a potential bias towards correlated predictive variables . If we ignore the computation cost of retraining the model, we can get the most accurate feature importance using a brute force drop-column im…

AI Ethics AI Research & Papers
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Cross Validated 2020-07-30 21:43 UTC Score 18.0 AI-113-20200730-social-media-787bbdeb

Quantifying the uncertainty of aggregated model predictions

Say I have a binary response variable, Y , that I model using a logistic model with four predictors, A , B , C and D . To make matters concrete, imagine that Y = 1 designates a respondent registering support for something, and 0 an absence of support. Having estimated the relevant parameters on some sample, S , I then want to see what proportion of 1s (i.e., support) I likely would have seen, had all observations in S taken on a particular value on A . Assume conditions for causal inference are satisfied for A , so that changing its value can be thought of as a (hypothetical) intervention. So I create a "new" sample, S *, identical, to S , save for each observation taking on the desired value on A . I then use the fitted model to “predict” the probability of Y = 1 for each observation in that sample. Taking the mean of those predictions I get an estimated proportion of support under the relevant intervention. My question is: how should I quantify the uncertainty of that estimate? I can think of three ways, but am not sure which one (if any) makes sense: Resample from the predicted probabilities of the model and bootstrap a confidence interval for the relevant mean that way. Calculate a confidence interval for the prediction made on each observation (the probability that respondent 1 registers support, etc.) like here , and then create a confidence interval for mean support by taking the means of the upr and lwr values of the individual predictions. Resample from S * to fit a…

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Jay Alammar Blog 2020-07-27 00:00 UTC Score 36.0 USR-0113-20200727-ai-specialis-7d5fd94d Full article

How GPT3 Works - Visualizations and Animations

Discussions: Hacker News (397 points, 97 comments), Reddit r/MachineLearning (247 points, 27 comments) Translations: German, Korean, Chinese (Simplified), Russian, Turkish The tech world is abuzz with GPT3 hype. Massive language models (like GPT3) are starting to surprise us with their abilities. While not yet completely reliable for most businesses to put in front of their customers, these models are showing sparks of cleverness that are sure to accelerate the march of automation and the possibilities of intelligent computer systems. Let’s remove the aura of mystery around GPT3 and learn how it’s trained and how it works. A trained language model generates text. We can optionally pass it some text as input, which influences its output. The output is generated from what the model “learned” during its training period where it scanned vast amounts of text.

AI Chips & Hardware
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Inria AI 2020-07-24 08:06 UTC Score 25.0 USR-0036-20200724-research-aca-27c9c66c Full article

Lille by Inria, n°11

Lille by Inria, n°11 decarpig ven, 07/24/2020 - 10:06 Le onzième numéro de Lille by Inria, le magazine du centre de recherche Inria Lille - Nord Europe vient de sortir. On y trouve un dossier dédié au numérique pour l'environnement et la biodiversité. Vous y découvrirez aussi un face à face sur l'action COVIDー19 au sein du centre, un focus sur la reconnaissance et le respect de la vie privée. Et comme à chaque numéro, retrouvez les rubriques habituelles, une BD sur les fibres optiques, le portrait d'Arnaud Liefooghe de l'équipe Bonus, ainsi que vos rubriques Best-of. Retrouvez le magazine en ligne standard-width

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Cross Validated 2020-07-01 05:16 UTC Score 15.0 AI-113-20200701-social-media-29daf536

Likelihood function for switchpoint analysis--in this case example from PyMC3

I was watching a video on PyMC3 for fitting Bayesian models, and an example they gave was of "switchpoint" analysis for coal mining disasters. A picture of the data is below, and the goal is to identify the year in which the distribution changes from a high number of disasters to low number of disasters regime. My question is, what is the likelihood function for a switchpoint model? That was not mentioned in the talk or documentation. I understand the priors, but the likelihood almost seems like a mixture of poissons or something. I was just wondering what the precise mathematical specification of the likelihood function was--or at least the probability distribution used to setup the likelihood. Now, the model is specified as: $$ \begin{aligned} D_{t} & \sim \operatorname{Pois}\left(r_{t}\right), r_{t}=\left\{\begin{array}{ll} e, & \text { if } t \leq s \\ l, & \text { if } t>s \end{array}\right.\\ s & \sim \operatorname{Unif}\left(t_{l}, t_{h}\right) \\ e & \sim \exp (1) \\ l & \sim \exp (1) \end{aligned} $$ And the PyMC3 code is with pm.Model() as disaster_model: switchpoint = pm.DiscreteUniform('switchpoint', lower=years.min(), upper=years.max(), testval=1900) # Priors for pre- and post-switch rates number of disasters early_rate = pm.Exponential('early_rate', 1) late_rate = pm.Exponential('late_rate', 1) # Allocate appropriate Poisson rates to years before and after current rate = pm.math.switch(switchpoint >= years, early_rate, late_rate) disasters = pm.Poisson('disaste…

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Cross Validated 2020-06-28 14:44 UTC Score 12.0 AI-113-20200628-social-media-c79df7d5

Where to learn dealing with audio data?

I want to learn how to deal with audio data. But, I did not find any course on signal processing. Can someone suggest me the course or give me a link to that course about signal processing in machine learning.

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Distill Archive 2020-06-17 20:00 UTC Score 8.0 AI-038-20200617-ai-specialis-aa48e75a Full article

Curve Detectors

Part one of a three part deep dive into the curve neuron family.

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Cross Validated 2020-06-13 23:15 UTC Score 28.0 AI-113-20200613-social-media-eabee5a9

Can you use the isolation forest algorithm on a large sample size?

I've been using the scikit learn sklearn.ensemble.IsolationForest implementation of the isolation forest to detect anomalies in my datasets that range from 100s of rows to millions of rows worth of data. It seems to be working well and I've overridden the max_samples to a very large integer to handle some of my larger datasets (essentially not using sub-sampling). I noticed that the original paper ( https://ieeexplore.ieee.org/abstract/document/4781136 ) states that larger sample sizes create risk of swamping and masking. Is it okay to use the isolation forest on large sample sizes if it seems to be working okay? I tried training with a smaller max_samples and the testing produced too many anomalies. My data has really started to grow and I'm wondering if a different anomaly detection algorithm would be better for such a large sample size.

AI Research & Papers
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Andrej Karpathy Blog 2020-06-11 10:00 UTC Score 28.0 USR-0115-20200611-ai-specialis-27cfb0c3 Full article

Biohacking Lite

Throughout my life I never paid too much attention to health, exercise, diet or nutrition. I knew that you’re supposed to get some exercise and eat vegetables or something, but it stopped at that (“mom said”-) level of abstraction. I also knew that I can probably get away with some ignorance while I am young, but at some point I was messing with my health-adjusted life expectancy. So about halfway through 2019 I resolved to spend some time studying these topics in greater detail and dip my toes into some biohacking. And now… it’s been a year! A "subway map" of human metabolism. For the purposes of this post the important parts are the metabolism of the three macronutrients (green: lipids, red: carbohydrates, blue: amino acids), and orange: where the magic happens - oxidative metabolism, including the citric acid cycle, the electron transport chain and the ATP Synthase. full detail link. Now, I won’t lie, things got a bit out of hand over the last year with ketogenic diets, (continuous) blood glucose / beta-hydroxybutyrate tests, intermittent fasting, extended water fasting, various supplements, blood tests, heart rate monitors, dexa scans, sleep trackers, sleep studies, cardio equipments, resistance training routines etc., all of which I won’t go into full details of because it lets a bit too much of the mad scientist crazy out. But as someone who has taken plenty of physics, some chemistry but basically zero biology during my high school / undergrad years, undergoing some o…

AI Research & Papers
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How to calculate the expected value of $k$ heads in this case?
Cross Validated 2020-06-04 14:05 UTC Score 9.0 AI-113-20200604-social-media-aaf128a9 Full article

How to calculate the expected value of $k$ heads in this case?

I'm having some trouble on how to tackle the following problem $X_1$ is a random variable with probability density $f(x)$ in the range $[0,1]$ . A value of $X_1$ is picked, call its value $p$ . A coin is played $n$ times with a probability $p$ to come up heads in each time. Calculate the expected value of the number of $k$ heads in the $n$ plays in the following cases: Each coin toss is independent and the $p$ value is the same for all of them. Find an expression for $E [k]$ in the case of a general $f(x)$ Find $E[k]$ if $f(x)$ is uniform over $[0,1]$ Find $E[k]$ if $f(x)$ is uniform over $[0,1]$ and a new $p$ value is picked before each coin flip. I'm not sure I'm interpreting this correctly and honestly I think it's a little bit confusing. If $p$ is fixed, the PMF would be the binomial distribution. In the case of a general $f(x)$ , I assume I've to first derive a posterior distribution for $X_1$ , where $f(x)$ is the prior. I'd proceed by finding the likelilhood based on the information that the coin was flipped $n$ times with a probability $p$ to come up heads. Then I could find the distribution for the next $m$ plays and calculate the expected value for this case. Here starts the trouble for me - it's asking for the expected value of the same $n$ plays I'm using to construct the likelihood. Because of that I'm not sure my approach is correct. Also, I would appreciate some insight in the case where $X_1$ is picked before each coin flip. Thanks.

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Cross Validated 2020-05-30 07:28 UTC Score 20.0 AI-113-20200530-social-media-fc6d7429

Binomial Regression and Grouping Data

I'm currently trying to model Data where the dependent variable is either 0 or 1. Binary outcome. This would be the same as to say the data comes from a binomial distribution B(1, p). So I could use various link functions like probit, logit or log, etc. In my dataset some rows are completely the same, as far as I understood so far, this would mean that I have to group the data otherwise my X matrix would not be invertible. R still calculates this regression - is it just throwing out duplicated cases? Is it better to group the data, if R is throwing out rows, that would mean that some sort of information is lost, about the frequency of these double entries? And another question regarding the grouping process itself. If I use grouped data, I just have to throw out double rows and record the frequency and use that as weights?

AI Research & Papers
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Cross Validated 2020-05-15 10:52 UTC Score 9.0 AI-113-20200515-social-media-d44899f2

Yeo-Johnson does not increase normality

I have used Box-Cox Yeo-Johnson transformation to make my skewed data columns less skewed and more normal so that I can remove outliers. e.g. originally most of my columns have a 'skewness' of 400! After applying Box Cox they reduce to -36.965404. This is a huge difference and is still somewhat skewed. I then apply quantile based method to remove outliers (by column) and a lot of the data is removed (50%) so this method doesn't seem appropriate. def remove_outlier_by_Col(df,col,low_q,hi_q): low = low_q high = hi_q quant_df = df.quantile([low, high]) df = df[(df[col] > quant_df.loc[low, col]) & (df[col] I am doing this to minimize the effect the 'outliers' have on xgboost but I am having trouble deciding how to treat these outliers when my distribution is heavily skewed. I have thought about simply Winsorizing, but is this appropriate when data is skewed? Can somebody please advise what is best thing to do in this situation! Before Yeo-Johnson transformation on one column: After Yeo-Johnson on the same column:

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Distill Archive 2020-05-05 20:00 UTC Score 16.0 AI-038-20200505-ai-specialis-6cedbca3 Full article

Exploring Bayesian Optimization

How to tune hyperparameters for your machine learning model using Bayesian optimization.

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How to deal with problems like this ? What machine learning algorithms should be used?
Cross Validated 2020-05-02 15:31 UTC Score 26.0 AI-113-20200502-social-media-602cfe3b Full article

How to deal with problems like this ? What machine learning algorithms should be used?

I am new to machine learning and this community too. So please pardon me if i make any mistake while putting up this question. I am trying this https://www.kaggle.com/doaaalsenani/usa-cers-dataset problem from kaggle where i am trying to predict price of cars based on various parameters. And I am not sure which algorithm to apply for this type of problem as it is having both categorical and numeric data but then also i tried to apply linear regression to it on price feature as price is dependent on all other features but after converting all categorical features such as color , model , brand to one hot encoding and applying feature scaling to all it gave me 2.4 mean squared error which is terribly bad , may be i am using irrelevant or too many features but i don't feel straight forward linear regression is good choice. I completed a course on machine learning where when i applied linear regression all data was in numeric form but not in this case and i know linear regression is not a good choice for this problem or may be i need to make some modifications to it but i don't know what to do else. Can anybody suggest me what should i begin with problems like this ? What algorithms are used or what kind of modifications can be used. I am open to any suggestions as i really don't have a path for solving this type of problem. I dont know how to include dataset in question so i had given a link to problem and including a snapshot of dataset too. And in my computation i dropped vin…

AI Research & Papers
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Cross Validated 2020-04-21 05:44 UTC Score 10.0 AI-113-20200421-social-media-f381e80a

Bayesian Networks Node Definition

The educational content online for Bayesian Networks is not the best. (It's a subtle topic which leads to subtle questions and I'm having a hard time understanding it.) It is my understanding that every node of a Bayesian Network is a probability distribution and that a node is conditionally independent of its non-descendants given its parents. Is this correct? Can nodes have multivariate distributions? If so, doesn't this mean that every statistical model can be represented as a one-node "bayesian network"? (a trivial one for sure, but still)

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Cross Validated 2020-04-20 20:15 UTC Score 27.0 AI-113-20200420-social-media-95dbe6a7

Finding patterns in binary files using deep learning

I am a newbie in deep learning and wanted to know if the problem I have at hand is a suitable fit for deep learning algorithms. I have thousands of fragments each of about 1000 bytes size (i.e. numbers in the range of 0 to 255). There are two classes in the fragments: Some fragments have a high frequency of two particular byte values appearing next to one another: "0 and 100". This kind of pattern roughly appears once every 100 to 200 bytes. In the other class, the byte values are more randomly distributed. We have the ability to produce as many numbers of instances of each class as needed for training purposes. However, I would like to differentiate with a machine learning algorithm without explicitly identifying the "0 and 100" pattern in the 1st class myself. Can deep learning help us solve this? If so, what kind of layers might be useful? As a preliminary experiment, we tried to train a deep learning network made up of 2 hidden layers of TensorFlow's "Dense" layers (of size 512 and 256 nodes in each of the hidden layers). However, unfortunately, our accuracy was indicative of simply a random guess (i.e. 50% accuracy). We were wondering why the results were so bad. Do you think a Convolutional Neural Network will better solve this problem?

RAG
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Cross Validated 2020-04-03 03:08 UTC Score 12.0 AI-113-20200403-social-media-e3375272

The question is related to stochastic process and the Markov chains

This is the complete question. Jim is currently living in Scranton. Each year that he lives in Scranton, he has a probability of 1/2 of staying in Scranton the next year. Otherwise, he has an equally likely chance of moving to Chicago, Philadelphia, New York, or Seattle, the next year. On any given year that he lives in Philadelphia, he has a 1/4 probability of moving to Seattle, a probability of 3/8 of moving to Scranton and a probability of 3/8 of moving to Chicago the next year. On any year that he lives in Chicago, he has a probability of 1/2 of moving to New York, a probability of 3/8 of moving to Scranton and a probability of 1/8 of moving to Philadelphia the next year. In answering the questions below, assume Jim will be living in one of the 5 cities forever. Also assume, for parts (a)-(e) that if Jim moves to Seattle or New York, he will stay there and will not relocate again. Is this a valid Markov chain? Create the transition graph and matrix. What is the probability that Jim eventually will leave the non-Coastal cities (i.e., Scranton, Chicago, Philadelphia) permanently? What is the probability that Jim will eventually relocate permanently to New York? What is the expected number of years until Jim leaves Scranton permanently? Jim’s friend Karen also started out like Jim but in Chicago. She also eventually relocated to New York or Seattle. What is the expected number of years she lived in Scranton? for the first five parts i could only be able to obtained the prob…

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Distill Archive 2020-04-01 20:00 UTC Score 12.0 AI-038-20200401-ai-specialis-5adbe118 Full article

An Overview of Early Vision in InceptionV1

An overview of all the neurons in the first five layers of InceptionV1, organized into a taxonomy of 'neuron groups.'

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Distill Archive 2020-03-16 20:00 UTC Score 12.0 AI-038-20200316-ai-specialis-ca0a82a8 Full article

Visualizing Neural Networks with the Grand Tour

By focusing on linear dimensionality reduction, we show how to visualize many dynamic phenomena in neural networks.

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Cross Validated 2020-02-10 20:44 UTC Score 21.0 AI-113-20200210-social-media-95a4e54e

Extract confidence intervals confint() for random estimates of lmer models

I want to test the significance of the random slope in my model, i.e. if there is significant individual difference in change. I am using lmer() and confint() in R The model is: model time: 4 time points, values 1,2,3,4. n: continuous dependent variable for neuroticism summary(model) Linear mixed model fit by REML. t-tests use Satterthwaite's method [ lmerModLmerTest] Formula: n ~ time + (1 + time | id) Data: long REML criterion at convergence: -421 Scaled residuals: Min 1Q Median 3Q Max -3.6702 -0.4900 -0.0058 0.4802 3.4323 Random effects: Groups Name Variance Std.Dev. Corr id (Intercept) 0.14163958 0.376350 time 0.00008384 0.009157 0.39 Residual 0.01127142 0.106167 Number of obs: 842, groups: id, 250 Fixed effects: Estimate Std. Error df t value Pr(>|t|) (Intercept) 2.185644 0.025323 248.552766 86.312 When I extract the confidence intervals, this is the output: confint(linear.mod.n) 2.5 % 97.5 % .sig01 0.340460916 0.415590685 .sig02 -1.000000000 1.000000000 .sig03 0.000000000 0.026388745 .sigma 0.098924884 0.112977148 (Intercept) 2.135917316 2.235365845 time -0.009836903 0.003374645 I am trying to figure out which confidence intervals are presented here. .sig01 appears to match the random intercept standard deviations, .sig03 for random slope time , .sigma for random residuals, and (Intercept) and time for the fixed effects. Is this correct? If so, what is .sig02 providing the confidence interval for? Thank you all in advance!

AI Chips & Hardware
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Within-subject centering of a repeatedly measured dichotomous variable in a multilevel model?
Cross Validated 2019-11-06 13:04 UTC Score 12.0 AI-113-20191106-social-media-18f3e195 Full article

Within-subject centering of a repeatedly measured dichotomous variable in a multilevel model?

I'm currently working on a nested data set consisting of 100 subjects which answered several questions at home on five consecutive days (ecological momentary assessment). Among them, they were asked if they adhered to the study protocoll on each given day (no/ yes), leading to 0 (no) or 1 (yes) in the data for each of the five days. The continous outcome variable was also assessed on each day. The reserach question is whether beeing adherent on a given day has an effect on the outcome. Now, to explore the effect of the adherence on the outcome variable, I built a nested multilevel model (days within subjects) predicting the outcome by some covariates, a random intercept (high ICC) and the variable coding for adherence (0 or 1). Normally, I would proceed and disentangle within-subject variations (person mean centered) from between-subject variations (grand mean centering of the person means) for the adherence variable. However, it seems rather odd to me to center a dichotomous variable. On the other hand, I know that it is necessary in order to get a clear picture of the within-subject effect. When not centering, I could enter the 0/1 adherence variable as a factor. However, in this case, it would confound within- and between-subject variations (because subjects do not only differ as compared to themselves but also in their total amount of adherence in comparision to the group, the grand mean). Do you have any advice on whether I should center the 0/1 variable? If yes, how wo…

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Why are probability problems involving combinations often solved indirectly?
Cross Validated 2019-09-14 19:25 UTC Score 12.0 AI-113-20190914-social-media-816b04de Full article

Why are probability problems involving combinations often solved indirectly?

Let's say you have a box with 25 cell phones in it, of which 2 are defective. If a person selects 10 cell phones at random, without replacement, what is the probability that both defective cell phones will be selected? I've only seen problems of this sort solved "indirectly". For example, one would solve for the probability of selecting 8 non-defective cell phones and not for selecting the 2 defective cell phones. Why is this? How do you solve for selecting the 2 defective cell phones directly? If it helps, here is how I've seen these types of problems solved. If 8 out of the 10 cell phones are non-defective , then the remaining 2 cell phones selected must be the defective cell phones. So, you first figure out the total number of ways selecting 10 cell phones at random from 25 cell phones (without replacement) which is 25 choose 10 or $\binom{25}{10} = 3,268,760$ using the binomial coefficient formula. Then, calculate the number of ways to select 8 non-defective cell phones from 23 total non-defective cell phones (25 total cell phones minus 2 defective cell phones) so $\binom{23}{8}=490,314$ . Therefore, the probability of selecting the 8 non-defective cell phones is $490,314 / 3,268,760 = 0.15$ which also equals the probability of selecting 2 defective cell phones. But, I've never seen this type of problem solved for in a "direct" manner.

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What is the probability that exactly $k$ tosses are required to get exactly $2$ Heads
Cross Validated 2019-09-04 22:04 UTC Score 9.0 AI-113-20190904-social-media-14c71bf0 Full article

What is the probability that exactly $k$ tosses are required to get exactly $2$ Heads

I have the following homework problem: What is the probability of that exactly k tosses are required to get exactly 2 Heads I wanted to validate that my approach to solving this is correct and if my answer is on the right track: $$A = P(\text{only 1 H in first $k-1$ tosses}) \cdot P(\text{H in last toss}) $$ $$A=P(\text{only 1 H in first $k-1$ tosses}) \cdot .5 $$ Is this the correct approach? Also below is my answer; is this correct or am I way off? $$ \frac{k-1}{2 ^{k-1}} \cdot .5 $$

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Cross Validated 2019-09-03 07:45 UTC Score 15.0 AI-113-20190903-social-media-c6699ce0

How to exclude events with low data (eg. threshold, outliers)

I have this data set and I want to filter only "Event" with a good conversion rate. We can say that good are those that have a higher than average conversion (but maybe you have better ideas). Since the average conversion is 0.8% I will also select those events with too little data (eg Impressions = 1 or impressions = 10). What formula can I use to quickly exclude events with too little data and set a minimum threshold for impressions? I can't use an hard-coded threshold because it has to be different for each account. Impressions/clicks are not normally distributed. Maybe long-tail distribution makes more sense.

RAG
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MIT CSAIL Research 2019-07-01 14:07 UTC Score 43.0 USR-0009-20190701-research-aca-21aed578 Full article

Teaching AI to create visuals with more common sense

Teaching AI to create visuals with more common sense aconner Mon, 07/01/2019 - 10:07 Article July 01 '19 Adam Conner-Simons, MIT CSAIL An MIT/IBM system could help artists and designers make quick tweaks to visuals while also helping researchers identify “fake” images. Today’s smartphones often use artificial intelligence (AI) to help make the photos we take crisper and clearer. But what if these AI tools could be used to create entire scenes from scratch? A team from MIT and IBM has now done exactly that with “GANpaint Studio,” a system that can automatically generate realistic photographic images and edit objects inside them. In addition to helping artists and designers make quick adjustments to visuals, the researchers say the work may help computer scientists identify “fake” images. David Bau, a PhD student at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL), describes the project as one of the first times computer scientists have been able to actually “paint with the neurons” of a neural network — specifically, a popular type of network called a generative adversarial network (GAN). Available online as an interactive demo , GANpaint Studio allows a user to upload an image of their choosing and modify multiple aspects of its appearance, from changing the size of objects to adding completely new items like trees and buildings. Boon for designers Spearheaded by MIT professor Antonio Torralba as part of the MIT-IBM Watson AI Lab he directs, the project has vas…

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MIT CSAIL Research 2019-06-10 16:37 UTC Score 43.0 USR-0009-20190610-research-aca-3ad70f6f Full article

MIT simulator lets users design wide range of functional soft robots

MIT simulator lets users design wide range of functional soft robots aconner Mon, 06/10/2019 - 12:37 Article June 10 '19 Adam Conner-Simons, MIT CSAIL MIT simulator lets users design wide range of functional soft robots To get robots to do things, computer scientists often use systems called physics simulators that reflect how a robot’s actions will impact the real world. These simulators don’t work particularly well, however, when it comes to soft robots made of flexible, deformable materials. This is because the underlying physical laws of deformable objects are much more complicated, requiring a lot more computational power to simulate. But in a new paper, a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed a new simulator made specifically for soft robots, and have shown that it can realistically simulate an eclectic mix of robotic designs, from a crawling robot to a four-legged running robot. The simulator doesn’t just efficiently evaluate robot designs, but also provides feedback on how designs can be improved. (The system’s feedback is computed based on something called “the chain rule,” and so the team has dubbed the simulator “ChainQueen”.) The team developed a high-performance GPU implementation of the simulator that they hope to eventually make open-source. “We believe this system has the potential to dramatically accelerate the development of soft robots,” says PhD student Andrew Spielberg, one of the co-authors of the…

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MIT CSAIL Research 2019-05-28 17:35 UTC Score 30.0 USR-0009-20190528-research-aca-6e3ec7c0 Full article

Wireless health monitoring system shows promise in clinical trials

Wireless health monitoring system shows promise in clinical trials aconner Tue, 05/28/2019 - 13:35 Article May 30 '19 Novartis Wireless health monitoring system shows promise in clinical trials Over the past year MIT CSAIL has worked with Novartis to test a novel technology for passive, contactless monitoring of physiological signals that may be used to monitor clinical trial patients in their homes. Developed by Professor Dina Katabi and her students, the technology consists of a Wi-Fi-like device that transmits low-powered radio signals and uses machine learning algorithms to analyze their reflections and produce physiological metrics. The device can gather data on patient mobility, gait, breathing, heart rate, sleep stages, sleep apnea, and other metrics without requiring the patient to wear sensors or change their behavior in any way. Novartis and the MIT team explored the potential use of this technology in clinical trials to collect digital biomarkers, both existing and new, and potentially allow continuous, real-time monitoring of patients in their own homes. As part of the collaboration, Novartis deployed the technology in a Novartis facility, as well as in a life sciences facility with a living lab, sleep monitoring, motion and behavior monitoring. Individuals were studied for multiple days in the lab, and their motion, breathing, sleep, and behavior were measured using the technology and compared against existing standards for such measurements. Comparison to the g…

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MIT CSAIL Research 2019-05-22 14:30 UTC Score 40.0 USR-0009-20190522-research-aca-9ba8b03c Full article

This robot helps you lift objects — by looking at your biceps

This robot helps you lift objects — by looking at your biceps rachelg Wed, 05/22/2019 - 10:30 Video May 22 '19 Rachel Gordon CSAIL system can mirror a user's motions and follow nonverbal commands by monitoring arm muscles. We humans are very good at collaboration. For instance, when two people work together to carry a heavy object like a table or a sofa, they tend to instinctively coordinate their motions, constantly recalibrating to make sure their hands are at the same height as the other person’s. Our natural ability to make these types of adjustments allows us to collaborate on tasks big and small. But a computer or a robot still can’t follow a human’s lead with ease. We usually either explicitly program them using machine-speak, or train them to understand our words, à la virtual assistants like Siri or Alexa. In contrast, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) recently showed that a smoother robot-human collaboration is possible through a new system they developed, where machines help people lift objects by monitoring their muscle movements. Dubbed RoboRaise, the system involves putting electromyography (EMG) sensors on a user’s biceps and triceps to monitor muscle activity. Its algorithms then continuously detect changes to the person’s arm level, as well as discrete up-and-down hand gestures the user might make for finer motor control. The team used the system for a series of tasks involving picking up and assembling mock…

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MIT CSAIL Research 2019-05-08 14:20 UTC Score 32.0 USR-0009-20190508-research-aca-4d6aaf93 Full article

CSAIL's Daskalakis wins ACM Grace Murray Hopper Award

CSAIL's Daskalakis wins ACM Grace Murray Hopper Award rachelg Wed, 05/08/2019 - 10:20 Article May 08 '19 Rachel Gordon Constantinos (“Costis”) Daskalakis, an MIT professor and CSAIL principal investigator, has won the 2018 ACM Grace Murray Hopper Award. Constantinos (“Costis”) Daskalakis, an MIT professor and CSAIL principal investigator, has won the 2018 ACM Grace Murray Hopper Award. Announced today, the prize is awarded yearly to a computer scientist on the basis of a single recent major technical or service contribution, made at or before age 35 at the time of the contribution. Daskalakis was honored for “ proving that the computational complexity of finding Nash equilibria is the same as that of finding Brouwer fixed points, a proof since extended to several other equilibrium notions.” “By challenging equilibrium theory, his work has triggered an ongoing reshaping of our understanding of strategic behavior, showing that computation must play an essential role in the foundations of game theory and economics.” His research, a fusion of computer science, economics and game theory, focuses in part on how strategic behavior complicates large-scale technological systems. To study these systems, researchers typically use equilibrium concepts, and very prominently the concept of Nash equilibrium, which occurs when every player does the best they can given other players’ choices, so no player can benefit from unilaterally changing their choice. However, Nash’s equilibrium existe…

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