Project Veritas leaked bombshell documents yesterday concerning Google’s “Machine Learning Fairness” program, where fairness is defined by their social agenda. (Disclosure: I largely support Google’s social agenda, just not promotion of that agenda through mass, hidden manipulation). In my recent article “What Journalists Need to Know About Artificial Intelligence” [2], I discussed the insertion of…

# Tag: machine learning

## what journalists need to know about artificial intelligence

why this article? A journalist recently asked me to comment on the feasibility of a conspiracy theory involving one of Facebook’s AI algorithms. He wanted to know whether it was likely, or even possible, that Facebook was using its existing algorithm for suicide video detection to screen and censor conservative media sources. To answer the…

## machine learning in FOREX (part one: establishing a performance baseline)

Introduction We’ve been applying machine learning to FOREX price prediction. The performance of our models varies widely, so to establish a baseline we created a simple linear regression model with which we can compare performance of more sophisticated models against. What We Are Trying To Do Given a time-series of 26 four-hour price samples, we…

## artificial intelligence in fashion (part one: brainstorming)

Brainstorming as usual: Fashion dictums involve many IF-THEN-ELSE rules. One can convert this into a decision engine (inference engine). User specifies their body shape, and a recommendation engine selects suitable clothing for them, taking into account the user’s tastes. Upload an image of a dress you want to buy, and specify the dress’s given size….

## pseudo-harmonic FOREX prediction with machine learning (part one)

“Harmonic” trading methods seek patterns in the relationships between neighboring peaks and valleys in the time series. Particularly, harmonic traders seek pre-specified ratios in the price differences among a series of peaks and valleys. For example, a trader might observe the following pattern: Let A, B, C, D, and E be the points in the…

## Bayesian method for filtering out mRNA turnover rate bias from siRNA knockdown measurements

Abstract siRNA performance prediction calculations for a given siRNA may be divided into two broad categories: functions of the siRNA’s sequence, hereafter referred to as “intrinsic” properties of the siRNA, and functions of the target mRNA, hereafter referred to as “extrinsic” properties of the siRNA. When training a statistical or machine learning model to select…

## overfitting in statistics and machine learning (part one)

Overfitting is a common risk when designing statistical and machine-learning models. Here I give a brief demonstration of overfitting in action, using simple regression models. A later post will more rigorously address how to quantify and avoid overfitting. We start by sampling data from the process using the R code: Then we produce a linear…

## simulated ROC curves

How receiver operating characteristic (ROC) curves vary with simulated data having stepped degrees of separation: Computational Notes These were created in R using the “ROCR” package. Be sure to say “ROCR” really fast! The simulated data are normally distributed within each group.