blockchain is punk

Wide distribution of power lies at the heart of anarchistic thinking. While “punk” and “anarchy” do not necessarily imply one another, they often overlap. Punks tend to balk at centralized authority, as do anarchists. A short leap of logic concludes that we therefore dislike centralized technology. Concentration in technology long parallels concentration in political power. […]

fans control the music: using AI to measure fan enthusiasm at EDC

We invented technology to enhance the fan/performer connection. Vote for Team Ambience at EDC! DJs and more traditional musicians require realtime audience feedback during performances. However, often we cannot see our audience—their movement, their facial expressions, etc.—during shows due to stage lighting. Therefore we cannot gauge their enthusiasm, and therefore cannot alter our performance to respond. […]

toward a gene panel for psychiatric violence

I recently developed a method for specifying a comprehensive gene list for investigating genes related to psychiatric violence, which I describe below. First though, here’s a cool picture from the analysis: Method I started by extracting a list of diseases involving violence from [1], removing epilepsy, dementia, mental retardation (is there a better word for […]

artificial intelligence in fashion (part two: a first step)

In my last post, “artificial intelligence in fashion (part one: brainstorming)“, I produced a list of big ideas on how machine learning and artificial intelligence may be applied to the fashion industry. I addressed sizing, marketing, and design activities when brainstorming this list. This post doesn’t specifically cover an artificial intelligence solution, but it lays […]

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. […]

rapidly identifying potential CRISPR/Cas9 off-target sites (part one)

Before we can score segments in the genome having a small number of mismatches to a CRISPR for their off-target risk, we must first find these segments. Searching for every possible mismatch permutation proves computationally expensive, so we apply the following heuristic: We only search for mismatches in the top positions relevant to CRISPR efficiency. […]

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 […]

picking stocks by graph database (part 2: machine learning)

In our last post, we demonstrated a graph database created to enable study of the stock market, particularly the study of causality relationships. So how to proceed from there? At this stage we want to pick winning stocks, not write an academic paper, so our focus turns toward practical machine learning. Source Data We start […]

picking stocks by graph database (part one)

Historical stock price data comes readily available at daily resolution. So we calculated the Granger causality for each pair of stocks we hold data for, at one and two day lags (testing the question “does daily percent change in volume for stock X Granger cause daily percent change in adjusted close price for stock Y?”). […]