Machine Learning Roulette

1. A computer can predict red or black. Machine learning has nothing to do with it. ML is attempting many different techniques very fast, and figuring out which method works.
2. The simplest roulette computer I offer is called the 'Basic roulette computer'. It is just a basic roulette computer using the basic design described above. It is FREE to my roulette system players because it realistically can beat only perhaps 5% of wheels, and still the accuracy is nowhere what could be achieved.
3. One of the great things about machine learning is that there are so many different approaches to solving problems. Neural networks are great for finding patterns in data, resulting in predictive capabilities that are truly impressive. Reinforcement learning uses rewards-based concepts, improving over.

Selfies are being ripped apart by an AI-driven web experiment that uses a huge image database to classify pictures of people.

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From 'timid defenceless simpleton' to 'insignificant student', the online project ImageNet Roulette has handed out brutal assessments to an increasingly long list of users keen to experiment.

The web page launched as part of Training Humans, a photography exhibition conceived by Professor Kate Crawford and artist Trevor Paglen.

Ever wonder how algorithms trained on human classification categories type you? Thanks to this new tool from @katecrawford and @trevorpaglen's 'Training Humans' project now you can: https://t.co/ESrpzyjtxU

weird flex but ok #imagenetpic.twitter.com/0EWCoZzmhz

The gallery contains several collections of pictures used by scientists to train AI in how to 'see and categorise the world', and ImageNet Roulette is based on this research.

The tech has been trained using the existing ImageNet database and is designed to be a 'peek into the politics of classifying humans in machine learning systems and the data they are trained on'.

It has since gone viral on social media, with huge numbers of users ignoring a warning that the AI 'regularly classifies people in dubious and cruel ways'.

While some have been left flattered by being assigned descriptors like 'enchantress', others have been told they fall into categories like 'offender' and 'rape suspect'.

I am flattered by ImageNet's classification of me pic.twitter.com/6yHE3vESyZ

📯 mortal soul, available for recitals.
(via ImageNet https://t.co/6wDgGC9cXH) pic.twitter.com/jWwIRtqyeu

In a bid to explain why people might receive unflattering designations, a post on the site says they are all based on existing data already assigned to pictures in the ImageNet database.

The original database was developed in 2009 by scientists at Princeton and Stanford universities in the US, and has since assigned more than 20,000 categories across millions of images.

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ImageNet Roulette is 'meant in part to demonstrate how various kinds of politics propagate through technical systems, often without the creators of those systems even being aware of them'.

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ImageNet Roulette is 'meant in part to demonstrate how various kinds of politics propagate through technical systems, often without the creators of those systems even being aware of them'.

Hmmm. Not sure what I make of this ImageNet algorithm... pic.twitter.com/PTCVevgfCJ

The page also states that it 'does not store the photos people upload or any other data' - reassuring those who may have been put off by privacy concerns surrounding other recent picture-driven internet phenomena.

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Earlier this year, hundreds of thousands of people began to share their photos from FaceApp, which alters selfies to make them look older, younger, or to change their gender or hair style.

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Some users expressed fears over its terms and conditions allowing the app to collect data from phones, and a claim that its parent company was based in Russia and had received funds from the Russian government.