When I used to start a conversation about neural networks over a bottle of beer, people were casting glances at me of what seemed to be fear; they grew sad, sometimes with their eyelid twitching. In rare cases, they were even eager to take refuge under the table. Why? These networks are simple and instinctive, actually. Yes, believe me, they are! Just let me prove this is true!

Suppose there are two things I’m aware of about the girl: she looks pretty to my taste or not, and I have lots to talk about with her or I haven’t. True and false will be one and zero respectively. We’ll take similar principle for appearance. The question is: “What girl I’ll fall in love with, and why?”

We also can think it straight and uncompromisingly: “If she looks pretty and there’s plenty to talk about, then I will fall in love. If neither is true, then I quit”.

But what if I like the lady but there’s nothing to talk about with her? Or vice versa?

Hi guys,

Before you is an article guide to open data sets for machine learning. In it, I, for a start, will collect a selection of interesting and fresh (relatively) datasets. And as a bonus, at the end of the article, I will attach useful links on independent search of datasets.

Less words, more data.

A selection of datasets for machine learning:

• Data deaths and battles from the game of thrones — This data set combines three data sources, each based on information from a series of books.
• Global Terrorism Database — Over 180,000 terrorist attacks worldwide, 1970-2017.
• Bitcoin, historical data — Bitcoin data with an interval of 1 minute from selected exchanges, January 2012 — March 2019

Inspiration sometimes arrives in strange ways. Here is the story of how a dirty disposable diaper led to the development of an artificial intelligence (AI) solution to help rid the world’s coasts of massive amounts of waste and garbage.

With the recent progress in Neural Networks in general and image Recognition particularly, it might seem that creating an NN-based application for image recognition is a simple routine operation. Well, to some extent it is true: if you can imagine an application of image recognition, then most likely someone have already did something similar. All you need to do is to Google it up and to repeat.

However, there are still countless little details that… they are not insolvable, no. They simply take too much of your time, especially if you are a beginner. What would be of help is a step-by-step project, done right in front of you, start to end. A project that does not contain «this part is obvious so let's skip it» statements. Well, almost :)

In this tutorial we are going to walk through a Dog Breed Identifier: we will create and teach a Neural Network, then we will port it to Java for Android and publish on Google Play.

For those of you who want to see a end result, here is the link to NeuroDog App on Google Play.

Web site with my robotics: robotics.snowcron.com.
Web site with: NeuroDog User Guide.

Here is a screenshot of the program:

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The original post has been updated based on community input in order to remove confusion.

Final version of the whitepaper is available here:

https://github.com/cybercongress/cyber/releases

The biggest issue with AI is not that it is stupid but a lack of definition for intelligence and hence a lack of formal measure for it [1a] [1b].

Turing test is not a good measure because gorilla Koko [2a] and bonobo Kanzi [2b] wouldn't pass though they could solve more problems than many disabled human beings.

It is quite possible that people in the future might wonder why people back in 2019 thought that an agent trained to play a fixed game in a simulated environment such as Go had any intelligence [3a] [3b] [3c] [3d] [3e] [3f] [3g] [3h].

Intelligence is more about applying/transferring old knowledge to new tasks (playing Quake Arena good enough without any training after mastering Doom) than compressing agent's experience into heuristics to predict a game score and determining agent's action in a given game state to maximize final score (playing Quake Arena good enough after million games after mastering Doom) [4].

Human intelligence is about ability to adapt to the physical/social world, and playing Go is a particular adaptation performed by human intelligence, and developing an algorithm to learn to play Go is a more performant one, and developing a mathematical theory of Go might be even more performant.