• Dog Breed Identifier: Full Cycle Development from Keras Program to Android App. on Play Market

      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:

      image

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    • Developer’s Guide to Building AI Applications

        Create your first intelligent bot with Microsoft AI


        Artificial intelligence (AI) is accelerating the digital transformation for every industry, with examples spanning manufacturing, retail, finance, healthcare, and many others. At this rate, every industry will be able to use AI to amplify human ingenuity. In this e-book, Anand Raman and Wee Hyong Tok from Microsoft provide a comprehensive roadmap for developers to build their first AI-infused application.


        Using a Conference Buddy as an example, you’ll learn the key ingredients needed to develop an intelligent chatbot that helps conference participants interact with speakers. This e-book provides a gentle introduction to the tools, infrastructure, and services on the Microsoft AI Platform, and teaches you how to create powerful, intelligent applications.

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      • Progress and hype in AI research

        The biggest issue with AI is not that it is stupid but lack of definition for intelligence and hence lack of measure for it [1] [2].


        Turing test is not a good measure because gorilla Koko wouldn't pass but she could solve more problems than lots of disabled human beings [3] [4].


        It is quite possible that people in the future will wonder why so many people back in 2019 thought playing Go and other fixed games in simulated environments after long training had anything to do with intelligence.


        Intelligence is more about adapting/transferring old knowledge to new task (playing Quake Arena quite good without any training after mastering Doom) than it is about compressing experience into heuristics to predict outcome and determining action to maximize predicted outcome value in given state (playing Quake Arena quite good after million games after mastering Doom).


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


        It makes more sense to compare a human and AI not by effectiveness/efficiency of end product of adaptation (in games played between human and agent) but by effectiveness/efficiency of process of adaptation (in games played between human-coded agent and machine-learned agent after limited practice).

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