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  1. codepo8 revised this gist Dec 4, 2018. 1 changed file with 6 additions and 0 deletions.
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    * The [Vision API](https://aka.ms/vision-api) of Microsoft's Cognitive Services analyses images and detects what is in them. You get a list of tags and a human readable description. It also detects known entities, faces, celebrities and gives you the colours used in the image.
    * The [Face API](https://aka.ms/face-api) of Microsoft's Cognitive Services detects human faces in an image. You get a truckload of data back: Age, Emotion, Gender, Pose, Smile, and Facial Hair along with 27(!) landmarks for each face in the image.
    * Emotion detection is interesting and can be done by pretty basic means [Susan Hinton's Emoji Face Demo](https://github.com/noopkat/face-api-emoji-face) is a good example how you can run this on your own machine.
    * [Ganpaint](http://gandissect.csail.mit.edu/) filling images with Gan generated content.

    # Speech Services
    * The [Speech API](https://aka.ms/text-to-speech) of Microsoft's Cognitive Services and Bing turns spoken words into text and speaks out text content using generated voices in various languages. Try it out on this demo.
    @@ -25,6 +26,7 @@
    * Converting speech to text is much easier when you trained the system on the person speaking. The [Speaker recognition API](https://aka.ms/speaker-recognition) allows you to detect who spoke a certain audio file and allows you to train the recognition algorithm on your own voice for much better results.

    # Ethics, Security and AI
    * China's [Social Credit System](https://futurism.com/china-social-credit-system-rate-human-value/) planned to be rolled out by 2020
    * Chinese authorities are considering using "gait recognition" to detect people according to [this Associated Press report](https://apnews.com/bf75dd1c26c947b7826d270a16e2658a)
    * You can generate photos of people by merging lots of celebrity photos and [create fake avatars](https://blog.insightdatascience.com/
    generating-custom-photo-realistic-faces-using-ai-d170b1b59255)
    @@ -39,9 +41,13 @@ generating-custom-photo-realistic-faces-using-ai-d170b1b59255)
    * Suz Hinton's [Emoji face overlay demo](https://github.com/noopkat/face-api-emoji-face)
    * Linda Liukas' [Hello Ruby](https://helloruby.com) - a kid's book series going from "what is a computer" to "how does machine learning work?
    * [De-oldify](https://github.com/jantic/DeOldify) - colouring old photos by comparison
    * [Inspirobot](http://inspirobot.me) - a network trained by inspirational posters to create new ones. With hilarious results.
    * [Google's Deep Mind teaching a sensor array to walk like humans](https://www.youtube.com/watch?v=gn4nRCC9TwQ) and almost hitting it, except for the arms


    # Interesting further materials

    * [W3C discussion group on Machine Learning on device in JavaScript](https://w3.org/community/webmachinelearning)
    * Microsoft's [AI for good](https://aka.ms/ai-for-good) program
    * Object detection of ML is not infallible, it is actually pretty easy to [Trick AI into recognising the wrong things using a few image tricks](https://aka.ms/tricking-ai)
    * [CGP Grey's "How machines learn"](https://youtube.com/watch?v=R9OHn5ZF4Uo) is a great 7 minutes video debunking a few AI myths.
  2. codepo8 revised this gist Nov 19, 2018. 1 changed file with 1 addition and 0 deletions.
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    * Charlie Gerard's [Teachable Keyboard](https://charliegerard.github.io/teachable-keyboard/)
    * Suz Hinton's [Emoji face overlay demo](https://github.com/noopkat/face-api-emoji-face)
    * Linda Liukas' [Hello Ruby](https://helloruby.com) - a kid's book series going from "what is a computer" to "how does machine learning work?
    * [De-oldify](https://github.com/jantic/DeOldify) - colouring old photos by comparison

    # Interesting further materials

  3. codepo8 revised this gist Nov 19, 2018. 1 changed file with 19 additions and 1 deletion.
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    * [ReCaptcha](https://google.com/recaptcha/intro) is a CAPTCHA engine that feeds the data back into Google's ML systems. For example, currently being asked to detect street signs or cars is a good indicator that this data will go into the self-driving cars project.
    * [Amazon's Mechanical Turk](https://mturk.com) is a service by Amazon to get humans to do things for you. A lot of the data accumulated with that one could be used to train models. One very famous examples back then was to ask people to [paint sheep facing left](http://www.aaronkoblin.com/work/thesheepmarket/).
    * All the big players in IT offer AI/ML services, like [Google](https://cloud.google.com/products/machine-learning/), [Microsoft](https://azure.microsoft.com/en-us/services/cognitive-services/) and [Amazon](https://aws.amazon.com/machine-learning/)
    * NVIDIA released an interesting new algorithm to [automatically fill missing parts of images](aka.ms/nvidia-fix-image) using a deep learning network trained on faces.
    * NVIDIA released an interesting new algorithm to [automatically fill missing parts of images](https://aka.ms/nvidia-fix-image) using a deep learning network trained on faces.

    # Vision services and recognition
    * There is a bot scouring Twitter that turns images into alternative text. All you have to do is to tag a thread with an image with `#vision_api`.
    @@ -24,9 +24,27 @@
    * [LUIS](https://luis.ai), an interface allowing you to train your own data sets for trigger words in spoken sentences, based on the [LUIS API](https://aka.ms/luis-api)
    * Converting speech to text is much easier when you trained the system on the person speaking. The [Speaker recognition API](https://aka.ms/speaker-recognition) allows you to detect who spoke a certain audio file and allows you to train the recognition algorithm on your own voice for much better results.

    # Ethics, Security and AI
    * Chinese authorities are considering using "gait recognition" to detect people according to [this Associated Press report](https://apnews.com/bf75dd1c26c947b7826d270a16e2658a)
    * You can generate photos of people by merging lots of celebrity photos and [create fake avatars](https://blog.insightdatascience.com/
    generating-custom-photo-realistic-faces-using-ai-d170b1b59255)
    * [Social Mapper](https://github.com/SpiderLabs/social_mapper) is an attack tool to detect users across dozens of social networks using facial recognition

    # Having fun

    * [PointerPointer](http://pointerpointer.com) - finding photos pointing at where the mouse is currently on the screen
    * [Move Mirror](https://experiments.withgoogle.com/move-mirror) Google experiment finding photos with your current pose
    * Cassie Evan's [Shy Blob](https://codepen.io/cassie-codes/pen/jKaVqo/) that only moves when you don't look
    * Charlie Gerard's [Teachable Keyboard](https://charliegerard.github.io/teachable-keyboard/)
    * Suz Hinton's [Emoji face overlay demo](https://github.com/noopkat/face-api-emoji-face)
    * Linda Liukas' [Hello Ruby](https://helloruby.com) - a kid's book series going from "what is a computer" to "how does machine learning work?

    # Interesting further materials

    * Microsoft's [AI for good](https://aka.ms/ai-for-good) program
    * Object detection of ML is not infallible, it is actually pretty easy to [Trick AI into recognising the wrong things using a few image tricks](https://aka.ms/tricking-ai)
    * [CGP Grey's "How machines learn"](https://youtube.com/watch?v=R9OHn5ZF4Uo) is a great 7 minutes video debunking a few AI myths.
    * [Chris Heilmann's AI course on Skillshare](https://skl.sh/christian)
    * Microsoft offers a [full length Machine Learning course for free](https://aka.ms/learn-ai) ending with an "Microsoft Professional Program Certificate in Artificial Intelligence". You will learn about
    * The Math behind ML
    * The ethics of AI
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    * Machine Learning Models
    * Deep Learning Models
    * Reinforcement Learning Models

    * [Skillshare Course on Demystifying Artificial Intelligence](https://skl.sh/christian)

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    # Slides

    * The slides of the [Intel AIDC keynote are available here as a PDF](https://www.dropbox.com/s/jyktab6bigumihg/AIDC-printout.pdf?dl=0)
    * The slides of this talk are available on [Dropbox as a PDF](https://www.dropbox.com/s/oyn80rf2ecyi57m/AI-emerge.pdf?dl=0)

    # Intro and history of ML on the web
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    # Slides

    * The slides of this talk are available on [Dropbox as a PDF](https://www.dropbox.com/s/oyn80rf2ecyi57m/AI-emerge.pdf?dl=0)

    # Intro and history of ML on the web

    * [Autodraw](https://autodraw.com) by Google is a tool that allows you to doodle what you want to paint and turns it into a proper icon by detecting the outline and making an ML based assumption what it could be.
    * [Quickdraw](https://quickdraw.withgoogle.com) by Google is a game they created a few years before Autodraw to train the model.
    * [ReCaptcha](https://google.com/recaptcha/intro) is a CAPTCHA engine that feeds the data back into Google's ML systems. For example, currently being asked to detect street signs or cars is a good indicator that this data will go into the self-driving cars project.
    * [Amazon's Mechanical Turk](https://mturk.com) is a service by Amazon to get humans to do things for you. A lot of the data accumulated with that one could be used to train models. One very famous examples back then was to ask people to [paint sheep facing left](http://www.aaronkoblin.com/work/thesheepmarket/).
    * All the big players in IT offer AI/ML services, like [Google](https://cloud.google.com/products/machine-learning/), [Microsoft](https://azure.microsoft.com/en-us/services/cognitive-services/) and [Amazon](https://aws.amazon.com/machine-learning/)
    * NVIDIA released an interesting new algorithm to [automatically fill missing parts of images](aka.ms/nvidia-fix-image) using a deep learning network trained on faces.

    # Vision services and recognition
    * There is a bot scouring Twitter that turns images into alternative text. All you have to do is to tag a thread with an image with `#vision_api`.
  7. codepo8 revised this gist Apr 11, 2018. 1 changed file with 6 additions and 6 deletions.
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    * Object detection of ML is not infallible, it is actually pretty easy to [Trick AI into recognising the wrong things using a few image tricks](https://aka.ms/tricking-ai)
    * [CGP Grey's "How machines learn"](https://youtube.com/watch?v=R9OHn5ZF4Uo) is a great 7 minutes video debunking a few AI myths.
    * Microsoft offers a [full length Machine Learning course for free](https://aka.ms/learn-ai) ending with an "Microsoft Professional Program Certificate in Artificial Intelligence". You will learn about
    ** The Math behind ML
    ** The ethics of AI
    ** Working with Data using Python
    ** Machine Learning Models
    ** Deep Learning Models
    ** Reinforcement Learning Models
    * The Math behind ML
    * The ethics of AI
    * Working with Data using Python
    * Machine Learning Models
    * Deep Learning Models
    * Reinforcement Learning Models


  8. codepo8 revised this gist Apr 11, 2018. 1 changed file with 18 additions and 12 deletions.
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    * All the big players in IT offer AI/ML services, like [Google](https://cloud.google.com/products/machine-learning/), [Microsoft](https://azure.microsoft.com/en-us/services/cognitive-services/) and [Amazon](https://aws.amazon.com/machine-learning/)

    # Vision services and recognition
    * `#vision_api`
    * https://aka.ms/vision-api
    * https://aka.ms/face-api
    * https://github.com/noopkat/face-api-emoji-face
    * There is a bot scouring Twitter that turns images into alternative text. All you have to do is to tag a thread with an image with `#vision_api`.
    * The [Vision API](https://aka.ms/vision-api) of Microsoft's Cognitive Services analyses images and detects what is in them. You get a list of tags and a human readable description. It also detects known entities, faces, celebrities and gives you the colours used in the image.
    * The [Face API](https://aka.ms/face-api) of Microsoft's Cognitive Services detects human faces in an image. You get a truckload of data back: Age, Emotion, Gender, Pose, Smile, and Facial Hair along with 27(!) landmarks for each face in the image.
    * Emotion detection is interesting and can be done by pretty basic means [Susan Hinton's Emoji Face Demo](https://github.com/noopkat/face-api-emoji-face) is a good example how you can run this on your own machine.

    # Speech Services
    * https://aka.ms/text-to-speech
    * https://aka.ms/conversation-ui
    * https://luis.ai
    * https://aka.ms/luis-api
    * https://aka.ms/speaker-recognition
    * The [Speech API](https://aka.ms/text-to-speech) of Microsoft's Cognitive Services and Bing turns spoken words into text and speaks out text content using generated voices in various languages. Try it out on this demo.
    * An excellent article on Smashing Magazine by Burke Holland on [The Rise Of Intelligent Conversational UI](https://aka.ms/conversation-ui) with examples on why this is a useful thing and how to build your own speech recognition services without using the Alexa API.
    * [LUIS](https://luis.ai), an interface allowing you to train your own data sets for trigger words in spoken sentences, based on the [LUIS API](https://aka.ms/luis-api)
    * Converting speech to text is much easier when you trained the system on the person speaking. The [Speaker recognition API](https://aka.ms/speaker-recognition) allows you to detect who spoke a certain audio file and allows you to train the recognition algorithm on your own voice for much better results.

    # Interesting further materials
    * https://aka.ms/tricking-ai
    * https://youtube.com/watch?v=R9OHn5ZF4Uo
    * https://aka.ms/learn-ai
    * Object detection of ML is not infallible, it is actually pretty easy to [Trick AI into recognising the wrong things using a few image tricks](https://aka.ms/tricking-ai)
    * [CGP Grey's "How machines learn"](https://youtube.com/watch?v=R9OHn5ZF4Uo) is a great 7 minutes video debunking a few AI myths.
    * Microsoft offers a [full length Machine Learning course for free](https://aka.ms/learn-ai) ending with an "Microsoft Professional Program Certificate in Artificial Intelligence". You will learn about
    ** The Math behind ML
    ** The ethics of AI
    ** Working with Data using Python
    ** Machine Learning Models
    ** Deep Learning Models
    ** Reinforcement Learning Models


  9. codepo8 revised this gist Apr 11, 2018. 1 changed file with 13 additions and 7 deletions.
    20 changes: 13 additions & 7 deletions ai-for-humans.md
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    * https://autodraw.com
    * https://quickdraw.withgoogle.com
    * https://google.com/recaptcha/intro
    * https://mturk.com
    * https://cloud.google.com/products/machine-learning/
    * https://azure.microsoft.com/en-us/services/cognitive-services/
    * https://aws.amazon.com/machine-learning/
    # Intro and history of ML on the web

    * [Autodraw](https://autodraw.com) by Google is a tool that allows you to doodle what you want to paint and turns it into a proper icon by detecting the outline and making an ML based assumption what it could be.
    * [Quickdraw](https://quickdraw.withgoogle.com) by Google is a game they created a few years before Autodraw to train the model.
    * [ReCaptcha](https://google.com/recaptcha/intro) is a CAPTCHA engine that feeds the data back into Google's ML systems. For example, currently being asked to detect street signs or cars is a good indicator that this data will go into the self-driving cars project.
    * [Amazon's Mechanical Turk](https://mturk.com) is a service by Amazon to get humans to do things for you. A lot of the data accumulated with that one could be used to train models. One very famous examples back then was to ask people to [paint sheep facing left](http://www.aaronkoblin.com/work/thesheepmarket/).
    * All the big players in IT offer AI/ML services, like [Google](https://cloud.google.com/products/machine-learning/), [Microsoft](https://azure.microsoft.com/en-us/services/cognitive-services/) and [Amazon](https://aws.amazon.com/machine-learning/)

    # Vision services and recognition
    * `#vision_api`
    * https://aka.ms/vision-api
    * https://aka.ms/face-api
    * https://github.com/noopkat/face-api-emoji-face

    # Speech Services
    * https://aka.ms/text-to-speech
    * https://aka.ms/conversation-ui
    * https://luis.ai
    * https://aka.ms/luis-api
    * https://aka.ms/speaker-recognition

    # Interesting further materials
    * https://aka.ms/tricking-ai
    * https://youtube.com/watch?v=R9OHn5ZF4Uo
    * https://aka.ms/learn-ai
  10. codepo8 revised this gist Apr 11, 2018. 1 changed file with 19 additions and 19 deletions.
    38 changes: 19 additions & 19 deletions ai-for-humans.md
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    https://autodraw.com
    https://quickdraw.withgoogle.com
    https://google.com/recaptcha/intro
    https://mturk.com
    https://cloud.google.com/products/machine-learning/
    https://azure.microsoft.com/en-us/services/cognitive-services/
    https://aws.amazon.com/machine-learning/
    #vision_api
    https://aka.ms/vision-api
    https://aka.ms/face-api
    https://github.com/noopkat/face-api-emoji-face
    https://aka.ms/text-to-speech
    https://aka.ms/conversation-ui
    https://luis.ai
    https://aka.ms/luis-api
    https://aka.ms/speaker-recognition
    https://aka.ms/tricking-ai
    https://youtube.com/watch?v=R9OHn5ZF4Uo
    https://aka.ms/learn-ai
    * https://autodraw.com
    * https://quickdraw.withgoogle.com
    * https://google.com/recaptcha/intro
    * https://mturk.com
    * https://cloud.google.com/products/machine-learning/
    * https://azure.microsoft.com/en-us/services/cognitive-services/
    * https://aws.amazon.com/machine-learning/
    * `#vision_api`
    * https://aka.ms/vision-api
    * https://aka.ms/face-api
    * https://github.com/noopkat/face-api-emoji-face
    * https://aka.ms/text-to-speech
    * https://aka.ms/conversation-ui
    * https://luis.ai
    * https://aka.ms/luis-api
    * https://aka.ms/speaker-recognition
    * https://aka.ms/tricking-ai
    * https://youtube.com/watch?v=R9OHn5ZF4Uo
    * https://aka.ms/learn-ai

  11. codepo8 created this gist Apr 11, 2018.
    20 changes: 20 additions & 0 deletions ai-for-humans.md
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    https://autodraw.com
    https://quickdraw.withgoogle.com
    https://google.com/recaptcha/intro
    https://mturk.com
    https://cloud.google.com/products/machine-learning/
    https://azure.microsoft.com/en-us/services/cognitive-services/
    https://aws.amazon.com/machine-learning/
    #vision_api
    https://aka.ms/vision-api
    https://aka.ms/face-api
    https://github.com/noopkat/face-api-emoji-face
    https://aka.ms/text-to-speech
    https://aka.ms/conversation-ui
    https://luis.ai
    https://aka.ms/luis-api
    https://aka.ms/speaker-recognition
    https://aka.ms/tricking-ai
    https://youtube.com/watch?v=R9OHn5ZF4Uo
    https://aka.ms/learn-ai