Synthesizing your voice: WaveNet by Google DeepMind

I remember the day (somewhen in the 90s), when computer generated voices sounded - well synthetic. Today you can still tell the difference between a human and a machine speaking to you. Although they have gotten very good. On the other hand, it's probably good that you can tell a difference. Think about all the implicit expectations, that we would have, if we'd thought a human was speaking to us...


But then on the other hand, think about how much already existing content that exists in text form we can leverage given we have a natural sounding voice reading it to us. Many e-learning platforms are already using it but to be honest most of them do not cut it, when they use TTS. It's diffent to watch a youtube tutorial with an energetic tutor that grabs my attention.


But technology keep catching on: WaveNet by Google DeepMind is promising, generating voices from actual audio samples. Imagine: Hearing your voice reading a book or a tutorial, without reading it (yes I know it's akward to hear you own voice when you are not used to it).


Based in deep learning techniques WaveNet picks up subtle notions such as breathing rhythm and individual intonation. Probably energizing the generated TTS with some markup is not so far away...

Categories: AI, Article, YouTube

Machine Learning Recipes

A cool series for learning the principles of machine learning...

Categories: AI, Learn, YouTube

Voice User Interface - The New UI

It's amazing how quickly the AI topic develops. In the past few months, I have seens so much new stuff emerging.

When I first saw this, I was amazed (happens a lot lately - maybe I am just too easy to motivate ;-). Then I thought: well voice enabled computing isn't new really, I mean all the hotlines do it. So while the Conversion UI basically serves the same purpose as traditional hotline conversation machines, the new thing about  it is really the context based natural language interaction opposed to: request - response that it was in the past.


Goal for user:
Triggering a particular service


Goal for the AI:
Fetching all neccessary parameters from a natural language interaction in order to invoke the service.


Benefits for the User:
Natural language conversation, where the AI considers context (what has been said during the conversation, what environment the user is currently in (etc. location, time). Habits of the user are also taken into account as the history of how the user has been interacting with Google devices and services is tracked.

Benefits for the Developer:

Declarative approach to defining the conversation. It's based on examples rather than fixed rules, that the service can then turn into algorithms using it's AI.

Categories: AI, API, Featured, Learn, YouTube

A.I. Experiments

Sometimes I stumble across things that are just jaw dropping. I have read a lot of articles and watched a lot of YouTube videos about AI. Google has great platforms and technology to power this, but what really is fascinating when you find something that steps from behind the glass wall of "me staring at it astonished" to "oh, hi there, let's play". This is what


A.I. Experiments

is. An experimental Site where you can find an A.I. driven virtual playground. It may seem a little childish at first but then again children grow up really fast and become adults. This is something you don't want to be missing... not just watching but being a part of it.

I got caught on watching this...



...and then this...



...and guess what: you can try that for yourself!



And this here could be something, really useful for analytic technology fields. Probably one can find way's to apply this kind of pattern recognition to Business Intelligence releated questions. Isn't BI all about people trying to find patterns in data using visual tools like graphs and charts. We'll maybe machines can do that job quite well finding patterns in massive amounts of data...

Categories: AI, Learn, YouTube

MarI/O - Machine Learning for Video Games

For everybody who want's to get started with the concepts of machine learning in a fun way... As far as I get it, the basic idea to mimic evolutionary or learning based on experience.

A computer player in this scenario basically needs:

1. Senses like sight: Abstracting the actual screen to individual items like save ground, blocks/walls, gaps, enemies. Fortunately platforming games like Mario support abstraction really well as sprites can be though of as blocks of the above mentioned types
2. The ability to push buttons on the controller: Well there are APIs for that, right ;-)
3. Knowing what success means: e.g. advancing to the right side of the level without getting killed in the shortest amount of time
4. Patience, Endurance or the ability to massively parallelize the process of trying, trying, trying... as you are basically brute forcing every combination of button presses. But fortunately when you are failing you will have a sense of how successful you were based on how far you got in the level and how quickly you got there. Then you can take that as a starting point for further tries... or the next generation when shifting to evolutionary terms...

Worth a read is this paper which explains it much better than I tried (maybe my children will be more successful in explaining ;-)



And while we are at it... here goes Mario Kart

Categories: AI, Article, Learn, YouTube

ReactCasts Episode 1

I really enjoy generic approaches to common problems. Abstraction - once mastered - makes it easy to dump off mental stress and advance.

Categories: JavaScript, React, YouTube

Immutable User Interfaces (Lee Byron) - Full Stack Fest 2016

Enlightning talk on modern web application achitecture - including a deeper, challenging message. Really you should not miss it!

Categories: Featured, Frontend, Learn, Patterns, YouTube

MobX tutorials - MobX + React is AWESOME

Not that I have tried it yet, but MobX looks like a straight forward alternative, especially for developers, that used to work with classical Model-Classes for a long time. It just seems so familiar.

Categories: Featured, Framework, JavaScript, Learn, React, YouTube

GITHUB PULL REQUEST, Branching, Merging & Team Workflow

It's embarassing enough that I don't work with GitHub more than downloading repositories as ZIPs... Oh man, so much more to it...

Categories: Learn, Tools, YouTube

Github Tutorial For Beginners - Github Basics for Mac or Windows & Sourc...

Well this one is to get started. Yes and really everyone in the software industry should know source/version control..

Categories: Learn, Tools, YouTube

Ryan Florence - ‹Rethinker stop={false}/›

Sometimes it's good to rethink our common practises. I'm not sure if I would apply this to a productive app, but it's worth thinking about the approaches presented.

Categories: Featured, Patterns, React, YouTube