[FRIAM] more fun with AI

[Steven A Smith]

holy shite REC!   Looks like pretty good KoolAid!

I cut my teeth 40 years ago on APL.  Feels like what I *wished for* back 
then (studying Physics/Math with CS "just a tool").

As we talked a few years ago, I have a (still open, hanging fire) 
project to do real-time stitching on a 360 stereographic camera (84 
cameras in a spherical array with more than 50% overlap with each 
neighbor, E/W and N/S)...

- Steve

On 2/16/17 8:57 AM, Roger Critchlow wrote:
> I watched the livestream from the TensorFlow Dev Summit in 
> Mountainview yesterday.  The individual talks are already packaged up 
> as individual videos at 
> https://events.withgoogle.com/tensorflow-dev-summit/videos-and-agenda/#content, 
> but watching the livestream with the enforced moments of deadtime 
> filled with vaguely familiar music (was that Phillip Glass, or a 
> network trained on him?) was very instructive.
>
> TensorFlow is a data graph language where the data is all Tensors, ie 
> vectors, matrices, and higher dimensional globs of numbers.  Google 
> open sourced it as python scripts and a C++ kernel about a year ago, 
> updated with minor releases monthly, and released 1.0 yesterday.   
> It's been used all over the place at Google, it's the top machine 
> learning repo at github, and its products have made the cover of 
> Nature twice or three times in the past year.
>
> New stuff yesterday:
>
>   * an LLVM compiler to native x86, arm, nvidia, etc.,
>   * new language front ends,
>   * pre-built networks and network components
>   * classical ML techniques in case deep learning networks aren't your
>     thing
>   * distributed model training on pcs, servers, and GPUs
>   * a server architecture for delivering inferences at defined latency
>   * embedded inference stacks for Android, iOS, and Raspberry Pi
>   * a very sweet visualizer, TensorBoard, for network architectures,
>     parameters, and classified sets
>   * higher level APIs
>   * and networks trained to find network architectures for new classes
>     of problems
>
> You can get a lot of this by just watching the keynote, even just the 
> first 10 minutes of the keynote.
>
> Whether you buy the KoolAid or not, it's an impressive demonstration 
> of the quantity and quality of KoolAid that the Google mind can 
> produce when it decides that it needs KoolAid.
>
> An LSTM is a Long Short-Term Memory node, a basic building block of 
> the networks that translate languages or process other variable length 
> symbol strings.
>
> -- rec --
>
>
>
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