[Info-vax] HPE buys Cray

[Arne Vajhøj]

On 5/18/2019 2:49 PM, Grant Taylor wrote:
> I feel like much of the AI / ML is noise if not hype of said noise.
> 
> $LastJob tried adding IBM Watson to system administration.  They ran 
> into a number of problems doing so.  First, the amount of noise made the 
> quality of the signal so low that it wasn't worth wasting time on. 
> Second, the systems weren't nearly as consistent (cattle) as people 
> thought they were.  In fact, they were quite unique (pets) on many 
> different levels.  This made it quit difficult to get anything of value 
> out of the AI / ML.
> 
> What they did get out of the AI / ML was largely already known.  Watch 
> all your logs.
> 
> 10  Look for anything that's unknown.
>         If you're starting at zero, everything is unknown, and this is 
> okay.
> 20  Simply pick the most common thing and decide if it's acceptable or not.
> 30  Then filter /that/ thing out of the stream of data to process.
> 40  GOTO 10
> 
> As you iterate through the above process you will learn more about your 
> environment, what is normal, what is abnormal, and what warrants 
> immediate action.
> 
> The thing that AI / ML did do was to find some things that were many 
> Many MANY iterations deep.  Some of which did have a cross interaction 
> with each other.  In hindsight, they made obvious sense.  But they were 
> of relatively low value, especially compared to the other more 
> significant things that the humans saw using traditional methods.
> 
>> I do know one large site I was at was looking at adapting Splunk to do 
>> big data style analysis of all the infrastructure (servers, storage, 
>> network) / application / appliance log files in their IT environment.
> 
> I think that taking raw data streams and hiding known good signals to 
> the point that only unknown and / or bad signals show up is a decent 
> idea.  But I don't think that AI or ML is necessary.  Especially when 
> what is functionally a filter can do much of what is needed.

There is definitely a lot of hype about ML/AI.

And the distinction between let us call it traditional
and ML/AI is not that well defined either.

But for the example you give I consider it relative clear.

IF a human spend time evaluating potential filters and decide
which filters to apply then it is traditional.

If the system itself creates filters that filter out all the
usual and only keep the unusual, then I would say that it quacks
like a duck.

Arne