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Comment by ilaksh | Hacker News Reader

I thinks this type of paradigm shift is what will be needed for the next level of performance for AI. But it also seems like for that to be competitive with current methods, they need to reduce the size by a factor of 2-3 and also make bigger chips.

I just feel like IBM has a track record recently of doing great research but not actually manufacturing anything. Kind of like Google.

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Usually IBM doesn't sell to the kind of HN crowd, rather people happy with Aix, IBM i and z/OS, DB2, cloud for mainframes, own JVM implementations, Java EE servers, offshoring services,...

Some of the research does end in such products, that are outside HN radar, because IBM isn't cool, yet many HN startups won't achieve a legacy like it and still be pumping money like IBM, even for boring business.

IBM revenue has been shrinking over the last 10 years. They aren't selling well.

Many companies would dream of a revenue with that many digits.

Yes, IBM reached a peak in the past from which a gradual fall still leaves them raking in money from largely legacy enterprise clients for a very long time.

That doesn't make them competitive in new markets, though.

Unfortunately I won't be around to check on it, lets see in 100 years who would still be around.

Your repeated discount of "it's shrinking and has been shrinking" with "we just don't know" is like saying that because you won't be around in 100 years you also can't make claims about whether the sun will come up.

I won't be around in 100 years either but I am sure IBM won't survive for the same reason I'm sure the sun will rise: all rigorous analysis today indicates it.

>are outside HN radar, because IBM isn't cool

More like outside people's radar because IBM only sells to big companies.

I would bet a large portion of software engineers work in Big Companies. FANG employees comment regularly here and they work for the biggest companies in Tech.

Even in a FANG you can't just walk to your manager and tell them to get a mainframe so you can play around with z/OS.

No one will let you play with a mainframe. I’ve worked for finical services companies and toured a data center with large IBM mainframes I wasn’t allowed to get close to much less touch the software.

I’m sure if I wanted to move to the team it would’ve been possible to play with z/OS.

In 1994's Summer I was doing AS/400 backups during my internship.

It is a matter of being on the right team.

I feel like the irony here is that for all the fences most companies put around their mainframes... at the end of the day and inside the fence it's usually 1-2 people running everything who still know how to hack JCL et al.

Those are the cool ones, the "do no evil" stuff.

I don't think anyone disputes that ibm is the heir to an impressive tech empire. In its day it was definitely something.

They just haven't done a whole lot lately, and most of what they have done is support people locked into their legacy solutions.

As far as I'm concerned that's a bug not a feature for them.

Just a laymen here, but if the organic brain is something we're trying to model I always ached for some kind of analog processor for things like neural networks. Op-amps, not logic gates.

(Quick google found this fun hobbyist article of a perceptron built with op-amps: https://www.nutsvolts.com/magazine/article/the_perceptron_ci...)

We're not really trying to model the organic brain; while some structures are somewhat inspired by some structures in the brain, the current learning structures are fundamentally quite different from biological brains.

While there is a bunch of research for modeling organic brains, IMHO it is primarily driven by neuroscience trying to understand how humans work, and not directly applicable to making computation more efficient.

But wouldn't some computations be more efficient it they could run on brain-like synapses/clusters or hardware analogs thereof? Pattern recognition, multi-modal perception and such.

Maybe? This presupposes we know how the brain works (we don't).

Then you can kind of walk it to "but what if it kinda worked like [my understanding of how the brain works], would it be more efficient?", but it's not really clear what conversation we're having at that point.

The closest equivalent Google has is the TPU which is very much a manufactured thing at large scale.

TPUs are a lot more like GPUs, they're just a bunch of tensor cores and high speed memory.

The chip in the article gains its advantages from doing the compute directly in the memory cells. It saves power by moving information around less.

Yea I'm just saying it's not like all hardware projects that Google does are "great research but not actually manufacturing anything." as claimed by OP.

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