Month: October 2022

FAST(HARD) or Slow(soft)AGI takeoff – AGI Part 6

Posted on by Ray in AGI, Cognitive computing, Distributed computing, Forecasting, Scenario planning, Strategic Inflection Points

I was listening to a podcast a couple of weeks back and the person being interviewed made a comment that he didn’t believe that AGI would have a fast (hard) take off rather it would be slow (soft). Here’s the podcast John Carmack interviewed by Lex Fridman).

Hard vs. soft takeoff

A hard (fast) takeoff implies a relatively quick transition (seconds, hours, days, or months) between AGI levels of intelligence and super AGI levels of intelligence. A soft (slow) takeoff implies it would take a long time (years, decades, centuries) to go from AGI to super AGI.

We’ve been talking about AGI for a while now and if you want to see more about our thoughts on the topic, check out our AGI posts (in most recent order: AGI part 5, part 4, part 3 (ish), part (2), part (1), and part (0)).

The real problem is that many believe that any AGI that reaches super-intelligence will have drastic consequences for the earth and especially, for humanity. However, this is whole other debate.

The view is that a slow AGI takeoff might (?) allow sufficient time to imbue any and all (super) AGI with enough safeguards to eliminate or minimize any existential threat to humanity and life on earth (see part (1) linked above).

A fast take off won’t give humanity enough time to head off this problem and will likely result in an humanity ending and possibly, earth destroying event.

Hard vs Soft takeoff – the debate

I had always considered AGI would have a hard take off but Carmack seemed to think otherwise. His main reason is that current large transformer models (closest thing to AGI we have at the moment) are massive and take lots of special purpose (GPU/TPU/IPU) compute, lots of other compute and gobs and gobs of data to train on. Unclear what the requirements are to perform inferencing but suffice it to say it should be less.

And once AGI levels of intelligence were achieved, it would take a long time to acquire any additional regular or special purpose hardware, in secret, required to reach super AGI.

So, to just be MECE (mutually exclusive and completely exhaustive) on the topic, the reasons researchers and other have posited to show that AGI will have a soft takeoff, include:

  • AI hardware for training and inferencing AGI is specialized, costly, and acquisition of more will be hard to keep secret and as such, will take a long time to accomplish;
  • AI software algorithmic complexity needed to build better AGI systems is significantly hard (it’s taken 70yrs for humanity to reach todays much less than AGI intelligent systems) and will become exponentially harder to go beyond AGI level systems. This additional complexity will delay any take off;
  • Data availability to train AGI is humongous, hard to gather, find, & annotate properly. Finding good annotated data to go beyond AGI will be hard and will take a long time to obtain;
  • Human government and bureaucracy will slow it down and/or restrict any significant progress made in super AGI;
  • Human evolution took Ms of years to go from chimp levels of intelligence to human levels of intelligence, why would electronic evolution be 6-9 orders of magnitude faster.
  • AGI technology is taking off but the level of intelligence are relatively minor and specialized today. One could say that modern AI has been really going since the 1990s so we are 30yrs in and today have almost good AI chatbots today and AI agents that can summarize passages/articles, generate text from prompts or create art works from text. If it takes another 30 yrs to get to AGI, it should provide sufficient time to build in capabilities to limit super-AGI hard take off.

I suppose it’s best to take these one at a time.

  • Hardware acquisition difficulty – I suppose the easiest way for an intelligent agent to acquire additional hardware would be to crack cloud security and just take it. Other ways may be to obtain stolen credit card information and use these to (il)legally purchase more compute. Another approach is to optimize the current AGI algorithms to run better within the same AGI HW envelope, creating super AGI that doesn’t need any more hardware at all.
  • Software complexity growing – There’s no doubt that AGI software will be complex (although the podcast linked to above, is sub-titled that “AGI software will be simple”). But any sub-AGI agent that can change it’s code to become better or closer to AGI, should be able to figure out how not to stop at AGI levels of intelligence and just continue optimizating until it reaches some wall. i
  • Data acquisition/annotation will be hard – I tend to think the internet is the answer to any data limitations that might be present to an AGI agent. Plus, I’ve always questioned if Wikipedia and some select other databases wouldn’t be all an AGI would need to train on to attain super AGI. Current transformer models are trained on Wikipedia dumps and other data scraped from the internet. So there’s really two answers to this question, once internet access is available it’s unclear that there would be need for anymore data. And, with the data available to current transformers, it’s unclear that this isn’t already more than enough to reach super AGI
  • Human bureaucracy will prohibit it: Sadly this is the easiest to defeat. 1) there are roque governments and actors around the world with more than sufficient resources to do this on their own. And no agency, UN or otherwise, will be able to stop them. 2) unlike nuclear, the technology to do AI (AGI) is widely available to business and governments, all AI research is widely published (mostly open access nowadays) and if anything colleges/universities around the world are teaching the next round of AI scientists to take this on. 3) the benefits for being first are significant and is driving a weapons (AGI) race between organizations, companies, and countries to be first to get there.
  • Human evolution took Millions of years, why would electronic be 6-9 orders of magnitude faster – electronic computation takes microseconds to nanoseconds to perform operations and humans probably 0.1 sec, or so. Electronics is already 5 to 8 orders of magnitude faster than humans today. Yes the human brain is more than one CPU core (each neuron would be considered a computational element). But there are 64 core CPUs/4096 CORE GPUs out there today and probably one could consider similar in nature if taken in the aggregate (across a hyperscaler lets say). So, just using the speed ups above it should take anywhere from 1/1000 of a year to 1 year to cover the same computational evolution as human evolution covered between the chimp and human and accordingly between AGI and AGIx2 (ish).
  • AGI technology is taking a long time to reach, which should provide sufficient time to build in safeguards – Similar to the discussion on human bureaucracy above, with so many actors taking this on and the advantages of even a single AGI (across clusters of agents) would be significant, my guess is that the desire to be first will obviate any thoughts on putting in safeguards.

Other considerations for super AGI takeoff

Once you have one AGI trained why wouldn’t some organization, company or country deploy multiple agents. Moreover, inferencing takes orders of magnitude less computational power than training. So with 1/100-1/1000th the infrastructure, one could have a single AGI. But the real question is wouldn’t a 100- or 1000-AGis represent super intelligence?

Yes and no, 100 humans doesn’t represent super intelligence and a 1000 even less so. But humans have other desires, it’s unclear that 100 humans super focused on one task wouldn’t represent super intelligence (on that task).

Interior view of a data center with equipment

What can be done to slow AGI takeoff today

Baring something on the order of Nuclear Proliferation treaties/protocols, putting all GPUs/TPUs/IPUs on weapons export limitations AND restricting as secret, any and all AI research, nothing easily comes to mind. Of course Nuclear Proliferation isn’t looking that good at the moment, but whatever it’s current state, it has delayed proliferation over time.

One could spend time and effort slowing technology progress down. Such as by reducing next generation CPU/GPU/IPU compute cores , limiting compute speedups, reduce funding for AI research, putting a compute tax, etc. All of which, if done across the technological landscape and the whole world, could give humanity more time to build in AGI safeguards. But doing so would adversely impact all technological advancement, in healthcare, business, government, etc. And given the proliferation of current technology and the state actors working on increasing capabilities to create more, it would be hard to envision slowing technological advancement down much, if at all.

It’s almost like putting a tax on slide rules or making their granularity larger.

It could be that super AGI would independently perceive itself benignly, and only provide benefit to humanity and the earth. But, my guess is that given the number of bad actors intent on controlling the world, even if this were true, they would try to (re-)direct it to harm segments of humanity/society. And once unleashed, it would be hard to stop.

The only real solution to AGI in bad actor hands, is to educate all of humanity to value all humans and to cherish the environment we all live in as sacred. This would eliminate bad actors,

It sounds so naive, but in reality, it’s the only thing, I believe, the only way we can truly hope to get us through this AGI technological existential crisis.

Just like nuclear, we as a society will keep running into technological existential crisis’s like this. Heading all these off, with a better more all inclusive, more all embracing, and less combative humanity could help all of them.

Comments?

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The Hollowing out of enterprise IT

Posted on by Ray in Cloud services, Forecasting, IoT, Market dynamics, Storage, Strategic Inflection Points, Strategic planning, Visionary leadershp

We had a relatively long discussion yesterday, amongst a bunch of independent analysts and one topic that came up was my thesis that enterprise IT is being hollowed out by two forces pulling in opposite directions on their apps. Those forces are the cloud and the edge.

Western part of the abandoned Packard Automotive Plant in Detroit, Michigan. by Albert Duce

Cloud sirens

The siren call of the cloud for business units, developers and modern apps has been present for a long time now. And their call is more omnipresent than Odysseus ever had to deal with.

The cloud’s allure is primarily low cost-instant infrastructure that just works, a software solution/tool box that’s overflowing, with locations close to most major metropolitan areas, and the extreme ease of starting up.

If your app ever hopes to scale to meet customer demand, where else can you go. If your data can literally come in from anywhere, it usually lands on the cloud. And if you have need for modern solutions, tools, frameworks or just about anything the software world can create, there’s nowhere else with more of this than the cloud.

Pre-cloud, all those apps would have run in the enterprise or wouldn’t have run at all. And all that data would have been funneled back into the enterprise.

Not today, the cloud has it all, its siren call is getting louder everyday, ever ready to satisfy every IT desire anyone could possibly have, except for the edge.

The Edge, last bastion for onsite infrastructure

The edge sort of emerged over the last decade or so kind of in stealth mode. Yes there were always pockets of edge, with unique compute or storage needs. For example, video surveillance has been around forever but the real acceleration of edge deployments started over the last decade or so as compute and storage prices came down drastically.

These days, the data being generated is stagering and compute requirements that go along with all that data are all over the place, from a few ARMv/RISC V cores to a server farm.

For instance, CERN’s LHC creates a PB of data every second of operation (see IEEE Spectrum article, ML shaking up particle physics too). But they don’t store all that. So they use extensive compute (and ML) to try to only store interesting events.

Seismic ships roam the seas taking images of underground structures, generating gobs of data, some of which is processed on ship and the rest elsewhere. A friend of mine creates RPi enabled devices that measure tank liquid levels deployed in the field.

More recently, smart cars are like a data center on tires, rolling across roads around the world generating more data than you want can even imagine. 5G towers are data centers ontop of buildings, in farmland, and in cell towers doting the highways of today. All off the beaten path, and all places where no data center has ever gone before.

In olden days there would have been much less processing done at the edge and more in an enterprise data center. But nowadays, with the advent of relatively cheap computing and storage, data can be pre-processed, compressed, tagged all done at the edge, and then sent elsewhere for further processing (mostly done in the cloud of course).

IT Vendors at the crossroads

And what does the hollowing out of the enterprise data centers mean for IT server and storage vendors, mostly danger lies ahead. Enterprise IT hardware spend will stop growing, if it hasn’t already, and over time, shrink dramatically. It may be hard to see this today, but it’s only a matter of time.

Certainly, all these vendors can become more cloud like, on prem, offering compute and storage as a service, with various payment options to make it easier to consume. And for storage vendors, they can take advantage of their installed base by providing software versions of their systems running in the cloud that allows for easier migration and onboarding to the cloud. The server vendors have no such option. I see all the above as more of a defensive, delaying or holding action.

This is not to say the enterprise data centers will go away. Just like, mainframe and tape before them, on prem data centers will exist forever, but will be relegated to smaller and smaller, niche markets, that won’t grow anymore. But, only as long as vendor(s) continue to upgrade technology AND there’s profit to be made.

It’s just that that astronomical growth, that’s been happening ever since the middle of last century, happen in enterprise hardware anymore.

Long term life for enterprise vendors will be hard(er)

Over the long haul, some server vendors may be able to pivot to the edge. But the diversity of compute hardware there will make it difficult to generate enough volumes to make a decent profit there. However, it’s not to say that there will be 0 profits there, just less. So, when I see a Dell or HPE server, under the hood of my next smart car or inside the guts of my next drone, then and only then, will I see a path forward (or sustained revenue growth) for these guys.

For enterprise storage vendors, their future prospects look bleak in comparison. Despite the data generation and growth at the edge, I don’t see much of a role for them there. The enterprise class feature and functionality, they have spent the decades creating and nurturing aren’t valued as much in the cloud nor are they presently needed in the edge. Maybe I’m missing something here, but I just don’t see a long term play for them in the cloud or edge.

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For the record, all this is conjecture on my part. But I have always believed that if you follow where new apps are being created, there you will find a market ready to explode. And where the apps are no longer being created, there you will see a market in the throws of a slow death.

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