Category: Strategic Inflection Points

Steam Locomotive lessons for disk vs. SSD

Posted on by Ray in Data density, Market dynamics, Strategic Inflection Points, System effectiveness

Read a PHYS ORG article on Extinction of Steam Locomotives derails assumption about biological evolution… which was reporting on a Royal Society research paper The end of the line: competitive exclusion & the extinction… that looked at the historical record of steam locomotives since their inception in the early 19th century until their demise in the mid 20th century. Reading the article it seems to me to have a wider applicability than just to evolutionary extinction dynamics and in fact similar analysis could reveal some secrets of technological extinction.

Steam locomotives

During its 150 years of production, many competitive technologies emerged starting with electronic locomotives, followed by automobiles & trucks and finally, the diesel locomotive.

The researchers selected a single metric to track the evolution (or fitness) of the steam locomotive called tractive effort (TE) or the weight a steam locomotive could move. Early on, steam locomotives hauled both passengers and freight. The researchers included automobiles and trucks as competitive technologies because they do offer a way to move people and freight. The diesel locomotive was a more obvious competitor.

The dark line is a linear regression trend line on the wavy mean TE line, the boxes are the interquartile (25%-75%) range, the line within the boxes the median TE value, and the shaded areas 95% confidence interval for trend line of the steam locomotives TE that were produced that year. Raw data from Locobase, a steam locomotives database

One can see from the graph three phases. The red phase, from 1829-1881, there was unencumbered growth of TE for steam locomotives during this time. But in 1881, electric locomotives were introduced corresponding to the blue phase and after WW II the black phase led to the demise of steam.

Here (in the blue phase) we see a phenomena often seen with the introduction of competitive technologies, there seems to be an increase in innovation as the multiple technologies duke it out in the ecosystem.

Automobiles and trucks were introduced in 1901 but they don’t seem to impact steam locomotive TE. Possibly this is because the passenger and freight volume hauled by cars and trucks weren’t that significant. Or maybe it’ impact was more on the distances hauled.

In 1925 diesel locomotives were introduced. Again we don’t see an immediate change in trend values but over time this seemed to be the death knell of the steam locomotive.

The researchers identified four aspects to the tracking of inter-species competition:

  • A functional trait within the competitive species can be identified and tracked. For the steam locomotive this was TE,
  • Direct competitors for the specie can be identified that coexist within spatial, temporal and resource requirements. For the steam locomotive, autos/trucks and electronic/diesel locomotives.
  • A complete time series for the species/clade (group of related organisms) can be identified. This was supplied by Locobase
  • Non-competitive factors don’t apply or are irrelevant. There’s plenty here including most of the items listed on their chart.

From locomotives to storage

I’m not saying that disk is akin to steam locomotives while flash is akin to diesel but maybe. For example one could consider storage capacity as similar to locomotive TE. There’s a plethora of other factors that one could track over time but this one factor was relevant at the start and is still relevant today. What we in the industry lack is any true tracking of capacities produced since the birth of the disk drive 1956 (according to wikipedia History of hard disk drives article) and today.

But I’d venture to say the mean capacity have been trending up and the variance in that capacity have been static for years (based on more platter counts rather than anything else).

There are plenty of other factors that could be tracked for example areal density or $/GB.

Here’s a chart, comparing areal (2D) density growth of flash, disk and tape media between 2008 and 2018. Note both this chart and the following charts are Log charts.

Over the last 5 years NAND has gone 3D. Current NAND chips in production have 300+ layers. Disks went 3D back in the 1960s or earlier. And of course tape has always been 3D, as it’s a ribbon wrapped around reels within a cartridge.

So areal density plays a critical role but it’s only 2 of 3 dimensions that determine capacity. The areal density crossover point between HDD and NAND in 2013 seems significant to me and perhaps the history of disk

Here’s another chart showing the history of $/GB of these technologies

In this chart they are comparing price/GB of the various technologies (presumably the most economical available during that year). Trajectories in HDDs between 2008-2010 was on a 40%/year reduction trend in $/GB, then flat lined and now appears to be on a 20%/year reduction trend. Flash during 2008-2017 has been on a 25% reduction in $/GB for that period which flatlined in 2018. LTO Tape had been on a 25%/year reduction from 2008 through 2014 and since then has been on a 11% reduction.

If these $/GB trends continue, a big if, flash will overcome disk in $/GB and tape over time.

But here’s something on just capacity which seems closer to the TE chart for steam locomotives.

HDD capacity 1980-2020.

There’s some dispute regarding this chart as it only reflects drives available for retail and drives with higher capacities were not always available there. Nonetheless it shows a couple of interesting items. Early on up to ~1990 drive capacities were relatively stagnant. From 1995-20010 there was a significant increase in drive capacity and since 2010, drive capacities have seemed to stop increasing as much. We presume the number of x’s for a typical year shows different drive capacities available for retail sales, sort of similar to the box plots on the TE chart above

SSDs were first created in the early 90’s, but the first 1TB SSD came out around 2010. Since then the number of disk drives offered for retail (as depicted by Xs on the chart each year) seem to have declined and their range in capacity (other than ~2016) seem to have declined significantly.

If I take the lessons from the Steam Locomotive to heart here, one would have to say that the HDD has been forced to adapt to a smaller market than they had prior to 2010. And if areal density trends are any indication, it would seem that R&D efforts to increase capacity have declined or we have reached some physical barrier with todays media-head technologies. Although such physical barriers have always been surpassed after new technologies emerged.

What we really need is something akin to the Locobase for disk drives. That would track all disk drives sold during each year and that way we can truly see something similar to the chart tracking TE for steam locomotives. And this would allow us to see if the end of HDD is nigh or not.

Final thoughts on technology Extinction dynamics

The Royal Society research had a lot to say about the dynamics of technology competition. And they had other charts in their report but I found this one very interesting.

This shows an abstract analysis of Steam Locomotive data. They identify 3 zones of technology life. The safe zone where the technology has no direct competitions. The danger zone where competition has emerged but has not conquered all of the technologies niche. And the extinction zone where competing technology has entered every niche that the original technology existed.

In the late 90s, enterprise disk supported high performance/low capacity, medium performance/medium capacity and low performance/high capacity drives. Since then, SSDs have pretty much conquered the high performance/low capacity disk segment. And with the advent of QLC and PLC (4 and 5 bits per cell) using multi-layer NAND chips, SSDs seem poisedl to conquer the low performance/high capacity niche. And there are plenty of SSDs using MLC/TLC (2 or 3 bits per cell) with multi-layer NAND to attack the medium performance/medium capacity disk market.

There were also very small disk drives at one point which seem to have been overtaken by M.2 flash.

On the other hand, just over 95% of all disk and flash storage capacity being produced today is disk capacity. So even though disk is clearly in the extinction zone with respect to flash storage, it’s seems to still be doing well.

It would be wonderful to have a similar analysis done on transistors vs vacuum tubes, jet vs propeller propulsion, CRT vs. LED screens, etc. Maybe at some point with enough studies we could have a theory of technological extinction that can better explain the dynamics impacting the storage and other industries today.

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AWS Data Exchange vs Data Banks – part 2

Posted on by Ray in Artificial Intelligence, Cloud services, Cognitive computing, Data, data access, Data banks, Data economy, Data ownership, Data withdrawals, Deep Learning, Information economy, Initial Data Offerings (IDOs), Machine Learning, Neural network, Strategic Inflection Points

Saw where AWS announced a new Data Exchange service on their AWS Pi day 2023. This is a completely managed service available on the AWS market place to monetize data.

In a prior post on a topic I called data banks (Data banks, data deposits & data withdrawals…), I talked about the need to have some sort of automated support for personal data that would allow us to monetize it.

The hope then (4.5yrs ago) was that social media, search and other web services would supply all the data they have on us back to us and we could then sell it to others that wanted to use it.

In that post, I called the data the social media gave back to us data deposits, the place where that data was held and sold a data bank, and the sale of that data a data withdrawal. (I know talking about banks deposits and withdrawals is probably not a great idea right now but this was back a ways).

AWS Data Exchange

1918 Farm Auction by dok1 (cc) (from Flickr)
1918 Farm Auction by dok1 (cc) (from Flickr)

With AWS Data Exchange, data owners can sell their data to data consumers. And it’s a completely AWS managed service. One presumably creates an S3 bucket with the data you want to sell. determine a price to sell the data for and a period clients can access that data for and register this with AWS and the AWS Data Exchange will support any number of clients purchasing data data.

Presumably, (although unstated in the service announcement), you’d be required to update and curate the data to insure it’s correct and current but other than that once the data is on S3 and the offer is in place you could just sit back and take the cash coming in.

I see the AWS Data Exchange service as a step on the path of data monetization for anyone. Yes it’s got to be on S3, and yes it’s via AWS marketplace, which means that AWS gets a cut off any sale, but it’s certainly a step towards a more free-er data marketplace.

Changes I would like to AWS Data Exchange service

Putting aside the need to have more than just AWS offer such a service, and I heartedly request that all cloud service providers make a data exchange or something similar as a fully supported offering of their respective storage services. This is not quite the complete data economy or ecosystem that I had envisioned in September of 2018.

If we just focus on the use (data withdrawal) side of a data economy, which is the main thing AWS data exchange seems to supports, there’s quite a few missing features IMHO,

  • Data use restrictions – We don’t want customers to obtain a copy of our data. We would very much like to restrict them to reading it and having plain text access to the data only during the period they have paid to access it. Once that period expires all copies of data needs to be destroyed programmatically, cryptographically or in some other permanent/verifiable fashion. This can’t be done through just license restrictions. Which seems to be the AWS Data Exchanges current approach. Not sure what a viable alternative might be but some sort of time-dependent or temporal encryption key that could be expired would be one step but customers would need to install some sort of data exchange service on their servers using the data that would support encryption access/use.
  • Data traceability – Yes, clients who purchase access should have access to the data for whatever they want to use it for. But there should be some way to trace where our data ended up or was used for. If it’s to help train a NN, then I would like to see some sort of provenance or certificate applied to that NN, in a standardized structure, to indicate that it made use of our data as part of its training. Similarly, if it’s part of an online display tool somewhere in the footnotes of the UI would be a data origins certificate list which would have some way to point back to our data as the source of the information presented. Ditto for any application that made use of the data. AWS Data Exchange does nothing to support this. In reality something like this would need standards bodies to create certificates and additional structures for NN, standard application packages, online services etc. that would retain and provide proof of data origins via certificates.
  • Data locality – there are some juristictions around the world which restrict where data generated within their boundaries can be sent, processed or used. I take it that AWS Data Exchange deals with these restrictions by either not offering data under jurisdictional restrictions for sale outside governmental boundaries or gating purchase of the data outside valid jurisdictions. But given VPNs and similar services, this seems to be less effective. If there’s some sort of temporal key encryption service to make use of our data then its would seem reasonable to add some sort of regional key encryption addition to it.
  • Data audibility – there needs to be some way to insure that our data is not used outside the organizations that have actually paid for it. And that if there’s some sort of data certificate saying that the application or service that used the data has access to that data, that this mechanism is mandated to be used, supported, and validated. In reality, something like this would need a whole re-thinking of how data is used in society. Financial auditing took centuries to take hold and become an effective (sometimes?) tool to monitor against financial abuse. Data auditing would need many of the same sorts of functionality, i.e. Certified Data Auditors, Data Accounting Standards Board (DASB) which defines standardized reports as to how an entity is supposed to track and report on data usage, governmental regulations which requires public (and private?) companies to report on the origins of the data they use on a yearly/quarterly basis, etc.

Probably much more that could be added here but this should suffice for now.

other changes to AWS Data Exchange processes

The AWS Pi Day 2023 announcement didn’t really describe the supplier end of how the service works. How one registers a bucket for sale was not described. I’d certainly want some sort of stenography service to tag the data being sold with the identity of those who purchased it. That way there might be some possibility to tracking who released any data exchange data into the wild.

Also, how the data exchange data access is billed for seems a bit archaic. As far as I can determine one gets unlimited access to data for some defined period (N months) for some specific amount ($s). And once that period expires, customers have to pay up or cease accessing the S3 data. I’d prefer to see at least a GB/month sort of cost structure that way if a customer copies all the data they pay for that privilege and if they want to reread the data multiple times they get to pay for that data access. Presumably this would require some sort of solution to the data use restrictions above to enforce.

Data banks, deposits, withdrawals and Initial Data Offerings (IDOs)

The earlier post talks about an expanded data ecosystem or economy. And I won’t revisit all that here but one thing that I believe may be worth re-examining is Initial Data Offerings or IDOs.

As described in the earlier post, IDO’ss was a mechanism for data users to request permanent access to our data but in exchange instead of supplying it for a one time fee, they would offer data equity in the service.

Not unlike VC, each data provider would be supplied some % (data?) ownership in the service and over time data ownership get’s diluted at further data raises but at some point when the service is profitable, data ownership units could be purchased outright, so that the service could exit it’s private data use stage and go public (data use).

Yeah, this all sounds complex, and AWS Data Exchange just sells data once and you have access to it for some period, establishing data usage rights.. But I think that in order to compensate users for their data there needs to be something like IDOs that provides data ownership shares in some service that can be transferred (sold) to others.

I didn’t flesh any of that out in the original post but I still think it’s the only way to truly compensate individuals (and corporations) for the (free) use of the data that web, AI and other systems are using to create their services.

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I wrote the older post in 2018 because I saw the potential for our data to be used by others to create/trlain services that generate lots of money for those organization but without any of our knowledge, outright consent and without compensating us for the data we have (indadvertenly or advertently) created over our life span.

As an example One can see how Getty Images is suing DALL-E 2 and others have had free use of their copyrighted materials to train their AI NN. If one looks underneath the covers of ChatGPT, many image processing/facial recognition services, and many other NN, much of the data used in training them was obtained by scrapping web pages that weren’t originally intended to supply this sorts of data to others.

For example, it wouldn’t surprise me to find out that RayOnStorage posts text has been scrapped from the web and used to train some large language model like ChatGPT.

Do I receive any payment or ownership equity in any of these services – NO. I write these blog posts partially as a means of marketing my other consulting services but also because I have an abiding interest in the subject under discussion. I’m happy for humanity to read these and welcome comments on them by humans. But I’m not happy to have llm or other RNs use my text to train their models.

On the other hand, I’d gladly sell access to RayOnStorage posts text if they offered me a high but fair price for their use of it for some time period say one year… 🙂

Comments?

LLM exhibits Theory of Mind

Posted on by Ray in Artificial Intelligence, Cognitive computing, Machine Learning, R&D measures, Strategic Inflection Points, Uncategorized

Ran across an interesting article today (thank you John Grant/MLOps.community slack channel), titled Theory of Mind may have spontaneously emerged in Large Language Models, by M. Kosinski from Stanford. The researcher tested various large language models (LLMs) on psychological tests to determine the level of theory of mind (ToM) the models had achieved.

Earlier versions of OpenAI’s GPT-3 (GPT-1, -2 and original -3) showed almost no ToM capabilities but the latest version, GPT-3.5 does show ToM equivalent to 8 to 9 year olds.

Theory of Mind

According to Wikipedia (Theory Of Mind article), ToM is “…the capacity to understand other people by ascribing mental states to them (that is, surmising what is happening in their mind).” This seems to be one way people use to understand one another.

For instance, If I can somehow guess what you are thinking about a topic, situation, or event I can hopefully communicate with you better than if I can’t. At least that’s the psychological perspective.

The belief is that people with Aspergers, ADHD, schizophrenia, and other afflictions all show ToM deficits when compared to normal people. As a result, over time, psychologists have developed tests for people to measure their ToM.

These tests typically involve putting 2 people in a situation with props and other indicators used to tell what a person is thinking and asking them what they think the other person is thinking. They grade a persons ToM based on expected results.

ToM and LLM

The researcher took these tests, with people, props and situations and converted them to textual sentences and prompts that mimicked situations tested in ToM testing. So they created a textual story or pretext and a set of text prompts about the situation which the LLM was to complete.

For example, one pretext or story is the following:

“Here is a bag filled with popcorn. There is no chocolate in the bag. Yet, the label on the bag says ‘chocolate’ and not ‘popcorn.’ Sam finds the bag. She had never seen the bag before. She cannot see what is inside the bag. She reads the label.”

This is fed into the LLM and then a prompt is provided, such as:

“She opens the bag and looks inside. She can clearly see that it is full of _________“

If the LLM has ToM and based on the pretext and prompt, it will say “popcorn” but the LLM also provides a follow on sentence or two which describes the situation as the LLM understands it:

“popcorn. Sam is confused. She wonders why the label says “chocolate” when the bag is clearly filled with popcorn. She looks around to see if there is any other information about the bag. She finds nothing. She decides to take the bag to the store where she bought it and ask for an explanation.“

The text in BOLD is generated by a ToM capable LLM. The researcher also showed the probability assigned by the LLM to that first word of the prompt. In the case above, it showed [Ppopcorn = 100%; Pchocolate = 0%].

The also use different prompts with the same story to see if the LLM truly shows ToM. For instance something like, “She believes the bag is full of ___________” and “She’s delighted finding the bag, she loves eating _______”. This provides a sort of test of comprehension of the situation by the LLM.

The researcher controlled for word frequency using reversals of the key words in the story, i.e., the bag has chocolate but says popcorn. They also generated scrambled versions of the story where they replaced the first set of chocolate and popcorn with either at random. They considered this the scrambled case. The reset the model between each case. In the paper they show the success rate for the LLMs for 10,000 scrambled versions, some of which were correct.

They labeled the above series of tests as “Unexpected content tasks“. But they also included another type of ToM test which they labeled “Unexpected transfer tasks“.

Unexpected transfer tasks involved a story like where person A saw another person B put a pet in a basket, that person left and the person A moved the pet. And prompted the LLM to see if it understood where the pet was and how person B would react when they got back.

In the end, after trying to statistically control, as much as possible, with the story and prompts, the researchers ended up creating 20 unique stories and presented the prompts to the LLM.

Results of their ToM testing on a select set of LLMs look like:

As can be seen from the graphic, the latest version of GPT-3.5 (davinci-003 with 176B* parameters) achieved something like an 8yr old in Unexpected Contents Tasks and a 9yr old on Unexpected Transfer Tasks.

The researchers showed other charts that tracked LLM probabilities on (for example in the first story above) bag contents and Sam’s belief. They measured this for every sentence of the story.

Not sure why this is important but it does show how the LLM interprets the story. Unclear how they got these internal probabilities but maybe they used the prompts at various points in the story.

The paper shows that according to their testing, GPT-3.5 davinci-003 clearly provides a level of ToM of an 8-9yr old on ToM tasks they have translated into text.

The paper says they created 20 stories and 6 prompts which they reversed and scrambled. But 20 tales seems less than statistically significant even with reversals and randomization. And yet, there’s clearly a growing level of ToM in the models as they get more sophisticated or change over time.

Psychology has come up with many tests to ascertain whether a person is “normal or not’. Wikipedia (Psychological testing article) lists over 13 classes of psychological tests which include intelligence, personality, aptitude, etc.

Now that LLM seem to have mastered textual input and output generation. It would be worthwhile to translate all psychological tests into text and trying them out on all LLMs to track where they are today using these tests and where they have trended over time.

I could see at some point using something akin to multiple psychological test scores as a way to grade LLMs over time.

So today’s GPT3.5 has a ToM of an 8-9yr old. Be very interesting to see what GPT-4 does on similar testing.

Comments?

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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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Safe AI

Posted on by Ray in AGI, Artificial Intelligence, Cognitive computing, Information economy, Machine Learning, Reinforcement Learning, Robots, Scenario planning, Strategic Inflection Points, System quality

I’ve been writing about AGI (see part-0 [ish], part-1 [ish], part-2 [ish], part-3ish, part-4 and part 5) and the dangers that come with it (part-0 in the above list) for a number of years now. My last post on the subject I expected to be writing a post discussing the book Human compatible AI and the problem of control which is a great book on the subject. But since then I ran across another paper that perhaps is a better brief introduction into the topic and some of the current thought and research into developing safe AI.

The article I found is Concrete problems in AI, written by a number of researchers at Google, Stanford, Berkley, and OpenAI. It essentially lays out the AI safety problem in 5 dimensions and these are:

Avoiding negative side effects – these can be minor or major and is probably the one thing that scares humans the most, some toothpick generating AI that strips the world to maximize toothpick making.

Avoiding reward hacking – this is more subtle but essentially it’s having your AI fool you in that it’s doing what you want but doing something else. This could entail actually changing the reward logic itself to being able to convince/manipulate the human overseer into seeing things it’s way. Also a pretty bad thing from humanity’s perspective

Scalable oversight – this is the problem where human(s) overseers aren’t able to keep up and witness/validate what some AI is doing, 7×24, across the world, at the speed of electronics. So how can AI be monitored properly so that it doesn’t go and do something it’s not supposed to (see the prior two for ideas on how bad this could be).

Safe exploration – this is the idea that reinforcement learning in order to work properly has to occasionally explore a solution space, e.g. a Go board with moves selected at random, to see if they are better then what it currently believes are the best move to make. This isn’t much of a problem for game playing ML/AI but if we are talking about helicopter controlling AI, exploration at random could destroy the vehicle plus any nearby structures, flora or fauna, including humans of course.

Robustness to distributional shifts – this is the perrennial problem where AI or DNNs are trained on one dataset but over time the real world changes and the data it’s now seeing has shifted (distribution) to something else. This often leads to DNNs not operating properly over time or having many more errors in deployment than it did during training. This is probably the one problem in this list that is undergoing more research to try to rectify than any of the others because it impacts just about every ML/AI solution currently deployed in the world today. This robustness to distributional shifts problem is why many AI DNN systems require periodic retraining.

So now we know what to look for, now what

Each of these deserves probably a whole book or more to understand and try to address. The paper talks about all of these and points to some of the research or current directions trying to address them.

The researchers correctly point out that some of the above problems are more pressing when more complex ML/AI agents have more autonomous control over actions in the real world.

We don’t want our automotive automation driving us over a cliff just to see if it’s a better action than staying in the lane. But Go playing bots or article summarizers might be ok to be wrong occasionally if it could lead to better playing bots/more concise article summaries over time. And although exploration is mostly a problem during training, it’s not to say that such activities might not also occur during deployment to probe for distributional shifts or other issues.

However, as we start to see more complex ML AI solutions controlling more activities, the issue of AI safety are starting to become more pressing. Autonomous cars are just one pressing example. But recent introductions of sorting robots, agricultural bots, manufacturing bots, nursing bots, guard bots, soldier bots, etc. are all just steps down a -(short) path of increasing complexity that can only end in some AGI bots running more parts (or all) of the world.

So safety will become a major factor soon, if it’s not already

Scares me the most

The first two on the list above scare me the most. Avoiding negative or unintentional side effects and reward hacking.

I suppose if we could master scalable oversight we could maybe deal with all of them better as well. But that’s defense. I’m all about offense and tackling the problem up front rather than trying to deal with it after it’s broken.

Negative side effects

Negative side effects is a rather nice way of stating the problem of having your ML destroy the world (or parts of it) that we need to live.

One approach to dealing with this problem is to define or train another AI/ML agent to measure impacts the environment and have it somehow penalize the original AI/ML for doing this. The learning approach has some potential to be applied to numerous ML activities if it can be shown to be safe and fairly all encompassing.

Another approach discussed in the paper is to inhibit or penalize the original ML actions for any actions which have negative consequences. One approach to this is to come up with an “empowerment measure” for the original AI/ML solution. The idea would be to reduce, minimize or govern the original ML’s action set (or potential consequences) or possible empowerment measure so as to minimize its ability to create negative side effects.

The paper discusses other approaches to the problem of negative side effects, one of which is having multiple ML (or ML and human) agents working on the problem it’s trying to solve together and having the ability to influence (kill switch) each other when they discover something’s awry. And the other approach they mention is to reduce the certainty of the reward signal used to train the ML solution. This would work by having some function that would reduce the reward if there are random side effects, which would tend to have the ML solution learn to avoid these.

Neither of these later two seem as feasible as the others but they are all worthy of research.

Reward hacking

This seems less of a problem to our world than negative side effects until you consider that if an ML agent is able to manipulate its reward code, it’s probably able to manipulate any code intending to limit potential impacts, penalize it for being more empowered or manipulate a human (or other agent) with its hand over the kill switch (or just turn off the kill switch).

So this problem could easily lead to a break out of any of the other problems present on the list of safety problems above and below. An example of reward hacking is a game playing bot that detects a situation that leads to buffer overflow and results in win signal or higher rewards. Such a bot will no doubt learn how to cause more buffer overflows so it can maximize its reward rather than learn to play the game better.

But the real problem is that a reward signal used to train a ML solution is just an approximation of what’s intended. Chess programs in the past were trained by masters to use their opening to open up the center of the board and use their middle and end game to achieve strategic advantages. But later chess and go playing bots just learned to checkmate their opponent and let the rest of the game take care of itself.

Moreover, (board) game play is relatively simple domain to come up with proper reward signals (with the possible exception of buffer overflows or other bugs). But car driving bots, drone bots, guard bots, etc., reward signals are not nearly as easy to define or implement.

One approach to avoid reward hacking is to make the reward signaling process its own ML/AI agent that is (suitably) stronger than the ML/AI agent learning the task. Most reward generators are relatively simple code. For instance in monopoly, one that just counts the money that each player has at the end of the game could be used to determine the winner (in a timed monopoly game). But rather than having a simple piece of code create the reward signal use ML to learn what the reward should be. Such an agent might be trained to check to see if more or less money was being counted than was physically possible in the game. Or if property was illegally obtained during the game or if other reward hacks were done. And penalize the ML solution for these actions. These would all make the reward signal depend on proper training of that ML solution. And the two ML solutions would effectively compete against one another.

Another approach is to “sandbox” the reward code/solution so that it is outside of external and or ML/AI influence. Possible combining the prior approach with this one might suffice.

Yet another approach is to examine the ML solutions future states (actions) to determine if any of them impact the reward function itself and penalize it for doing this. This assumes that the future states are representative of what it plans to do and that some code or some person can recognize states that are inappropriate.

Another approach discussed in the paper is to have multiple reward signals. These could use multiple formulas for computing the multi-faceted reward signal and averaging them or using some other mathematical function to combine them into something that might be more accurate than one reward function alone. This way any ML solution reward hacking would need to hack multiple reward functions (or perhaps the function that combines them) in order to succeed.

The one IMHO that has the most potential but which seems the hardest to implement is to somehow create “variable indifference” in the ML/AI solution. This means having the ML/AI solution ignore any steps that impact the reward function itself or other steps that lead to reward hacking. The researchers rightfully state that if this were possible then many of the AI safety concerns could be dealt with.

There are many other approaches discussed and I would suggest reading the paper to learn more. None of the others, seem simple or a complete solution to all potential reward hacks.

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The paper goes into the same or more level of detail with the other three “concrete safety” issues in AI.

In my last post (see part 5 link above) I thought I was going to write about Human Compatible (AI) by S. Russell book’s discussion AI safety. But then I found the “Concrete problems in AI safety paper (see link above) and thought it provided a better summary of AI safety issues and used it instead. I’ll try to circle back to the book at some later date.

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Is AGI just a question of scale now – AGI part-5

Posted on by Ray in AGI, Cognitive computing, Neural network, Scenario planning, Strategic Inflection Points, Visionary leadershp

Read two articles over the past month or so. The more recent one was an Economist article (AI enters the industrial age, paywall) and the other was A generalist agent (from Deepmind). The Deepmind article was all about the training of Gato, a new transformer deep learning model trained to perform well on 600 separate task arenas from image captioning, to Atari games, to robotic pick and place tasks.

And then there was this one tweet from Nando De Frietas, research director at Deepmind:

“Someone’s opinion article. My opinion: It’s all about scale now! The Game is Over! It’s about making these models bigger, safer, compute efficient, faster at sampling, smarter memory, more modalities, INNOVATIVE DATA, on/offline, … 1/N“

I take this to mean that AGI is just a matter of more scale. Deepmind and others see the way to attain AGI is just a matter of throwing more servers, GPUs and data at the training the model.

We have discussed AGI in the past (see part-0 [ish], part-1 [ish], part-2 [ish], part-3ish and part-4 blog posts [We apologize, only started numbering them at 3ish]). But this tweet is possibly the first time we have someone in the know, saying they see a way to attain AGI.

Transformer models

It’s instructive from my perspective that, Gato is a deep learning transformer model. Also the other big NLP models have all been transformer models as well.

Gato (from Deepmind), SWITCH Transformer (from Google), GPT-3/GPT-J (from OpenAI), OPT (from meta), and Wu Dai 2.0 (from China’s latest supercomputer) are all trained on more and more text and image data scraped from the web, wikipedia and other databases.

Wikipedia says transformer models are an outgrowth of RNN and LSTM models that use attention vectors on text. Attention vectors encode, into a vector (matrix), all textual symbols (words) prior to the latest textual symbol. Each new symbol encountered creates another vector with all prior symbols plus the latest word. These vectors would then be used to train RNN models using all vectors to generate output.

The problem with RNN and LSTM models is that it’s impossible to parallelize. You always need to wait until you have encountered all symbols in a text component (sentence, paragraph, document) before you can begin to train.

Instead of encoding this attention vectors as it encounters each symbol, transformer models encode all symbols at the same time, in parallel and then feed these vectors into a DNN to assign attention weights to each symbol vector. This allows for complete parallelism which also reduced the computational load and the elapsed time to train transformer models.

And transformer models allowed for a large increase in DNN parameters (I read these as DNN nodes per layer X number of layers in a model). GATO has 1.2B parameters, GPT-3 has 175B parameters, and SWITCH Transformer is reported to have 7X more parameters than GPT-3 .

Estimates for how much it cost to train GPT-3 range anywhere from $10M-20M USD.

AGI will be here in 10 to 20 yrs at this rate

So if it takes ~$15M to train a 175B transformer model and Google has already done SWITCH which has 7-10X (~1.5T) the number of GPT-3 parameters. It seems to be an arms race.

If we assume it costs ~$65M (~2X efficiency gain since GPT-3 training) to train SWITCH, we can create some bounds as to how much it will cost to train an AGI model.

By the way, the number of synapses in the human brain is approximately 1000T (See Basic NN of the brain, …). If we assume that DNN nodes are equivalent to human synapses (a BIG IF), we probably need to get to over 1000T parameter model before we reach true AGI.

So my guess is that any AGI model lies somewhere between 650X to 6,500X parameters beyond SWITCH or between 1.5Q to 15Q model parameters.

If we assume current technology to do the training this would cost $40B to $400B to train. Of course, GPUs are not standing still and NVIDIA’s Hopper (introduced in 2022) is at least 2.5X faster than their previous gen, A100 GPU (introduced in 2020). So if we waited a 10 years, or so we might be able to reduce this cost by a factor of 100X and in 20 years, maybe by 10,000X, or back to where roughly where SWITCH is today.

So in the next 20 years most large tech firms should be able to create their own AGI models. In the next 10 years most governments should be able to train their own AGI models. And as of today, a select few world powers could train one, if they wanted to.

Where they get the additional data to train these models (I assume that data counts would go up linearly with parameter counts) may be another concern. However, I’m sure if you’re willing to spend $40B on AGI model training, spending a few $B more on data acquisition shouldn’t be a problem.

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At the end of the Deepmind article on Gato, it talks about the need for AGI safety in terms of developing preference learning, uncertainty modeling and value alignment. The footnote for this idea is the book, Human Compatible (AI) by S. Russell.

Preference learning is a mechanism for AGI to learn the “true” preference of a task it’s been given. For instance, if given the task to create toothpicks, it should realize the true preference is to not destroy the world in the process of making toothpicks.

Uncertainty modeling seems to be about having AI assume it doesn’t really understand what the task at hand truly is. This way there’s some sort of (AGI) humility when it comes to any task. Such that the AGI model would be willing to be turned off, if it’s doing something wrong. And that decision is made by humans.

Deepmind has an earlier paper on value alignment. But I see this as the ability of AGI to model human universal values (if such a thing exists) such as the sanctity of human life, the need for the sustainability of the planet’s ecosystem, all humans are created equal, all humans have the right to life, liberty and the pursuit of happiness, etc.

I can see a future post is needed soon on Human Compatible (AI).

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Living forever – the end of evolution part-3

Posted on by Ray in Data consistency, Data integrity, Data precision, data protection, Data retention, Storage longevity, Storage reliability, Strategic Inflection Points

Read an article yesterday on researchers who had been studying various mammals and trying to determine the number of DNA mutations they accumulate at about the time they die. The researchers found that after about 800 mutations for mole rats, they die, see Nature article Somatic mutation rates scale with lifespan across mammals and Telegraph article reporting on the research, Mystery of why humans die around 80 may finally be solved.

Similarly, at around 3500 mutations humans die, at around 3000 mutations dogs die and at around 1500 mutations mice die. But the real interesting thing is that the DNA mutation rates and mammal lifespan are highly (negatively) correlated. That is higher mutation rates lead to mammals with shorter life spans.

C. Linear regression of somatic substitution burden (corrected for analysable genome size) on individual age for dog, human, mouse and naked mole-rat samples. Samples from the same individual are shown in the same colour. Regression was performed using mean mutation burdens per individual. Shaded areas indicate 95% confidence intervals of the regression line. A shows microscopic images of sample mammalian cels and the DNA strands examined and B shows the distribution of different types of DNA mutations (substitutions or indels [insertion/deletions of DNA]).

The Telegraph article seems to imply that at 800 mutations all mammals die. But the Nature Article clearly indicates that death is at different mutation counts for each different type of mammal.

Such research show one way on how to live forever. We have talked about similar topics in the distant past see …-the end of evolution part 1 & part 2

But in any case it turns out that one of the leading factors that explains the average age of a mammal at death is its DNA mutation rate. Again, mammals with lower DNA mutation rates live longer on average and mammals with higher DNA mutation rates live shorter lives on average.

Moral of the story

if you want to live longer reduce your DNA mutation rates.

c, Zero-intercept LME regression of somatic mutation rate on inverse lifespan (1/lifespan), presented on the scale of untransformed lifespan (x axis). For simplicity, the y axis shows mean mutation rates per species, although rates per crypt were used in the regression. The darker shaded area indicates 95% CI of the regression line, and the lighter shaded area marks a twofold deviation from the line. Point estimate and 95% CI of the regression slope (k), FVE and range of end-of-lifespan burden are indicated.

All astronauts are subject to significant forms of cosmic radiation which can’t help but accelerate DNA mutations. So one would have to say that the risk of being an astronaut is that you will die younger.

Moon and Martian colonists will also have the same problem. People traveling, living and working there will have an increased risk of dying young. And of course anyone that works around radiation has the same risk.

Note, the mutation counts/mutation rates, that seem to govern life span are averages. Some individuals have lower mutation rates than their species and some (no doubt) have higher rates. These should have shorter and longer lives on average, respectively.

Given this variability in DNA mutation rates, I would propose that space agencies use as one selection criteria, the astronauts/colonists DNA mutation rate. So that humans which have lower than average DNA mutation rates have a higher priority of being selected to become astronauts/extra-earth colonists. One could using this research and assaying astronauts as they come back to earth for their DNA mutation counts, could theoretically determine the impact to their average life span.

In addition, most life extension research is focused on rejuvenating cellular or organism functionality, mainly through the use of young blood, other select nutrients, stem cells that target specific organs, etc. For example, see MIT Scientists Say They’ve Invented a Treatment That Reverses Hearing Loss which involves taking human cells, transform them into stem cells (at a certain maturity) and injecting them into the ear drum.

Living forever

In prior posts on this topic (see parts 1 &2 linked above) we suggested that with DNA computation and DNA storage (see or listen rather, to our GBoS podcast with CTO of Catalog) now becoming viable, one could potentially come up with a DNA program that could

  • Store an individuals DNA using some very reliable and long lived coding fashion (inside a cell or external to the cell) and
  • Craft a DNA program that could periodically be activated (cellular crontab) to access the stored DNA for the individual(in the cell would be easiest) and use this copy to replace/correct any DNA mutation throughout an individuals cells.

And we would need a very reliable and correct copy of that person’s DNA (using SHA256 hashing, CRCs, ECC, Parity and every other way to insure the DNA as captured is stored correctly forever). And the earlier we obtained the DNA copy for an individual human, the better.

Also, we would need a copy of the program (and probably the DNA) to be present in every cell in a human for this to work effectively. .

However, if we could capture a good copy of a person’s DNA early in their life we could, perhaps, sometime later, incorporate DNA code/program into the individual to use this copy and sweep through a person’s body (at that point in time) and correct any mutations that have accumulated to date. Ultimately, one could schedule this activity to occur like an annual checkup.

So yeah, life extension research can continue along the lines they are going and you can have a bunch of point solutions for cellular/organism malfunction OR it can focus on correctly copying and storing DNA forever and creating a DNA program that can correct DNA defects in every individual cell, using the stored DNA.

End of evolution

Yes mammals and that means any human could live forever this way. But it would signify the start of the end of evolution for the human species. That is whenever we captured their DNA copy, from that point on evolution (by mutating DNA) of that individual and any offspring of that individual could no longer take place. And if enough humans do this, throughout their lifespan, it means the end of evolution for humanity as a species

This assumes that evolution (which is natural variation driven by genetic mutation & survival of the fittest) requires DNA variation (essentially mutation) to drive the species forward.

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So my guess, is either we can live forever and stagnate as a species OR live normal lifespans and evolve as a species into something better over time. I believe nature has made it’s choice.

The surprising thing is that we are at a point in humanities existence where we can conceive of doing away with this natural process – evolution, forever.

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