Turtles all the way Down

What have I learned from working inside the AI black box with Aman & Henry?

I've been working with generative artificial intelligence with students in my computer technology program since 2018 when we were fortunate to get a new grade 9 whose dad was on the team that brought IBM Watson to Jeopardy. That got us connected to IBM cloud and building AI chatbots five years before the "AI revolution" everyone has been caught out by.

That wasn't our first point of contact with AI though. I'd been keeping an eye on AI dev as far back as 2015 because we launched our gamedev course then and getting handle on building intelligent responses to player actions in our games immediately became our biggest challenge. Thanks to Gord and IBM we were able to get our juniors familiar with AI prior to asking them to take on significant software engineering challenges with it in the senior grades.

I presented on AI use in the classroom at the ECOO conference pre-COVID in fall of 2019. Gord from IBM even came all the way down to Niagara Falls to offer world class suppport. The room was all but empty:

This is how many Ontario educators (already interested in edtech because this is ECOO!) you get in an introduction to gnerative AI in 2019 (yes, it was four in an otherwise empty room). Ahead of our time (again)? Four years later it's an emergency and suddenly there are education AI experts everywhere. I wonder where they were in 2019.

If you ever wonder why education always seems two steps behind emerging technologies that will have profound impacts on classrooms, here's a fine example. Except you won't even see four people sitting in an empty room in 2024 because all edtech conferences like ECOO focused on teacher technology integration have evaporated in Ontario.

***

OK, so I've been banging my head against pedagogically driven AI engagement in education for almost a decade only to see it swamp an oblvious education system anyway, so what's happening now? I'm ressearching the leading edge of this technology to see if we can't still rescue a pedagogically meaningful approach to it.

In the summer Katina Papulkas from Dell Canada put out a call for educators interested in action research on AI use in learning. I've been talking to Aman Sahota and Henry Fu from Factors Education over the past year looking for an excuse to work on something like this, so I pitched this idea: De-blackboxing AI technology and using it to understand how it works.

Our plan is to use the Factors AI engine that Henry himself has built and Aman administrates to build custom data libraries that will support an AI agent that will interact with students and encourage them to ask questions to better understand how generative AI works. As mentioned before on Dusty World, GenAI isn't intelligent and it's important that people realize what it is and how it works to demystify it and then apply it effectively. Getting misdirected by the marketing driven AI hype isn't helpful.

So far we've built modules that describe the history and development of AI, how AI works and the future of AI. In the process of doing this I've come across all sorts of public facing research material that breaks down generative AI for you (like Deep Learning from MIT Press), but it's technically dense and not accessible to the casual reader.

During the last week of August Factors had a meeting with interested educators through UofT OISE (their AI system came out of the OISE edtech accelerator). I demonstrated in the presentation how the AI engine might be used to break down a complex article for easier consumption through agent interaction. The example was WIRED's story on how Google employees developed the transformers that moved generative AI from a curiosity to real world useful in the late teens. I picked this one because it explains some of what happens in the 'blackbox' that AI is often hidden in.

With some well crafted prompting and then conversational interaction, students can get clear, specific answers to technical details that might have eluded them in the long form article. The reading support side of GenAI hasn't been fully explored yet (though WIRED did a recent interesting piece on cloning famous authors to become AI reading buddies as you tackle the classics which is in the ballpark).

What have I learned from working in the engine room (BTW, that image at the top is Adobe Firefly's AI image generator) building an AI data library and then tuning it? AI isn't automatic at all. It demands knowledgable people providing focus and context to aim it in the right direction and maximize productive responses with users. An interesting example of this was finding documents that provided relevant data on the subjects we wanted the AI to respond to. When I couldn't find specific ones Henry suggested using Perplexity, an AI research tool that coalates online sources and then gives you concise summaries along with a bibliography of credible sources.

I thought I was being perverse asking Factors to design an AI that expalins AI using AI, but Henry's always a step ahead. His suggstion is to use an AI to build a library of information to feed the AI engine that then uses AI to interact with the user... about AI. It's turtles all the way down!

A.I. Isn't What You Think It Is

 I've been in a series of presentations over the past couple of months where organizations are getting frantic about catching the 'AI Wave'. This urgent need to feel like they aren't missing out on a fad is understandable, but like so many emerging technologies, getting 'into it' won't be effective if you ignore the foundations its built on, and the foundations of AI and the technology itself are... problematic.

You can't have 'generative' AI without massive data sets to train it on. This data is scraped from the internet and then fed into systems that can eventually give users "a statistically likely configuration of words" that look like an answer. That's right, the brilliant answer you just got on a generative AI platform isn't really an answer, it's a cloud computer cluster giving you its best guess based on crowdsourced data. None of that stops people from thinking it's intelligent (it isn't), and being in a panic about missing out.

Putting the fact that AI isn't nearly as smart as the marketing portrays it aside for a moment, large data and the cloud infrastructure that stores and delivers it are a house of cards teetering on the edge of collapse. You can't have AI without climbing to the stop of this wobbly infrastructure. How precarious is it? Data growth worldwide is in an explosive phase of growth (partially driven by the AI fad). Our overloaded storage infrastructure is under pressure because AI uses it much more aggressively that simply storing information. AI demands fast data retrieval and constant interaction making the rise in AI particularly problematic for our stressed storage systems.

We're facing data storage shortages in the next couple of years because of our belief that the cloud is an infinite resource. It isn't, it's an artfully hidden technological sleight of hand. The irony is that our digital storage infrastructure limitations will also end up limiting our current crop of AIs as well.

The staggering environmental costs that underlie our myth of an infinite digital cloud haven't  been mentioned yet, but like many of our other ecological marketing myths (electric vehicles), pushing the messy business of how it works out of sight of the consumer is a great way to market a green future while doing the opposite. Data centres in the US consume over 2% of all electricity in the country. There are benefits to scaling large data centres, but the trend into the foreseeable future is that the cloud will continue consuming more energy out here in the real world. That we're increasingly throwing limited resources at building AI guessing machines tells you something about our priorities.

One of the first posts on Dusty World was about dancing in this datasphere twelve years ago. Back then I'd found a quote by Google CEO Eric Schmidt talking about the coming information revolution:

I'd make a distinction between information and data. One is useful, the other is raw binary numbers and storing the majority of it is a complete waste of time and resources. Sussing out information from data is an ongoing challenge. That doesn't change the fact that the amount of data being generated back then wouldn't even register on the graph below, which looks like a runaway growth curve - you can make good money from all that data.

So, we live in a world that is well into an aggressive phase of digital growth, though very few people understand how any of that works. Even as we compile more content than we have in the entirety of human history to feed the attention economy, we also decide to play a sleight of hand game with machine learning on massive datasets just to see if it'll work.

From an educational perspective, AI is in the wild now and ignoring it will only get you and your students in trouble. If we're going to make functional use of this progenitor of true artificial intelligence, we need to teach the media literacy around it so that people understand what it is, how it works and how best to use it to amplify rather than replace their humanity.

I've seen a lot of people panicking about AI taking their jobs away, but if your work output is a statistically likely configuration of words, then you're not applying much of your vaunted human intellect to the task at hand and probably should be replaced by one of these meh AIs. But if you're one of those humans who actually thinks, even this stunted AI can be a powerful ally. In a fight for intellectual supremacy who would you think would win?

1. just machines
2. just people
3. an empowered hybrid of the two

The move here during this awkward adolescence of artificial intelligence where we're faking it until we make it is to leverage the tool to best effect. If effective use of AI speeds up our ability to gain actionable information in the chaos of data that surrounds us, then we can more quickly move on to the next real steps in technology evolution.

The other day I described AI as we currently define it as a hack to keep classical computing ahead of the data tsunami we're living in. At the time I was surprised by how I described it, but classical computing is reaching the limit of what it can do. For the past few years we've been finding speed in parallel processing such as adding computing cores to CPUs rather than making faster ones. We've also been finding efficiencies in how we manage data such as creating more organized memory caches to better feed our processors. Ultimately, I feel like generative AI in 2023 is another one of these patches. It's a way to make our overwhelming data cloud more functional to us.

This is from a presentation I've been giving that attempts to bring people into a better understanding of the hype. AI (even this meh one) will replace you if you let it, so don't!

Digital technologies aren't going to go anywhere, but they are a 'low resolution' way to compute. There is also the problem of reducing the complexity of reality to ones and zeroes. Mathematical concepts can help us understand relationships, but they will always be inherently reductive; they're never the thing itself but a simplified abstraction of it. Digital reduces the world to ones and zeros, but there is a better way.

When we run out of nanometres like we have with electronics, the next step is a big one, but it's one we're working on globally as a species right now. In the next decade we're gong to figure out how to use the building blocks of nature itself to compute at speeds that classical computers can't imagine. What will this do for our data clogged world? One of my hopes is that it will process much of that data into usable information, information that we can then use to solve this mess we've gotten ourselves into. When you have an answer you no longer need to keep the information leading to it.

I'll weather the current AI hype storm, but if you ask me what I'm really excited about it's artificial intelligence realized on a fault tolerant quantum computer. The future beyond that moves in directions I can't begin to guess, and that is exciting. imminent and absolutely necessary if we're going to prevent a global collapse of human civilization. Some people might get panicky about that, but they're the same ones who think a cloud based statistical guessing machine will replace them.

Dancing in the Datasphere 2022 Edition: AI Refined User Interfaces!

This quote is 12 years old now, but it's more true than
ever, and our technology is about to take another leap
forward that will make our current passive information
/screen based approach to digital look
as outdated as a fax machine.

Way back in 2011 I made one of my first presentations for a provincial education conference (Dancing in the Datasphere).  Leveraging years in IT prior to teaching, I tried to edge teachers closer to an understanding of how the rest of the world had moved on in terms of their digital engagement.  Stepping out of IT in 2003 to become a teacher felt like time warping back 20 years, so out of date was the use of technology in education.  In 2019 I attended Cisco Live and discovered that the rest of the world has moved on again, leveraging cloud based systems in a way that no one in education is, so the anti-tech habits of education are still there.  The need for online/cloud based systems in education is apparent (especially since the pandemic began), but poor cybersecurity management is often used as an excuse to stay out of it.  We're still the only school in South Western Ontario doing CyberTitan and one of only five in the province with any kind of cyber-focus.

In the past decade education has staggered into the 21st Century, though Ontario has gone out of its way to fear and shun it until all the tech-haters suddenly desperately needed it during the pandemic.  The past two years have forced a recognition of the importance of digital fluency, though there are still no mandatory digital literacy courses in any Ontario high school.

On To The Future, Ready or Not...

With all that in mind, what's coming next offers some exciting possibilities, not that education will leverage them before I retire.  Machine learning and the artificial intelligence growing out of it is already offering students a silent AI partner for coding with Github's Copilot.  The GPT-3 OpenAI system Copilot runs on is already producing original text, and perhaps even some of the original essays that teachers think are written by students.

As systems become smarter information falls to hand more readily and old habits become irrelevant (like memorizing phone numbers).  With all that in mind, I've had grade 10s building IBM Watson AI powered chatbots for several years now, and this past semester several of my seniors used Copilot to make their culminating coding projects.  Being able to communicate effectively with ML & AI is going to become increasingly important in the next decade.

But what really excites me about intelligent machines is how they're able to simulate activities with human users in order to streamline and improve the human-machine interface.  Last week we were watching FITC's Spotlight UX, an online conference about the multidisciplinary field of User Experience (UX) based on digital design, ergonomics and user interfaces.  UX opens things up to consider all aspects of digital design from a user's point of view; it has a lot in common with student centered learning in education.  The opening speaker was formerly an ethnologist before getting into UX and her background allowed her to dismantle many of the assumptions that alienate users, especially in online systems that may be designed in one country and used many others.

At the same time I was reading Guy Huntington's piece on The Coming Classroom Revolution.  One of the things he covers is the concept of a virtual-self personal learning assistant.  Guy is looking at the AssistBot from a legal/privacy perspective in the article, but a complex digital model of a students' learning habits offers some interesting possibilities.  What if the virtual student could be run through simulations using various software?  User interface issues could be recognized even before a student picks up a new device or software for the first time.  Interfaces that have been refined by AI driven user simulations would feel intuitive in a way they never have been before because each user would be interacting with digital information on an interface that was custom designed for them based on thousands of hours of simulation prior to them ever picking it up for the first time.

The learning benefits should also be apparent if everyone is walking around with a digital doppelganger in tow.  A teacher might pitch a lesson into a simulation space and the virtual student-bots would be able to show where it does and doesn't work for them, and the lesson could then be customized for each student as needed prior to them ever seeing it for the first time.  Classrooms would become radically personalized after over a century of factory conformity and low resolution information sharing.

A buzzword flying about at the moment is 'metaverse', especially after Facebook rebranded itself Meta.  In the last post I talked about my long involvement with interactive and immersive virtual reality, and after years of development we are close to finally making it happen on a system-wide scale, but it's going to happen while the systems themselves are becoming intelligent and the web itself is attempting to evolve itself past the attention merchant economy that web2.0 became.

Back in April I watched FITC's big early conference and they had Jared Ficklin keynoting about how web3 (driven by blockchain encryption) might give us back control of our own data and change the paradigm we're stuck in online with multi-nationals selling our data as if they owned it.  It was a thrilling talk and I've since come across similar thinking in WIRED.

Web3's a bit of a dog's breakfast thanks to crypto and the mess it has made, but the possibility of individuals owning their online presence is a thrilling return to what the internet once was and might be again.

Combining all of these converging ideas into a viable technological future is ambitious, but it's something worth pursuing because if you don't push for the best outcome for the most people we end up with what we have now.

Could the internet provide us with secure interaction and storage without abusing our information?  Could we move past the low-resolution two dimensional windows that we all peer into the datasphere with now?  Could we leverage machine intelligence to treat each other in a more human way than our 'superior' one teacher to 30+ student brick-in-the-wall classrooms continue to do even now?

Imagine if you will a future where you are able to move in and out of digital information at will without it ever distracting you from the real world as it does now.  Peripheral user interface ergonomics will drastically improve as we get clear of the smartphone myopia we're currently stuck in.  When deep diving into digital data you'll be able to do it using complex multi-dimensional interfaces that make our current screen fixation look positively archaic.  Haptic IoT devices mean you'll interact with data with more than your fingers, allowing for much more nuanced control of your digital interactions.  Your awareness of that environment will also be dimensionally greater than peering through a 2d screen.  Moving three dimensionally in digital data offers you a much richer connection to your digital self.

A better interface with digital information is already here and will only improve, and though Web3 struggles to make sense at the best of times, the idea that we could bring our shared network back to a user-centric experience where our privacy and personal information is owned and controlled by users points to a possible future beyond the tyranny of the attention economy.  But what's most exciting to me is the idea that we can have virtual versions of our habits that we can run simulations on in order to produce software experiences unlike any we've had before.  The efficiency in that combined with all these other converging technologies points to a digital future much richer than the step we're stuck on now.

Imagine opening up a brand new app to discover that it intuitively makes sense to you because it was designed using thousands of simulated hours with your digital avatar.  This also offers some interesting security opportunities because no two interfaces would be the same since each would be tailored to its user.  Combined with a more privacy friendly web, multi-dimensional user interfaces and machine learning that enables us to refine the human-machine connection even before first use, the cybernaut of the future will be doing things in digital spaces that will challenge what we think is possible, which is vital because we will interacting with more and more complex artificial intelligences when digitally connected and if we don't refine and improve our ability to operate in digital spaces, we'll rapidly lose touch with what these automated intelligences are doing.

Rachel, one of our founding Terabytches who took care of Cisco networking during cybersecurity competitions has moved on to computer science at university.  Back when she was doing coop it she developed a simple machine intelligence to develop an understanding of what was going on in large datasets.  One of the biggest surprises for us both was how much work is involved in the Explore/Transform section of this hierarchy.  ML, AI and deep learning offer us a new way to understand large, complex data-sets, but they also need human oversight to make them work.  The automation possible in modern data science is another one of those 21st Century skills most classrooms don't consider.

The Bar For Being Human Just Got Raised Again

As artificial intelligence continues to improve through both hardware and software engineering advances it'll get to the point where it starts to overtake low functioning human beings.  Then it'll overtake average human beings.  At some point it'll overtake the smartest human beings on the planet.  Ray Kurzweil called this point the technological singularity.  It's the point where we won't know why machine intelligence is doing what it's doing any more than an ant would understand why the humans are having a picnic.

You can read up on GPT-3, the latest step forward in A.I. here, or here, but I'm curious about it from an educational point of view.  GPT-3 is the latest iteration of OpenAI's research into text prediction machine intelligence.  Version three isn't that architecturally different from GPT-2, but it's much, much bigger, by many orders of magnitude.  This brute force approach allows it to adapt and respond with such high fidelity that it surprises people.

What does this mean in education?  GPT-3 based online systems are going to start appearing in the next year.  These systems will take a few suggestions from a human user and create text outputs that will stress a Turing test in terms of how well they are put together and what is being said.  With sufficient training and some smart engineering around focusing inputs, GPT-3 based online systems will write an accurate, original essay on any subject.  It could be used to answer any questions in any subject or formulate text responses even in abstract areas like poetry .  It'll also translate better than anything we've seen so far.  It's GPT-3's agile Swiss-Army-knife effectiveness that will see it falling into student hands sooner rather than later.  Which students?  The ones it already sounds like:

"GPT-3’s ability to dazzle with prose and poetry that sounds entirely natural, even erudite or lyrical, is less surprising. It’s a parlor trick that GPT-2 already performed, though GPT-3 is juiced with more TPU-thirsty parameters to enhance its stylistic abstractions and semantic associations. As with their great-grandmother ELIZA, both benefit from our reliance on simple heuristics for speakers’ cognitive abilities, such as artful and sonorous speech rhythms. Like the bullshitter who gets past their first interview by regurgitating impressive-sounding phrases from the memoir of the CEO, GPT-3 spins some pretty good bullshit."

http://dailynous.com/2020/07/30/philosophers-gpt-3

Dig up some GPT-3 output online and you'll see it uses the fact that it has figured out human speech patterns to smoothly say very little; it's like listening to a slick salesman  This complex machine learning formula is the perfect tool for weak students answering rote, repetitive school assignments, because both those students and the school system they're responding too are so low functioning that this rudimentary A.I. can do the job better (and in less than a second).

"As AI researcher Julian Togelius put it: “GPT-3 often performs like a clever student who hasn’t done their reading, trying to bullshit their way through an exam. Some well-known facts, some half-truths, and some straight lies, strung together in what first looks like a smooth narrative.” (Though as many have pointed out: clever students who know how to bullshit go far in this world because people don’t always scrutinize what they’re saying.)"

https://www.theverge.com/21346343/gpt-3-explainer-openai-examples-errors-agi-potential

So, the bar for human expectation has just moved again.  If you're operating as a teacher or student at the sharp end of human achievement, this is well beneath you, but if you like to trot out the same old material year after year, don't bother assessing process and don't really pay much attention to student work you do mark, this'll fool you.  For a student looking to get something for nothing this is a dream come true.

"GPT-3 would never kill jobs skilled developers. Instead its a wake-up call for cargo coders and developers. It’ll urge them to buckle up and upskill to ensure they’re up for solving complex computer programming problems." (cargo coders are weak programmers who copy and paste code rather than generating it themselves - they're like many students)

https://towardsdatascience.com/openais-gpt-3-the-end-of-cargo-cult-programmers-23102f70f855

The obvious answer to this is to assess process, since a student attempting to hand in work this way would have none.  Of more interest from a pedagogically standpoint is how we should integrate this evolving technology into our learning processes.  OpenAI isn't doing this in an evil attempt to create an entire generation of illiterate children, they're doing it to create A.I. that assists and supports human endeavour and raises it to a higher level.

The last year I was teaching English I had my 3Us try and beat Turnitin.com.  The standard usage seemed to be an 'aha, I caught you plagiarizing!" punitive response after a minimally reviewed writing process, all done behind a curtain.  By turning that all around  and giving students transparent access to this punitive tool, I had students come to the realization that they could beat turnitin's plagiarism check, but it took so much work to do it effectively that it was easier and more functional to just write the damned thing yourself.  Instead of using tech to catch cheating or banning it outright, we used it to test limitations and validate a meaningful writing process.

I imagine education's first response to GPT-3 driven plagiarism tools will be to try and ban them, but as usual that's backwards.  A.I. supported human intelligence isn't being developed for us to do less, but to enable us to do more.  From that point of view, an A.I. supported writing process should move rubric expectations for everyone upwards.  What used to meet expectations should now fail to meet expectations.  A digitally supported writer should already be leveraging tools to mitigate grammar and spelling errors, and teachers should be teaching effective use of these tools.  Where 5-10 grammar errors in a paper might have gotten you a level three/meets expectation before, there should now be none because digital supports should be integrated, proficiency in them expected and output from them meeting raised expectations.  With that technical work supported, student writers should be focusing on developing rhetoric, continuity of thought, voice and style.

The same goes for A.I. supported writing as we enter the Twenty-Twenties.  We should be evolving writing processes to include A.I. editorial review, A.I. supported enhanced research and maybe even A.I. driven originality of thinking.  Can you imagine a Turing test as a part of writing process that tells a student that their writing isn't as human as a GPT-3 piece?  That's using A.I. to raise the bar.  Can you imagine what student writing might look like if advanced word prediction A.I.s like GPT-3 were integrated into student writing processes?  We all need to be thinking about that, now.  It's what literacy is going to look like in the next decade.

Beyond writing you're going to see GPT-3 driven online tools rock rote, standardized (lazy) learning.  Like your worksheets?  A student will be able to scan a worksheet and receive accurate, textually correct responses instantly, to any question, in any subject.  If you're using the same old assignments over and over, the A.I. will find that and use previous examples to produce even more complex and relevant answers.  If you assess student learning through limited, repeated single-assessment final products, you just got punked.

The irony is the teachers who struggle most with this new threshold of human expectation are also the ones who will use it to mark student work.  In those teach-like-it's-1960 classes, A.I. written papers will be handed in by students and then marked by A.I. markers - no humans will have played a part in any of that 'learning'.





OTHER READING

https://www.wired.co.uk/article/gpt-3-openai-examples  "the world’s most impressive AI. Humans are being given limited use – for now – to make sure things don’t go wrong"

https://lambdalabs.com/blog/demystifying-gpt-3/
A technical analysis of GPT-3:  OpenAI recently published GPT-3, the largest language model ever trained. GPT-3 has 175 billion parameters and would require 355 years and $4,600,000 to train

We're Not Ready For This: A.I.

I saw this the other day:

He goes over deep learning, self-directed computer intelligence for the first fifteen minutes or so and summarizes at about 17:00 minutes.  The social implications of deep machine learning are quite profound.

Here are some other artificial intelligence related media that you might want to peruse:

Ray Kurzweil's The Singularity is Near (a long and tedious mathematical read with some wonderful implications mixed in.)

Her, Spike Jonze' ode to A.I..

A lot of Hollywood A.I. talk falls short into HAL type horror, but this one doesn't, it goes all the way.  By the end you'll be questioning our short comings rather than fearing what a superior intelligence might do.  I wonder what Kurzweil thought of the A.I. in this film and what it ends up doing.

Better education doesn't help? Work is irrelevant? What do we do
in a world of human pets that serve no real function in terms of survival?
This could be an age of unprecedented creativity, or the beginning of the end.

The TED talk has an interesting moment in those final two minutes where Howard is talking about the social implications of an imminent (the next five years!) machine intelligence revolution.  He talks about computers taking over jobs that we consider to be human-only and doing them better than people ever could.  This isn't about coding a better piece of software, it's about computers coding themselves in a never ending cycle of improvement.  It's also about people no longer having to be responsible for their own survival decisions.


What happens to insurance companies when automotive accidents are a thing of the past?  Accidents don't happen when the A.I. managing it can not only control the car in question, but also move the entire traffic jam up ten feet to avoid accidents.  This is often misunderstood as people say that A.I. driven vehicles could have bad code that causes a massive pile up.  These aren't machines running code, these are machines that create code as they need it, kind of like people do, but much faster, and with absolute precision.  And however well they do it now, they'll do it better tomorrow.

What happens to human beings when they are no longer
responsible for their own survival?

The busy truck driver still needs to sleep, what replaces him won't.  It'll never drive tired or hungry or angry or distracted either.  It'll only ever use the least amount of gas to get where it's going.  One of the tricky things about trying to grasp human superior A.I. is in trying to envisage all the ways that it would be superior.  That superior A.I. would never stop improving, it would take over any concept of efficiency in business.

As Howard says, machines that are able to build machines in a continuously improving manner are going to make the social change caused by the industrial revolution look like a blip on the radar.

Perhaps the hardest implication of a machine intelligence revolution is the idea that your income is tied to your usefulness.  Our entire society is predicated on the idea that your income somehow reflects your usefulness.  If human usefulness is no longer tied to social status, what would society look like?

During the big market bailouts in 2008 someone online described business as the cockroaches that feed off the work of human society.  He suggested that you don't feed them steak, you just let them thrive on the waste.  The implication was that capitalism is a necessary evil that serves human beings, not the other way around as it's often stated (people are a necessary evil in capitalism).

The idea that people could be free to pursue their own excellence in the future without having to work for the cockroaches is quite thrilling, though it would require a huge jump in social maturity for human beings.  We'd have to begin identifying our own self worth through our own actions rather than our education and employment.  I suspect most people aren't close to that.  We'd also have to recognize that everyone has a unique and valuable place in society, which sounds like socialism!

Education is as guilty as any social construction in aiming children towards the idea of success being employability and income.  We stream students according to their intellectual capital and then tell them to work hard in order to achieve financial success in the future.  The very idea of effort is tied to financial success - something we'd have to change in a machine intelligent future.  Can humans value themselves and seek excellence without the yoke of survival hung around their necks?

Universal income is an idea being floated in Switzerland and elsewhere.  If the future is one where people are no longer integral to their own survival, we better find something other than a survival instinct to base our self value on, or we're going to quickly run out of reasons for being.