[endisneigh]

The irony is that the world where AI could reasonably explain and teach any level of math is the same world where most of such graduates would be unemployed anyways.

You’re already seeing oversupply of educated folks in India and China. Graduate students working as baristas in USA, etc. Sadly inevitable.

In any case it’s good to have the resources I suppose.

I’m also fundamentally skeptical of these stats around math struggling. What percentage of kids who would be very likely to use Khan Academy are struggling with math? And what percentage who are not would even use khan academy to begin with?

Most of the problems with students doing poorly are sadly societal - not to say that this isn’t useful, though.

[dghlsakjg]

I disagree. There is no such thing as a societal oversupply of educated people. There might be a market oversupply, but a well educated society is a stupid thing to avoid. I would also argue that we are seeing an oversupply of people with education credentials, rather than an education. Even in high end universities in China cheating is rampant (this isn't isolated to China, but my experience is that Chinese students are VERY open about it). For many people in University, the goal isn't to get an education, it is to get a degree.

As to my own experience: I am, according to most standardized tests, very apt at quantitative reasoning, but I never progressed far in math in school. Why? because I was placed on the standard track in math in a public school with a bunch of students who didn't care, and teachers who didn't have time to care, and to be honest, the attitude rubbed off. I once got in trouble because I programmed a python script to do my problem sets when I was 13 because it was faster than doing it by hand for me. In retrospect, that form of "cheating" was a sign that my teachers should have picked up on.

Quite simply, I never had access to a good math instructor throughout my schooling.

Now, decades later, I am intensely interested in a lot of subjects that require a background in math that I don't have, and I am becoming interested in Math for Math's sake. I have been using open access textbooks, and an AI assistant of my own creation to help me learn.

[deeznuttynutz]

I'm similar to you in many regards. I had no desire to learn math early in school due to the education system, how it is taught and lack of meaning for math portrayed early on. I coasted through high school like a zombie without meaning until 11th grade when I took Physics. Suddenly everything clicked, I magically became good at math despite not performing well at it before in my math classes. I ended up studying engineering and working in the EV industry. Now, I am studying pure math for the sake of my own curiosity and I'm passionate about developing AI tools to help people learn and see the why behind math as early as possible. I think I would have accomplished much more if I was exposed to Physics in elementary or middle school or at least a "History of Math" philosophy based class.

[endisneigh]

> I disagree. There is no such thing as a societal oversupply of educated people.

I disagree with this assessment. You don’t need everyone to have a phd. There’s education and there’s Education. With respect to the article, sure it’s great if everyone is good at math, but not everyone needs a math PhD. Additionally there are diminishing returns on the time spent learning more and more math vs. other things.

In any case there have already been tons of math resources available for a while now.

A better example would be chemistry. Should everyone be spending time learning chemistry, why or why not?

Intellectual obesity is a thing - simply knowing more things isn’t inherently useful.

[dghlsakjg]

The rest of my comment mostly agrees with that. I highlighted that there is an overage of people with degrees rather than actual education.

> In any case there have already been tons of math resources available for a while now.

That's great if you have the ability to learn everything from a static resource, and have the time to wait for asynchronous guidance. But what if you just sort of get it, but there is no one that can explain the parts you don't get? I've been to places where the educational standards are low enough that making change from a payment was an impossible task without a calculator. A keen student in that particular village has no one they can easily ask about math when they hit a sticking point.

> Intellectual obesity is a thing - simply knowing more things isn’t inherently useful.

I don't disagree for extreme cases, but creating more resources for people to learn has an incredible payoff, and is almost certainly morally right. Erring on the side of overeducated rarely turns out poorly at the societal, or personal level. Across the world, more highly educated societies tend to be better off across most QOL metrics. I'm well aware that there are anecdotal counterexamples of math PHDs living off of ramen. But there's a damn good reason that brain drain is a concern in many places, and other places favour immigration candidates with higher education.

[nathan_compton]

Usefulness isn't the end all and be all of the human condition. In fact, I'd argue that the only reason we care about useful things is because of the non-useful things.

[lupire]

It's complicated: https://en.wikipedia.org/wiki/Elite_overproduction

Also, writing a computer program because you are slow at solving math problems is a sign that you may need more math practice. It's not like you were handing my in your homework with solutions your wrote for harder problems that you wrote.

[dghlsakjg]

Elite overproduction theory deals with class and power. The linking of education with power and class isn't inherent, and is a very modern phenomenon. There have been plenty of stupid, uneducated, yet powerful elites. In other historical periods elite membership was more related to heritage, or physical fighting abilities.

> Also, writing a computer program because you are slow at solving math problems is a sign that you may need more math practice.

I didn't say I was slow, I just realized Python was faster than doing the same boring problem with different numbers. I seriously doubt there are people that can do high school level math faster than a computer at any level.

In retrospect a 13 year old knowing the problem area and programming well enough to get a computer to do their homework was a sign of mastery and boredom rather than a need for more practice. You can't get a python script to do your algebra homework without a pretty good understanding of the subject. So much so that in later math courses you are required to buy a programmable calculator to avoid wasting your time on the basic and tedious parts of math problems.

[nathan_compton]

I think evidence for disgruntled failed elites is pretty thin on the ground.

[PebblesRox]

Yes, the studies tend to exclude the kids who aren't using the tools "enough," which can end up being 95% of the students! [0]

On the other hand, I know there are people who enjoy math but struggle to pay attention and learn from a "sit quietly and listen" lecture-based model. (My husband is in this camp.) I can see how the back-and-forth conversational approach of these AI tutors could be really helpful for students like this.

They're still a small minority of the student population, but maybe a different minority from the students who are already excelling academically. If Khanmigo helps only 5% but it's a slightly different 5% from the students who are best served by the current system, I see that as a win, even if it's not the educational revolution that is being hyped.

[0] https://danmeyer.substack.com/p/the-kids-that-edtech-writes-...

[newswasboring]

> What percentage of kids who would be very likely to use Khan Academy are struggling with math?

A lot. A lot, lot. There are entire states full of people in India who don't have access to good teaching. Who travel absurd distances and stay away from their family for years to get access to good education. In the last few years as kids in my extended family grew up, I am amazed by how much they just learn from YouTube. One of the biggest educational startups started from a free education YT channel.

[endisneigh]

Interesting - from my time in India most kids don’t even have reliable access to internet and electricity and are very unlikely to be using khan academy to begin with

[lupire]

India has more Internet users than most continents have total population.

https://en.m.wikipedia.org/wiki/Internet_in_India

[endisneigh]

So? Doesn’t contradict what I said and even supports it if you read it…

[newswasboring]

When was your time in India?

[endisneigh]

Nine months ago (BLR)

[curiousllama]

Or, it's a world where people understand math in non-math jobs. What a world that would be!

[bilsbie]

You’re already unemployed if you focus on history or literature but we still learn it.

It teaches you how to think and how to learn which is valuable no matter what.

[beezlebroxxxxxx]

> You’re already unemployed if you focus on history or literature but we still learn it.

Most people are still employed if they focus on history or literature. They just might not be making a lot of money or working in history or literature, though some do. Plenty of people in highly "employable" degrees also find themselves unemployed for a long time once they graduate.

The reality is that getting a college degree, regardless of the major, increases your chances of employment afterwards and potential lifelong earnings enormously over only a HS degree.

But as you implied, most of the worst trends in higher-ed, a real corruption of its original intentions and values, have come from a obsession with the idea that higher-ed is solely about employee training.

[lupire]

Higher Ed was invented as vocational training for clergy.

[trod123]

AI won't ever be able to teach math because AI cannot reason. It handles information very differently than humans do. It is at best a flawed oracle that may lie through omission without you being able to tell.

This is supported by the fact that there are hard limits to computation, in terms of both Computability Theory, and Complexity Theory that are often disregarded by the novice and magical thinker alike despite being largely solved fields by experts in Computer Science, at least in terms of Computability Theory.

Most of this work was completed in the 1950s.

This is not a difficult a subject, but most people online simply are unwilling to do the work to rationally learn this and instead choose to try and hide their own ignorance because they feel inadequate, or perhaps they are engaging in something more malevolent. The reasoning (false justification) doesn't really matter.

It is, however; quite telling when expert's have validated these things, and yet any mention contrary to a narrative provokes downvotes in a public forum to remove it from view. A perfect example of intentional actions done by third-parties trying to misinform others about the risks (by those parties actions removing legitimate and valid information).

For those with the cajones to actually learn this stuff. Here is a link. It seems very jaron laden but it provides a true understanding of how computers actually work which is sorely lacking in the youth of today.

MIT 18.404J Theory of Computation https://www.youtube.com/watch?v=9syvZr-9xwk&list=PLUl4u3cNGP...

The only net benefit AI has towards society is towards destructive ends because there are far more destructive people whose efforts scale more in general when compared to the good people today. These people often do things which cannot be undone (salting the earth and burning the bridge).

Simply not having a sufficient background, or failing to perform basic due dilligence often will place most people in that destructive cohort regardless of their own personal beliefs.

Outcomes matter more than intentions, and the devil is always in the details.

A perfect example of this potential landmine of a field would be anything dealing with the underlying mechanisms involved in human to human communication.

There is an uncanny valley, and distorted reflected appraisal will occur inevitably in any human to AI interface seeking to mimic human communication.

Case studies from torture during the Korean Conflict (1950s) show that distorting reflected appraisal leads to either psychotic or dissociative behavior that progresses as exposure increases, it can permanently break people to the point where they cannot recover. Often, the first thing to go is rational thought.

Multiple experts cover this and confirm the findings (Lifton, Meerloo).

So what do you suppose will happen when AI distorts reflected appraisal (because it can't be tested sufficiently to the contrary) and its then used on the next generation of children? (who are inherently vulnerable to permanent change at that stage of development).

If as a result, they end up either killing themselves or others, or become people incapable of rational thought (which impulse control is correlated with), do you think they'll be alive or survive very long? What indicators would there be that this is happening (none, other than increasing chaotic violence)

Who do you think will be responsible for crippling them if that were to come to pass? Would any kind of justification ever be able to justify that outcome? Could we even recover from intentional crazy-making as a society? (likely not, insanity cannot be cured).

These things are not toys. Outcomes matter.

[nathan_compton]

Other posters have told you that computability has nothing to do with what AI can and can't do relative to humans and I agree with them, but its not exactly something universally agreed upon.

However, I think its reasonable to say that if you think that some fundamental thing keeps computers from doing what people do then you believe that people are somehow magic. This isn't a super unusual perspective among humans, but its certainly not a particularly common scientific one.

[trod123]

> Other posters have told you that computability has nothing to do with what AI can and cant do relative to humans.

This is improper paraphrasing (its ambiguous), the claim was, "computability theory has nothing to say about which (if any) cognitive tasks people can do that AI cannot."

This claim is a statement based in irrational fallacy. Computability Theory discusses the limits of the underlying mathematical operations performed by a computer (DFA). A Counter-example is provided in the very first lecture of that linked playlist that includes a task that humans can verify (in practice), that is not possible for computers. Formal verification is a very tricky subject with regards to computation, and is an open area of research but it provides a tidy contradictory example that the claimed statement must be false (a priori).

There is no sound or valid claim or proof that AI can somehow exceed the limitations of its dependent and underlying architecture (which is based in mathematical properties), once those properties are broken.

Additionally, taking isolated subject matter, overgeneralizing it in isolation, and ignoring the dependent abstraction layers is fallacious and magical thinking. It is a simple cognitive mistake that often occurs when people don't have expert knowledge of the whole system (sans abstraction).

As for my perspective, it is based in a decent amount of mathematics and science. Aside from the mathematical proofs, and properties, on to something you might find a bit more interesting, how familiar are you with wave collapse theory in neuroscience?

[nathan_compton]

I'm a physicist and I have published research in neuroscience. I don't feel like you understand the material you are talking about. Let's consider the question of formal verification. It is entirely possible for a computer to generate a proof for any proposition for which a person can generate a proof if by no other means than randomly printing out characters that represent the proof and then checking if the proof is correct. A proof is always a finite number of characters and the verification of a proof is always a finite number of steps (otherwise we wouldn't be able to write it down and to convince ourselves that the proof is true).

There are certainly problems which are hard for computers which are easy for people (and vice versa) but I don't know of any formal version of the claim that people can perform actions which are in principal impossible for human beings. You seem to be alluding to the claims of Penrose that humans can somehow do things machines cannot, but this isn't even what Penrose is claiming in the book. Instead, Penrose claims humans _somehow perceive_ the truth of propositions which cannot be proven in a given formal system, but this is a very vague claim which few scholars take seriously. He also thinks quantum mechanics is involved somehow, but this is also entirely vague. Even if one were to demonstrate that quantum mechanics were somehow involved in cognition there isn't any account that I know of that would describe how, per se, merely having a quantum computer involved would somehow introduce computations that classical computers could not do. I mean for one thing one can always simulate any quantum process on a classical computer. I'm not an expert in complexity theory, but as far as I know there are no known problems for which it is proven that there are no non-deterministic classical algorithms that perform as well as quantum algorithms. So we cannot even demonstrate with certainty that quantum computers are in fact better than classical computers with randomness. We only have situations where quantum algorithms are theoretically better than any known classical algorithm. It is also worth noting that most neuroscientists are dubious about the importance of quantum mechanical effects in cognition.

All that said, some people believe that there is some property of people that allows them to do things that computers cannot do (at the very least generate or have consciousness). But I don't know of any way of supporting that idea that doesn't involve dualism of some kind. That is, that asserts that there is something more than material that somehow constitutes a person. Like I said, a lot of people believe that there is. Most people. But a plurality of scientists do not believe this. I do not believe it. While I wouldn't say I'm a materialist, I would say I am a monist and that belief basically makes the assertion that a machine can do what a person does trivially true.

All that aside, I agree with you that language models cannot reason, almost certainly have no consciousness, and are have the propensity to be bad for people "spiritually" (speaking loosely of course). I'm not sure things are quite as dire as you assert. The moral rot of working in a call center, indeed, the existence of call centers, is surely worse than the imposition of an AI agent between the caller and the worker.

[trod123]

Apologies for not being able to respond sooner. I don't know what is wrong with YN, but when attempting to respond a number of times now over the past few days I've repeatedly received the YN error message saying you've posted too much in too short a time, despite only having posted what's shown up on my public dashboard (which is hardly anything).

As for formal verification of software, there are often common problems with decidability that go ignored, or design problems within the model being tested where similar looking but ultimately different objects are treated the same in error (apples to oranges). This latter part is often quite common with abstraction and its acting as a conceptual black box.

Unfortunately, this is not just a simple brute force problem. Required properties for the system need to be preserved, and these properties may or may not be explicitly defined in the formal model under test, not to mention that many tools designed for this purpose also have a corresponding combinatorial explosion with regards to complexity in many cases. Termination as a property is one that is quite common, and famous in Computer Science (i.e. the Turing Halting problem).

We can design a model that will guarantee termination, but we cannot easily determine if an arbitrary model will terminate without first running it. The hard part is in the definition and coming up with the proof of correctness, not necessarily the actual verification of correctness part.

The former as far as I'm aware is always done by humans because machines above a trivial complexity will get stuck in loops largely due to the halting problem, or the predictability paradox (which also has roots in halting). They will chug away without realizing they've repeated the same steps ad infinitum. Inductive proofs as a class are one that a lot of machines choke on or run forever.

It seems to me like you may be confounding my meaning of 'non-determinism' in a context from Floyd, instead of from a system's property context (such as found in EE coursework on Signals and Systems).

The former context is a bit more relaxed than the latter (i.e. its similar to the same way as describing the absence of time in-variance [system property]), while the latter requires unique inputs to unique output maps (i.e. preserving that 1:1 function test at each step of a computation) The property basically acts as a virtual rail for the CPU states to follow some computational graph with at most only one edge being followed based on inputs.

Von Neumann Architectures are all inherently deterministic at the signal domain level, the instructions are static and usually in the same place, and its processed in the CPU (also a single fixed place, at a single time). The preserved property guarantees that each unique instruction will will always have a single unique output (given care with the design of the instructions), and will provide that same output regardless of time, absent further compromises as you go up the abstraction layers of the architecture.

Non-determinism can be introduced through coding of interfaces and other methods of abstraction that may break the property (such as flattening, shifting, etc).

Overall non-determinism's usefulness is vanishingly small (mostly being bugs, errors, and undefined behavior), and its introduction breaks any guarantees that further automation (which may be passed output from non-deterministic code) may work at all.

Non-determinism is largely uncharacterizable (outputs don't corresponding to input as potential causes) and as a result lacks the properties for troubleshooting (aside from a guess and check model), and in practice exception handling cannot generally correct these type of errors given the unpredictability.

Troubleshooting of digital systems is largely just an application of the structured properties. Absent those properties, you cannot isolate the problem without full knowledge of the system including its design spec for inputs and outputs (which often isn't available, or economic).

Unfortunately, the role of determinism or its absence as a system's properties doesn't allow any direct comparison without knowledge of the architecture. While we can make some assumptions, we wouldn't have any sound basis because the devil really is in the details with regards to how superposition can be leveraged, and the instruction set.

Sorry if this seemed a bit run-on. I find it is important to be clear without ambiguity with the meaning of the words which is why I'm clarifying (also given that I may not be able to respond timely if YN keeps blocking me). Hopefully this was sufficiently clear in identifying any discrepancies of underlying meaning.

> I'm not sure things are quite as dire as you assert.

The direness of the assertion comes from long experience and intimate understanding of feedback systems, and a realization of the dependencies for acting on problems.

Generally speaking, Feedback systems are normally considered broken when they no longer appropriately react to signals they should normally react to.

Perception in people is just one such feedback system, and there are structures of stimuli that can bypass it in most people. Cialdini has documented a number of these in his book Influence. Other areas such as Reflected Appraisal and its distortion, along with destructive interference have also been investigated related to our biological stages of development for self concept the main way we transfer culture to our offspring (Ericksonian Identity), as well as some subtle applications of Sapir-Whorf in the 1950s on PoWs (which follow Cialdini's Consistency Principle).

The general rule of thumb for feedback systems are when they continue running, once broken the longer they take to catastrophic breakdown the higher the overall cost.

In an ecological overshoot environment, where failure of society to organize (such as happens in a breakdown) or even react leads to a failure to feed ourselves, it requires a critical view based solely on the existential risk.

Now if your perception is changed without you recognizing or alerting to it, such that you no longer recognize certain problems and discount them from your awareness, you become blind, and without knowing/being able to react you cannot act to correct it. Critically thinking people may be able to cope better than others, but no one I know is hyper-rational all the time.

Perception underlies most of what we do, so when these structures and the following consequences happen, you ultimately get stuck in a feedback loop as an unthinking automata without realizing it. You may perform regular fixed action patterns but its still the same output oblivious to the danger signals you may be receiving.

Associative priming of unrelated things is one such method discovered in the past 70 years, similar to attenuating signals (jamming), or front of line blocking (more common in bureacracy); those are the two most common ways these systems fail.

Would you not consider a subtle, indirect, and blinding madness that is uncharacterizable but spreading and overtaking people, and chaotically increasing across a population a greater imposition than an AI agent crudely disrupting people's ability to communicate (which the underlying mechanic this agent performs has been shown to break people; with a cohort trending towards this type of psychotic behavior or alternatively dissassociating completely as automata)?

Its been well documented that the first thing to go in torture is often rational thought, which would be our sole defense.

Authors Meerloo and Lifton both cover case studies of these type of mental torture structures which have been used both by Nazi's and Mao, as well as so haphazardly and carelessly today by businesses towards manipulating sentiment and marketing, none more commonly than social media and adtech.

Meerloo covers WW2 Nazi Thought Reform through Mao (where they broke perception). Lifton documents in detail the case studies from victim POWs in the Korean Conflict (Mao). These structures are even commonly found in schools.

Personally, I think a blinding illness that spreads subtly through communication, that you can't react to without first being inoculated against is a pretty dire threat.

Predictability Paradox and links to Halting (overview) https://link.springer.com/content/pdf/10.1007/s10670-020-003...

[nathan_compton]

All very interesting, but much of it is besides the simple point I am trying to make. I know of no formal demonstration that definitively distinguishes between the kinds of processes a person can do and the kinds of processes a computer can do in principal. In practice various processes are easy for computers and hard for people and vice versa, but any product which manifests itself as a finite document (clearly all proofs are finite documents) whose validity can be determined in a finite number of steps can obviously be created by a computer although getting it to do so may be hard. Indeed, the production of proofs is quite hard for human beings as well.

One other note:

> Overall non-determinism's usefulness is vanishingly small .

This is really not true. There are many problems which have no useful deterministic solution but for which non-deterministic solutions exist which can get very close to an optimal answer, and non-determinism is extremely important. In fact, in complexity theory Turing machines with and without a random oracle are distinguished precisely because the former is so useful and, in many respects, the big question in the complexity theory of quantum computers comes down to: is the randomness of a quantum computer more powerful than mere classical randomness?

People perform certain kinds of proof tasks in practice better than computers, in my opinion, only because we have a large volume of heuristic strategies for proving things which are embedded in the culture of mathematics and which are difficult but in principal not impossible to embed in a machine. In a sense, humans prove things by _guessing correctly_ which turns out to be how we get computers to do these kinds of non-trivial search tasks as well.

Anyway, on the cultural end of things I agree with the basic idea that some applications of language models and other AI are probably bad for society, but I think you're just overstating the case that this particular technology is so catastrophic. In fact, this technology merely takes effort out of a person and puts it into a machine: in a call center situation if a person gets angry at the call center employee, that is itself an error on the part of the caller, since the person at the center isn't personally responsible for anything and is, in fact, placed there almost entirely to sop up the emotional energy of the customer. The call center employee in turn has to do the internal work of "throwing away" the emotions of the customer so as to continue to respond in the robotic way that their bosses require them to perform. Merely being in a call center is intensely dehumanizing and, presently, places the burden of that dehumanization on the employee: they must dehumanize _themselves_ or face the economic consequences. This technology displaces some of that labor. Does it make the entire "call center" locus more dehumanizing? Hard to say, in total, but if so, its marginal. The employee in this situation is by far the one with the shortest end of the stick and I have trouble getting really bent out of shape about a technology which might make their lives slightly more pleasant.

If the product in question here were a secret AI agent that intermediated between a partners in a romantic relationship or between parent and child (especially) then I would get your point, but the call center is such a moral morass that this piece of technology hardly matters.

Speaking more broadly: you have a grand narrative, clearly, in your mind, but I think its preventing you from looking closely at the things that are really happening and responding rationally to them. In particular, in the cases to which you refer, where various sorts of strategies to disrupt cognition were so damaging, one thing stands out in particular: the overwhelming, credible, threat of actual violence to the subject from a difficult or impossible to escape force. Without that critical ingredient I would expect most of the effects you describe to be missing or highly attenuated.

[hollerith]

>The moment that any element has more than one underlying meaning the problem class exceeds what is capable by a computer (also known as a deterministic finite automata).

This is complete bullshit, and computability theory has nothing to say about which (if any) cognitive tasks people can do that AI cannot.

[trod123]

> This is complete bullshit

That may be your belief, but it is not, and beliefs mean little in the face of reality.

Saying the same thing in a more formalized way (so there can be no ambiguity), it is inherent in the 1:1 unique input to output relationship that is required for each state transition on a state graph, for the system's property of determinism to be preserved.

No disrespect intended, but you don't seem to have actually taken or learned important parts regarding this subject matter.

> computability theory has nothing to say about which (if any) cognitive tasks people can do that AI cannot.

You are incorrect. The very first video in that linked coursework (from MIT) provides a counterexample to this statement. The professor clearly states this is impossible, and the proofs that come later in the class prove it.

This statement is an over-generalization based in fallacy.

[hollerith]

>you don't seem to have actually taken or learned important parts regarding this subject matter.

If I hadn't mastered the concepts of computability and deterministic finite automata, I wouldn't've written what I did.

[trod123]

I'd have responded sooner but YN stopped accepting submissions, sorry if they made you wait.

We will have to disagree.

I feel my position still stands correct on solid rational foundation, since you haven't provided any rational basis other than an appeal to authority, and there remain several unanswered contradictions in the supposition you made.