LeadIQ

LeadIQ AI

LeadIQ AI

LeadIQ AI

LeadIQ AI

LeadIQ AI

LeadIQ AI

LeadIQ AI

LeadIQ AI

LeadIQ AI

LeadIQ AI

Could AI be indirectly addressing the imbalance in equality of opportunity due to our differences in IQ?

I had been thinking about how schools work when I realised it seems as though you're first taught how to work then why to do the work. I think that was a perfectly reasonable mode of operation at the time formal education was being introduced because it wasn't at a time when we were exactly as skeptical as we are now about the corrupt foundations of our systems of authority. This is to say that, back then, because of how high stakes survival was, people weren't so comfortable existing without order. This also isn't to say that established order is perfect, and nothing of value can be found through exploration, but in fact to say that this is how innovations come to be, and that there was a lot more respect for keeping things in order because the other option was effectively desperation. Nowadays, with the justification upon which western and westernised civilisations developed being shaken, as in the belief in Judeo-Christian values, the established order seems archaic, which is usually the first step towards a sweeping change, which could be revolutionary improvement or a flood. Why does that matter? While I believe getting entirely rid of the influence that our foundational belief has on our culture would be catastrophic, i don't think there are no improvements to be made and in fact can't conceptualise the point where there exists no improvement). Think of the foundational belief/philosophy of 'Loving the Lord your God (which I understand as having the utmost respect for pure truth which leads to true love) and then loving your neighbour as you love yourself' as a current that carries us through time. Some currents are full of rocks while some provide safe passage. This current has led to the greatest civilisation man has recorded thus far. So to get rid of surfaces you can do without to further avoid collisions is what we're supposed to do. We're now at a point where 'switching streams' seems to be a central focal point of cultural, political and philosophical conversations, meaning the respect for the old mode is quickly disappearing and so, for example, few really think about the reasoning behind being educated in the first place. We effectively now aim for careers with shining titles rather than those whose effect we first identified as positively impacting a community, or end up aiming in other directions which is more often than not a very good idea. The reasoning behind the greatness of a doctor is now reflected by their paycheck, when in fact the paycheck is actually effectively determined by the value the community sees in their effort, or at least that comes as an afterthought. If schools increase focus on expressing why and what effect the subject is important they can peak the interest of students in their subjects. The fundamental things we seek as humans are quite constant, they're just 'flavoured' by the culture you're in. From this perspective, a teacher can understand how to frame lessons to specific students. Of course, even in the things we want fundamentally there exist those we ought not to give into, as in, exactly what would constitute falsehood and not loving your neighbour as you do yourself. This is the true basis of what we have now thats any good, that is, look into yourself to find out what people appreciate, look for the resource to build it and bring it to the community in hopes that they appreciate it, then the community reciprocates through a token of appreciation, which they themselves think is a 'fair compensation for your troubles in bringing them the convenience'. What we have a lot of nowadays are people selling the illusion of convenience, and people convinced that this is the method. We actively look inside ourselves for ways to successfully deceive, and use this to guide other into their own loss at our profit, which is practically flipping our foundational belief on its head. I think a lot of this is caused by the hopelessness some may feel struggling to understand something they can't and are constantly berated without even knowing what they're working for, or others simply driven by a spotlight. With AI which can understood to be a heightened IQ for all, ignoring all the controversy that can't be concluded on, with such an approach we can have a lot more people working toward identifying problems and easily finding technical solutions to them, which would definitely create more job opportunities even temporarily, as AI develops to complete even more complicated tasks, with the ease with which these conveniences are produced increasing, lowering costs and therefore prices. We may end up with a culture more focused on understanding oneself in order to benefit others and thrive yourself. Ai will know how to do complex tasks, but expecting it to understand what people will appreciate to the point of being profitable requires us to make it perfectly in tune with the nature of human experience, which we ourselves aren't, but are definitely closer to, and ap

AGI won't create new jobs and here is why

If we define AGI as something that performs as well as humans on all current economically valuable tasks, then it could theoretically be true that new tasks will be created that the AGI is not good at, which humans could then make their new niche. In the following argument, I'd like to show that it is possible and likely for AGI to replace all jobs and future jobs (at least for the jobs where success is measured in productivity/quality). Argument of feasibility: Intelligence on the known dimensions can generalize to new unmeasured dimensions For this, I would first like to show that there is a finite-dimensional solution to human intelligence in general. This is easily understood by looking at the total parameter space of the human-brain: if we assume 1 parameter per neuron, or if you want to model the brain in slightly higher resolution, 100-1000 parameters per neuron, we end up with ~86 billion - 86 trillion parameters / dimensions. That is a huge amount, but most importantly, it is finite. Secondly, I'd like to show that human intelligence likely lies on a much, much lower dimensional manifold. For this, look at IQ tests: basically, what IQ tests have shown is that we can decompose intelligence into a handful of broad cognitive components, which identify roughly 7 to 10 broad abilities that account for 50% of all variance in human cognitive performance. What IQ tests have shown is some form of PCA of human intelligence: appearantly, this highly complex thing (intelligence) can be decomposed into just a handful of components that can explain 50% of the performance on human cognitive tasks. This doesn't mean that the rank of intelligence is 7-10, but rather that the functional rank is likely quite low for intelligence tasks, much lower than the ~86 trillion dimensions of the brain itself. Now, the amount of cognitive dimensions measured is only a subset of the total dimensions of the human brain. The point however is that since we know the g-factor is so highly predictive of many cognitive tasks, its unlikely that we will find many new tasks / dimensions that show a low or no correlation to the g-factor. Therefore, we can already get an accurate picture of human intelligence just by this rank 7-10 space. Considering that the human brain has managed to decompose all these cognitive tasks down into a 10-dimensional manifold, shows us that it is atleast feasible to find a low rank solution to cognitive tasks that generalizes to new unmeasured dimensions. 2) Current AI systems show the g-factor already: Secondly, I'd like to make the case for the g-factor of AI. In essence, this is also what the 'g' in AGI stands for. What we care for here is exactly the same thing as in IQ tests: that performance on one benchmark translates to performance on other benchmarks. To measure every possible dimension of human intelligence is infeasible (as i said, up to ~86 trillion dimensions). To test every human economically valuable task is less infeasible, as its a subset of this ~86 trillion, but still infeasible. Luckily, we don't have to if models generalize. If models were to act like chinese room experiments, where they have a 1-1 mapping from input to output, they would be strictly memorizing. In this case, we would need to measure every economical task, since their solution would be brittle and not generalize at all. Now the first evidence that they generalize atleast within the same data distribution is that they perform well on test sets of unseen data. So the most extreme version of this assumption clearly can't be true. Secondly, we've seen that especially bigger models tend to generalize well. One explanation is the lottery ticket hypothesis, where the latent space in the model is used to try out many different solutions, in which only the best solution wins. This shows models compressing something like the mona lisa down 1000 fold, storing it as simple rules. This compression is essentially what generalization entails: finding the lowest rank solution such that it still carries the signal and ignores the noise (perfectly in line with occams razor). Thirdly, posttraining has unlocked a whole new level of generalizing capabilities. Empirically we see that reasoning models greatly carryover performance on math/coding benchmarks to unseen reasoning benchmarks that have nothing to do with math or coding. This makes intuitive sense: reasoning is the ability to produce new objects from in-distribution components. THe first layers of a network do some form of PCA on the input, decomposing it into its simplest elements. Each consecutive layer then composes it into something more complex. Since the network uses compressed, generalizable rules, it is able to generate new objects it has never seen before. The more OOD the object is, the more layers are needed. SOmetimes this exceeds the amount of layers in the architecture, aka for hard problems, and then the model needs to loop back into itself: recursion. This is