Making left turns...stopping at red lights...these are success/failure criteria.
In contrast, having a favorite food, or an opinion on politics, or a preference on what should be considered the best movie from the 1990's, or what kind of music you want to blast on your stereo to listen to on your drive as you make your left turn...these are not success/failure criteria.
No. Wrong. Not a physical property. At all. This is completely the wrong way to describe it.
Consciousness is better described as an "emergent phenomenon". This is how serious researchers and academics define it.
Like I said in another reply to you, you really should go talk with a neuroscientist or cognitive psychologist. It sounds like you need to catch up quickly on the field of research.
>We're not interested in people's extremely poor self-modelling which is pragmatically useful for managing their lives, we're interested in what they are trying to model: their properties.
>Experiments, measures, validity, reliability, testing, falasification, hypotheses, properties and their degrees....This is required, it is non-negotiable.
Whoa, whoah, whoah, hold on there.
Who says that Q&A in Psychological Research doesn't involve "Experiments, measures, validity, reliability, testing, falasification, hypotheses, properties and their degrees...."
?
Where are you coming from? Your responses don't sound very scientific. You don't sound like you're even aware of the different research methods within neuroscience and cognitive psychology. Your responses sound like someone who wants to be perceived as supporting a scientific approach, but doesn't understand how to actually do these things.
This is why I quized you and gave you the chance to respond about your issues with Q&A in psychological research. You just came back with surface level platitudes. which doesn't lend much confidence to the ideathat you have anything other prejudice.
Go talk to a neuroscientist, a cognitive psychologist, you need to catch up and quick if you want to speak on these topics.
Here's an example of what ZeroGravitas mentioned.
This is a blog from the "old" Web2.0 days, when social media was nascent.
This old man still updates his blog almost daily with content that would have him banned on Twitter. He's one of the most insightful and inciteful voices in the world of advertising.
Like did the evolution of AI & ML research go like this?
> We're stuck. After decades of research, we've hit a dead end. All we're left with is a byzantine maze of IF/THEN statements. We cannot simulate intelligence using pure logic. We have failed.
> Ok but what if we throw in PROBABILITIES into a byzantine maze of IF/THEN statements??????
Feels like Economics in undergrad, listening to professors repeat broken, oversimplified models that are so hilariously wrong in their assumptions that they have to invent entirely new definitions to deal with their own failings.
Who put the statistics nerds in charge of AI? Is this really the best we got? Chained probabilities? Gradient descent?
>”Namely you have to simplify any problem in order to talk about it, solve it, teach it (making some of those reductions) but there is a certain amount of complexity that is fundamental to the problem.
For instance you can sometimes get away with treating a concurrent process as sequential, sometimes you can't.
The reductionist prays for the wisdom to know which simplifications they can get away with and which ones they can't. If your model captures the essential features you are OK, otherwise you are lost in the woods.”
Yeah I've come across ReLU, and Leaky ReLU, tanh, Maxout, ELU.
I guess I really, really need someone to explain to me like I'm a total idiot why I'd want to use a 2D function on data that might be (and probably is) multidimensional and interdependent.
>"The big trouble with probabilities is that, potentially, every event is contingent on every other event and the joint probability distribution of all possible inputs and outputs is a huge dimensional space."
Maybe! But this seems like a way more interesting challenge, with the potential to handle way more dimensions, and be way closer to "reality" and "truth", whatever those definitions really are or really mean.
>" It's not good enough to estimate that A has a 80% probability of being true, in general you need to estimate what the probability of A is if B is true and C is false."
Doesn't that still seem a little lame though? And massively error prone?
Filtering problems through a stochastic process of probabilities kinda feels like trying to rig a pachinko machine.
PaulHoule, on the other hand, gave a great example:
>For instance, where should a modern book on digital photography be filed in the library? Should it go in the 000's with computing? In the 700's under art? Or in the 600's with technology (an application of optics, electronics, etc.)
Your example wasn't a good example. For starters, it's literally a binary decision to make (yes/no: is this jstx1 trying to unlock the phone?).
Most of the tough, interesting, challenging problems in this world don't boil down to binary decisions.
Second, facial recognition doesn't depend (inherently) on artificial intelligence. It's not a great example. It's not a truly interesting, tough problem. It's not in the realm of fuzzy logic, concurrency, periodic or aperiodic behavior, or nonlinear relationships.
Could a Neural Net do it faster? Yeah, sure, maybe. But so what? You have a quicker algorithm, a faster heuristic.
> "It's built into the task, not into the solution. "
Who says? Who's defining the task?
I promise I'm not trying to get too philosophical here, but this is a long-standing issue in all areas of Science - the tendency towards reductionism.
Keep turning the dials on the oscilloscope to try and eliminate the signal noise...but what if the noise itself is an essential part of the phenomenon you're trying to study and understand? You see where I'm going with this?
> "In practice it works much better than all the alternatives that we've tried."
I was waiting for someone to just come out and say "It's the best we got". I'll grant that it might be true, but I don't like it and I don't accept it.
:D Oh, nonlinear equations! This is something I know a lot about.
:) I think I see...so they use nonlinear equations in the activation function. This helps to create divergence, or sensitive dependence on initial conditions.
:| Wait it's a sigmoid function?? Wtf that's boring.
:( They're just trying to min/max a data set, and figure out probability as it relates to that min/max. But that sucks, because most of the interesting phenomenon in nature exists in BETWEEN zero and one! All the fun, cool stuff happens in the middle! You can't reduce it down to a probability, there's no way that's going to do a good job describing anything!
By that same logic, there is no such thing as "justice" and the entire legal system is a mirage? :)