I'd like to argue that it's rather the extreme connection density and feedback loops that connect all these different concepts. Taken on their own, each of these models that the brain (and perhaps artificial neural networks) construct are weak predictors. This is compensated for by their sheer number and the plasticity of the feedback loops between them.
As you say, when a human observes a plastic bag, a vast number of different models and transformations aggregate their predictions in a highly nonlinear fashion:
The bag has a plasticky look, it seems to be flopping around, it is slightly see-through, it produces a certain sound that implies a hollow cavity etc... these primary observations are processed by the sensory neurons, which do a first pass filter to remove noise completely subconsciously. If they don't get enough feedback, perhaps it was just a mirage of a bag - not real - and you do a double take and realize it was just a play of shadows.
But let's assume first pass feedback confirms that it is likely a real sensation. The primary inputs are then confirmed by secondary predictions:
The bag is carried by the wind, implying low density, the sound it makes is common for empty thin plastic materials, it has a matte surface that lets through some amount of light, etc... the subconscious thus makes the conclusion that it is indeed probably made of thin plastic and therefore of low density and low hardness and therefore not a threat in terms of high velocity impact. Your swerve reflex thus doesn't kick in and you drive straight.
But this reasoning requires an in-depth model of the world. It isn't enough to just recognize the shape of a bag, because that could be a myriad of other things. Only by having a model and thus understanding of all these different aspects of reality can one make a prediction as robustly as a human. And that is not a high bar, because humans are not good at predictions, let alone on short timescales. We are prone to biases, sensory errors, local minima from past bad experiences, basically the lot.
>> But this reasoning requires an in-depth model of the world. It isn't enough to just recognize the shape of a bag, because that could be a myriad of other things. Only by having a model and thus understanding of all these different aspects of reality can one make a prediction as robustly as a human.
This is a great summary of why I think current deep-learning based methods will never lead to 'intelligence' that is good enough to e.g. navigate the real world like humans do. They are all based on learning to recognize patterns to infer which things look the same as whatever was in their training set, but they have no semantic capabilities beyond simple classification.
>> And that is not a high bar, because humans are not good at predictions, let alone on short timescales. We are prone to biases, sensory errors, local minima from past bad experiences, basically the lot.
This observation I don't really follow, I would say the bar to match human reasoning abilities is extremely high for exactly the reasons you described yourself.
>> This observation I don't really follow, I would say the bar to match human reasoning abilities is extremely high for exactly the reasons you described yourself.
Sorry I should've phrased it better. I was trying ti imply that just matching human reasoning abilities is indeed an undertaking of incomprehensible complexity, _and_yet_ it is still highly error prone. I believe a system that replaces humans will be under close scrutiny and just being at par won't be enough.
>> This is a great summary of why I think current deep-learning based methods will never lead to 'intelligence' that is good enough to e.g. navigate the real world like humans do. They are all based on learning to recognize patterns to infer which things look the same as whatever was in their training set, but they have no semantic capabilities beyond simple classification.
I'm not a neurologist or cutting edge ML researcher by any measure, but this is my viewpoint as well. The astounding amount of information and internal models, and the astounding complexity of these models in terms of connections and feedback loops (and their plasticity) implies to me that our current pedestrian attempts at AI are nowhere near what is required for GAI, let alone human level GAI.
It seems to me like a lot of hubris to suggest (as I've seen people do) that in just a couple of years we could get there. Currently we have not even a clue how consciousness arises. We have evidence that it is physically possible, but that's it.
The leading enterprise in the area, Google/Youtube routinely fail to identify objects and sounds in videos.
My prediction is that what we have currently is a local optimum that expands our capabilities a lot, compared to what we had before, but in terms of genuine insight into human level AI, it will prove to be a dead end.
Regarding models of the world: isn't it conceivable that a computer could have a smaller, specialized, model of the world specific to its task?
A car could have a model of reality whose scope is only encompassed by the context of roads and driving. It is conceivable to me that a car could have an in-depth model of the "driving-world" that would allow it to make multi-sensory, tiered observations and predictions akin to human cognition.
> They are all based on learning to recognize patterns to infer which things look the same as whatever was in their training set, but they have no semantic capabilities beyond simple classification
Deep learning is more than just imagenet classification or object detection.
There are many approaches that require more understanding, such as future video prediction, captioning, question answering, reinforcement learning requiring an implicitly learned model of how the environment works beyond mere appearances, image generation, structure extraction, anomaly detection, 3d reasoning, external memory, few/one/zero shot learning, meta-learning, etc etc.
The field is huge and whatever "obvious shortcomings of deep learning" non-specialists come up with after reading popular articles are probably being tackled already in many groups and have several lines of approaches and papers already.
As you say, when a human observes a plastic bag, a vast number of different models and transformations aggregate their predictions in a highly nonlinear fashion:
The bag has a plasticky look, it seems to be flopping around, it is slightly see-through, it produces a certain sound that implies a hollow cavity etc... these primary observations are processed by the sensory neurons, which do a first pass filter to remove noise completely subconsciously. If they don't get enough feedback, perhaps it was just a mirage of a bag - not real - and you do a double take and realize it was just a play of shadows.
But let's assume first pass feedback confirms that it is likely a real sensation. The primary inputs are then confirmed by secondary predictions:
The bag is carried by the wind, implying low density, the sound it makes is common for empty thin plastic materials, it has a matte surface that lets through some amount of light, etc... the subconscious thus makes the conclusion that it is indeed probably made of thin plastic and therefore of low density and low hardness and therefore not a threat in terms of high velocity impact. Your swerve reflex thus doesn't kick in and you drive straight.
But this reasoning requires an in-depth model of the world. It isn't enough to just recognize the shape of a bag, because that could be a myriad of other things. Only by having a model and thus understanding of all these different aspects of reality can one make a prediction as robustly as a human. And that is not a high bar, because humans are not good at predictions, let alone on short timescales. We are prone to biases, sensory errors, local minima from past bad experiences, basically the lot.