I’ve said this before, but I think that a lack of physical modeling might be the key barrier for AV technology. Human drivers have a mental model of physics that they’ve honed for 17-18 hours a day since they were born.
Don't sell biology short like that. Human driver are born with a mental model of physics that's been honed 24 hours a day since before they were diatoms.
I don't think that's quite right. I believe that humans are essentially born as blank neural networks; it's the structure, and the graph of connections between brain structures and sensory inputs, that is effectively primed for learning certain tasks that we find to be intuitive.
A baby is not born with the knowledge of body movement, for example, but through natural exploration of the body and environment, almost all physically capable humans learn to walk.
>action patterns are said to be produced by the innate releasing mechanism, a "hard-wired" neural network, in response to a sign stimulus or releaser
This is exactly what I'm talking about. Just like a baby deer "instinctively" can walk, but wobbles around for the first few hours, what you're seeing is something very similar to a purpose evolved neural network structure who's weights are being set through the principle of firing and wiring together (I forget what it's called).
I can't believe I got -4 for that!
Edit: hebbian learning. Point is it's probably far too much information to encode in DNA, but if you structure your neural network properly, you encode, how could I put it, the general topology of the problem you are attempting to solve, and through reinforcement learning "fill in the blanks" by training weights (or hebbian learning which functions similarly).
Your hypothesis feel correct, and is one of two three parts that I feel are missing from current deep learning networks.
1) Pre-existing structures that are already specialized for the necessary tasks, but untrained. We kind of mimic this with transfer learning, and by discovering more appropriate general architectures by hand.
2) Training while inferring. We very crudely approximate this by releasing updated models every month but I think it would be best if also performed at the edge. Google has begun doing this, I have hope for 'federated learning'[0].
3) 20+ years of exaflop training.
More narrowly focused to this article, I believe researchers keep finding that models which are architected to solve the most "general case" possible to solve consistently perform better on highly specific tasks than models trained only on those specific tasks. Definitely creating models that understand general physics follows that trend. Although I suspect, (as I believe you do), that scaling will be hampered without some sort of ML "fixed action patterns".
My thinking about this topic has been strongly guided by a special issue of Scientific American: Mind that I read in 2013 [1]. The issue was hard for me to find today because it's not listed in the usual archives, due to being a special edition. SCIENTIFIC AMERICAN MIND September 2013 Volume 22, Issue 3s
The whole issue is devoted to optical illusions and what they can tell us about how our brain uses evolutionary shortcuts to efficiently determine things in the real world. "In the wild", these shortcuts improve accuracy and speed of inference. But with artificial stimuli, they can lead us astray, and do in the case of artificially generated optical illusions.
As for the -4 (which is the maximum negative you can go on HN) I think some people just saw the first part and clicked downvote at that point.
> I don't think that's quite right. I believe that humans are essentially born as blank neural networks
I wouldn't worry about the vote counter. "Those who play for applause, that's all they'll get." -Wynton Marsalis' dad.
Following up like this to clarify for us idiots is really the best thing to do, maybe editing the original comment for clarity if you really feel like it.
Eh, I've seen my baby sucking on its thumb, and then the baby jerks and the thumb goes away, baby cries, somehow the wild flailing gets the thumb back to the mouth. Baby happy again, sucking on its own thumb.
I agree that we are not born a blank slate, but at the same time, there's a lot of knowledge missing on a newborn baby.
Even if the baby has trouble getting the thumb into the mouth at first, it has no trouble sucking it. Or any of thousands of other instinctive behaviors.
Perhaps born is the wrong word. That freshly-born baby did have nine months of gestation during which it's undoubtedly been exploring how to move about and sense its very limited environment.
Right but what it's doing in the womb is also lots of moving around ("kicking" and "jumping") so I just don't think this argument makes much sense to anyone who has experienced having a baby.
They're capable of movement, I wouldn't say they have knowledge of it though. It can take them a while to even figure out suckling, and deliberate directed movements can take a month or two. Until then they're pretty much flailing randomly and gathering training data.
One thing that makes babies hard to analyze is that it is difficult to tell what they do not know how to do vs what they are not physically able to do reliably vs unlearning coping mechanisms for their limited abilities once they start being able to. Failing randomly could certainly be trying out different movements, but it could also be lack of hand eye coordination making it difficult to apply instinctual knowledge
True, but I think it’s fair to assume that when they’re happily sucking on a finger, suddenly rip their hand away from their face, and then punch themselves in the face repeatedly before starting to cry, that is not deliberate.
>Which is why there is such a thing as "human nature" that is meaningful to talk about.
That kind of "human nature" that comes as a conclusion from the fact of what's hardcoded in a newborn baby is only a trivial kind, and not generally what people mean when they talk about "human nature" any more than the fact that babies are born with different eye colours tells us about human nature. Human nature, by definition, is found common to all humans, so a difference in "skills and aptitudes" does not say anything with regards to human nature (or the essence or appearance of it) other than "humans have skills, aptitudes and personality traits hardcoded" (which seems like a very strong claim to me anyway), but that itself would only be a trivial statement. It wouldn't tell us whether it's human nature (in the transhistorical, transsocietal sense) to be cooperative or greedy, violent or peaceful, etc.
Even so, understanding the fact that there is a human nature does not bring us much closer to what that human nature entails. Anthropologists, historians, economists, philosophers, and (some) evolutionary psychologists have a lot to say on the topic. To say that something is "just human nature" requires more evidence than "human nature is unchanging, applicable to all, and transhistorical".
Accepting that there is such a thing as "human nature" is a big thing, and at odds with much current thinking.
Many modern people assume human behavior is an effect of upbringing and social cues. A mental model where it is a mix of upbringing and people's inherent human nature can be shocking to many.
Turkheimer's Three Laws of Behavior Genetics is a good world shaking introduction to this world:
I disagree with this, sort of completely, after having been attacked by an animal. There is millions of years of evolution that will wake up and save you from getting hurt as it recognizes what's happening and instantly formulates a response to it.
You are likely correct. I think most researchers would agree, however. The bigger issue is actually learning how to form complex models. People want networks to just learn this implicitly, believing that we would likely impose counterproductive models. Other people simply struggle to incorporate models into the training process.
Yes. That usage of the acronym works when there are enough context clues. I think it will supplant the "audio/visual" meaning as autonomous vehicles become more salient. Here is text from an old job posting at Ford quoted on TechCrunch:
"We are seeking exceptional candidates to join our growing Autonomous Vehicle (AV) business team!"
I'm working on autonomous off-road vehicles, and while this is (probably) true for autonomous cars, dynamics modeling for wheeled robots on rough terrain is another beast where these approaches could very much help.
People in space robotics have been working on that (moon and mars rovers need to deal with this). Perception is also a bottleneck; you have to see rocks, root, grass, mud and predict the effects on the dynamics.
Sure, but to be clear, I meant physical modeling which includes real-time modeling of all salient objects and surfaces in the immediate and foreseeable environment. I mean going as far as creating a physical model for deer, their range of behavior and speed, weight distribution, predictive modeling for subsequent behavior, etc...
But not outside those teams. If you want to put something together in a few weeks your options are relatively limited in that collecting accurate data is fairly hard. The actual dynamics of the car is fairly simple but the forces applied to it are quite hard to model (I don't know how much Michelin charge to use TameTire but I'm guessing not cheap)