Cheapest Lidar I know of with reasonable quality is a Neato vacuum cleaner. Go to Target, get a vacuum, give it a lidar-ectomy, throw away the sucky parts, and you still have the cheapest LIDAR in town. 350 pionts, 5HZ scan rate. Indoor range about 5m, outdoor 2+m with a sun shade, accuracy at 5m about +/- 2cm. Great for hobby robots. Totally not sufficient to drive a car at highway speeds.
When an implementation of AGI comes around (yes, it will come around) it will inevitably involve a number of different neural nets working together in concert as separate subsystems. That's what makes these "Neural Nets Will Never Become Conscious!" articles so hilarious.
But yeah, I could see feeding the output of an array of sub-networks into a parent network. So think one NN for vision, one for hearing, etc, etc, all of those outputs feed into a parent level network that could be your abstraction network that deals with making executive level decisions.
For the analogy to hold, it's more of a question of whether or not ML algorithms operate in the same way as the brain. Right now, ML models use algorithms from continuous optimization that require certain structure. Namely, we require a Hilbert space, so that we can define things like derivatives and gradients. This puts certain requirements on the kinds of functions that we can minimize and the kinds of spaces that we can work with. These are requirements that are difficult to have precise analogies in biology. What does it mean to have an inner product in the brain? We does twice continuously differentiable mean in the context of a neuron? Even if there is a minimization principle, which I am not sure there is or is not, if ML uses algorithms, which are fundamentally not realizable in biology, how can we say it replicates the brain?
Based on what goes on in every cell in our bodies when it comes to the information processing involved with DNA, I don't think there is any such algorithm which is fundamentally not realizable in biology. I'll grant you, I don't think biological neurons are calculating derivatives across connection strengths, but there must be some analogous process to control neural connection strengths.
That may very well be and I think it's a fantastic area to do research on. Namely, can we accurately model the body with an algorithmic process and what does this process look like? However, unless ML directly mirrors: the algorithms involved in the body, the models used by the body, and the the misfit function used by the body, which together already assumes that the body really does operate on a strict minimization principle, then I contend it's improper to anthropomorphize the algorithms. They're good algorithms, but a better name would be empirical modeling since we're creating models from empirical data.
Pretty interesting that he says reasoning and long term planning are impossible tasks for a neural net, when those tasks are done by billions of neural nets every day. :^)
Once people come up with a way to have a computer think abstractly it's just a matter of linking together a bunch of different subsystems. Your brain works the same way (try getting your visual cortex to come up with your next tic-tac-toe move).
Shouldn't we be researching ways to terraform Mars from afar rather than ship a couple humans to go live in the middle of red rocks for a few decades and then die?
