How insects like bumblebees do so much with tiny brains

Portia spiders are also referenced in Peter Watts' Echopraxia. (Some spoilers could be inferred from thinking about this too hard while reading the novel.)

Their hunting strategies are awesome:

http://portiaspiders.weebly.com/hunting-strategies.html

Quickly skimming those but I think I've read most of those I do recall reading the Portia spider's eyes don't move but instead it rotates its brain.

It can mimic the footsteps of other spiders by plucking the web of the spider it's going to kill.

Doesn't the name comes from Portia in the Merchant of Venice a devious character?

Jumping spiders are cool we have the stripy ones in my region. Spider give me the creeps but not jumping spiders I think it's due to the shorter legs and small size.

That's so different from our concept of vision. It would be like watching a web cam that surveils a park, but only refreshes small bits of the screen at a time. You might see a headless woman strolling along, etc

It's interesting that bees can learn new tasks, but this is amazing:

> Other bumblebees learned by observing trained demonstrators from a distance. Only a small minority solved the task spontaneously. The experiments suggest that learning a nonnatural task in bumblebees can spread culturally through populations.

Bees, ants, and (I think) other hive living insects share ~75% of their DNA with their hive mates.

This makes it reasonable to think of the hive as one individual!

Just like we are made of individual cells and organs, an ant hill is made up of individual ants. That the parts can move separately is a fairly superficial difference.

I bet the drone industry will do work in that direction.

Probably a little bit of both, but definitely a huge amount of the former. Simplistic models of human intelligence being the result of neurons ignore huge amounts of human physiology, such as the fact that we have over 400 recognized types of neurons. Marvin Minsky has a lot of criticism of the overly fantastical fantasies of Neural Networks, and this is one of them.

Two points:

First, it's important to keep in mind the difference between artificial "neurons" and real neurons. Real neurons, with their complicated dendritic arbors, are much more complicated than anything you'll see in a typical ANN. So there isn't a one to one correspondence between the "few hundred or thousand" neurons in a bee and the number of units in an ANN. Now is there a one to thousand correspondence? I don't know. There's probably research on it, but I'm unfamiliar. Certainly for some neurons even a thousand unit ANN would seem inadequate (look at the arborization of a Purkinge cell, for example).

Point two: Absolutely modern ANNs are missing something fundamental. I would wager obscenely large amounts of money that they are missing more than one fundamental idea, and I doubt I could find another neuroscientist who'd take that wager. What are ANNs missing? Obviously I don't know or I would have published it already. But I'll guarantee you the first step is recurrence. Hell, intelligent recurrence might be the only thing missing and I'd lose my bet. But recurrence is hard. And anyway, back in point one, even the simple facial recognition in a bee using only a thousand neurons would take a few hundred thousand to a few tens of millions of modularly-recurrently connected "neurons." Not exactly a laptop simulation.

If I had to guess: neural networks have to operate on pixel data whereas real neurons don't. Brains and eyes have evolved in tandem. Perhaps what makes them so efficient is that the eyes handle some of the processing as a consequence of their physical shape and characteristics.

Look at the eyes of bees. Very different from our own (and from the cameras we build) and perhaps very specialized to the limited set of tasks that bees carry out?

Reminds me of a fascinating talk by Gerald Sussman at Strangeloop.

We Really Don't Know How to Compute! https://www.youtube.com/watch?v=O3tVctB_VSU

Maybe it's the difference between digital and analog?

Consider a vinyl record and record player. It's super simple - a long groove with smooth bumps and a needle that slides in the groove. Recording and playback via analog methods are super simple.

Compare that to a compact disk or MP3 file and the complexity required to encode, store, and playback the sound.

I would also suggest anyone that is interested in BEAM robotics to take a look at Braitenberg Vehicles[1]. These are simple reflex-based robots that can exhibit complex behaviors due to their interaction with their environment. Originally, they were thought experiments by the psychologist Valentino Braitenberg who showed in his book, Vehicles[2], how one might ascribe complex emotional and mental states to a simple automaton if one only observed its external behavior. For a fascinating earlier example of analog robotics, (and the first example of true autonomous robotics to my knowledge) one has the work of Grey Walter and his "tortoise" robots[3].

[1] https://en.wikipedia.org/wiki/Braitenberg_vehicle

[2] https://books.google.com/books?id=7KkUAT_q_sQC (there appears to be excerpts available for free as pdfs)

[3] https://en.wikipedia.org/wiki/William_Grey_Walter

Maybe something along the lines of "our brains would overheat" - there's a small temperature window in which proteins won't denature, it takes k T log 2 joules to erase one bit of information (Landauer's principle), and our brain uses around 20 Watts of power.

Maybe tin foil hats make good heatsinks..

Interestingly enough there is some evidence that the Gibbs free energy of ketone metabolism is more thermodynamically efficient than glucose in the brain (c.f. Dr. Richard Veech's work). You can measure a lower temperature gradient between the tongue temperature and the brain's.

*edit: s/Beech/Veech..

Give bumblebees radio, tv, reddit, hackernews, facebook and whatsapp. Pretty soon they too will be doing pretty much nothing...

Also, give them money and the idea that they will have to amass this stack pile paper before they die, instead of gathering the stupid nectar...

Or rather, why our digital neurons do so little. Mammal brains do quite a lot IMO.

Read Blindsight by Peter Watts. Apart from being an incredible novel it gets into this question, and it's freely available from the author's site: http://www.rifters.com/real/Blindsight.htm

Trying to avoid spoilers here, so ROT13 - Gur nyvraf rapbhagrerq va gur abiry ner abg pbafpvbhf, naq uhzna pbafpvbhfarff vf cerfragrq nf na ribyhgvbanel fvqr rssrpg / zvfgnxr juvpu erdhverf hf gb jnfgr uhtr nzbhagf bs cbjre guvaxvat nobhg guvaxvat, vafgrnq bs whfg guvaxvat. Bhe phygher, oebnqpnfg vagb fcnpr, nccrnef yvxr n QQBF nggnpx bs vafnar vachgf gb gur aba-pbafpvbhf nyvraf orpnhfr bs ubj zhpu rssbeg vg gnxrf gb cnefr guebhtu vg nyy.

(The neurological phenomena of blindsight is also very very interesting, and suggests that our brains may do more work than is strictly required)

Tiny ASIC vs large SoC with CPUs and FPGAs?

Can we do so little?

I think not.

I see a brain like a FPGA or a programming language, it can become anything, but it has to be led in the right direction.

Like, some people did Facebook with PHP and some people hacked together one in a million CMS.

Some people wrote a OS in C and I wrote a asteroids clone.

Why? That analogy is so widely recognised and accepted that electronic engineers/neuroscientists are experimenting on computer circuit boards to assess the understanding of neuroscience.

"It does so by way of neuroscience’s favourite analogy: comparing the brain to a computer. Like brains, computers process information by shuffling electricity around complicated circuits."

http://www.economist.com/news/science-and-technology/2171497...