Highly relevant is the Portia genus of spiders ([0], [1]) and apparently other related jumping spiders ([2]).
One of my favorite excerpts from [1]:
> Harland says Portia’s eyesight is the place to start. Jumping spiders already have excellent vision and Portia’s is ten times as good, making it sharper than most mammals. However being so small, there is a trade-off in that Portia can only focus its eyes on a tiny spot. It has to build up a picture of the world by scanning almost pixel by pixel across the visual scene. Whatever Portia ends up seeing, the information is accumulated slowly, as if peering through a keyhole, over many minutes. So there might be something a little like visual experience, but nothing like a full and “all at once” experience of a visual field.
Portia spiders are also referenced in Peter Watts' Echopraxia. (Some spoilers could be inferred from thinking about this too hard while reading the novel.)
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.
Eusocial insects like honeybees, some wasps, and ants take this even one step further by combining the extraordinary but limited powers of one individual insect to make highly-complex decisions with the coordinated input from thousands or millions of individuals. It's often stated that after humans, these eusocial insects are the most advanced life form on earth. After studying, for example, the fascinating voting process of how honeybees choose a new home during swarming, I am in full agreement.
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.
Read Complex Worlds from Simpler Nervous Systems[1] if you want more information on how very complex behavior can emerge from jumping spiders and bees. After reading that book, it seemed to me that AI researchers should focus much more time on duplicating the feats of these simpler animals (in similarly computationally limited contexts) rather than focusing on duplicating extremely high level human faculties like reasoning and even playing Go. I mean, imagine a robot with the intelligence of a parrot or the smartest birds in the corvidae family[2]. I think people would probably be afraid of autonomous robots of this level of intelligence.
"With just a few hundred or thousand neurons, you can easily recognise perhaps a hundred faces".
It makes me think we are missing something when creating arificial neural networks which needs much more neurons to achieve only this specific task. Maybe artificial neurons are too simplified models compared to biological ones, maybe our training process could be much more efficient?
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?
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.
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.
Slightly off-topic but related.
BEAM robots created by Mark Tilden who later founded WowWee might be of interest to readers.
From Wikipedia[0]:
BEAM robotics (from Biology, Electronics, Aesthetics and Mechanics) is a style of robotics that primarily uses simple analogue circuits, such as comparators, instead of a microprocessor in order to produce an unusually simple design.
....
BEAM robots may use a set of the analog circuits, mimicking biological neurons, to facilitate the robot's response to its working environment.
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].
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.
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)
The article mentions lots of different insect skills such as spatial representation, route finding, pattern recognition, predation, etc, and then says that "Neurons act a little like wires, carrying electrical signals from one part of the brain to another. They are a biological version of the circuit board in a computer".
After reading the article, I was impressed by the insects, not by the BBC explainers.
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."
I was hoping there would be something in the article about emergent behavior in colonies of insects. If you think of the bees as a big group of automata, the complexity isn't so much in the individual bee's brain, but in the collective mind of the hive.
The research on nematodes, fruit flies and dragonflies - non-colony creatures - seems to contradict this, except none of their behaviors seemed as complex to me as the problem the bees were solving. But that might just be my programmer's brain over-simplifying the seek and flee behaviors I've put into NPCs.
The article frames the behaviors it describes as "clever", but they're really not. The bees detect some pollen, get as near as they can, and their brain tells their bodies "try stuff!", and they exhibit some behaviors they typically use to get into small spaces or past objects. A "small minority" of the bees (quote from video of the test) get the pollen when they happen to try the right stuff. They excrete a chemical signaling to the other bees that they did something that worked, and the other bees can gradually copy the behavior.
None of this is "clever" -- it's some amazing mechanisms they've selected into, but it's not figuring things out, using logic, or using tools. It's much more akin to a simple learning network playing super mario brothers and dying frequently until it eventually succeeds. It shouldn't surprise us that it only takes a few hundred thousand neurons to do (including a tiny, low-res, colorless visual cortex and olfactory system that maintain a tiny, low-res representation of their surroundings).
I'm not dismissing the wonders of nature, just trying to add some detail to a write-up that glosses over _how_ these things are working.
It's astonishing to me that on the one hand the article upends what had been common sense with experimental results, but then blithely goes on and makes assertions about the nature of cognition based on no other justification than it being the au courant model.
[+] [-] joshmarlow|9 years ago|reply
One of my favorite excerpts from [1]:
> Harland says Portia’s eyesight is the place to start. Jumping spiders already have excellent vision and Portia’s is ten times as good, making it sharper than most mammals. However being so small, there is a trade-off in that Portia can only focus its eyes on a tiny spot. It has to build up a picture of the world by scanning almost pixel by pixel across the visual scene. Whatever Portia ends up seeing, the information is accumulated slowly, as if peering through a keyhole, over many minutes. So there might be something a little like visual experience, but nothing like a full and “all at once” experience of a visual field.
[0] - https://en.wikipedia.org/wiki/Portia_(spider) [1] - http://www.dichotomistic.com/mind_readings_spider%20minds.ht... [2] - http://news.nationalgeographic.com/2016/01/160121-jumping-sp...
[+] [-] pavel_lishin|9 years ago|reply
[+] [-] nickpsecurity|9 years ago|reply
http://portiaspiders.weebly.com/hunting-strategies.html
[+] [-] dghughes|9 years ago|reply
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.
[+] [-] stil|9 years ago|reply
[+] [-] bartread|9 years ago|reply
Quite extraordinary.
[+] [-] nostromo|9 years ago|reply
> 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.
[+] [-] jly|9 years ago|reply
[+] [-] BurningFrog|9 years ago|reply
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.
[+] [-] hexagonc|9 years ago|reply
[1] https://mitpress.mit.edu/books/complex-worlds-simpler-nervou...
[2] https://en.wikipedia.org/wiki/New_Caledonian_crow
[+] [-] thinkling|9 years ago|reply
[+] [-] JohnGoGoGo|9 years ago|reply
It makes me think we are missing something when creating arificial neural networks which needs much more neurons to achieve only this specific task. Maybe artificial neurons are too simplified models compared to biological ones, maybe our training process could be much more efficient?
[+] [-] asgraham|9 years ago|reply
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.
[+] [-] chongli|9 years ago|reply
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?
[+] [-] saosebastiao|9 years ago|reply
[+] [-] Tenhundfeld|9 years ago|reply
We Really Don't Know How to Compute! https://www.youtube.com/watch?v=O3tVctB_VSU
[+] [-] criddell|9 years ago|reply
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.
[+] [-] unknown|9 years ago|reply
[deleted]
[+] [-] unknown|9 years ago|reply
[deleted]
[+] [-] unknown|9 years ago|reply
[deleted]
[+] [-] richardboegli|9 years ago|reply
From Wikipedia[0]: BEAM robotics (from Biology, Electronics, Aesthetics and Mechanics) is a style of robotics that primarily uses simple analogue circuits, such as comparators, instead of a microprocessor in order to produce an unusually simple design.
....
BEAM robots may use a set of the analog circuits, mimicking biological neurons, to facilitate the robot's response to its working environment.
[0]: https://en.wikipedia.org/wiki/BEAM_robotics
[+] [-] hexagonc|9 years ago|reply
[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
[+] [-] midgetjones|9 years ago|reply
[+] [-] pizza|9 years ago|reply
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..
[+] [-] redacted|9 years ago|reply
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)
[+] [-] babyrainbow|9 years ago|reply
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...
[+] [-] morsch|9 years ago|reply
[+] [-] k__|9 years ago|reply
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.
[+] [-] tambourine_man|9 years ago|reply
[+] [-] KineticLensman|9 years ago|reply
After reading the article, I was impressed by the insects, not by the BBC explainers.
[+] [-] chatwinra|9 years ago|reply
"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...
[+] [-] padobson|9 years ago|reply
The research on nematodes, fruit flies and dragonflies - non-colony creatures - seems to contradict this, except none of their behaviors seemed as complex to me as the problem the bees were solving. But that might just be my programmer's brain over-simplifying the seek and flee behaviors I've put into NPCs.
[+] [-] allworknoplay|9 years ago|reply
None of this is "clever" -- it's some amazing mechanisms they've selected into, but it's not figuring things out, using logic, or using tools. It's much more akin to a simple learning network playing super mario brothers and dying frequently until it eventually succeeds. It shouldn't surprise us that it only takes a few hundred thousand neurons to do (including a tiny, low-res, colorless visual cortex and olfactory system that maintain a tiny, low-res representation of their surroundings).
I'm not dismissing the wonders of nature, just trying to add some detail to a write-up that glosses over _how_ these things are working.
[+] [-] it|9 years ago|reply
[1] https://www.ebiomedia.com/the-biology-classics-paramecium-be...
[2] http://www.ruf.rice.edu/~bioslabs/studies/invertebrates/micr...
[+] [-] JackFr|9 years ago|reply
[+] [-] sitkack|9 years ago|reply
[+] [-] PublicFace|9 years ago|reply
[+] [-] NotThe1Pct|9 years ago|reply
[deleted]