[ExI] thawing of ai winter?
Angel Z. Lopez
zoielsoy at gmail.com
Sun Apr 19 11:50:52 UTC 2020
if the brain doesn't have the ability to recondition itself then maybe this
game of life is in fact all a predetermined outcome?
On Sun, Apr 19, 2020 at 12:41 AM Rafal Smigrodzki via extropy-chat <
extropy-chat at lists.extropy.org> wrote:
> On Sat, Apr 18, 2020 at 7:17 AM Kunvar Thaman <
> f20170964 at pilani.bits-pilani.ac.in> wrote:
>> The convergence of GANs is still not proved, correct?
> ### Explain?
>> >As the brain matures in contact with the real world, each of the
>> pre-wired layers is then fleshed out, and thanks to the pre-wired scaffold
>> the amount of data needed to optimize each layer is low.
>> We don't yet know distributed information is coded in brains, but we do
>> know that the cortex is not so specialized as to be able to represent only
>> one modality in one region. For example, consider experiments of re-wiring
>> to enable seeing with tongue, etc. So, is it pre-wired, really?
> ### Yes, the cortex has a relatively uniform general wiring pattern
> throughout, and many cortical areas can take on tasks that are different
> from their usual ones. However, you need to consider the larger picture, of
> which the cortex is but one part.
> Our nervous system is much more than the cortex, most of it is hardwired
> or partially hardwired. The pattern of nuclei and tracts within the spinal
> cord, brainstem and the peripheral nervous system is coded in the genome,
> with automated routines achieving connection milestones well before birth.
> This is why babies in the womb kick - they are calibrating the reciprocal
> reflexive connections within the spinal cord. The pattern of connections in
> the basal ganglia is very complex and innate. The cortex cannot work at all
> without a huge amount of input from the basal ganglia - if your caudate or
> thalamus are destroyed, you become severely demented, even if the cortex is
> And of course, the ability of one cortical area to learn different tasks
> does not mean that all cortical areas are computationally equivalent. While
> the general pattern of short intracortical connections is as I said
> relatively uniform, there is variability, not as much as the differences
> between various subcortical structures but still substantial. If you look
> at the cortex under the microscope, which is a very crude tool, you can
> already discern a lot of subtle variability (Broadman's cortical areas),
> and the closer you look, using various functional techniques, the more
> complex it is. The wiring pattern for the macaque visual cortex has more
> than a hundred distinct functional parts, and it's just a very small part
> of the overall structure. Some of that is likely to be encoded
> genetically, with a suite of task-specific connection patterns (routines)
> ready to be deployed in a cortical area, and the specific routine depending
> on pattern of input from basal ganglia. This accounts for the ability to
> re-wire sensory areas to respond to different formats of sensory input
> (visual, tactile, etc.) and it is still pre-wired, in the genome.
> It is estimated that the brain contains more than 10,000 distinct types of
> neurons and hundreds of thousands of distinct neuroanatomical areas. But
> this is not all - the pattern of connections between cortical areas is also
> very non-random. There are hundreds of thousands of tracts connecting
> different areas of the cortex, and many of these connections are under
> close genetic (i.e. pre-wired) control. For example, disruption of the
> Foxp2 gene causes a language development impairment due to mis-wired
> connections to and from the Broca's area. Doubtless there are thousands of
> other specific genetically determined connections within the cortex.
> This clearly shows that the cortex does not just wire itself up from
> scratch, like deep-learning networks did when research on them started
> years ago. The cortex is built atop a huge hardwired pile of complex
> computing machinery, it is dependent on the pre-processed input from that
> machinery and it relies on a lot of hardwired machinery to process its
> outputs. Intracortical connections are also pre-wired and genetically
> controlled, not relying on a uniform simple algorithm for creating
>> What do you mean pre-wired? Our brains are constantly changing, in fact,
>> every bye of information you process causes a physical change in the brain
> ### As I was explaining above, by "pre-wired" I mean the complex pattern
> of nuclei and tracts throughout the spinal cord, brainstem, cerebellum,
> basal ganglia that is largely or even exclusively genetically determined,
> as well as the more responsive to experience but still genetically planned
> pattern of connections within the cortex. The process of individual
> learning changes very little on the general wiring pattern, instead works
> on the microscale of individual synapses and cortical columns. This is a
> bit analogous to hardware and software in a computer. The general inherited
> (genetic) wiring pattern is the hardware, and individual synapse creation
> in response to various inputs is analogous to software.
> In this analogy, the contest between modern deep learning systems and
> biological brains is like a contest between a general software emulation
> competing against a hardware-accelerated machine highly optimized for a
> complex task. You need stupendous general computing power to develop an
> emulator capable of outrunning a task-oriented hardware accelerated machine.
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