I am not interested in this conversation, but I think I should clear up the misinformation spewed about factorials.

I'd also say that a human could calculate 1,000,000! before they can build a computer to do be able to do it without imploding.

It took me 1 minute to find and install GMP, 2 minutes to write this code:

#include <gmpxx.h>
#include <iostream>
 
int main() {
  mpz_class f=1;
  for (int n=1; n<=1000000; ++n)
    f *= n;
  std::cout << f << '\n';
}

2 more minutes to figure out the right g++ invocation, a few seconds to compile and less than 2 minutes to run it.

Now, 1000! has around half a million digits.

1000! has 2568 digits. The precise value is

40238726007709377354370243392300398571937486421071463254379991042993851239862902
05920442084869694048004799886101971960586316668729948085589013238296699445909974
24504087073759918823627727188732519779505950995276120874975462497043601418278094
64649629105639388743788648733711918104582578364784997701247663288983595573543251
31853239584630755574091142624174743493475534286465766116677973966688202912073791
43853719588249808126867838374559731746136085379534524221586593201928090878297308
43139284440328123155861103697680135730421616874760967587134831202547858932076716
91324484262361314125087802080002616831510273418279777047846358681701643650241536
91398281264810213092761244896359928705114964975419909342221566832572080821333186
11681155361583654698404670897560290095053761647584772842188967964624494516076535
34081989013854424879849599533191017233555566021394503997362807501378376153071277
61926849034352625200015888535147331611702103968175921510907788019393178114194545
25722386554146106289218796022383897147608850627686296714667469756291123408243920
81601537808898939645182632436716167621791689097799119037540312746222899880051954
44414282012187361745992642956581746628302955570299024324153181617210465832036786
90611726015878352075151628422554026517048330422614397428693306169089796848259012
54583271682264580665267699586526822728070757813918581788896522081643483448259932
66043367660176999612831860788386150279465955131156552036093988180612138558600301
43569452722420634463179746059468257310379008402443243846565724501440282188525247
09351906209290231364932734975655139587205596542287497740114133469627154228458623
77387538230483865688976461927383814900140767310446640259899490222221765904339901
88601856652648506179970235619389701786004081188972991831102117122984590164192106
88843871218556461249607987229085192968193723886426148396573822911231250241866493
53143970137428531926649875337218940694281434118520158014123344828015051399694290
15348307764456909907315243327828826986460278986432113908350621709500259738986355
42771967428222487575867657523442202075736305694988250879689281627538488633969099
59826280956121450994871701244516461260379029309120889086942028510640182154399457
15680594187274899809425474217358240106367740459574178516082923013535808184009699
63725242305608559037006242712434169090041536901059339838357779394109700277534720
00000000000000000000000000000000000000000000000000000000000000000000000000000000
00000000000000000000000000000000000000000000000000000000000000000000000000000000
00000000000000000000000000000000000000000000000000000000000000000000000000000000
00000000

It seems to me that humans and computers currently are good at what the other one's bad at:

Vision/Hearing (humans MUCH better than computers in all aspects):
- Humans have a massive parallel signal processing network which recognizes an astounding amount of different kinds of features in visual/audio input VERY quickly.
- Computers have a tough time with this because we don't have hardware that's as good as human eyes/ears yet, parallelism that's massive enough to process the signal quickly, or the wide variety of different types of feature recognizers that humans have.

Control (robots are better in controlled situations, humans are better in novel situations):
- Robots are better for speed and precision.
- Humans are better for adaptation (for instance, if a servo/muscle stop functioning, robots often cannot perform their task anymore, whereas a human can probably figure out an alternate way to perform the task)

Natural languages (neither human nor computer is very good at this):
- NLP systems have thousands of individual systems all working together to handle typos, solve ambiguities, reason about likely meanings, and learn new meanings on the fly. Humans have lots of trouble getting their intended meaning across to other people. Computers have trouble dealing with imperfect grammar, meanings, ambiguity, error correction, and learning.

Logic and reasoning:
- Computers quickly follow the rules they're given, and have problems when the rules they've been provided aren't sufficient. Humans are slow at following rules but can adapt to cases they haven't seen before. Computers won't make mistakes. Humans make lots of mistakes.

Arithmetic:
- Computers are much better in all aspects.

System Interop:
- AI research has typically been approached one isolated piece at a time (vision, NLP, control, logic/reasoning, planning). Lots of complex AI problems need to be solved by having the different systems help each other out, but generally AI researchers focus only on their individual problems without seeking to interop with other fields. For example, when attempting to handle NLP input, you won't get very far without a logic/reasoning system to help resolve ambiguities and likely meanings. When handling vision, recognizing characters from a language requires dealing with different orientations/mirroring/perspective changes, stylistic variations, and reasoning about what a heavily corrupted glyph probably is based on the other glyphs around it.

(long comparison of different fields)

Nice writeup. In summary, I think one must admit that while computers do have some fields where they clearly beat the human (in some fields in a stellar manner!), they still have large deficiencies at what one would commonly call "intelligence". Finding the shortest route is not intelligence, nor is minimizing a graph in general.

It's about doing things correctly (or mostly correctly) that you haven't been taught and which you've not been given a set of rules for, and it's about combining tasks and abilities in a sensible manner (again, without someone telling you).

For example, I could tell a human "Get me the book about that wizard kid from my room, please", and a human would almost certainly come back with the correct book. Failure rate pretty much 0%.

A computer might find the correct route to the room (or it might not know that my room is upstairs, or it might not be programmed to walk stairs) and it might not stumble on the stairs, might not bump into a closed door, and it might successfully identify the shapes of two dozen books in the room with an error rate of only 2%. It might even do OCR to read the book titles, but it will almost certainly fail to bring the correct book back anyway.

If that isn't enough, imagine I'm just reading a book by Tom Clancy, and I tell my favorite human to get me my Grisham book. And imagine that I'm saying: "Can you get me oh fuck it's snowing again my Grisham book?".

She will know that I don't mean to have intercourse, and she will know that "it's snowing again" isn't the book's title, and she will know that I mean Clancy, not Grisham.

A computer might know that I am reading Clancy, but it would still go for a Grisham book because that is what I ask for (or, it might reject the request because my syntax is wrong and won't go at all). Or, the computer might simply answer: "Yes I can".

I'm afraid this topic is a complete nonsense.

Computers are finite-state machines, human brain is not.

Comparing them makes no sense ...

And it will probably end in a sterile flame war in which I don't want to participate

The power of the (human) brain is that it can optimize the decisions, like a muscle can be stronger.

The power of a computer is that it can compute faster and without the factor "error".

"In some mounths" .... serioulsy ? Simulating a neural population that represents a single neo-cortical column needs a super-computer ...

They are so different machines !