Cryonics scam

It's amazing how many smart people fall prey for the Cryonics scam.

Here're my reasons why cryonics is a scam.

1) The chances of successful revival are extremely slim.
The process of reviving frozen people was never tested.
That means that most likely something would almost definitely go wrong:
Either freezing process, or maintaining frozen body, or unfreezing.
Most likely failures would be in every step.
I'd say that the chances of successful revival of the dead body are well below one in one thousand.

2) The cost of maintaining frozen body for several hundred years is pretty high. The chance that frozen body would never be heated up to unacceptable temperature during these these hundred years is pretty low.
In fact such accidents have been reported already. We should assume that many more accidents like that were never reported, because it's not in the interests of Cryonics companies to report them.

3) Even if it would be possible to revive your frozen body -- what would be the motivation to unfreeze you? In 25th century it would be much more productive to clone genetically modified super-humans (or better yet -- silicon AGIs) than revive hardly functional brain of person who was frozen with terminal decease in 21st century.


What causes people to believe in Cryonics?
I guess it's the same reason that pushes people toward religion -- they're terrified by their own death.

The catch is that Cryonics makes people die even earlier than they would die otherwise.

Enjoy Penn and Teller take on Cryonics:


Cryonics competes for people's money on the same level as any other religions do. I think that eventually Cryonics will be fully transformed into religion (like it happened with Scientology).
Scientology and Cryonics might even merge with each other
:-)

The only weakness of Artificial Intelligent Systems

~4 years ago I wrote small article about Weaknesses of Artificial Intelligent System.
That article listed only one weakness: artificial system didn't pass natural selection, while human evolution did.
I think I should clarify what exactly this "no evolution" weakness mean.

Natural selection = millions of years of testing

I'm looking at AIS (Artificial Intelligent System) from engineering perspective. All systems need to be tested, and all discovered problems need to be fixed.
Humans have billions years of testing and fixing bugs.
Artificial Intelligent Systems wouldn't have such luxury.
That would mean that some obscure (but important) design problems most likely won't be found, and under certain circumstances these design problems may hurt AI System or even significantly damage the whole society of AI Systems.

Natural Selection and Emotions

Noel Anthony Pierre in his article Social Considerations for Artificial Intelligence assumes that artificially crafted Intelligent System would rely on logic only and wouldn't use emotions.
That's not correct. Logic cannot work without low level intelligent support that emotions provide. That's why Artificial Intelligent Systems would have emotions.
However because of limited testing period (years of testing by engineers versus millions of years of testing by Evolution), artificially crafted emotions wouldn't be as carefully tuned as human emotions.

See also:
More discussions about AI weaknesses

Narrow AI in PostJobFree.com

I strongly believe that the best way to AGI (Artificial General Intelligence) is building narrow AI and then gradually extend it toward more and more General Intelligence.

Finally, I implemented some of my AI techniques in real-life web site PostJobFree.com.
Now PostJobFree.com intelligently calculates Daily Job Posting Limit. The calculations are based on how many times recruiter's postings were viewed, and how many times these postings were reported as spam.
I cannot claim that this feature has "advanced intelligence", but it is intelligent nevertheless.

Here are intelligent techniques we used to build that feature:

1) Preprocessing data prior to using it in decision making.
Row data is coming in the form of "page views" and "spam report clicks".
Special process raw input into RecruiterRating and JobRating tables.

2) Forgetting.
The most recent data is usually more valuable for decision making.
That's why yet another PostJobFree process makes sure that old data is slowly losing it's value (and disappears if the value is too low).
We implemented it by simply decreasing values in some columns in RecruiterRating and JobRating tables by 1% every night.


Here's what I've learned from implementing my first real-life intelligent feature:
1) The best working formulas and algorithms are relatively simple.
2) Still it takes time to carefully propose, test, chose, and implement intelligent algorithm.
3) If the system is designed properly - performance is not an issue.

Artificial General Intelligence project

Funny quote from AGI mailing list:

=======
Vladimir Nesov wrote:
> On Tue, Mar 11, 2008 at 7:20 AM, Linas Vepstas wrote:

Linas Vepstas: How about joining effort with one of the existing AGI projects?

Vladimir Nesov: "They are all hopeless, of course. That's what every AGI researcher
will tell you... ;-)"

Richard Loosemore: "Oh no: what every AGI researcher will tell you is that every project is hopeless EXCEPT one. ;-)"
=======

How do we learn

Mark Gluck gives an interesting explanation about cognitive processes in human brain:
The Cognitive and Computational Neuroscience...

Mark explains that we learn both from observation and from experiment.