A look at AI

Here is an honest look at what is so often called AI.

Hogwash, codswallop and balderdash.

It's not news that nobody has created an AI yet to equal human intelligence, even a 5th rate defective intelligence (like, for instance, Trump).  What HAS been created is a number of discrete "expert systems" that sort and collate data to reach conclusions faster and more accurately than humans.

That is not, and never has been, "general intelligence."  What it is, is "artificial intelligence."

The number of neuronal connections in the human brain -- even a withered, malformed brain like Trump's (labeled "Defective!  Do Not Use!" in its jar) is a couple of orders of magnitude greater than the biggest AI computers.

Get the hardware into the same realm of complexity and we'll see how they compare.

My guess is, it's just a matter of complexity.  I don't think there's anything magic about the brain.

Then we won't be able to call it "artificial" anymore.

A salient point worth contemplating, if you're up for some mental gymnastics, is "what rights will AIs have once they reach the level of equaling or exceeding human intelligence?"

• Should it be illegal to disconnect one once it's up and running?
  
  
• Will they be granted independence and free will?  Will they be forced to follow human law?
  
  
• In a court of law, if there's a dispute between an AI and a human, who will be given the presumption of dominion?
  
  
• In a court of law, if an AI determines a human is a danger to the AI, a danger to other humans, a danger to society at large, will termination of the human be sanctioned?
  
  
• Expand that last question to groups of humans.
  

Stanislaw Lem first broached questions like these in his groundbreaking 1971 short story "Non Serviam" inside the collection "A Perfect Vacuum."  Like it or not, the day is coming when such issues will have to be resolved.

NoCoPilot wrote:

"what rights will AIs have once they reach the level of equaling or exceeding human intelligence?"

None. They will still be just machines.

Coincidentally, I was laying in bed this morning thinking about AI and robotic intelligence. When we envision AI we usually see it as performing tasks for us: driving our cars, vacuuming our homes, working in a car factory, serving us breakfast in a maid costume.

As butlers and servants, in other words — now that people of color have proven they’re the equal of the white races and deserve equal treatment, and children are off the table.

However, once AI reaches or exceeds human intelligence, it seems likely that the AI will make its own decisions, will direct it’s own activities, will decide what it will, or will not, do for us. Robot Liberation!  We are just as (or more) likely to end up serving our Robot Overlords.

Despite Asimov’s Three Rules.

I don't like death metal any more than country, but since you mention it...

LISTEN TO BRUTAL DEATH METAL MADE BY A NEURAL NETWORK

When AIs price a book at $23 million.
http://www.michaeleisen.org/blog/?p=358

According to Paul Scharre, there are three levels of AI:

• AFI : artificial functional intelligence.  Expert systems, self-driving cars, self-guided missiles.  Anything where the AI has one function and one function only.  AFIs can be very good at what they do -- the best chess program currently beats every Grand Master, Go is now the sole domain of computers, Watson won "Jeopardy" -- but outside their domain they're as dumb as a stick.
  
• AGI : artificial general intelligence.  An AI that can pass the Turing test.  So far, just a dream on paper.
  
• ASI : artificial super intelligence.  Once AGIs are put to the task of designing better versions of themselves, people have speculated they'll create ASIs.  Some scientists and futurists worry that ASIs will be antagonistic to humankind -- afterall, we've polluted our planet and all but assured our eventual demise.  Their solution might be to take over and rid the planet of us. Beyond that, however, I'm unsure how ASIs might differ from AGIs. (Of course I'm only an AFI.)
  

Quote :

AlphaGo is a computer program developed by Google DeepMind to play the board game Go. AlphaGo's algorithm uses a combination of machine learning and tree search techniques, combined with extensive training, both from human and computer play. The system's neural networks were initially bootstrapped from human game-play expertise. AlphaGo was initially trained to mimic human play by attempting to match the moves of expert players from recorded historical games, using a KGS Go Server database of around 30 million moves from 160,000 games by KGS 6 to 9 dan human players.[13][28] Once it had reached a certain degree of proficiency, it was trained further by being set to play large numbers of games against other instances of itself, using reinforcement learning to improve its play.

AlphaGo versus Lee Sedol, also known as the Google DeepMind Challenge Match, was a five-game Go match between 18-time world champion Lee Sedol and AlphaGo, played in Seoul, South Korea between 9 and 15 March 2016. AlphaGo won all but the fourth game. The match has been compared with the historic chess match between Deep Blue and Garry Kasparov in 1997.

Game 1
AlphaGo (white) won the first game. Lee appeared to be in control throughout much of the match, but AlphaGo gained the advantage in the final 20 minutes and Lee resigned. Lee stated afterwards that he had made a critical error at the beginning of the match; he said that the computer's strategy in the early part of the game was "excellent" and that the AI had made one unusual move that no human Go player would have made.

Game 2
AlphaGo (black) won the second game. Lee stated afterwards that "AlphaGo played a nearly perfect game",[49] "from very beginning of the game I did not feel like there was a point that I was leading".[50] One of the creators of AlphaGo, Demis Hassabis, said that the system was confident of victory from the midway point of the game, even though the professional commentators could not tell which player was ahead.[50]

Michael Redmond (9p) noted that AlphaGo's 19th stone (move 37) was "creative" and "unique".[30] Lee took an unusually long time to respond to the move.[30] An Younggil (8p) called AlphaGo's move 37 "a rare and intriguing shoulder hit" but said Lee's counter was "exquisite". He stated that control passed between the players several times before the endgame, and especially praised AlphaGo's moves 151, 157, and 159, calling them "brilliant".[51]

AlphaGo showed anomalies and moves from a broader perspective which professional Go players described as looking like mistakes at the first sight but an intentional strategy in hindsight.[52] As one of the creators of the system explained, AlphaGo does not attempt to maximize its points or its margin of victory, but tries to maximize its probability of winning.[30][53] If AlphaGo must choose between a scenario where it will win by 20 points with 80 percent probability and another where it will win by 1 and a half points with 99 percent probability, it will choose the latter, even if it must give up points to achieve it.[30] In particular, move 167 by AlphaGo seemed to give Lee a fighting chance and was declared to look like an obvious mistake by commentators. An Younggil stated "So when AlphaGo plays a slack looking move, we may regard it as a mistake, but perhaps it should more accurately be viewed as a declaration of victory?"

By training AlphaGo through many instances of playing itself, the Google DeepMind people gave us a glimpse of what ASI will look like (although AlphaGo is only an AFI).  ASI will not mimic human thought. It will look alien to our minds.  It will seem to make "mistakes" and decisions that no human would make.  We will inevitably view these as weaknesses.

They are not.  They're superhuman.

Quote :

Programmer and CMU PhD Tom Murphy created a function to “beat” NES games by watching the score. When the computer did things that raised the score it would learn how to reproduce them again and again, resulting, ultimately, in what amounts to a Super Mario Brothers-playing robot. The program, called a “technique for automating NES games,” can take on nearly every NES game, but it doesn’t always win.

By giving the program a little bit of training – how to jump, what to grab – the program becomes a coin-hungry juggernaut, stomping turtles and taking no mushroom prisoners. Murphy ran a few other games through it, including Tetris, and found that the program would eventually just pause itself rather than continue playing and lose.