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The Evolutionary Perspective
Category Archives: Superintelligence
Posted: February 25, 2021 at 1:11 am
Artificial Intelligence, Machine Learning, and Deep Learning are terms that often overlap with each other and are easily confused. Lets discuss all three in detail and go through their applications and uses.
Have you ever noticed how effortlessly we calculate the environment around us and keep learning from past experiences? Well, Artificial Intelligence (AI) is a method to teach a computer the same thing.
Artificial Intelligence is used to build tools, agents, bots, and robots that can predict human behavior & act on a human basis. Teslas auto-driving cars, Amazons Alexa, and Siri are all examples of Artificial intelligence.
AI has three different levels:
First, Artificial Narrow Intelligence (ANI) is the only type of AI we have successfully accomplished to date. ANI is designed to perform singular tasks & is goal-oriented. ANI is very capable of completing specific tasks it is programmed to do. A few examples of ANI are voice assistants, facial recognition, or driving a car.
Second, Artificial General Intelligence (AGI) is the concept of a machine with general intelligence that can mimic human intelligence and behaviors, with the ability to learn from data and apply its intelligence to solve any problem. Artificial General Intelligence can think, understand, and act in a somewhat similar way to a human in any given situation.
Artificial Superintelligence (ASI) is the hypothetical where machines can become self-aware and surpass human ability and intelligence. Practically, we are far away from achieving this form of AI in real life.
While Artificial Intelligence is a concept of imitating human abilities, Machine Learning is a subset of Artificial Intelligence that teaches a machine to learn from previous outcomes.
Machine learning models look for patterns in the data and try to conclude you or I would based on previous outcomes and data. And once the algorithm gets really good at drawing outcomes, it starts applying the knowledge to the new data sets and keeps improving.
In a nutshell, Artificial Intelligence is the science of computers copying human behavior, while Machine Learning is the method behind how machines learn from data.
Supervised Learning is when a large amount of labeled data is fed to the algorithms, and variables that the algorithm needs to assess for correlations are also defined. However, supervised learning needs a vast pool of data to master the tasks.
Unsupervised Learning helps the algorithm look for patterns and data sets that dont have labeled responses. You would use this technique to explore your data but dont yet have a specific goal. The algorithm scans the data sets and starts segregating data into groups based on characteristics they share.
The mix of supervised & unsupervised learning is called semi-supervised learning. In semi-supervised learning mostly labeled data is fed to an algorithm, yet the model is free to explore & develop its own understanding of the data set.
Reinforcement learning is teaching a machine to complete a multi-step process with clearly defined rules. The algorithm takes its own decisions along the way & gets rewards or penalties for the actions it takes
It would not be an exaggeration to say that deep learning is a technique for implementing Machine Learning. Deep Learning is a subset of machine learning that uses deep neural networks and imitates the network of neurons in a brain, and allows machines to make accurate decisions without humans help.
However, deep learning is sometimes seen as an evolution of machine learning.The depth of a model is represented by the number of layers it has. Deep learning is the new state of the art in terms of Artificial Intelligence. In deep learning, the training is done through a neural network.
Deep learning has empowered many practical applications in Artificial Intelligence. Self-driving cars, better healthcare, even better product recommendations are all here today or on the horizon.
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Posted: January 29, 2021 at 11:14 am
Business magnate Elon Musk has come out with harsh criticisms for AI and called it the biggest existential threat. Artificial intelligence is spreading its wings across all the industries making automation an actual thing. Artificial intelligence, deep learning, machine learning, and other technologies have proven beneficial for all industries.
The Covid-19 pandemic made the contribution of AI in healthcare more visible. AI has reached inside our homes, making them smart homes. Is the interference increasing? Is AI taking over humanity? Fear of artificial intelligence has a lot to do with fiction and movies. Several Sci-Fi movies like Terminator portrays AI and robots as villains mercilessly hijack the human race.
The truth remains that these are just fiction, stories that sprung from human brains. The artificial intelligence we are using now is not capable of behaving like a human.
Artificial General Intelligence (AGI) would be capable of doing tasks like humans. It will think and reason like us to understand the world. This theoretical concept has made way for many movies like Startrek. This machine is understood as a threat to human lives because it carries reasoning abilities like humans and also owns machine intelligence and computational powers. They might outdo humans and end the need for human labor. AGI can pave its way towards attaining superintelligence.
There is no need to worry about them since the AGI remains a concept. It might take hundreds of years to become a reality.
Narrow artificial intelligence enables machines to perform a single task. The existing AI-driven technologies use narrow AI. They use machine learning and datasets to acquire information and do the limited tasks assigned to them. These machines do not possess human intelligence and thus should not be feared. The voice assistants and automatic self-driving cars use narrow artificial intelligence. However, they work at a faster pace than human brains and produce more accurate insights.
Other than the fear of evil robots taking our space, the existing AI has also been under the radar of human fear. Let us look at some of the reasons.
AI has faced the question of taking up jobs ever since it came into existence. Fear of losing jobs is a reality. AI has taken up a lot of tasks that were earlier carried out by people. This enables humans to focus on more rational tasks and alleviate the overload of repetitive and mundane jobs. AI does not terminate jobs as a whole. It cuts down some unproductive job categories and tasks.
Arriving at biased decisions is another fear. AI works on datasets and algorithms which are created by human beings. These datasets might carry the biases of their organization or creator. AI analyses them to provide insights and predictions. This can be rectified with proper governance of algorithmic data and corrections in case of wrong information.
The fear of AI reaching in the wrong hands is also prevalent. Misusing technological advancements have been there in human history. AI cannot explain why it arrived at an answer. It cannot reason nor think rationally. Once it archives the human intelligence, humans might use them to cause a negative impact. AIs black box has been in discussions for a long time. This caters to another fear fear of the unknown.
A progression towards artificial general intelligence and terminators are not in the foreseeable future. It will take time to arrive at such advanced technology. To incorporate human intelligence into machines, there is a need to understand the human brain completely. It is a very complicated strategy. Hence, the solutions are far away.
For the time being, existing AI is not a threat to humanity. It might take over some jobs but also provide a new set of jobs. Dealing with these systems seem easier and achievable with improved human governance.
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Researchers Say Humanity Will Be Officially Screwed If Artificial Intelligence Keeps Learning To Teach Itself – BroBible
Posted: at 11:14 am
Based on some of the advancements in artificial intelligence that have been unveiled in recent years, it seems like there are way too many people out there whove watched an episode of Black Mirrorand thought, Hey, thats a great idea, as youre kind of missing the point if you use a show thats devoted to highlighting the potential pitfalls of our crippling reliance on technology as a good source of inspiration.
Im sure most of the people whove devoted their lives to figuring out how to harness the power of artificial intelligence have good intentions, but the same could be said for the researchers who brought Jurassic Park to life (and we all know how well that worked out for them). Theres no denying that its wild to live in a world where we can talk with people after theyve died and even have a computer predict when youre going to die, but as the aforementioned movie taught us, its easy to get so preoccupied with whether or not youcando something to take a second to ask yourself if you should.
Whenever Boston Dynamics releases a video showcasing a new skill one of its robots has learned, theres always an avalanche of people who respond by joking about the robot overlords that will eventually bring humanity to its knees. However, theres plenty of evidence that suggests that outcome isnt a laughing matter, as some people who more know about A.I. than I ever will believe that dystopian future is a very real possibility.
Now, we have even more proof courtesy of researchers at the Max-Planck Institute for Humans and Machines, who recently published a paper in The Journal of Artificial Intelligence Research with a fun little title containing the words Superintelligence Cannot Be Contained, which is totally, definitely not a cause for concern whatsoever.
The authors of the paper took a closer look at the Three Laws of Robotics author Isaac Asimov famously said could prevent an I, Robot scenario from unfolding, which appear to be about as foolproof in the real world as they were in that work of fiction.
While some experts have posited you can control A.I. by limiting its access to the internet or writing algorithms in an attempt to control its behavior, the chance of those strategies actually working becomes increasingly unlikely as humans willingly push the limits of what artificial intelligence can do, with researcher Iyad Rahwan saying:
The ability of modern computers to adapt using sophisticated machine learning algorithms makes it even more difficult to make assumptions about the eventual behavior of a superintelligent AI.
Oh well. At least society had a good run.
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Posted: November 26, 2020 at 10:47 pm
In this new Melissa McCarthy comedy, directed by her husband and frequent collaborator Ben Falcone (who has a supporting role), she plays Carol, described by another character as the most average person on earth. This pronouncement catches the ear of a roving artificial intelligence one that travels from smartphone to TV to rice cooker at will which decides on Carol, a former Silicon Valley star turned do-gooder, as its test subject.
Taking on the voice of Carols favorite celeb, James Corden (who stars as his own voice), the superintelligence, a.k.a. the A.I., gives Carol a big bank account, a self-driving car and a snazzy apartment. In return, she must teach it about humanity. If it doesnt like what it learns, it will end the human race.
Jexi meets The Day the Earth Stood Still it is, then. Carols task is to revive her failed romance with George, a good-natured academic played good-naturedly by Bobby Cannavale. The countdown to extinction hooks up with what film scholars call the comedy of remarriage. (That is, the happy relitigation of a stalled alliance.) And the movie saunters between these two modes with minimal rhyme or reason. The couple is placed, to visual advantage, in many attractive Seattle locations the city has never looked more sparkly than it does here.
This is a movie of bits, enacted by varied comic luminaries. McCarthys who me? winsomeness, running neck and neck with her quick-witted cheekiness, is familiar. A new dynamic is added by the inspired Brian Tyree Henry, who, as Carols best friend and digital guru, hilariously crushes on the movies American president (Jean Smart).
This is nice theyre nice people, Falcones character, an F.B.I. agent tailing Carol, says while observing Carol and George at play. That is about the best recommendation one can give Superintelligence.
SuperintelligenceRated PG for impending apocalypse and language. Running time: 1 hour 46 minutes. Watch on HBO Max.
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Posted: at 10:47 pm
News 12 Staff
Nov 26, 2020, 7:10am EST
Updated on:Nov 26, 2020, 7:10am EST
If you are looking to watch something new, this is what is streaming this weekend:
Melissa McCarthy stars in a new comedy. "SuperIntelligence" starts streaming today on HBO Max.
Kaley Cuoco stars in dark comedic thriller "The Flight Attendant." The murder mystery just landed on HBO Max.
The "Saved By the Bell" reboot is a Bayside reunion 27 years in the making. It just started streaming on Peacock.
There are new holiday movies to get you in the spirit - including "Christmas Chronicles 2." This action packed adventure is fun for the whole family. Kurt Russell and Goldie Hawn return as Santa and Mrs. Claus. It premieres today on Netflix.
Dolly Parton's "Christmas on The Square" movie musical is new on Netflix.
And for adventure through beloved Star Wars moments from all nine saga films, "The Lego Star Wars Holiday Special" is now on Netflix.
New to streaming this week: ‘Saved by the Bell’ and Melissa McCarthy’s ‘Superintelligence’ – Chicago Daily Herald
Posted: at 10:46 pm
Here's a collection curated by The Associated Press' entertainment journalists of what's arriving on TV, streaming services and music platforms this week.
The Christmas movie, that yuletide evergreen, is subtly changing. "Happiest Season," which premieres Wednesday on Hulu, has many of the genre's comforting standards -- a homecoming trip, family discord, a secretly planned engagement -- but it opens the holiday comedy to a fresh cast of characters, and comes away all the more charming for it. Writer-director Clea DuVall's film -- originally planned as a theatrical release by Sony Pictures -- stars Kristen Stewart and Mackenzie Davis as Harper and Abby, a couple who travel to Harper's Waspy family for the holidays. Just before they arrive, Harper confesses she isn't out to her family. The spirited supporting cast includes Aubrey Plaza, Mary Steenburgen and Daniel Levy.
Kristen Stewart, left, and Mackenzie Davis in "Happiest Season," which premieres Wednesday on Hulu.- Courtesy of Hulu
"Superintelligence," too, is a studio film uprooted to a streaming service by the pandemic. The Melissa McCarthy comedy, her latest with director-husband Ben Falcone ("Tammy," "The Boss"), had been headed to theaters but will instead debut Thursday on HBO Max. In it, an artificial-intelligence supercomputer voiced by James Corden tasks McCarthy's unemployed character with saving the world.
Glenn Close stars in "Hillbilly Elegy." After two weeks in select cinemas, Ron Howard's film begins streaming Tuesday on Netflix.- Courtesy of Netflix
Ironically, the week's top Netflix release is the one that's been playing in theaters. After two weeks in select cinemas, Ron Howard's "Hillbilly Elegy" begins streaming Tuesday. The adaptation of J.D. Vance's much-talked-about 2016 bestseller hasn't been a hit with critics (including this one), but it's also a kind of regular feature to the season: a big 'ol helping of awards bait, with a handful of big performances by elite actors (Glenn Close, Amy Adams).
--AP Film Writer Jake Coyle
"Plastic Hearts" by Miley Cyrus.- Courtesy of RCA
Miley Cyrus is ready to rock 'n' roll on her new album. The pop star recruited some famous rock stars to help on her seventh studio release "Plastic Hearts," including Stevie Nicks, Billy Idol and Joan Jett. And Mick Rock, the iconic rock 'n' roll photographer who has shot everyone from David Bowie to Debbie Harry, photographed the "Plastic Hearts" cover art. But pop fans shouldn't worry too much about Miley's rock sound, the album -- out Friday -- also features a collaboration with hitmaker Dua Lipa and includes producers like Mark Ronson (Amy Winehouse, Bruno Mars) and Louis Bell (Post Malone).
Speaking of Dua Lipa, the Brit has had a major year in music thanks to the success of her sophomore album, "Future Nostalgia," and the smash hit single "Don't Start Now." She'll celebrate her big year on Friday with "Studio 2054," a multidimensional live experience where Lipa is promising fans "a night of music, mayhem, performance, theater, dance and much more." The singer said there will be "surprise superstar guests" at the event. Standard tickets costs $11.99.
"CYR" by Smashing Pumpkins.- Courtesy of Sumerian Records
Grammy-winning Chicago-based rockers Smashing Pumpkins will release a double album on Friday. "CYR" features 20 tracks produced by founding member and frontman Billy Corgan. The band's 11th album also features founding members James Iha and Jimmy Chamberlin as well as guitarist Jeff Schroeder. "CYR" is the follow-up to 2018's "SHINY AND OH SO BRIGHT, VOL. 1 / LP: NO PAST. NO FUTURE. NO SUN" -- Corgan, Iha and Chamberlin's first collaborative album in 18 years.
-- AP Music Editor Mesfin Fekadu
If you like "Bones" and "CSI" but just need more French accents, your best bet is the terrific NOVA special "Saving Notre Dame." The hourlong PBS documentary airing Wednesday shows the incredible lengths architects, engineers and craftspeople have gone to restore the iconic Paris cathedral stricken by 2019's fire. There is detective work -- where did the original limestone come from? -- and painstaking efforts to reclaim the building's glory, like stained glass specialists using cotton swabs to remove toxic lead. Everyone wears full hazard protection gear as they navigate a "giant house of cards."
Elizabeth Berkley as Jessica Spano, Mario Lopez as A.C. Slater, Tiffani Thiessen as Kelly Kapowski and Mark-Paul Gosselaar as Zack Morris star in the reboot of "Saved By the Bell."- Courtesy of Peacock
Can you have a "Saved by the Bell" without Screech? Peacock is hoping fans won't notice that character's absence when its sequel to the popular TV series brings back members of the original cast -- Elizabeth Berkeley, Mario Lopez, Tiffani Thiessen and Mark-Paul Gosselaar -- but not Dustin Diamond, who played the quirky Screech. In this sequel kicking off Wednesday, Gosselaar is the governor of California who has a son at Bayside High, Berkeley is a guidance counselor and Lopez is once again A.C. Slater, now a gym teacher.
Griffin Matthews, left, and Kaley Cuoco star in the HBO Max series "The Flight Attendant."- Courtesy of HBO Max
It happens all the time: You wake up next to a dead body in a Bangkok hotel. In the case of HBO Max's adaptation of "The Flight Attendant," the comedy and darkness work simultaneously. Kaley Cuoco of "The Big Bang Theory" plays an air hostess with a drinking problem whose looney attempts to cover up her part in the death place her in the crosshairs of the FBI. The first three episodes of the limited series premiere Thursday, with the first one free now if you're willing to give HBO Max your email.
-- AP Entertainment Writer Mark Kennedy
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Posted: at 10:46 pm
Heres whats happening in the world of television for Thursday, November 26. All times are Eastern.
12 Dates Of Christmas (HBO Max, 3:01 a.m., series premiere, first three episodes): Tis the season to put on some super lightweight TV while you make another damn pie crust because the first one fell apart!
This HBO Max reality series (from an executive producer of Love Is Blind) smushes together a bunch of elements familiar to anyone who watches The Bachelor and/or Nancy Meyers movies: A bunch of singles spend some magical time in a wintry Austrian castle, searching for someone they can bring home for the holidays. Insecures Natasha Rothwell narrates the ugly sweater parties, ski outings, cookie-decorating sessions, and whatever other Hallmarky things they might do on their dates. Check out Gwen Ihnats pre-air review.
Can you binge it? The first three episodes arrive today. Next week three more drop, with a final two the week following.
The Flight Attendant (HBO Max, 3:01 a.m., series premiere, first three episodes): The Flight Attendant lands on HBO Max this week with a look and feel that fairly screams Hitchcock homageinitially, at least. Chris Bohjalians novel of the same name might be the source material for Steve Yockeys adaptation, but its far from the only inspiration. Strangers meet on a train, er, plane, a beautiful blond slowly loses her grip on reality, and theres an unreliable narrator at the center of a possible international conspiracy. But as this lively pastiche unfolds, it recalls a different type of thriller altogether, the kind of blue sky series that made USA Network the (ultimately temporary) home of the breezy watch. Read the rest ofDanette Chavezs pre-air review.
Can you binge it? As with 12 Dates Of Christmas, this one arrives in several small bursts, with episodes 1-3 premiering today.
Star Trek: Discovery(CBS All Access, 3:01 a.m.)The Masked Singer(Fox, 8 p.m., special night): Keep an eye out for our news coverage, as well as Angelica Cataldos coverage (created with some help from her dad).
Superintelligence (HBO Max, 3:01 a.m., premiere): Melissa McCarthy is a nice person, Brian Tyree Henry is the cool work buddy, and James Corden is the voice of an alien superintelligence thats going to destroy the planet. Its cinema!
Craftopia (HBO Max, 3:01 a.m., premiere): This kids crafting competition show returns with two holiday-centric episodes.
Full Bloom (HBO Max, 3:01 a.m., episodes 5 and 6): Florists compete for cash, glory, and job satisfaction in this reality series, which kicked off earlier this month.
Posted: September 18, 2020 at 1:15 am
Its not paranoia if the threat is real.
Silicon Valley pioneer Paul LeBlanc discovers that one of his own creations -- a powerful A.I. called neXt -- might spell doom for humankind, so he tries to shutter the project, only to be kicked out of the company by his own brother, leaving him with nothing but mounting dread about the fate of the world.
When a series of unsettling tech mishaps points to a potential worldwide crisis, LeBlanc joins forces with Special Agent Shea Salazar, whose strict moral code and sense of duty have earned her the respect of her team.
Now, LeBlanc and Salazar are the only ones standing in the way of a potential global catastrophe, fighting an emergent superintelligence that, instead of launching missiles, will deploy the immense knowledge it has gleaned from the data to recruit allies, turn people against each other and eliminate obstacles to its own survival and growth.
From the Executive Producer of "24" comes an epic event series. Catch NEXT beginning Tuesday, Oct. 6 at 8 p.m. only on FOX6.
Posted: July 21, 2020 at 12:17 pm
PDF: We made a fancy PDF of this post for printing and offline viewing. Buy it here. (Or see a preview.)
Note: The reason this post took three weeks to finish is that as I dug into research on Artificial Intelligence, I could not believe what I was reading. It hit me pretty quickly that whats happening in the world of AI is not just an important topic, but by far THE most important topic for our future. So I wanted to learn as much as I could about it, and once I did that, I wanted to make sure I wrote a post that really explained this whole situation and why it matters so much. Not shockingly, that became outrageously long, so I broke it into two parts. This is Part 1Part 2 is here.
We are on the edge of change comparable to the rise of human life on Earth. Vernor Vinge
What does it feel like to stand here?
It seems like a pretty intense place to be standingbut then you have to remember something about what its like to stand on a time graph: you cant see whats to your right. So heres how it actually feels to stand there:
Which probably feels pretty normal
Imagine taking a time machine back to 1750a time when the world was in a permanent power outage, long-distance communication meant either yelling loudly or firing a cannon in the air, and all transportation ran on hay. When you get there, you retrieve a dude, bring him to 2015, and then walk him around and watch him react to everything. Its impossible for us to understand what it would be like for him to see shiny capsules racing by on a highway, talk to people who had been on the other side of the ocean earlier in the day, watch sports that were being played 1,000 miles away, hear a musical performance that happened 50 years ago, and play with my magical wizard rectangle that he could use to capture a real-life image or record a living moment, generate a map with a paranormal moving blue dot that shows him where he is, look at someones face and chat with them even though theyre on the other side of the country, and worlds of other inconceivable sorcery. This is all before you show him the internet or explain things like the International Space Station, the Large Hadron Collider, nuclear weapons, or general relativity.
This experience for him wouldnt be surprising or shocking or even mind-blowingthose words arent big enough. He might actually die.
But heres the interesting thingif he then went back to 1750 and got jealous that we got to see his reaction and decided he wanted to try the same thing, hed take the time machine and go back the same distance, get someone from around the year 1500, bring him to 1750, and show him everything. And the 1500 guy would be shocked by a lot of thingsbut he wouldnt die. It would be far less of an insane experience for him, because while 1500 and 1750 were very different, they were much less different than 1750 to 2015. The 1500 guy would learn some mind-bending shit about space and physics, hed be impressed with how committed Europe turned out to be with that new imperialism fad, and hed have to do some major revisions of his world map conception. But watching everyday life go by in 1750transportation, communication, etc.definitely wouldnt make him die.
No, in order for the 1750 guy to have as much fun as we had with him, hed have to go much farther backmaybe all the way back to about 12,000 BC, before the First Agricultural Revolution gave rise to the first cities and to the concept of civilization. If someone from a purely hunter-gatherer worldfrom a time when humans were, more or less, just another animal speciessaw the vast human empires of 1750 with their towering churches, their ocean-crossing ships, their concept of being inside, and their enormous mountain of collective, accumulated human knowledge and discoveryhed likely die.
And then what if, after dying, he got jealous and wanted to do the same thing. If he went back 12,000 years to 24,000 BC and got a guy and brought him to 12,000 BC, hed show the guy everything and the guy would be like, Okay whats your point who cares. For the 12,000 BC guy to have the same fun, hed have to go back over 100,000 years and get someone he could show fire and language to for the first time.
In order for someone to be transported into the future and die from the level of shock theyd experience, they have to go enough years ahead that a die level of progress, or a Die Progress Unit (DPU) has been achieved. So a DPU took over 100,000 years in hunter-gatherer times, but at the post-Agricultural Revolution rate, it only took about 12,000 years. The post-Industrial Revolution world has moved so quickly that a 1750 person only needs to go forward a couple hundred years for a DPU to have happened.
This patternhuman progress moving quicker and quicker as time goes onis what futurist Ray Kurzweil calls human historys Law of Accelerating Returns. This happens because more advanced societies have the ability to progress at a faster rate than less advanced societiesbecause theyre more advanced. 19th century humanity knew more and had better technology than 15th century humanity, so its no surprise that humanity made far more advances in the 19th century than in the 15th century15th century humanity was no match for 19th century humanity.1[footnote2]Gray squares are boring objects and when you click on a gray square, youll end up bored. These are for sources and citations only.[/footnote2] open these
This works on smaller scales too. The movie Back to the Future came out in 1985, and the past took place in 1955. In the movie, when Michael J. Fox went back to 1955, he was caught off-guard by the newness of TVs, the prices of soda, the lack of love for shrill electric guitar, and the variation in slang. It was a different world, yesbut if the movie were made today and the past took place in 1985, the movie could have had much more fun with much bigger differences. The character would be in a time before personal computers, internet, or cell phonestodays Marty McFly, a teenager born in the late 90s, would be much more out of place in 1985 than the movies Marty McFly was in 1955.
This is for the same reason we just discussedthe Law of Accelerating Returns. The average rate of advancement between 1985 and 2015 was higher than the rate between 1955 and 1985because the former was a more advanced worldso much more change happened in the most recent 30 years than in the prior 30.
Soadvances are getting bigger and bigger and happening more and more quickly. This suggests some pretty intense things about our future, right?
Kurzweil suggests that the progress of the entire 20th century would have been achieved in only 20 years at the rate of advancement in the year 2000in other words, by 2000, the rate of progress was five times faster than the average rate of progress during the 20th century. He believes another 20th centurys worth of progress happened between 2000 and 2014 and that another 20th centurys worth of progress will happen by 2021, in only seven years. A couple decades later, he believes a 20th centurys worth of progress will happen multiple times in the same year, and even later, in less than one month. All in all, because of the Law of Accelerating Returns, Kurzweil believes that the 21st century will achieve 1,000 times the progress of the 20th century.[footnote2]Kurzweil, The Singularity is Near, 39.[/footnote2]
If Kurzweil and others who agree with him are correct, then we may be as blown away by 2030 as our 1750 guy was by 2015i.e. the next DPU might only take a couple decadesand the world in 2050 might be so vastly different than todays world that we would barely recognize it.
This isnt science fiction. Its what many scientists smarter and more knowledgeable than you or I firmly believeand if you look at history, its what we should logically predict.
So then why, when you hear me say something like the world 35 years from now might be totally unrecognizable, are you thinking, Cool.but nahhhhhhh? Three reasons were skeptical of outlandish forecasts of the future:
1) When it comes to history, we think in straight lines. When we imagine the progress of the next 30 years, we look back to the progress of the previous 30 as an indicator of how much will likely happen. When we think about the extent to which the world will change in the 21st century, we just take the 20th century progress and add it to the year 2000. This was the same mistake our 1750 guy made when he got someone from 1500 and expected to blow his mind as much as his own was blown going the same distance ahead. Its most intuitive for us to think linearly, when we should be thinking exponentially. If someone is being more clever about it, they might predict the advances of the next 30 years not by looking at the previous 30 years, but by taking the current rate of progress and judging based on that. Theyd be more accurate, but still way off. In order to think about the future correctly, you need to imagine things moving at a much faster rate than theyre moving now.
2) The trajectory of very recent history often tells a distorted story. First, even a steep exponential curve seems linear when you only look at a tiny slice of it, the same way if you look at a little segment of a huge circle up close, it looks almost like a straight line. Second, exponential growth isnt totally smooth and uniform. Kurzweil explains that progress happens in S-curves:
An S is created by the wave of progress when a new paradigm sweeps the world. The curve goes through three phases:
1. Slow growth (the early phase of exponential growth)2. Rapid growth (the late, explosive phase of exponential growth)3. A leveling off as the particular paradigm matures[footnote2]Kurzweil, The Singularity is Near, 84.[/footnote2]
If you look only at very recent history, the part of the S-curve youre on at the moment can obscure your perception of how fast things are advancing. The chunk of time between 1995 and 2007 saw the explosion of the internet, the introduction of Microsoft, Google, and Facebook into the public consciousness, the birth of social networking, and the introduction of cell phones and then smart phones. That was Phase 2: the growth spurt part of the S. But 2008 to 2015 has been less groundbreaking, at least on the technological front. Someone thinking about the future today might examine the last few years to gauge the current rate of advancement, but thats missing the bigger picture. In fact, a new, huge Phase 2 growth spurt might be brewing right now.
3) Our own experience makes us stubborn old men about the future. We base our ideas about the world on our personal experience, and that experience has ingrained the rate of growth of the recent past in our heads as the way things happen. Were also limited by our imagination, which takes our experience and uses it to conjure future predictionsbut often, what we know simply doesnt give us the tools to think accurately about the future.2 When we hear a prediction about the future that contradicts our experience-based notion of how things work, our instinct is that the prediction must be naive. If I tell you, later in this post, that you may live to be 150, or 250, or not die at all, your instinct will be, Thats stupidif theres one thing I know from history, its that everybody dies. And yes, no one in the past has not died. But no one flew airplanes before airplanes were invented either.
So while nahhhhh might feel right as you read this post, its probably actually wrong. The fact is, if were being truly logical and expecting historical patterns to continue, we should conclude that much, much, much more should change in the coming decades than we intuitively expect. Logic also suggests that if the most advanced species on a planet keeps making larger and larger leaps forward at an ever-faster rate, at some point, theyll make a leap so great that it completely alters life as they know it and the perception they have of what it means to be a humankind of like how evolution kept making great leaps toward intelligence until finally it made such a large leap to the human being that it completely altered what it meant for any creature to live on planet Earth. And if you spend some time reading about whats going on today in science and technology, you start to see a lot of signs quietly hinting that life as we currently know it cannot withstand the leap thats coming next.
If youre like me, you used to think Artificial Intelligence was a silly sci-fi concept, but lately youve been hearing it mentioned by serious people, and you dont really quite get it.
There are three reasons a lot of people are confused about the term AI:
1) We associate AI with movies. Star Wars. Terminator. 2001: A Space Odyssey. Even the Jetsons. And those are fiction, as are the robot characters. So it makes AI sound a little fictional to us.
2) AI is a broad topic. It ranges from your phones calculator to self-driving cars to something in the future that might change the world dramatically. AI refers to all of these things, which is confusing.
3) We use AI all the time in our daily lives, but we often dont realize its AI. John McCarthy, who coined the term Artificial Intelligence in 1956, complained that as soon as it works, no one calls it AI anymore.[footnote2]Vardi, Artificial Intelligence: Past and Future, 5.[/footnote2] Because of this phenomenon, AI often sounds like a mythical future prediction more than a reality. At the same time, it makes it sound like a pop concept from the past that never came to fruition. Ray Kurzweil says he hears people say that AI withered in the 1980s, which he compares to insisting that the Internet died in the dot-com bust of the early 2000s.[footnote2]Kurzweil, The Singularity is Near, 392.[/footnote2]
So lets clear things up. First, stop thinking of robots. A robot is a container for AI, sometimes mimicking the human form, sometimes notbut the AI itself is the computer inside the robot. AI is the brain, and the robot is its bodyif it even has a body. For example, the software and data behind Siri is AI, the womans voice we hear is a personification of that AI, and theres no robot involved at all.
Secondly, youve probably heard the term singularity or technological singularity. This term has been used in math to describe an asymptote-like situation where normal rules no longer apply. Its been used in physics to describe a phenomenon like an infinitely small, dense black hole or the point we were all squished into right before the Big Bang. Again, situations where the usual rules dont apply. In 1993, Vernor Vinge wrote a famous essay in which he applied the term to the moment in the future when our technologys intelligence exceeds our owna moment for him when life as we know it will be forever changed and normal rules will no longer apply. Ray Kurzweil then muddled things a bit by defining the singularity as the time when the Law of Accelerating Returns has reached such an extreme pace that technological progress is happening at a seemingly-infinite pace, and after which well be living in a whole new world. I found that many of todays AI thinkers have stopped using the term, and its confusing anyway, so I wont use it much here (even though well be focusing on that idea throughout).
Finally, while there are many different types or forms of AI since AI is a broad concept, the critical categories we need to think about are based on an AIs caliber. There are three major AI caliber categories:
AI Caliber 1) Artificial Narrow Intelligence (ANI): Sometimes referred to as Weak AI, Artificial Narrow Intelligence is AI that specializes in one area. Theres AI that can beat the world chess champion in chess, but thats the only thing it does. Ask it to figure out a better way to store data on a hard drive, and itll look at you blankly.
AI Caliber 2) Artificial General Intelligence (AGI): Sometimes referred to as Strong AI, or Human-Level AI, Artificial General Intelligence refers to a computer that is as smart as a human across the boarda machine that can perform any intellectual task that a human being can. Creating AGI is a much harder task than creating ANI, and were yet to do it. Professor Linda Gottfredson describes intelligence as a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience. AGI would be able to do all of those things as easily as you can.
AI Caliber 3) Artificial Superintelligence (ASI): Oxford philosopher and leading AI thinker Nick Bostrom defines superintelligence as an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills. Artificial Superintelligence ranges from a computer thats just a little smarter than a human to one thats trillions of times smarteracross the board. ASI is the reason the topic of AI is such a spicy meatball and why the words immortality and extinction will both appear in these posts multiple times.
As of now, humans have conquered the lowest caliber of AIANIin many ways, and its everywhere. The AI Revolution is the road from ANI, through AGI, to ASIa road we may or may not survive but that, either way, will change everything.
Lets take a close look at what the leading thinkers in the field believe this road looks like and why this revolution might happen way sooner than you might think:
Artificial Narrow Intelligence is machine intelligence that equals or exceeds human intelligence or efficiency at a specific thing. A few examples:
ANI systems as they are now arent especially scary. At worst, a glitchy or badly-programmed ANI can cause an isolated catastrophe like knocking out a power grid, causing a harmful nuclear power plant malfunction, or triggering a financial markets disaster (like the 2010 Flash Crash when an ANI program reacted the wrong way to an unexpected situation and caused the stock market to briefly plummet, taking $1 trillion of market value with it, only part of which was recovered when the mistake was corrected).
But while ANI doesnt have the capability to cause an existential threat, we should see this increasingly large and complex ecosystem of relatively-harmless ANI as a precursor of the world-altering hurricane thats on the way. Each new ANI innovation quietly adds another brick onto the road to AGI and ASI. Or as Aaron Saenz sees it, our worlds ANI systems are like the amino acids in the early Earths primordial oozethe inanimate stuff of life that, one unexpected day, woke up.
Why Its So Hard
Nothing will make you appreciate human intelligence like learning about how unbelievably challenging it is to try to create a computer as smart as we are. Building skyscrapers, putting humans in space, figuring out the details of how the Big Bang went downall far easier than understanding our own brain or how to make something as cool as it. As of now, the human brain is the most complex object in the known universe.
Whats interesting is that the hard parts of trying to build AGI (a computer as smart as humans in general, not just at one narrow specialty) are not intuitively what youd think they are. Build a computer that can multiply two ten-digit numbers in a split secondincredibly easy. Build one that can look at a dog and answer whether its a dog or a catspectacularly difficult. Make AI that can beat any human in chess? Done. Make one that can read a paragraph from a six-year-olds picture book and not just recognize the words but understand the meaning of them? Google is currently spending billions of dollars trying to do it. Hard thingslike calculus, financial market strategy, and language translationare mind-numbingly easy for a computer, while easy thingslike vision, motion, movement, and perceptionare insanely hard for it. Or, as computer scientist Donald Knuth puts it, AI has by now succeeded in doing essentially everything that requires thinking but has failed to do most of what people and animals do without thinking.'[footnote2] Nilsson, The Quest for Artificial Intelligence: A History of Ideas and Achievements, 318.[/footnote2]
What you quickly realize when you think about this is that those things that seem easy to us are actually unbelievably complicated, and they only seem easy because those skills have been optimized in us (and most animals) by hundreds of millions of years of animal evolution. When you reach your hand up toward an object, the muscles, tendons, and bones in your shoulder, elbow, and wrist instantly perform a long series of physics operations, in conjunction with your eyes, to allow you to move your hand in a straight line through three dimensions. It seems effortless to you because you have perfected software in your brain for doing it. Same idea goes for why its not that malware is dumb for not being able to figure out the slanty word recognition test when you sign up for a new account on a siteits that your brain is super impressive for being able to.
On the other hand, multiplying big numbers or playing chess are new activities for biological creatures and we havent had any time to evolve a proficiency at them, so a computer doesnt need to work too hard to beat us. Think about itwhich would you rather do, build a program that could multiply big numbers or one that could understand the essence of a B well enough that you could show it a B in any one of thousands of unpredictable fonts or handwriting and it could instantly know it was a B?
One fun examplewhen you look at this, you and a computer both can figure out that its a rectangle with two distinct shades, alternating:
Tied so far. But if you pick up the black and reveal the whole image
you have no problem giving a full description of the various opaque and translucent cylinders, slats, and 3-D corners, but the computer would fail miserably. It would describe what it seesa variety of two-dimensional shapes in several different shadeswhich is actually whats there. Your brain is doing a ton of fancy shit to interpret the implied depth, shade-mixing, and room lighting the picture is trying to portray.[footnote2]Pinker, How the Mind Works, 36.[/footnote2] And looking at the picture below, a computer sees a two-dimensional white, black, and gray collage, while you easily see what it really isa photo of an entirely-black, 3-D rock:
Credit: Matthew Lloyd
And everything we just mentioned is still only taking in stagnant information and processing it. To be human-level intelligent, a computer would have to understand things like the difference between subtle facial expressions, the distinction between being pleased, relieved, content, satisfied, and glad, and why Braveheart was great but The Patriot was terrible.
So how do we get there?
First Key to Creating AGI: Increasing Computational Power
One thing that definitely needs to happen for AGI to be a possibility is an increase in the power of computer hardware. If an AI system is going to be as intelligent as the brain, itll need to equal the brains raw computing capacity.
One way to express this capacity is in the total calculations per second (cps) the brain could manage, and you could come to this number by figuring out the maximum cps of each structure in the brain and then adding them all together.
Ray Kurzweil came up with a shortcut by taking someones professional estimate for the cps of one structure and that structures weight compared to that of the whole brain and then multiplying proportionally to get an estimate for the total. Sounds a little iffy, but he did this a bunch of times with various professional estimates of different regions, and the total always arrived in the same ballparkaround 1016, or 10 quadrillion cps.
Currently, the worlds fastest supercomputer, Chinas Tianhe-2, has actually beaten that number, clocking in at about 34 quadrillion cps. But Tianhe-2 is also a dick, taking up 720 square meters of space, using 24 megawatts of power (the brain runs on just 20 watts), and costing $390 million to build. Not especially applicable to wide usage, or even most commercial or industrial usage yet.
Kurzweil suggests that we think about the state of computers by looking at how many cps you can buy for $1,000. When that number reaches human-level10 quadrillion cpsthen thatll mean AGI could become a very real part of life.
Moores Law is a historically-reliable rule that the worlds maximum computing power doubles approximately every two years, meaning computer hardware advancement, like general human advancement through history, grows exponentially. Looking at how this relates to Kurzweils cps/$1,000 metric, were currently at about 10 trillion cps/$1,000, right on pace with this graphs predicted trajectory:[footnote2]Kurzweil, The Singularity is Near, 118.[/footnote2]
So the worlds $1,000 computers are now beating the mouse brain and theyre at about a thousandth of human level. This doesnt sound like much until you remember that we were at about a trillionth of human level in 1985, a billionth in 1995, and a millionth in 2005. Being at a thousandth in 2015 puts us right on pace to get to an affordable computer by 2025 that rivals the power of the brain.
So on the hardware side, the raw power needed for AGI is technically available now, in China, and well be ready for affordable, widespread AGI-caliber hardware within 10 years. But raw computational power alone doesnt make a computer generally intelligentthe next question is, how do we bring human-level intelligence to all that power?
Second Key to Creating AGI: Making It Smart
This is the icky part. The truth is, no one really knows how to make it smartwere still debating how to make a computer human-level intelligent and capable of knowing what a dog and a weird-written B and a mediocre movie is. But there are a bunch of far-fetched strategies out there and at some point, one of them will work. Here are the three most common strategies I came across:
This is like scientists toiling over how that kid who sits next to them in class is so smart and keeps doing so well on the tests, and even though they keep studying diligently, they cant do nearly as well as that kid, and then they finally decide k fuck it Im just gonna copy that kids answers. It makes sensewere stumped trying to build a super-complex computer, and there happens to be a perfect prototype for one in each of our heads.
The science world is working hard on reverse engineering the brain to figure out how evolution made such a rad thingoptimistic estimates say we can do this by 2030. Once we do that, well know all the secrets of how the brain runs so powerfully and efficiently and we can draw inspiration from it and steal its innovations. One example of computer architecture that mimics the brain is the artificial neural network. It starts out as a network of transistor neurons, connected to each other with inputs and outputs, and it knows nothinglike an infant brain. The way it learns is it tries to do a task, say handwriting recognition, and at first, its neural firings and subsequent guesses at deciphering each letter will be completely random. But when its told it got something right, the transistor connections in the firing pathways that happened to create that answer are strengthened; when its told it was wrong, those pathways connections are weakened. After a lot of this trial and feedback, the network has, by itself, formed smart neural pathways and the machine has become optimized for the task. The brain learns a bit like this but in a more sophisticated way, and as we continue to study the brain, were discovering ingenious new ways to take advantage of neural circuitry.
More extreme plagiarism involves a strategy called whole brain emulation, where the goal is to slice a real brain into thin layers, scan each one, use software to assemble an accurate reconstructed 3-D model, and then implement the model on a powerful computer. Wed then have a computer officially capable of everything the brain is capable ofit would just need to learn and gather information. If engineers get really good, theyd be able to emulate a real brain with such exact accuracy that the brains full personality and memory would be intact once the brain architecture has been uploaded to a computer. If the brain belonged to Jim right before he passed away, the computer would now wake up as Jim (?), which would be a robust human-level AGI, and we could now work on turning Jim into an unimaginably smart ASI, which hed probably be really excited about.
How far are we from achieving whole brain emulation? Well so far, weve not yet just recently been able to emulate a 1mm-long flatworm brain, which consists of just 302 total neurons. The human brain contains 100 billion. If that makes it seem like a hopeless project, remember the power of exponential progressnow that weve conquered the tiny worm brain, an ant might happen before too long, followed by a mouse, and suddenly this will seem much more plausible.
So if we decide the smart kids test is too hard to copy, we can try to copy the way he studies for the tests instead.
Heres something we know. Building a computer as powerful as the brain is possibleour own brains evolution is proof. And if the brain is just too complex for us to emulate, we could try to emulate evolution instead. The fact is, even if we can emulate a brain, that might be like trying to build an airplane by copying a birds wing-flapping motionsoften, machines are best designed using a fresh, machine-oriented approach, not by mimicking biology exactly.
So how can we simulate evolution to build AGI? The method, called genetic algorithms, would work something like this: there would be a performance-and-evaluation process that would happen again and again (the same way biological creatures perform by living life and are evaluated by whether they manage to reproduce or not). A group of computers would try to do tasks, and the most successful ones would be bred with each other by having half of each of their programming merged together into a new computer. The less successful ones would be eliminated. Over many, many iterations, this natural selection process would produce better and better computers. The challenge would be creating an automated evaluation and breeding cycle so this evolution process could run on its own.
The downside of copying evolution is that evolution likes to take a billion years to do things and we want to do this in a few decades.
But we have a lot of advantages over evolution. First, evolution has no foresight and works randomlyit produces more unhelpful mutations than helpful ones, but we would control the process so it would only be driven by beneficial glitches and targeted tweaks. Secondly, evolution doesnt aim for anything, including intelligencesometimes an environment might even select against higher intelligence (since it uses a lot of energy). We, on the other hand, could specifically direct this evolutionary process toward increasing intelligence. Third, to select for intelligence, evolution has to innovate in a bunch of other ways to facilitate intelligencelike revamping the ways cells produce energywhen we can remove those extra burdens and use things like electricity. Its no doubt wed be much, much faster than evolutionbut its still not clear whether well be able to improve upon evolution enough to make this a viable strategy.
This is when scientists get desperate and try to program the test to take itself. But it might be the most promising method we have.
The idea is that wed build a computer whose two major skills would be doing research on AI and coding changes into itselfallowing it to not only learn but to improve its own architecture. Wed teach computers to be computer scientists so they could bootstrap their own development. And that would be their main jobfiguring out how to make themselves smarter. More on this later.
Rapid advancements in hardware and innovative experimentation with software are happening simultaneously, and AGI could creep up on us quickly and unexpectedly for two main reasons:
1) Exponential growth is intense and what seems like a snails pace of advancement can quickly race upwardsthis GIF illustrates this concept nicely:
2) When it comes to software, progress can seem slow, but then one epiphany can instantly change the rate of advancement (kind of like the way science, during the time humans thought the universe was geocentric, was having difficulty calculating how the universe worked, but then the discovery that it was heliocentric suddenly made everything much easier). Or, when it comes to something like a computer that improves itself, we might seem far away but actually be just one tweak of the system away from having it become 1,000 times more effective and zooming upward to human-level intelligence.
At some point, well have achieved AGIcomputers with human-level general intelligence. Just a bunch of people and computers living together in equality.
Oh actually not at all.
The thing is, AGI with an identical level of intelligence and computational capacity as a human would still have significant advantages over humans. Like:
AI, which will likely get to AGI by being programmed to self-improve, wouldnt see human-level intelligence as some important milestoneits only a relevant marker from our point of viewand wouldnt have any reason to stop at our level. And given the advantages over us that even human intelligence-equivalent AGI would have, its pretty obvious that it would only hit human intelligence for a brief instant before racing onwards to the realm of superior-to-human intelligence.
This may shock the shit out of us when it happens. The reason is that from our perspective, A) while the intelligence of different kinds of animals varies, the main characteristic were aware of about any animals intelligence is that its far lower than ours, and B) we view the smartest humans as WAY smarter than the dumbest humans. Kind of like this:
So as AI zooms upward in intelligence toward us, well see it as simply becoming smarter, for an animal. Then, when it hits the lowest capacity of humanityNick Bostrom uses the term the village idiotwell be like, Oh wow, its like a dumb human. Cute! The only thing is, in the grand spectrum of intelligence, all humans, from the village idiot to Einstein, are within a very small rangeso just after hitting village idiot level and being declared to be AGI, itll suddenly be smarter than Einstein and we wont know what hit us:
Gresham College: Prof. Yorick Wilks The State of AI: War,Ethics and Religion #3/3 Artificial Intelligence and Religion – stopthefud
Posted: May 15, 2020 at 8:07 am
About this series
Will you be murdered by AI? What if AI were conscious? And will a religion based on an AI god inevitably rise?
In his second series about the state of Artificial Intelligence, Professor Yorick Wilks will examine some of the tougher questions about ethics for AI in war zones, whether (and when) we should care about AI as we do about animals, and the impact AI could have on religion. Are we getting AI right?
About this lecture
This lecture addresses the potential links between AI and religious belief, which include the question of whether an artificial superintelligence, were one to arise, would be well-disposed towards us. Religious traditions historically assume that creations are well disposed to those who made them.
The lecture also looks at the recent US cults claiming to be ready to worship such a super-intelligence, if and when it emerges, as well as other futurist discourse on Transhumanism and its roots in 18th-century rationalism.
Professor Yorick Wilks
Yorick Wilks is Visiting Professor of Artificial Intelligence at Gresham College. He is also Professor of Artificial Intelligence at the University of Sheffield, a Senior Research Fellow at the Oxford Internet Institute, and a Senior Scientist at the Florida Institute for Human and Machine Cognition. Professor Wilks is especially interested in the fields of artificial intelligence and the computer processing of language, knowledge and belief. His current research focuses on the possibility of software agents having identifiable personalities.
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