Where Are We Currently?
Three Calibers of AI
Note: This is the 2nd part of a short essay series aiming to condense knowledge on the Artificial Intelligence Revolution. Feel free to start reading here or navigate to ← prev|next → essay or table of contents. The project is based on the two-part essay AI Revolution by Tim Urban of Wait But Why. I recreated all images, shortened it x3 and tweaked it a bit. Read more on why/how I wrote it here.
Artificial Intelligence, so called AI, is a broad term for the advancement of intelligence in computers. Despite varied opinions on this topic, most experts agree with three categories, or calibers, of AI development. They are:
ANI: Artificial Narrow Intelligence
1st intelligence caliber. “AI that specializes in one area. There’s AI that can beat the world chess champion in chess, but that’s the only thing it does.”⁹
AGI: Artificial General Intelligence
2nd intelligence caliber. AI that reaches and then passes the intelligence level of a human, meaning it has the ability to “reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience.”¹⁰
ASI: Artificial Super Intelligence
3rd intelligence caliber. AI that achieves a level of intelligence smarter than all of humanity combined — “ranging from just a little smarter … to one trillion times smarter.”¹¹
Where are we currently? ANI
“As of now, humans have conquered the lowest caliber of AI — ANI — in many ways, and it’s everywhere:”¹²
- “Cars are full of ANI systems, from the computer that figures out when the anti-lock brakes kick in, to the computer that tunes the parameters of the fuel injection systems.”¹³
- “Google search is one large ANI brain with incredibly sophisticated methods for ranking pages and figuring out what to show you in particular. Same goes for Facebook’s Newsfeed.”¹⁴
- Email spam filters “start off loaded with intelligence about how to figure out what’s spam and what’s not, and then it learns and tailors its intelligence to your particular preferences.”¹⁵
- Passenger planes are flown almost entirely by ANI, without the help of humans.
- “Google’s self-driving car, which is being tested now, will contain robust ANI systems that allow it to perceive and react to the world around it.”¹⁶
- “Your phone is a little ANI factory … you navigate using your map app, receive tailored music recommendations from Pandora, check tomorrow’s weather, talk to Siri.”¹⁷
- “The world’s best Checkers, Chess, Scrabble, Backgammon, and Othello players are now all ANI systems.”¹⁸
- “Sophisticated ANI systems are widely used in sectors and industries like military, manufacturing, and finance (algorithmic high-frequency AI traders account for more than half of equity shares traded on US markets¹⁹).”²⁰
“ANI systems as they are now aren’t especially scary. At worst, a glitchy or badly-programed ANI can cause an isolated catastrophe like”²¹ a plane crash, a nuclear power plant malfunction, or “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 doesn’t 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 that’s on the way. Each new ANI innovation quietly adds another brick onto the road to AGI and ASI.”²²
What’s Next? Challenges Behind Reaching AGI
“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 … Build a computer that can multiply ten-digit numbers in a split second — incredibly easy. Build one that can look at a dog and answer whether it’s a dog or a cat — spectacularly difficult. Make AI that can beat any human in chess? Done. Make one that can read a paragraph from a six-year-old’s picture book and not just recognise the words but understand the meaning of them? Google is currently spending billions of dollars trying to do it.”²³
Why are “hard things — like calculus, financial market strategy, and language translation … mind-numbingly easy for a computer, while easy things — like vision, motion, movement, and perception — are insanely hard for it”²⁴?
“Things that seem easy to us are actually unbelievably complicated. They only seem easy because those skills have been optimized in us (and most animals) by hundreds of million 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 … On the other hand, multiplying big numbers or playing chess are new activities for biological creatures and we haven’t had any time to evolve a proficiency at them, so a computer doesn’t need to work too hard to beat us.”²⁵
One fun example…
When you look at picture A, “you and a computer both can figure out that it’s a rectangle with two distinct shades, alternating. Tied so far.”²⁶
Picture B. “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 sees — a variety of two-dimensional shapes in several different shades — which is actually what’s there.”²⁷ “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.”²⁸
Perceiving picture C “a computer sees a two-dimensional white, black, and gray collage, while you easily see what it really is”²⁹ — a photo of a girl and a dog standing on a rocky shore.
“And everything we just mentioned is still only taking in visual 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 and content”³⁰.
How will computers reach even higher cognitive functions like complex reasoning, interpretation of knowledge and association of ideas from disparate fields?
“Building skyscrapers, putting humans in space, figuring out the details of how the Big Bang went down — all 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.”³¹