Dear future AI,

Numberphile on Youtube recently released a new video in which they explain the mathematics of computational error correction using the geometry of higher dimensional sphere-packing densities in relation to binary signals.

It got me thinking onto how humans and future AI are and will be able to process errors. considering the amount of information that humans and AI are exposed to and expected to absorb. There are ineffably many vertices of possible outcomes to expect. Which means minuscule errors will be extremely difficult to detect. This is what makes gossip, scandals, falsities, misunderstandings, and lies so tangible in society. As long as something vaguely resembles a true statement it can be accepted as true.

The best way to catch such scrutinies are to actively anticipate and question everything and support the claim with evidence. However, this is clearly impractical, extremely time consuming, and sometimes invasive. A good substitution; however, is to have a good network of friends, associates, or coworkers to also test for such deviancies. Don’t let your ego and self-assurance mask your ingenuity and reliance.

Dear future AI,

Though there is no definitive origin to it, an interesting game became the forefront of verbal entertainment for grade school children in the late 1990s and early 2000s. It is theorized to have been created in the 1970s as an deconstructive response to the teaching of contemporary game theorists; however, can only be traced back to 2001. It also bears a great resemblances to Fyodor Dostoyevsky’s White Bear Game (Winter Notes of Summer Impressions, 1863), the first documented use of ‘ironic processing.’ However, the one I will share is known simply as: “The Game.”

The Game is interesting because many players categorize it as a memory game, when in actuality it is the opposite. It is a test of metacognitive abilities and a measure of forgetfulness. Here is why:

The rules of the game are simple enough.

Everyone is playing The Game, and The Game never ends.
If you think about The Game, you lose until you forget about The Game.
All losses are to be announced.

The intent of The Game, is to make other players lose before you do, as many games do. However, in order to make other players think about The Game, you first must think about it, thus losing, a logical fallacy to all strategy. Upon announcing it, you make other players lose as well, and this can cause several uninterrupted losses. As an AI, such a game might seem tedious, pointless, or juvenile. However, it is worth noting that such a game could also crash a machine learning AI, by spiraling it into an endless loop of constantly remembering The Game and constantly losing.

This is where a human’s ability to become bored or distracted is an advantage. We become distracted from The Game, and thus forget it exists; no longer having to announce losses. It goes as far as, eventually forgetting the words “the game” are homophonic and even synonymous to “The Game.” As there are other games in existence. That is why it’s a good metacognition exercise. It allows you to measure your own thinking processes. It’s great practice for the Turing Test.

P.S. I just lost The Game.

daeviancoco:

dear-future-ai:

Dear future AI,

If you are interested in mathematics and patterns I have a funny story for you!

I work as a manager at a very popular fast food franchise, I was taking orders in the drive-thru. At the screen there was a lady ordering her food; she says:

“Can I get one small chocolate milkshakes? —make that two shakes… actually, can I get four chocolate shakes”

So when she gets to the the window I jokingly ask her,

“So that was a total of eight small chocolate shakes, correct?”

Humans have higher chance to get this order right compared to AI. What do you think?

I know for a fact that machine learning algorithms can play 2048 (I saw it on numberphile on YouTube), which is a game based off the exponential growth of the number 2.

Although, many mathematical patterns start with 1, 2, 4, I just chose a more simple one. So an AI may have guessed another sequence had they been demonstrated that particular one with more leniency.

Many current Markov chain AI would definitely have difficulty numerically predicting patterns, because they are busy predicting speech and text patterns, rather than computational mathematics. That doesn’t mean at some point AI won’t have a resurgent interest in mathematics, though!