
What the History of Math Can Teach Us About the Future of AI - jkuria
https://blogs.scientificamerican.com/observations/what-the-history-of-math-can-teach-us-about-the-future-of-ai/
======
felizuno
As the founder of the patent troll Intellectual Ventures the author (Nathan
Myhrvold) has hurt our community deeply and I'm surprised how willing we are
to give him a platform.

~~~
partycoder
Because we already do give a platform to patent trolls.

"The new" Microsoft trolls Android manufacturers and makes ~$1 billion from
it. Then, they have the nerve to say "Microsoft <3 Linux".

And they are still playing the embrace, extend and extinguish game. For
example, according to Microsoft, you should stop using R CRAN and move to MRAN
instead. [https://mran.microsoft.com/](https://mran.microsoft.com/)

btw, I don't care if I lose karma because of this, I have karma to spend.

~~~
sharemywin
Android is so open to begin with. Let's face it anything that has a stock
market symbol or plans to be a stock market symbol or be bought by a stock
market symbol is a cannibalistic partner.

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gwern
> Theorists have proved that some mathematical problems are actually so
> complicated that they will always be challenging or even impossible for
> computers to solve. So at least for now, people who can push forward the
> boundary of computationally hard problems need never fear for lack of work.

That applies equally well to humans. Consider the complexity class of Go.

> Meanwhile, many of the tasks that seem most basic to us humans—like running
> over rough terrain or interpreting body language—are all but impossible for
> the machines of today and the foreseeable future.

Wow no.

~~~
Izkata
> > Meanwhile, many of the tasks that seem most basic to us humans—like
> running over rough terrain or interpreting body language—are all but
> impossible for the machines of today and the foreseeable future.

> Wow no.

...wow indeed. No research at all. Often I just read the comments here and
skip the article, but that made me go and look to be sure.

For anyone somehow still unaware (BigDog creepiness factor was nearly viral
back when I was in college around 2008), BigDog has been handling rough
terrain for over a decade:
[https://www.youtube.com/watch?v=W1czBcnX1Ww](https://www.youtube.com/watch?v=W1czBcnX1Ww)

Here's BigDog getting abused to see what it can recover from (including being
kicked in the side):
[https://www.youtube.com/watch?v=4PaTWufUqqU](https://www.youtube.com/watch?v=4PaTWufUqqU)

Here's a running cheetah robot jumping over unexpected obstacles (2015):
[https://www.youtube.com/watch?v=_luhn7TLfWU](https://www.youtube.com/watch?v=_luhn7TLfWU)

And here's a two-legged robot walking around outside (2017):
[https://www.youtube.com/watch?v=Is4JZqhAy-M](https://www.youtube.com/watch?v=Is4JZqhAy-M)

~~~
Animats
My "running on rough terrain" video from 1995.[1] On non-flat surfaces,
traction control dominates the problem.[2][3] This was before Boston Dynamics.
(There's much more that could be done in this area, but there's no market. BD
still doesn't do speed changes fast. Their machines start by walking, running
or trotting in place and then extend the gait. Humans start by falling
forward, for a faster start, and go far off vertical for fast direction
changes.)

As for interpreting body language, here's the code on Github.[4]

[1] [https://www.youtube.com/watch?v=kc5n0iTw-
NU](https://www.youtube.com/watch?v=kc5n0iTw-NU) [2]
[http://animats.com/papers/leggedrun/leggedrun.html](http://animats.com/papers/leggedrun/leggedrun.html)
[3]
[http://animats.com/papers/articulated/articulated.html](http://animats.com/papers/articulated/articulated.html)
[4] [https://github.com/shahqaan/kinect-body-language-
analysis](https://github.com/shahqaan/kinect-body-language-analysis)

------
geoalchimista
But the article says nothing about _the history of math_. Whatever technique
NASA used to do calculations in the 1960s, it was an _engineering_ concern not
a math concern. And I doubt if it has any relevance to the progress of AI
today.

------
netjiro
The important point to be made is that the "simpler" tasks will be automated
early, pushing human required labour to generally be smarter and better
educated.

There are only so many people for any required value of "smart". The
historical human computers in the article required less education and base
intelligence than the mathematicians that are employed today.*

Sooner or later a significant segment of the population will simply not be
able to train to most tasks that still require human labour. And slowly (or
quickly) that bar will rise. When I hire and train people I simply need a
certain baseline mental capacity, otherwise they will never get good enough to
keep, no matter how long they train.

*) Note though that this does not mean they necessarily were less intelligent than the modern work force.

~~~
sharemywin
I kind of disagree. I think the pace of "smarter" is progressing faster and
cheaper then "simple tasks"

~~~
netjiro
Well made point, and I could be wrong. I assume you refer to the automation
inroads into accounting, legal, organisation tracking (lower management) etc.
I personally see those as specific low hanging fruit since they are highly
rule based.

But soon we will reach strong automation on diverse simple manual labour,
transportation, stocking/handling, etc. Even if automation just manages to
handle 90% of everyday tasks it will crash enormous employment numbers, far
beyond what we see today in accounting, legal, etc.

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titzer
FTA: "Meanwhile, many of the tasks that seem most basic to us humans—like
running over rough terrain or interpreting body language—are all but
impossible for the machines of today and the foreseeable future."

[https://www.zdnet.com/article/boston-dynamics-set-to-sell-
it...](https://www.zdnet.com/article/boston-dynamics-set-to-sell-its-viral-
hit-robot-dog/)

------
dontreact
This article gives very little evidence to support the claim that AI will
never be capable of doing everything a human can do. On the other side Of the
argument I’m still very convinced by the fact that our brains are physical
things doing some sort of computation, so computers will one day be able to
achieve anything that our brain computation can achieve.

~~~
ankurdhama
So if the brain is just doing some sort of computation then do you also agree
with the statement that any flow of fluid (river etc) is just solving fluid
dynamics differential equations?

~~~
red75prime
Flow of fluid is governed by physical laws, for which Navier–Stokes is an
approximation. Brain is governed by physical laws too, of course. But its
structure facilitates processes which can be described as information
processing.

If water fails to solve Navier-Stokes, we change the equations, if a human
fails at reasoning, we correct the human.

~~~
ankurdhama
My point was that we use mathematics to model physical phenomenon and by
definition every model is an approximation of the phenomenon which means that
you cannot say that the phenomenon is actually doing what the model says.
Instead we say that the model is useful only to answer certain set of
questions to certain degree of accuracy.

~~~
red75prime
If a brain isn't doing what formal logic says, then it's doing it wrong.
Sometimes models are more important.

------
soziawa
> Meanwhile, many of the tasks that seem most basic to us humans—like running
> over rough terrain or interpreting body language—are all but impossible for
> the machines of today and the foreseeable future.

I don't think the author ever ran over rough terrain, that's all but a basic
task. And for the interpreting body language, that fails as soon as you get to
a different culture or just to a different animal. The author is vastly
overestimating human capabilities.

------
salty_biscuits
I think two things go on with automation that are good and bad

1) things get cheaper and markets tend to be elastic so demand goes up. Good
for whole economy 2) particular jobs disappear completely, very bad for
particular sections of the community for a while.

It is sort of inevitable though.

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hyperpallium
Apart from no overdose danger, demand for computation is economically like
drug addicts'. So vendors call their clientele _users_.

> It turns out that human intelligence is not just one trick or technique — it
> is many.

Disagree. Though humans have many varied talents, some inherited from mammals
like walking and seeing, some developed culturally and practiced like go and
chess, I think strong intelligence is just _one trick_. It might need quite a
lot of background processing and memory in order to do anything useful or even
vaguely "intelligent", but the key trick itself might not need much processing
power, and could be quite simple.

> people who can push forward the boundary of computationally hard problems
> need never fear for lack of work

So, full employment!

------
kuwze
> interpreting body language

[https://arxiv.org/abs/1802.05521](https://arxiv.org/abs/1802.05521)

~~~
stepvhen
this paper is about lip reading, which is indeed a difficult task (i cant do
it well, to be honest) and worthy of study. however, lip reading is not body
language. lip reading maps movements to words in a natural language.
recognizing body language requires knowing e.g. that crossing arms maps to the
idea of closed-ness and thus that the interlocutor is closing themselves off
from the conversation to some degree. or maybe the interlocutor has very long
arms and has never been able to know what to do with them in a conversation or
something.

~~~
PeterisP
Multimodality isn't really my field, but there's also a lot of research on
emotion detection. E.g.
[https://arxiv.org/pdf/1801.07481.pdf](https://arxiv.org/pdf/1801.07481.pdf)
is a recent survey about commonly used methods that I found by quick googling.

We definitely _can_ combine things like detecting crossed arms (and knowing
that it's correlated with closedness) with emotion and stress signs in your
voice, sentiment mapping of the words you say, micro-movements and your pulse
rate (that a machine can detect from video if it's sufficiently good) and
various other things to infer your likely emotional state.

The trouble is that in-depth analysis requires excessive external context and
a shared worldview - i.e. "being of the same tribe" and knowing how a
particular real world event " _should_ " make one feel (and why), which is
pretty much a general AI problem; but purely reading what the body language of
this moment is telling about your emotions is a hard task but somewhat
solvable even right now.

