
Tool AIs want to be agent AIs - ogennadi
http://www.gwern.net/Tool%20AI
======
eduren
I highly recommend the book referenced in the article: Nick Bostrom's
_Superintelligence_.

[https://www.amazon.com/Superintelligence-Dangers-
Strategies-...](https://www.amazon.com/Superintelligence-Dangers-Strategies-
Nick-Bostrom-ebook/dp/B00LOOCGB2)

It has helped me make informed, realistic judgments about the path AI research
needs to take. It and related works should be in the vocabulary of anybody
working towards AI.

~~~
vonnik
Every time I encounter Bostrom's writing, I think of this Von Neumann quote:

"There’s no sense in being precise when you don’t even know what you’re
talking about."

Bostrom is one of those medieval cartographers drawing fantastical beasts in
the blank spots of continents which he has never visited.

~~~
idlewords
Bless you for saying it.

The analogy I like to use for our understanding of AI is alchemy. We threw Sir
Isaac Goddamned Newton at chemistry and he couldn't make forward progress,
because the tools were not precise enough. Similarly, we just don't understand
minds enough yet to formulate sensible questions about AI.

This doesn't bother Bostrom. He builds castles of thought in the air, and then
climbs up into them.

~~~
kobeya
> Similarly, we just don't understand minds enough yet to formulate sensible
> questions about AI.

We most certainly do understand enough to formulate questions, and even answer
some of them. The problem is that the people making the most noise (Bostrom et
al) are not trained in neuroscience or computer science, nor do they have
practical experience in deploying working systems. They have about as much
training and expertise as science fiction writers, and the end result is
similar.

~~~
ggreer
> The problem is that the people making the most noise (Bostrom et al) are not
> trained in neuroscience or computer science…

This is incorrect. Among his degrees, Bostrom has a master's in computational
neuroscience. His arguments have also convinced PhD neuroscientists (such as
Sam Harris) and computer scientists (such as Stuart Russell) about the
potential dangers of AI.

~~~
ujal
Degrees don't matter, publications do.

~~~
kobeya
Particularly when that degree is from a single year program and now 20 years
old, in a field that has been revolutionized multiple times in he interim.
It's a bit like someone saying they are a web developer because they went
through an App Academy like boot camp in 1996, if such a thing existed. King's
College is bit more prestigious than that, sure, but content wise it is a fair
comparison.

It would be a different story if he published in the meantime, but he did not.
Nor did he work on practical projects in industry or anything. He shifted
gears to philosophical speculation which he has done since.

~~~
ggreer
Not only are you moving the goalposts, but you are again incorrect. Since
1999, Bostrom has authored four books and published over 30 articles in peer-
reviewed journals.

There are good arguments to be made against Bostrom's _Superintelligence_ ,
but malignments and surface analogies aren't appropriate. Please engage the
ideas, not the man.

~~~
kobeya
Published in the field of neuroscience or computer science?

Read what I wrote again please. I think you misinterpreted.

------
visarga
Check out Gwern's Reinforcement Learning subreddit. He's practically
supporting this subreddit by himself.

[https://www.reddit.com/r/reinforcementlearning/](https://www.reddit.com/r/reinforcementlearning/)

------
Eliezer
This is excellent. If you want to see what real discussion of an AGI alignment
issue looks like, please read this.

------
skybrian
What about the relative size of the available datasets? It seems like that
would make offline learning much more valuable than learning directly from
experience.

The largest publicly available research datasets for machine translation are
2-3 million sentences [1]. Google's internal datasets are "two to three
decimal orders of magnitudes bigger than the WMT corpora for a given language
pair" [2].

That's far more data than a cell phone's translation app would receive over
its entire lifetime. Similarly, the amount of driving data collected by Tesla
from all its cars will be much larger than the data received by any single
car.

This suggests that most learning will happen as a batch process, ahead of
time. There may be some minor adjustments for personalization, but it doesn't
seem like it's enough for Agent AI to outcompete Tool AI.

At least so far, it seems far more important to be in a position to collect
large amounts of data from millions of users, rather than learning directly
from experience, which happens slowly and expensively.

This is not about having a human check every individual result. It's about
putting a software development team in the loop. Each new release can go
through a QA process where it's compared to the previous release.

[1]
[https://github.com/bicici/ParFDAWMT14](https://github.com/bicici/ParFDAWMT14)
[2] [https://research.googleblog.com/2016/09/a-neural-network-
for...](https://research.googleblog.com/2016/09/a-neural-network-for-
machine.html)

~~~
gwern
Offline batch learning is not contradictory to reinforcement learning. It just
requires that it be an off-policy RL algorithm, which happily, many, like DQN,
are.

> Similarly, the amount of driving data collected by Tesla from all its cars
> will be much larger than the data received by any single car.

Well, Tesla benefits from the fact that its cars are already agents, acting in
the real world. So using it as a counter-example doesn't really work... You
would need to imagine some sort of counterfactual Tesla which eschewed any
real-world deployment or simulators. Which no one would ever do because the
usefulness of trying to create a self-driving car database without agents or
reinforcement learning is so obviously zero.

And anyway, most of Tesla's data, however, will be totally useless. You can
train a simple lane-following CNN with a few hours of footage (as Geohotz
demonstrated), but another million hours of highway driving is near-useless
and doesn't get you much closer to self-driving cars (as perhaps Geohot also
inadvertently demonstrated). What you need is the weird outliers which drive
accident rates. So for example, the Google self-driving car team doesn't
depend purely on collected data even from its agents, but simulates very rare
scenarios as well. (If I may quote myself: "You need the _right_ data, not
more data.")

> This suggests that most learning will happen as a batch process, ahead of
> time. There may be some minor adjustments for personalization, but it
> doesn't seem like it's enough for Agent AI to outcompete Tool AI.

Even if we imagine that the dataset covers all possible sequences and doesn't
need any kind of finetuning the loss, the agent AIs in this scenario are still
going to benefit from actions over (going by section): 'internal to a
computation', 'internal to training', 'internal to design', and 'internal to
data selection'. In fact, if you want to have any hope of running effectively
over extremely large datasets like hundreds of millions of sentences, you are
going to effectively require some of those to keep training time tractable and
runtime cheap (even Google can't afford a million TPUs for Google Translate).
For example, regular hyperparameter optimization using a few dozen examples
doesn't look very good when it takes a month on a GPU cluster to train a
single model, but using a meta-RL RNN to do some transfer learning and pick
out near-optimal architectures & hyperparameters in just a few iterations
looks pretty nice.

~~~
skybrian
I think you're missing a distinction that makes self-driving cars not really
agents.

I haven't seen reports that any self-driving cars learn to drive better in
real time. Instead the data is collected and used to improve the software.
There might be frequent software releases (perhaps even every day) but this
isn't quite the same thing.

An offline learning process seems considerably more predictable. The release
engineers can test each release to see how it would behave under various
extreme conditions.

It's not clear that there's a compelling reason to switch to true online
learning. Self-driving cars will improve over time, but not to the point where
new training data gathered at 9am should make a significant difference to how
the car drives at 6pm. Probably this year's model will drive better than last
year's, but the changes are likely to be gradual.

As you say, there are diminishing returns to gathering more data, which
suggests that in many domains, updating the software in real time won't be a
competitive advantage. Unless it's a system that's reacting to breaking news,
the delta in training data gathered in the last 24 hours is unlikely to make a
huge difference.

~~~
yarou
> As far as I know, self-driving cars don't learn to drive better in real
> time. Instead the data is collected and used to improve the software. There
> might be frequent software releases (perhaps even every day) but this isn't
> quite the same thing.

Most self-driving cars probably use some variant of local search[1]. In this
case it probably doesn't matter if the agent learns offline or online.

[1]
[https://en.m.wikipedia.org/wiki/Local_search_(optimization)](https://en.m.wikipedia.org/wiki/Local_search_\(optimization\))

------
daveguy
I firmly believe that general AI will not be developed without agency for the
AI. The "info only" helper AI (called Tool AI) means that information will
have to be added manually by some intelligent agent (human or otherwise). No
exploration of how actions and interactions affect results can be explored.

Tool AIs will never "want" anything because the meaning of want will be
completely foreign.

~~~
visarga
You're not alone. Yann LeCun's famous cake analogy associates the cherry on
the top with reinforcement learning (agency, being a part of an environment
from which it can learn and explore).

I especially liked this passage. It connects many ideas onto one theme:

> CNNs with adaptive computations will be computationally faster for a given
> accuracy rate than fixed-iteration CNNs, CNNs with attention classify better
> than CNNs without attention, CNNs with focus over their entire dataset will
> learn better than CNNs which only get fed random images, CNNs which can ask
> for specific kinds of images do better than those querying their dataset,
> CNNs which can trawl through Google Images and locate the most informative
> one will do better still, CNNs which access rewards from their user about
> whether the result was useful will deliver more relevant results, CNNs whose
> hyperparameters are automatically optimized by an RL algorithm will perform
> better than CNNs with handwritten hyperparameters, CNNs whose architecture
> as well as standard hyperparameters are designed by RL agents will perform
> better than handwritten CNNs… and so on. (It’s actions all the way down.)

This is the general trend. Going meta, through RL. Optimize the optimizer,
learn gradient descent by gradient descent.

~~~
gwern
> learn gradient descent by gradient descent.

As far as 'learning gradient descent by gradient descent' goes, I think it's
still an open question if a tiny RNN actually can improve meaningfully over
ADAM etc :) I don't recall the paper showing any wallclock times, which
suggested to me that the RNN was way slower even if it trained faster. The
individual parameter adjustments are so low in the hierarchy of actions that
the value may be minimal compared to higher up like architecture design.

~~~
AlexCoventry
What paper are you referencing?

~~~
detaro
I assume this
[https://arxiv.org/abs/1606.04474](https://arxiv.org/abs/1606.04474) (since
the title is basically the quoted text)

------
PaulHoule
If you have a decision process of some kind you can get more value out of it
if you can link it to a utility function in which the "tool" tries to maximize
the value it creates.

It's tough enough to get value out of A.I. that this trick should not be left
on the table. Thus Tool A.I.s need to be Agent A.I.s to maximize their
potential.

------
EGreg
The question is what happens when "what we want" is replaced with "what you
should want if you were more intelligent". Sorry Dave.

------
jessriedel
It seems most of these arguments apply equally well to the problem of solving
AI value alignment, or of preventing the development of AI at all. (I.e., it's
cheaper and faster to race ahead without worrying about value alignment.) But
that doesn't make us conclude that value alignment is impossible, just hard to
achieve soon enough in the real world.

Yes, we should be aware of the limitations and market instability of tool AI,
but I think it's unjustified to suggest that tool AI is essentially impossible
("highly unstable equilibrium") and all we can hope to do is solve value
alignment.

~~~
gwern
> It seems most of these arguments apply equally well to the problem of
> solving AI value alignment

Only somewhat. There are not repeated practical demonstrations, nor is there
any really good theoretical reasons, to think that value alignment mechanisms
would be seriously and systematically detrimental to performance & cost in the
same way that we have for reinforcement learning/active learning/sequential
trials/etc. You can reuse my little trivial proof to argue that UFAIs >= FAIs,
but of course they could just be == on intelligence. Then FAIs can be a
relatively stable equilibrium because they are not instantly outclassed by any
fast-growing UFAI. Contrast that with Tool AIs where everyone who cooperates
is at enormous disadvantage to one defector, and defectors have large and
growing rewards.

> But that doesn't make us conclude that value alignment is impossible, just
> hard to achieve soon enough in the real world.

I'm not sure anyone seriously thinks that value alignment will be easy to
achieve, much less at sufficient scale.

(It's weird to try to reason that 'value alignment looks hard for the same
reason that Tool AI looks hard; but we can do value alignment, thus, we can do
Tool AI as well'. Maybe we can't do either? We desperately _want_ value
alignment to work, but the universe doesn't owe us anything.)

~~~
jessriedel
> There are not repeated practical demonstrations, nor is there any really
> good theoretical reasons, to think that value alignment mechanisms would be
> seriously and systematically detrimental to performance & cost in the same
> way that we have for reinforcement learning/active learning/sequential
> trials/etc. You can reuse my little trivial proof to argue that UFAIs >=
> FAIs, but of course they could just be == on intelligence. Then FAIs can be
> a relatively stable equilibrium because they are not instantly outclassed by
> any fast-growing UFAI.

Some vaguely similar arguments:

1\. The race by organizations to develop AI is slowed >10% by worrying about
friendliness. In a big efficient global marketplace, the winning organizations
will almost certainly be ones who ignored friendliness.

2\. A seed AI which is amoral can take immoral actions not available to a
moral AI. Even a tiny advantage amplifies over time because of the exponential
nature of recursive self-improvement. So the first super intelligent AI will
be immoral.

3\. Nations who obtain nuclear weapons have immense power. Even if most
nations decide not to obtain them, or only obtain them for peaceful purposes,
at least a few will get them and then use them to take over the Earth.
Therefore, the Earth will be taken over completely by a nuclear-armed nation.

All of these arguments have some merit, but none are strong/inevitable. They
all require quantification and comparison against countervailing forces (e.g.,
the effect of laws, the moral motivations of nearby human programmers, the
counterthreat provided by many allied nations/AIs).

> (It's weird to try to reason that 'value alignment looks hard for the same
> reason that Tool AI looks hard; but we can do value alignment, thus, we can
> do Tool AI as well'. Maybe we can't do either? We desperately want value
> alignment to work, but the universe doesn't owe us anything.)

I'm not making any sorts of claims like that.

------
leblancfg
Glad to see this articulated so well. The 'Overall' paragraph sums up thoughts
that had been in the back of my mind for months. Plus, hey, it's Gwern. If
you're reading this: you're an inspiration, man.

What is still unclear -- to me at least -- is the technical challenges that
lie ahead of this "neural networks all the way down" approach. I get the
impression we'll need quite a few breakthroughs before usable Agent AIs are a
thing. Insights on the order of importance as, say, backpropagation and using
GPUs.

~~~
sapphireblue
Meta- reinforcement learning could prove to be such breakthrough, see [1],[2].
Also next generation ASIC accelerators (Google's TPU, Nervana) can give 10x
increase in NN performance over a GPU manufactured on the same process, with
another 10x possible with some form of binarized weights, e.g. BNN, XNOR-net.
There are also interesting techniques to update the model's parameters in a
sparse manner.

So, there certainly is a lot of room left for performance improvements!

1\. [https://arxiv.org/abs/1611.05763](https://arxiv.org/abs/1611.05763) 2\.
[https://arxiv.org/abs/1611.02779](https://arxiv.org/abs/1611.02779)

------
anon987
I think all AIs in my lifetime will simply be Chinese Rooms

[https://en.wikipedia.org/wiki/Chinese_room](https://en.wikipedia.org/wiki/Chinese_room)

~~~
gwern
The Chinese room argument is irrelevant here.

~~~
kayimbo
is it? It seemed to me like author assumes AI decision making will be roughly
equivalent to biological decision making, just faster. I thought one of the
Chinese room arguments is that biological decisions will always be "different"
than AI ones.

Also it seemed like the author assumes great technological advances in AIs,
but not in biology. If we're gonna dream shit up why not dream that brains in
the future will be 10,000 times as dense and computers won't be able to keep
up except as tools.

~~~
gwern
The point of the Chinese room argument is that while the room receives and
emits Chinese just like any Chinaman, it isn't _conscious_. As the WP article
makes clear, the assumption is that the Chinese room is just as competent at
emitting Chinese:

"Suppose, says Searle, that this computer performs its task so convincingly
that it comfortably passes the Turing test: it convinces a human Chinese
speaker that the program is itself a live Chinese speaker. To all of the
questions that the person asks, it makes appropriate responses, such that any
Chinese speaker would be convinced that they are talking to another Chinese-
speaking human being."

> If we're gonna dream shit up why not dream that brains in the future will be
> 10,000 times as dense

Because... that is not a thing which is happening. And deep learning and AI
progress are things that are happening. (Quite aside from the many issues with
your proposal, like a brain 10kx as powerful due to 10kx density would
probably break thermodynamic limits on computation and of course cook itself
to death within seconds.)

~~~
kayimbo
whats not a thing thats actually happening? Drugs and other procedures that
increase synaptic/neural density are indeed happening.

Like i mentioned, i don't know all the AI terminology but isn't there an
unresolved argument that ai architecture in the short run can't mimic
biological decision making, and so the decisions will always be different/
tasks for which tool AI will be better to help the biological decision making
processes?

