Ask HN: What's the next big advance in AI you're looking forward to most? - crypticlizard
======
aabajian
I'm a medical resident. I see a patient in clinic, do a history and physical,
sit down at a computer, and write a note describing _what I just did._ That's
medical documentation, and it's a burdensome, growing problem.

The solution would be some combination of video or audio that records the
clinical encounter and automatically generates a note based on what was
discussed and performed. It falls under Paul Graham's "Schlep Task"
([http://www.paulgraham.com/schlep.html);](http://www.paulgraham.com/schlep.html\);)
you'd have to work with individual clinicians, get their (and the patient's!)
approval to record the encounter, record it from multiple angles, build tech
into dumb devices (e.g. stethoscope, Dopplers for pulses, O2 saturations, and
somehow use machine learning to integrate all that sensor data into a plain-
text note.

It's probably the #1 problem from a provider's viewpoint right now, especially
on the primary care side. Any individual provider will see 10-20 patients per
day and write just as many notes.

Edit: I'd add that you'd _also_ have to have access to existing notes because
progress notes typically summarize the patient's prior visits.

~~~
loxias
What an interesting idea. Would the final output be raw text, a sort of
English summary of what was said?

Or, to be useful, would the final output be structured data? (example: pre-
filling in a form with set number of fields and data types).

~~~
KingPrad
Most clinical documentation in EHRs is template based with lots of boilerplate
into which key words and phrases are dropped. And some template sections are
free text, enabling highly customizable final documentation.

------
pmontra
It runs on my laptop and phone and doesn't phone home. Example: I download a
trained network, it runs locally (maybe on some special purpose hw), discovers
remote resources (example train, metro, bus timetables) and tells me the
shortest route to destination without any company knowing where I'm going to.

This is a silly example because we don't need an AI for that but it's late
here and I should've given the idea. No centralized architecture, no spying,
no tracking. I'll pay for that.

~~~
nostrademons
There's a research paper published recently (ironically, by Google) on this
topic, which additionally describes how you could improve the model locally on
the device _without revealing the training data to the model 's owner_.

[https://research.googleblog.com/2017/04/federated-
learning-c...](https://research.googleblog.com/2017/04/federated-learning-
collaborative.html)

Previous HN discussion:
[https://news.ycombinator.com/item?id=14055655](https://news.ycombinator.com/item?id=14055655)

IMHO this is gonna be huge, but probably not gonna be huge for a decade. The
technical challenges seem large and it'll take a lot of experimentation by
entrepreneurs before they find a use-case where people are not currently
willing to use some big corp's solution.

~~~
lostmsu
I listened to a talk about this, and it seemed, that you can recover a lot of
data by looking at the "diffs". You could probably even train autoencoder-like
architecture to decode these deltas for you.

~~~
FlyingLawnmower
Very true, the paper calls out that there are no formal privacy guarantees on
this style of learning. The diffs that are sent ostensibly provide more
privacy than just the raw data, but it's hard to quantify how much "more"
privacy that really is, and where it applies/doesn't apply. I do applaud the
step in the right direction, however.

------
z3phyr
Currently, we are focusing on very narrow field of Artificial Intelligence.
Not that it is a bad thing, but we should not call it "AI". We are mostly
solving the problem of Data Analysis at Scale, which again, for businesses is
the most logical thing to do. I do not doubt that in the future, data analysis
will be an important jigsaw part in a true AI system.

Currently, I look forward to/want to see more research/movement in other
ideas/fields/methods which peep into AGI. Maybe Probabilistic (Quantum? or
Neuromorphic?) Computing? Maybe Artificial Life? Maybe Cognitive
Architectures? Recap into Symbolic?

PS: This is not a popular opinion, but I do want to share it with fellow
HNers. We do most of the computing to solve the hard problems in our society
and trade, and that is surely very noble. Having said that, When I was growing
up, I used to see/feel a lot of developments in Computers happened for the
sake of Computing. It felt like hacking was all together different field;
Linux, the all the Free Software movement, Windows NT, Doom, Quake, Huge
advances in Compilers, the whole culture around it etc.

Today I mostly see computing that works to advance and solve real world
business problems like better advertisements or helping with other noble
fields like astrophysics and genetics. I consider it as an advancement of our
tech society, but as immature as I am, I miss that time.

Anyway, I would love to see AI for the sake of AI.

------
petra
AI that looks at all the medical research in the world, and helps people get
the most accurate medical diagnosis and treatment.

Ah, and at the same time, why not create machines that
automatically,creatively and cheaply do biomedical r&d ? Or AI that
accelerates innovation around technologies that has an health impact ?

~~~
breitling
Isn't IBM's Watson trying to do the same

~~~
eat_veggies
And it's failing at it. Hard.

~~~
Density
Source?

------
mobileexpert
I want to see much better human in the loop systems in general. A simple
example is systems that will allow users to tweak the output of
creative/generative systems. E.g. Neural style transfer methods that allow the
user to easily adjust the output with human centric feedback. I want to say
"good start, but make the background more like Caravaggio would than it is
now, and lighten up the foreground to be more playful" and have the algorithm
adjust the output accordingly.

We aren't impossibly far off from this, but not that close yet either.

------
bhauer
As with everything in computing, what I want is more capability that runs on
my own devices—my own compute servers, my workstation, my laptop, or my mobile
device. Every application or service that has to interact with third-party
servers ("the cloud") to provide its functionality is, to my mind, defective
or at least deficient of the ideal.

AI is no exception. I have swarms of CPU cores in my life, the totality of
which could easily accomplish all of the "AI" functions of any value that I've
observed in my life and far more. Today's AI that works for me (rather than
against me) provides things like voice recognition, calendar management,
reminders, to-do list management, and route planning. None of these functions
requires even a small fraction of my personal CPU core armada, which are idle
for 99% of their clock cycles.

Today's all-too-popular refrain of leveraging the cloud to provide sufficient
compute capacity for these tasks is disingenuous and is too often accepted as
truth when it's just cover for data exfiltration.

~~~
p1esk
What does this have to do with the "next big advance in AI"?

~~~
TeMPOraL
Everything. But to rephrase GP's point in terms of the main question: the next
big advance in AI they - and I - are looking for is _AI being local_ , and not
in the cloud.

~~~
xaedes
I too am longing for local AI. That way the AIs I interact with can truly
begin to learn. From me. For me. Not the same bot learned from all the
aggregated Big Data for everyone. Personalized local AI. Also: Making AI local
opens the doors for privacy protection once again, I hope... I want my bot to
protect my data, not feed it to the big coorperations

------
bem94
I can't wait for the people building and deploying AI / Machine Learning tools
on a large scale to become aware of the broad societal implications of what
they are helping to do. Or, if they are, impress that knowledge loudly and
clearly onto their peers.

Not change, or stop nessesarily. Just be aware. This is going to be one of the
enablers for the biggest "behind the scenes" changes in how we work as a
society in our generation. As Devs, we should take some responsibility for how
we act in that regard. I don't see enough of that attitude at the moment.

------
komaromy
Maybe not the next big advance, but I'll be truly impressed when an AI system
with no game-specific prior knowledge is able to complete a game of Pokémon
Yellow within a reasonable amount of game time.

It requires:

\- Natural language understanding (choosing the right dialog options)

\- Interpretation of visual cues (navigating terrain and buildings)

\- Hierarchical planning (training certain Pokémon to relatively high levels
rather than a scattershot approach)

\- Puzzle solving (for gym access)

and quite a bit more.

~~~
Eridrus
Technically many of these are not really necessary IMO. The path that is most
likely result in a solution to this problem is some scaled up version of Deep
Reinforcement Learning (with a novelty reward) without any explicit natural
language understanding; though planning will probably be involved in scaling
it up and understanding visual cues is already trivial.

I think when thinking about these tasks, the right question to ask is "what's
the dumbest way this could be solved?" rather than "what high level knowledge
would I use to solve this?", otherwise you will be disappointed when the task
is solved but the methods do not look what you would like them to.

~~~
AstralStorm
You would be surprised how bad actual AI is at games. All kinds of, but
especially ones where the goal is far away and you don't readily know you're
succeeding. (Definition of a sparse reward game.)

Part of promising system is providing more understanding of output instead of
being blind and deaf, but indeed the main decision structure has to handle it
at all.

~~~
Eridrus
I think the Intrinsic Motivation is pretty important to solve this, and
DeepMind has shown it to work well for games, since it makes the reward far
less sparse, in a way that is very similar to our own way of playing games -
which IMO is somewhat important since we are the intended consumers.

Maybe making generic novelty metrics will turn out to be harder than anything
else anyway, but it has some intuitive appeal.

So I guess we're on the same page, we do need to understand the game output,
but IMO understanding it well enough to know when things are novel &
interesting vs when things are not is probably enough to get quite far.

------
ccvannorman
I want robust, packaged setups (docker, AWS, whatever) to be cheap/free,
accessible, and work out of the box with no end-user setup.

I am getting into DL myself and very excited about the potential, but I have
spent literally 5 hours on setup (python and DL-specific AWS Ubunut instance)
and have gotten exactly nowhere. Version conflicts, iPython not working in
venv, dependency mismatches where many people built demos I want to use on
python 2.7 that don't work with my 3.5 setup, can't get matplotlib to display
graphs over X11/remote ssh, etc, etc, etc. So frustrating.

I want programmers to be able to one-button setup a DL machine so they can
start tinkering and not wasting time on setup/dependencies/bugs.

~~~
saip
DevOps is indeed a huge bottleneck in deep learning. Provisioning machines,
installing drivers and packages and managing their dependency hell distracts
focus from the core deep learning. At FloydHub (I'm a co-founder), we're
building a zero-setup deep learning platform.

Spinning up a Jupyter notebook with Pytorch 0.2 is as simple as `floyd run
--env pytorch-0.2 --mode jupyter`. All the steps you mention in your comment
are automated.

DevOps hassles is, of course, just the first of many hurdles to doing
effective deep learning. Experiment management, version control,
reproducibility, sharing & collaboration, etc. are also other important
problems.

------
crypticlizard
Personally I'd love to talk to a bot that's mastered and memorized many books
and all of philosophy and science and is also researching philosophy. Imagine
getting his/her/their opinion on all the subtle questions life throws at you.

~~~
nemo1618
An "AI tutor," of any sort, would be incredible. One-on-one instruction,
directly from expert to novice, is leaps and bounds ahead of every other way
we commonly learn. It can be so frustrating to research a topic online and
find that all the resources are lacking in some way.

------
graycat
With the research directions and methodology that dominate AI now, I don't
look forward to anything significantly new or better.

To me, the core technology of my startup looks better than anything I've seen
in AI, but my technology is from some of my original research in applied math
based on some advanced pure/applied math prerequisites. I'm not calling my
work AI, but maybe some AI people would.

E.g., my work does well with the _meaning_ of content. Just how my applied
math does that has nothing to do with anything I've seen in current AI.

My view is that the current directions of AI are essentially out of gas --
there will continue to be new applications of the existing techniques, but new
techniques are not promising.

IMHO, for new techniques for AI we need to do much better with (A) applied
math and/or (B) implementations of relatively direct borrowing from natural
intelligence, e.g., at the level of a kitty cat and working up from there.

E.g., for the math, stochastic optimal control can look brilliant, even
prescient, and has had the basics on the shelves of the research libraries for
decades.

------
TeMPOraL
My dream: AI applied to _scientific research_.

For years now, if not decades, we've been creating science - as measured in
publications - _much_ faster than any human being, or group of humans, can
keep up with. Granted, lots of those papers are probably bogus, but then you
don't know which is which until you actually sit down and read it...

I believe we're missing _tons_ of insights and discoveries that we have all
the necessary components for making, and it would only take a smart person to
read the _right_ three papers to connect the dots. Alas, chances that any
human will do that are fast approaching zero. I think the only way to tackle
this is to employ computation.

What I would love to see, therefore, is an AI system that would a) filter out
bogus/bullshit papers and mark shoddy research that probably needs to be
redone, and b) mine the remaining ones for connections, correlations,
discovering which research supports which and which research contradicts each
other.

~~~
AstralStorm
We're very, very far from a good discovery AI. Currently world is at a stage
of "not get trapped in a local minimum when optimizing known decisions", which
is very far from actually generating decisions. Latest research in sparse
reward games is a good step forward, as research is generally one.

------
urlwolf
ML that helps me learn faster as a human. Let's be honest, watching videos
online is suboptimal. The middle-ages method of listening to a professor in a
large room is suboptimal.

Can you personalize what I learn? Can you find the exact best explanation for
my current level by looking at my facial expression?

------
blackbear_
Not needing millions of examples and being resilient to adversarial examples

------
Entangled
AI-NET. Specific AI that can communicate with other specialized AIs using a
simple protocol to solve specific problems in their field with the help of
others.

AIs don't need to know everything but they must know where everything is and
must know how to get that info. Sort of a DNS for intelligent repos and APIs
to access them. Wolphram Alpha would be one but we need more, like medicine
silos, agriculture silos, wikipedia, facebook, etc should have their own silos
with AI interfaces.

Then Google AI would help us search for silos of interest.

------
stillsut
An AI that can watch videos:

\- automated security guard, especially in sensitive areas like public
restrooms

\- watching and editing hours of stock footage for a good summary, or the
highlights

\- automated refereeing in sports

------
skierscott
I'm excited for self-driving cars and ML/AI tools that help professionals
(e.g., drug discovery, treatment).

The Keras lead developer wrote a post called "The future of deep learning"[1].
The podcast Partially Derivative has a good summary of it[2].

On the implementation side, I'm looking forward to distribution. Computers are
all around us, sitting by idly – how can we put them to use? How can we make
them secure? We're starting to see integration of ML models into mobile
systems (with Apple's .mlmodel and ARKit).

[1]:[https://blog.keras.io/the-future-of-deep-
learning.html](https://blog.keras.io/the-future-of-deep-learning.html)

[2]:[http://partiallyderivative.com/podcast/2017/07/25/the_future...](http://partiallyderivative.com/podcast/2017/07/25/the_future_of_deep_learning)

[3]:[http://papers.nips.cc/paper/4390-hogwild-a-lock-free-
approac...](http://papers.nips.cc/paper/4390-hogwild-a-lock-free-approach-to-
parallelizing-stochastic-gradient-descent)

------
larrydag
A conversational bot. One where you can not determine if you are talking to a
human or not. This could disrupt the huge call center business.

~~~
AstralStorm
Come on, they're using a lot of automation already. Do you really want more
failure on top of that?

------
kastnerkyle
Stronger incorporation of hard constraints / rule or program learning in
models for control and sequential decision making.

~~~
p1esk
Can you give an example?

~~~
kastnerkyle
[https://arxiv.org/abs/1703.07469](https://arxiv.org/abs/1703.07469)

------
mindhash
\- Dumbing down the implementation ... Most tools like tf, keras, pytorch are
heading that way .. but still there is a bit of expectation from engineer to
understand insides.. the future of AI depends on ability of every engineer to
use the techniques without the learning curve that is right now.

Please don't suggest - use of APIs they ain't cost effective and the future
lies in ability to tweak things on your own.

\- Also availability of DL libraries across different languages and stacks.
Yes there are methods but they need an extra effort to get working. At this
moment to learn AI you first need to either learn python, R or matlab.

The future of AI lies in its applications. And it can only happen through
experimentation. If you come across a possible application, it shouldn't take
you an year to really start experimenting.

------
d--b
AI toolbox for manipulating real life objects. Can't wait to make a robot that
slices tomatoes for me!

------
ltr_
Given the same algorithm implemented in any languages, the AI should be able
to compile(translate) every program to the same highly optimized machine code
or at least the same performance, learning only from sources of other programs
and their resulting compiled code.

~~~
AstralStorm
And what you would do with the result? It is worthless to just run it.

You're competing against optimizing compilers there.

------
grizzles
I'm looking forward to seeing the movement of software from our computers to
the third dimension in our daily lives, especially with robots in the
workplace. Computer Vision used to be the limiting factor, and now it's not.
Especially for things like science (eg. opentrons), the concept of brute force
and massively parallel experimentation & data collection will be a huge game
changer for discovery. There are areas in chemistry and materials science
alone where modest improvements to existing processes would have such massive
impacts to our society, we probably couldn't even estimate the resulting
benefits to their fullest extent.

------
thinbeige
Question to VCs on HN: Is AI declining? Not that it is dying down but my
feeling is that the general investment activity in AI slowed down but maybe I
am wrong.

1\. Do you get still that much AI deals in your inbox?

2\. Do you close this quarter as much deals as one year ago?

~~~
Top19
The head of Facebook AI said something a few years ago that AI has “died 4
times in 5 decades due to overpromising”.

AI in general is not a bubble, and progress will continue, but I imagine most
entrepreneurs are vastly overpromising, after all they only have more to gain
by doing this, and they might lose by not doing it if others are doing it (so
like a prisoners dilemma situation).

I should also say back in 1995 and 1996 a lot of people were saying we were in
a “tech bubble”, and they were technically right, but they underestimated just
how sustaining the euphoria was. They looked like idiots 2 years later when
stocks they decided against were quintupling in value, but less so 4 years
later when the stocks were worth nothing. So the main lesson is there are
really 3 bubbles, the actual bubble, the bubble where the elites and those in-
the-know finally can’t believe it anymore and bail out, shortly followed by
the bubble where all the others bail out (which triggers the most dramatic
decline).

------
mikkel
GAN improvements that lead towards finding global minima for any given
distribution mapping. GANs are versatile but still suffer from mode collapse
and instability, drastically reducing their utility.

------
idlewords
Regulation.

------
romanovcode
To be honest - games. AI that could write a story by itself and characters
that would interact based on AI and not some state machines would be amazing
experience.

------
yters
AI writing AI.

------
side_up_down
Big advance? AI than can learn conditional logic.

DL tops out as mapping vector spaces to less complicated vectors. Incredibly
powerful, but incredibly limited in what problems it can emulate.

------
tensormoon
Distributed and decentralized AI a la things like Synapse
[https://synapse.ai/](https://synapse.ai/)

------
qhoc
I want to see an "open models" community where trained models have been
validated with high accuracy that anyone can use and share.

------
scotty79
Neural network controllers for bipedal robots that grant them adaptability,
efficiency and elegance of movemeny.

------
pinouchon
I want an AI assistant that can do my taxes.

------
amelius
I'm looking forward to AI that can recognize ads visually, and can perfectly
extract ASCII text from an article.

------
twelfthnight
Not sure if it will happen, but I'd love to see life after back propagation
(1). In particular, I'd love to see advances in unsupervised learning, since
the amount of labeled data is a huge limit on what AI can currently do.

[https://www.axios.com/ai-pioneer-advocates-starting-
over-248...](https://www.axios.com/ai-pioneer-advocates-starting-
over-2485537027.html)

------
arithma
AI to help software engineers, since that would impact me the most in my daily
job.

~~~
mbrock
Specifically I would love an AI tool that looks at the context of the
expression I'm editing and finds suggestions that satisfy type checks.

~~~
arithma
Sure. Static analysis, scaffolding, a little bit of a conversation where the
AI is trained (by the developer themselves possibly) on the style. An AI
doesn't need to replace the developer, just let them think at a higher level
and the AI would deal with the lower level details, just like a compiler.
There's so much possibilities here, but alas, software development itself is
not exactly the most amenable to be addressable with machinery since it is at
the crux of both creativity and engineering when even the silliest things can
be uncomputable.

------
basicplus2
Self awareness.. Intellegence only exists where there is self awareness

------
miguelrochefort
People will realize how silly privacy is and start sharing their data with AI
for the benefit of everyone.

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deegles
Natural language generation.

------
k_lander
Developing a sense of humor

------
zackya89
advances in general intelligence

