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Predicting where AI is going in 2020

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Re: Predicting where AI is going in 2020

#71

Post by The_rationalist »

visarga wrote: post #67
y1tan wrote: post #64

I predict the broader ML/DL community will keep pumping out iterative papers that push the ball just a little bit forward while maintaining job security : Gatekeeping, no one thinking outside of the box, benchmark putting, just enough for the appearance of progress, and nothing broadly innovative or disruptive. The applications of ML/DL will continue to be gimmicky consumer products that have questionable valuable, q…

Apparently there is nothing positive possible in AI, based on your predictions. Is there?

Why would there be? Nobody has any sound roadmap on how to encode semantic meaning of words in a database. This preclude any hope of AGI.

Re: Predicting where AI is going in 2020

#72

Post by The_rationalist »

graycat wrote: post #18

I believe we will (1) find some basic data structures and algorithms to do real AI . (2) At first it will be able to do I/O only via text or simple voice. (3) Due to (1) it will learn very quickly from humans or other sources. (4) Soon it will be genuinely smart , enough, say, to discover and prove new theorems in math, to understand physics and propose new research directions, to understand drama and write good scre…

* I believe we will (1) find some basic data structures and algorithms to do real AI.*

There's no new kind of data structure to discover, humanity has made a disjonction of all possibles. The choice of data structure for semantic parsing is trivial, it's an hypergraph. The debate isn't the datastructure but how to fill it correctly while keeping the same Expressivity as in the original input (natural language). There's no reason to think we will make progress on this task beyond wishful thinking. Only a handful of humans are working on semantic parsing, which is the real AI task.

Re: Predicting where AI is going in 2020

#73

Post by graycat »

The_rationalist wrote: post #72
graycat wrote: post #18

I believe we will (1) find some basic data structures and algorithms to do real AI . (2) At first it will be able to do I/O only via text or simple voice. (3) Due to (1) it will learn very quickly from humans or other sources. (4) Soon it will be genuinely smart , enough, say, to discover and prove new theorems in math, to understand physics and propose new research directions, to understand drama and write good scre…

* I believe we will (1) find some basic data structures and algorithms to do real AI.* There's no new kind of data structure to discover, humanity has made a disjonction of all possibles. The choice of data structure for semantic parsing is trivial, it's an hypergraph. The debate isn't the datastructure but how to fill it correctly while keeping the same Expressivity as in the original input (natural language). There…

None of that is what I have in mind.

Re: Predicting where AI is going in 2020

#74

Post by The_rationalist »

graycat wrote: post #73
The_rationalist wrote: post #72

Earlier quoted context omitted.

* I believe we will (1) find some basic data structures and algorithms to do real AI.* There's no new kind of data structure to discover, humanity has made a disjonction of all possibles. The choice of data structure for semantic parsing is trivial, it's an hypergraph. The debate isn't the datastructure but how to fill it correctly while keeping the same Expressivity as in the original input (natural language). There…

None of that is what I have in mind.

What do you have in mind then?

Re: Predicting where AI is going in 2020

#75

Post by red75prime »

graycat wrote: post #18

I believe we will (1) find some basic data structures and algorithms to do real AI . (2) At first it will be able to do I/O only via text or simple voice. (3) Due to (1) it will learn very quickly from humans or other sources. (4) Soon it will be genuinely smart , enough, say, to discover and prove new theorems in math, to understand physics and propose new research directions, to understand drama and write good scre…

In short: someday we will create self-improving GOFAI (Good Old Fashioned Artificial Intelligence). Have I summarized correctly?

Re: Predicting where AI is going in 2020

#76

Post by taneq »

EliRivers wrote: post #46
taneq wrote: post #28

Earlier quoted context omitted.

I believe cartoons are our equivalent of adversarial images. They typically look nothing like (photos of) their namesake and yet we recognise them usually without prompting.

It is my understanding (although I sure don't have any evidence on me) that cartoons and such (at least, the ones where we haven't simply learned that this cartoon means this animal) work by being a picture of what we remember about an animal. Akin to a caricature; the cartoon contains the most salient features. It doesn't work by looking like the actual animal; it works by reacting with how we remember the animal.

Isn't that kind of the same thing? Adversarial examples work by matching what the neural net 'remembers' about the target classification, rather than being a picture of a thing in that class. Neural nets just find different features salient .

I've wondered in the past if we could use black box adversarial methods with Mechanical Turk to generate adversarial examples that work on humans. Maybe they'd end up looking like cartoons?

(Also agreed, some cartoon animals are just informed likeness - for instance Goofy doesn't look anything like a dog, at least to me.)

Re: Predicting where AI is going in 2020

#77

Post by nl »

riku_iki wrote: post #57
zak wrote: post #6

Earlier quoted context omitted.

(I'm one of the Cloud TPU product leads) We've seen multiple BERT-related PyTorch models training successfully on Cloud TPUs, including training at scale on large, distributed Cloud TPU Pod slices. Would you consider filing a GitHub issue at https://github.com/pytorch/xla or emailing pytorch-tpu@googlegroups.com to provide a bit more context about the specific issue you encountered? Here's the current PyTorch/TPU tro…

> BERT-related PyTorch models training successfully on Cloud TPUs How do you see it? Do you look at your client's code?

Google wrote BERT and they provide technical support to the FB Pytorch TPU port so it's not entirely surprising. RoBERTa, (Fb's variant) would be a good candidate to test it with.

Re: Predicting where AI is going in 2020

#78

Post by drongoking »

I'm generally pessimistic about predictions of the future. In this case I can't help but smile. They're trying to predict how a field (AI), which deals with complex adaptation, will intelligently adapt its adaptive techniques in the coming year, within an environment (we humans) that are themselves changing behavior while adapting to AI. That's approximately three meta levels. Good luck, guys!

Re: Predicting where AI is going in 2020

#79

Post by zak »

nl wrote: post #77
riku_iki wrote: post #57

Earlier quoted context omitted.

> BERT-related PyTorch models training successfully on Cloud TPUs How do you see it? Do you look at your client's code?

Google wrote BERT and they provide technical support to the FB Pytorch TPU port so it's not entirely surprising. RoBERTa, (Fb's variant) would be a good candidate to test it with.

We only see code when customers open-source it or otherwise explicitly share it with us. We are directly in touch with several customers who are using the PyTorch / TPU integration, so we hear feedback from them, and we also run a variety of open-source PyTorch models on Cloud TPUs ourselves as we continue to improve the integration.

Re: Predicting where AI is going in 2020

#80

Post by visarga »

The_rationalist wrote: post #71
visarga wrote: post #67

Earlier quoted context omitted.

Apparently there is nothing positive possible in AI, based on your predictions. Is there?

Why would there be? Nobody has any sound roadmap on how to encode semantic meaning of words in a database. This preclude any hope of AGI.

Have you heard of word embeddings, and recently of contextual word embeddings based on attention?
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