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Researchers: Are we on the cusp of an ‘AI winter’?

bbc.co.uk

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Re: Researchers: Are we on the cusp of an ‘AI winter’?

#81

Post by rytill »

drongoking wrote: post #64
rococode wrote: post #5

I think there's an interesting disconnect right now between research and practice. Cutting-edge research does feel like it's reaching a plateau - across most AI fields even "major" breakthroughs are only gaining a couple percentage points and we're probably starting to hit the limits of what current approaches can achieve. When the state-of-the-art is 97% on a task, there's only so much room for improvement. Yoav Gol…

I'm always bemused by the idea that AI is nothing but machine learning, and ML is nothing but predictive analytics. Equating research in AI with a "boring hyperparameter search" shows how narrow it's become; saying you've "gotten 97% on a problem" refers to, obviously, classification accuracy of a model on a set of labeled instances. "Use AI/ML to solve X" means finding a way to translate X into a prediction task ove…

If all you have is a function, everything starts to look like a mapping between two sets.

Re: Researchers: Are we on the cusp of an ‘AI winter’?

#82

Post by sgt101 »

mark_l_watson wrote: post #17

I have worked in the field since 1982, so I have experienced “the need to work on other things for a while” to earn a living. My prediction is that we are going to see a small revolution in cost reduction: hardware for deep learning will get cheaper; great educational materials like fast.ai and Andrew Ng’s lessons will increase the hiring pool of people who know enough to be useful; the large AI companies will contin…

Alot of the cost in AI projects now are in training or education, but instead in problem solving and plumbing. AI/ML free projects using things like Kafka and Flink are not cheap.

Coding up a CNN or MLP is not a big deal, but it never really was - it was work to build a c back propagation implementation but if I did it in 1995 then anyone could. The question and real differentiator is in answering three problems :

- what's the problem? - how can we get the data to the system? - how do we frame the data and output in terms of (any) AI technology?

All of these steps are closely coupled and require expertise.

On the programmer less modelling; I still have not seen a tool that is better than code for expressing a model precisely and testably, and my experience is that until we have some running code we don't really know that we understand the system.

Re: Researchers: Are we on the cusp of an ‘AI winter’?

#83

Post by antpls »

Veedrac wrote: post #23
rococode wrote: post #5

I think there's an interesting disconnect right now between research and practice. Cutting-edge research does feel like it's reaching a plateau - across most AI fields even "major" breakthroughs are only gaining a couple percentage points and we're probably starting to hit the limits of what current approaches can achieve. When the state-of-the-art is 97% on a task, there's only so much room for improvement. Yoav Gol…

> Cutting-edge research does feel like it's reaching a plateau It's really not. The second half of last year alone had MuZero and Megatron-LM, to name just a couple that most scream to me that we are actually progressing towards AGI. You say ‘When the state-of-the-art is 97% on a task’, but solved tasks are the least interesting tasks.

Also, Google released the Reformer just 2 days ago, and they claim it can ingest orders of magnitude more data than the Transformer.

https://ai.googleblog.com/2020/01/reformer-efficient-transfo...

TPUv3 is estimated to be 12 or 16nm process node, so the performance of TPU's next versions could still double over the next years (if needed).

At that pace of model research and hardware improvement, I would say we are still in the AI spring.

Re: Researchers: Are we on the cusp of an ‘AI winter’?

#84

Post by pauljurczak »

crubier wrote: post #77
mattnewport wrote: post #31

Earlier quoted context omitted.

Can you give some examples of applications that you think will have big impacts? I see places where current AI techniques can make incremental improvements but I just don't see any applications that really seem game changing. The ones that come closest tend to be dystopian unfortunately, like most applications of facial recognition.

My startup is using AI in many forms in order to build an accurate digital twin of the world cheaply, and extract valuable insights from it. In a few years we will have an accurate digital twin of the world, almost indistinguishable from the real world. this would have been impossible or way too expensive without massive automation with AI

> In a few years we will have an accurate digital twin of the world

No, you will not, unless you redefine what "accurate" means.

Re: Researchers: Are we on the cusp of an ‘AI winter’?

#85

Post by pauljurczak »

zelly wrote: post #40

Reminder: Most of the seminal accomplishments of this era's AI wave were actually developed in the 70s-90s. Yes, even GANs and RL. This industry has been riding on the NVIDIA welfare program for the past 10 yrs. How long until the hardware gets maxed out? http://people.idsia.ch/~juergen/deep-learning-miraculous-yea...

> This industry has been riding on the NVIDIA welfare program for the past 10 yrs

Nvidia has been very profitable for the past 10 years: https://www.macrotrends.net/stocks/charts/NVDA/nvidia/net-in.... I would call it a synergy, but it does smell of intellectual welfare.

Re: Researchers: Are we on the cusp of an ‘AI winter’?

#86

Post by catoc »

glial wrote: post #59
catoc wrote: post #53

Can't comment on every industry, but in medicine - especially the 'pattern-recognition-specialties' such as formost pathology and radiology - the actual implementation/usefulness/impact of "AI" (ML/DL) has not yet taken a foothold. Yes it's hyped, but the match between even the current state of DL and what is needed and possible in these specialties is so close to being perfect, and the gain is so close. What is hold…

There is a business opportunity here.

Oh yes - and it's seeing massive investments. Last RSNA - largest Radiology conference - saw >100 AI startups showcasing their products. (half of which will probably be gone by December this year, bankrupt, taken over or merged)

Re: Researchers: Are we on the cusp of an ‘AI winter’?

#87

Post by catoc »

m_ke wrote: post #6

Research progress might slow down for a bit in some areas of machine learning but the commercialization of existing technology will keep us busy for the next 10 years. Unlike the past two winters, deep learning is actually enabling a ton of applications that wouldn't have been possible otherwise and we now live in a world with a lot more data and computers to apply it to.

Exactly this

Re: Researchers: Are we on the cusp of an ‘AI winter’?

#88

Post by XorNot »

drongoking wrote: post #64
rococode wrote: post #5

I think there's an interesting disconnect right now between research and practice. Cutting-edge research does feel like it's reaching a plateau - across most AI fields even "major" breakthroughs are only gaining a couple percentage points and we're probably starting to hit the limits of what current approaches can achieve. When the state-of-the-art is 97% on a task, there's only so much room for improvement. Yoav Gol…

I'm always bemused by the idea that AI is nothing but machine learning, and ML is nothing but predictive analytics. Equating research in AI with a "boring hyperparameter search" shows how narrow it's become; saying you've "gotten 97% on a problem" refers to, obviously, classification accuracy of a model on a set of labeled instances. "Use AI/ML to solve X" means finding a way to translate X into a prediction task ove…

97% is also 3 failures out of every 100 attempts. In a lot of day to day experience I suspect humans do much better then this still.

Re: Researchers: Are we on the cusp of an ‘AI winter’?

#89

Post by fredguth »

rococode wrote: post #5

I think there's an interesting disconnect right now between research and practice. Cutting-edge research does feel like it's reaching a plateau - across most AI fields even "major" breakthroughs are only gaining a couple percentage points and we're probably starting to hit the limits of what current approaches can achieve. When the state-of-the-art is 97% on a task, there's only so much room for improvement. Yoav Gol…

The current SoTA achieve this 97% with a high cost in the number of samples. We are a living proof that it can be better. I believe that there Will be a push in achieving same generalization with less data.

Re: Researchers: Are we on the cusp of an ‘AI winter’?

#90

Post by solveit »

XorNot wrote: post #88
drongoking wrote: post #64

Earlier quoted context omitted.

I'm always bemused by the idea that AI is nothing but machine learning, and ML is nothing but predictive analytics. Equating research in AI with a "boring hyperparameter search" shows how narrow it's become; saying you've "gotten 97% on a problem" refers to, obviously, classification accuracy of a model on a set of labeled instances. "Use AI/ML to solve X" means finding a way to translate X into a prediction task ove…

97% is also 3 failures out of every 100 attempts. In a lot of day to day experience I suspect humans do much better then this still.

It depends. DL models have legitimately achieved superhuman accuracy on many tasks. Part of this is because deep learning is incredibly effective for a certain class of problems. But part of this is because humans are remarkably bad at some problems. Humans tend to be surprisingly bad at context-stripped tasks like "identify what object this blurry image is", and "what sequence of syllables is this short audio file?". But we have countermeasures to correct for our inaccuracy. Most importantly, we understand and use context to sanity-check the hell out of our imperfect senses, and nobody has any idea how we're going to get AI to do that.
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