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The recent advances in deep learning are good enough to keep industry busy for the next 20 years. Each year has brought us substantial improvements and if you l

by m_ke 5y ago

The recent advances in deep learning are good enough to keep industry busy for the next 20 years. Each year has brought us substantial improvements and if you look past the popular benchmarks there's no sign of things slowing down.

antupis 5y ago
I think it is that we are going to hit soon local maximum where modern NN architectures with better vertical integration and better hardware are going to create lots of value but other hand doesn't bring use anywhere near AGI.
- m_ke 5y ago
  No sane person believed that deep learning would get us to AGI withing the next few decades. Anyone saying so is doing it to fool VCs out of their money.
  - short_sells_poo 5y ago
    I'd like to agree with you but just 1 year ago every AI related thread here were chock full of people breathlessly predicting AGI within years... and presumably HN is a well informed community! It's often not about expertise or sanity, but AGI captures people's imagination to a huge extent and as evolution has conditioned us to do, we ascribe certain behaviors and desires to systems which only exhibit those by random chance.
    - m_ke 5y ago
      Sorry to break it to you but most of HN is not well informed. Look for some threads in your area of expertise for proof of that. Only people selling AGI are marketers and futurists.
0xFACEFEED 5y ago
As a layman I wonder when we're going to start bearing the fruit of all this effort in DL. In the average person's day to day life I mean. I don't rely on DL driven systems and I'm not even a skeptic. I want it to work. But I can't rely on these systems in the same way that I rely on my computer/phone, a light bulb, or a refrigerator. Is that ever going to change?
- m_ke 5y ago
  Most people unlock their phones using a deep learning model based facial recognition system, they talk to their devices thanks to deep learning, they translate documents with transformers, even google maps uses GNNs for ETA estimates and routing (https://arxiv.org/abs/2108.11482 https://arxiv.org/abs/2108.11482). The cameras on mobile phones got so much better thanks to deep learning, snapchat filters, zoom backgrounds, etc all use CNNs.
  - edgyquant 5y ago
    These things work so well and are so ingrained we don’t even notice them: which is what we should be going for
- potatolicious 5y ago
  DL is already bearing fruit everywhere - the key is that the places where it works are narrow domains. In general the larger the scope of intelligent behavior the less successful it has been. Someone has already mentioned face unlock, but also dictation is miles ahead of where it used to be. Similarly text-to-speech is absurdly better than it used to be and is approaching indistinguishability from human speech in some cases (again, narrow domains are more successful!) Smartwatches are capable of detecting falling motion and alerting emergency responders, and are increasingly able to detect (some types of) cardiac incidents. Again here the theme is intelligent behavior in very narrow domains, rather than some kind of general omni-capable intelligence. The list goes on, but I think there's a problem where so many companies have overpromised re: AI in more general circumstances. Voice assistants are still pretty primitive and unable to understand the vast majority of what users want to speak about. Self-driving still isn't here. To some degree I think the overpromising and underdelivering re: larger-scoped AI has poisoned the well against what is working, which is intelligent systems in narrow domains, where they are absolutely rocking it.
tmaly 5y ago
it will be interesting to see if they can start to load models like GPT-3 ASICs and realize some serious performance gains.

by m_ke 5y ago

antupis 5y ago

I think it is that we are going to hit soon local maximum where modern NN architectures with better vertical integration and better hardware are going to create lots of value but other hand doesn't bring use anywhere near AGI.

m_ke 5y ago
No sane person believed that deep learning would get us to AGI withing the next few decades. Anyone saying so is doing it to fool VCs out of their money.
- short_sells_poo 5y ago
  I'd like to agree with you but just 1 year ago every AI related thread here were chock full of people breathlessly predicting AGI within years... and presumably HN is a well informed community! It's often not about expertise or sanity, but AGI captures people's imagination to a huge extent and as evolution has conditioned us to do, we ascribe certain behaviors and desires to systems which only exhibit those by random chance.
  - m_ke 5y ago
    Sorry to break it to you but most of HN is not well informed. Look for some threads in your area of expertise for proof of that. Only people selling AGI are marketers and futurists.

0xFACEFEED 5y ago

As a layman I wonder when we're going to start bearing the fruit of all this effort in DL. In the average person's day to day life I mean. I don't rely on DL driven systems and I'm not even a skeptic. I want it to work. But I can't rely on these systems in the same way that I rely on my computer/phone, a light bulb, or a refrigerator. Is that ever going to change?

m_ke 5y ago
Most people unlock their phones using a deep learning model based facial recognition system, they talk to their devices thanks to deep learning, they translate documents with transformers, even google maps uses GNNs for ETA estimates and routing (https://arxiv.org/abs/2108.11482 https://arxiv.org/abs/2108.11482). The cameras on mobile phones got so much better thanks to deep learning, snapchat filters, zoom backgrounds, etc all use CNNs.
- edgyquant 5y ago
  These things work so well and are so ingrained we don’t even notice them: which is what we should be going for
potatolicious 5y ago
DL is already bearing fruit everywhere - the key is that the places where it works are narrow domains. In general the larger the scope of intelligent behavior the less successful it has been. Someone has already mentioned face unlock, but also dictation is miles ahead of where it used to be. Similarly text-to-speech is absurdly better than it used to be and is approaching indistinguishability from human speech in some cases (again, narrow domains are more successful!) Smartwatches are capable of detecting falling motion and alerting emergency responders, and are increasingly able to detect (some types of) cardiac incidents. Again here the theme is intelligent behavior in very narrow domains, rather than some kind of general omni-capable intelligence. The list goes on, but I think there's a problem where so many companies have overpromised re: AI in more general circumstances. Voice assistants are still pretty primitive and unable to understand the vast majority of what users want to speak about. Self-driving still isn't here. To some degree I think the overpromising and underdelivering re: larger-scoped AI has poisoned the well against what is working, which is intelligent systems in narrow domains, where they are absolutely rocking it.

tmaly 5y ago

it will be interesting to see if they can start to load models like GPT-3 ASICs and realize some serious performance gains.