8 ms·

I don't have ML or deep learning background (no Masters or PhD), adding comment from experience with backtesting trading systems. We will collect market data an

by kensoh 9y ago

I don't have ML or deep learning background (no Masters or PhD), adding comment from experience with backtesting trading systems. We will collect market data and design algorithms that seem to produce the kind of outcomes we want. Then test on some other data sets which the algorithms have never been applied on. Many iterations later, you can get a decent profitable algorithm. And if the 'holy grail' algo is run in market long enough, eventually there will be severe drawdown and going bust. The quality of the algo and I assume the deep learning model lies in the quality (breadth and depth) of the data, and how honest with himself the person choose to model it. There will be time and again new 'black swan' or edge events happening (remember LTCM), because using machine learning is like using the past to predict the future. I guess as long as the users' expectations are correct it can be useful in some very specific areas. Referencing the AlphaGo game last year, I was a Go player for more than a decade. But yet AlphaGo's weird move inspires new insights that break the conventional structure / thinking-framework of a Go player. From that angle, I do think that even though DL is somewhat a blackbox, humans can pick up new insights because it explores areas which are normally ridiculous to a human with 'common sense' to explore.

fspeech 9y ago
The "creative" moves may very well come from the search part of the AlphaGo algorithm, though of course the networks have done their jobs of pruning the search space.
- kensoh 9y ago
  I see.. That's true. Though credit still goes to the algo for choosing that particular weird move out of the entire search space (it's just 'weird' and something you will think is a move made by a total newbie to the game). I remembered for that whole week during lunchtime I would watch the broadcast live on YouTube. How devastated I was to see Lee Sedol losing match after match. It was a moment I would never forget, in my mind the computer had crossed an imaginary threshold and it won. I know ML/DL experts will say it is only for a very specific area. But what's stopping more mastery of enough 'specific' areas that the mastery will be broad enough to pass Turing tests?
  - webmaven 9y ago
    Careful, that's the sort of thinking that led to the last 'AI Winter': assuming that if enough rule-based expert systems were built, general-purpose systems could be assembled from them and/or enough could be learned to build general-purpose systems. Now, it is worth noting that DL models are already being assembled together (often with a coordinating DL model to switch between them). This can have the advantage of the smaller models being reusable to some extent (certainly more than expert systems ever were) but is not a panacea. The results are still essentially bespoke models rather than general purpose ones. Deep Learning obviously has a lot more mileage left in it, given that much human mental labor is 'just' training and using our general-purpose intellects for what amount to a series of rather narrowly defined tasks, but it won't surprise me if there is a wall of some sort lurking just over the horizon that will require a different approach (albeit one that may still be called 'deep learning') to cross. OTOH, it does seem as though the folks at DeepMind are fairly aggressively pursuing whatever is on the other side of that particular horizon: https://deepmind.com/blog/neural-approach-relational-reasoning/ https://deepmind.com/blog/neural-approach-relational-reasoni... https://deepmind.com/blog/cognitive-psychology/ https://deepmind.com/blog/cognitive-psychology/ https://deepmind.com/blog/imagine-creating-new-visual-concepts-recombining-familiar-ones/ https://deepmind.com/blog/imagine-creating-new-visual-concep...
    - eanzenberg 9y ago
      We can debate, but I don't think another AI winter will happen again in my lifetime. AI work is just earning way too much money for its funding to get cut, and a lot of funding is currently private too.
      - Aaliyah- 9y ago
        I just got paid $6784 working off my laptop this month. And if you think that's cool, my divorced friend has twin toddlers and made over $9k her first month. It feels so good making so much money when other people have to work for so much less. This is what I do,: :) AND GOOD LUCK.:) ►►www.joinmate2.comCOPY THE LINK
- mtremsal 9y ago
  My understanding is that innovation comes from reinforcement learning during self-play (rather than supervised learning of pro games), and thus goes against the best moves suggested by AlphaGo's policy network, in turn pushing it towards new options. In a sense, it seems innovation arises when the value network forces the policy network to expand the search space because an apparently unlikely move leads to downstream positions deemed favorable.
  - Cybiote 9y ago
    It's not that simple. The creativity is that the combination of rollouts, policy and value networks allow for more efficient traversal of the search space. Which gets you better exploration of possible paths, meaning more options than a human considered and therefore more creativity.
    - Abagail- 9y ago
      Google pay 97$ per hour my last pay check was $8500 working 1o hours a week online. My younger brother friend has been averaging 12k for months now and he works about 22 hours a week. I cant believe how easy it was once I tried it outit!... http://usawork.cn.toᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵ http://usawork.cn.xn--to-88raaaaaaaaa
    - Aubree- 9y ago
      Are you boring your life for pocket money.if u don’t worry for your pocket money I share my home profit system to everyone.i enjoy my life because I using this easy online jobs and earning $25986 daily wovrk for only 3 hours a day online for doing thivs easiest online home jobz.for more details visit this link… ᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵwww.joinmate2.comᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵCOPY THE LINK.:) AND GOOD LUCK.:)
    - Isabela32 9y ago
paganel 9y ago
> The quality of the algo and I assume the deep learning model lies in the quality (breadth and depth) of the data, and how honest with himself the person choose to model it. I've only dabbled with machine-learning here and there for the past 10 years or so, but if there's one thing I've learned so far is that the data behind your ML code (and the way it is structured) is responsible for almost all the success or failure of any given ML algorithm. I have an younger colleague at work who I've started tutoring, and he seems really interested in doing ML work (maybe because of all of the recent hype). I've tried to emphasize to him several times that ML algorithms come and go and that he should focus a lot of his time on the data itself (from where he intends to collect it? how is it structured? is it reliable? is it "enough"? etc), but it looks that my data-related advice falls on deaf ears every time, he's only interested in me pointing to him the latest cool ML algorithm. I guess he'll live and learn, so to speak.
- kensoh 9y ago
  Thanks for sharing your experience. I'm happy that my previous exposure to trading algorithms at least helped me understand more what the experts here are talking about. I believe the output model is only as good as the data (at least for the deep learning branch of ML). If the dataset does not cover data-points which exist in a wider space but in the same domain of the problem, or which haven't yet have a precedent, then we really can't simply assume that it is the algo/model that needs tweaking when shit hits the fan.
  - sandralisa667 9y ago
    For easy ways to make money online people need to check out https://is.gd/qKRnJt https://is.gd/qKRnJt I've been making over $70 per day from it and you guys can do the same tbh. Hope this helps.
- denzil_correa 9y ago
  > I've learned so far is that the data behind your ML code (and the way it is structured) is responsible for almost all the success or failure of any given ML algorithm Data is indeed a necessary condition but certainly not sufficient. You require a good marriage between engineering features and data to have a good success rate. Learning curves [0] are a good way to understand if your ML algorithm requires more data or better feature engineering. [0] http://mlwiki.org/index.php/Learning_Curves http://mlwiki.org/index.php/Learning_Curves

by kensoh 9y ago

fspeech 9y ago

The "creative" moves may very well come from the search part of the AlphaGo algorithm, though of course the networks have done their jobs of pruning the search space.

kensoh 9y ago
I see.. That's true. Though credit still goes to the algo for choosing that particular weird move out of the entire search space (it's just 'weird' and something you will think is a move made by a total newbie to the game). I remembered for that whole week during lunchtime I would watch the broadcast live on YouTube. How devastated I was to see Lee Sedol losing match after match. It was a moment I would never forget, in my mind the computer had crossed an imaginary threshold and it won. I know ML/DL experts will say it is only for a very specific area. But what's stopping more mastery of enough 'specific' areas that the mastery will be broad enough to pass Turing tests?
- webmaven 9y ago
  Careful, that's the sort of thinking that led to the last 'AI Winter': assuming that if enough rule-based expert systems were built, general-purpose systems could be assembled from them and/or enough could be learned to build general-purpose systems. Now, it is worth noting that DL models are already being assembled together (often with a coordinating DL model to switch between them). This can have the advantage of the smaller models being reusable to some extent (certainly more than expert systems ever were) but is not a panacea. The results are still essentially bespoke models rather than general purpose ones. Deep Learning obviously has a lot more mileage left in it, given that much human mental labor is 'just' training and using our general-purpose intellects for what amount to a series of rather narrowly defined tasks, but it won't surprise me if there is a wall of some sort lurking just over the horizon that will require a different approach (albeit one that may still be called 'deep learning') to cross. OTOH, it does seem as though the folks at DeepMind are fairly aggressively pursuing whatever is on the other side of that particular horizon: https://deepmind.com/blog/neural-approach-relational-reasoning/ https://deepmind.com/blog/neural-approach-relational-reasoni... https://deepmind.com/blog/cognitive-psychology/ https://deepmind.com/blog/cognitive-psychology/ https://deepmind.com/blog/imagine-creating-new-visual-concepts-recombining-familiar-ones/ https://deepmind.com/blog/imagine-creating-new-visual-concep...
  - eanzenberg 9y ago
    We can debate, but I don't think another AI winter will happen again in my lifetime. AI work is just earning way too much money for its funding to get cut, and a lot of funding is currently private too.
    - Aaliyah- 9y ago
      I just got paid $6784 working off my laptop this month. And if you think that's cool, my divorced friend has twin toddlers and made over $9k her first month. It feels so good making so much money when other people have to work for so much less. This is what I do,: :) AND GOOD LUCK.:) ►►www.joinmate2.comCOPY THE LINK
mtremsal 9y ago
My understanding is that innovation comes from reinforcement learning during self-play (rather than supervised learning of pro games), and thus goes against the best moves suggested by AlphaGo's policy network, in turn pushing it towards new options. In a sense, it seems innovation arises when the value network forces the policy network to expand the search space because an apparently unlikely move leads to downstream positions deemed favorable.
- Cybiote 9y ago
  It's not that simple. The creativity is that the combination of rollouts, policy and value networks allow for more efficient traversal of the search space. Which gets you better exploration of possible paths, meaning more options than a human considered and therefore more creativity.
  - Abagail- 9y ago
    Google pay 97$ per hour my last pay check was $8500 working 1o hours a week online. My younger brother friend has been averaging 12k for months now and he works about 22 hours a week. I cant believe how easy it was once I tried it outit!... http://usawork.cn.toᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵ http://usawork.cn.xn--to-88raaaaaaaaa
  - Aubree- 9y ago
    Are you boring your life for pocket money.if u don’t worry for your pocket money I share my home profit system to everyone.i enjoy my life because I using this easy online jobs and earning $25986 daily wovrk for only 3 hours a day online for doing thivs easiest online home jobz.for more details visit this link… ᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵwww.joinmate2.comᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵᴵCOPY THE LINK.:) AND GOOD LUCK.:)
  - Isabela32 9y ago

paganel 9y ago

> The quality of the algo and I assume the deep learning model lies in the quality (breadth and depth) of the data, and how honest with himself the person choose to model it. I've only dabbled with machine-learning here and there for the past 10 years or so, but if there's one thing I've learned so far is that the data behind your ML code (and the way it is structured) is responsible for almost all the success or failure of any given ML algorithm. I have an younger colleague at work who I've started tutoring, and he seems really interested in doing ML work (maybe because of all of the recent hype). I've tried to emphasize to him several times that ML algorithms come and go and that he should focus a lot of his time on the data itself (from where he intends to collect it? how is it structured? is it reliable? is it "enough"? etc), but it looks that my data-related advice falls on deaf ears every time, he's only interested in me pointing to him the latest cool ML algorithm. I guess he'll live and learn, so to speak.

kensoh 9y ago
Thanks for sharing your experience. I'm happy that my previous exposure to trading algorithms at least helped me understand more what the experts here are talking about. I believe the output model is only as good as the data (at least for the deep learning branch of ML). If the dataset does not cover data-points which exist in a wider space but in the same domain of the problem, or which haven't yet have a precedent, then we really can't simply assume that it is the algo/model that needs tweaking when shit hits the fan.
- sandralisa667 9y ago
  For easy ways to make money online people need to check out https://is.gd/qKRnJt https://is.gd/qKRnJt I've been making over $70 per day from it and you guys can do the same tbh. Hope this helps.
denzil_correa 9y ago
> I've learned so far is that the data behind your ML code (and the way it is structured) is responsible for almost all the success or failure of any given ML algorithm Data is indeed a necessary condition but certainly not sufficient. You require a good marriage between engineering features and data to have a good success rate. Learning curves [0] are a good way to understand if your ML algorithm requires more data or better feature engineering. [0] http://mlwiki.org/index.php/Learning_Curves http://mlwiki.org/index.php/Learning_Curves