51–60 of 296 posts
This is why I don't know if it will be possible (at current limitations) to let insect like brains to fully drive our cars. It may never be good enough.
I think the requirement for a large amount of data is the biggest objection to the reflex "AI will replace [insert your profession here] soon" that many techies, in particular on HN, have. There are many professions where there is very little data available to learn from. In some case (self-driving), companies will invest large amount of money to build this data, by running lots of test self-driving cars, or paying p…
> and will keep doing so until the cost of developing and maintaining software or AI has dropped to zero. I have no idea about the progress of AI, but normal software will get an order of magnitude cheaper to develop as we slowly wake up from the Unix/worse-is-better/everything-is-text mindset and abandon the dynamically typed and imperative languages, broken systems abstractions, etc. that hold us back.
To the vast majority of the educated population, software is very much a black art and people would have no idea of how to do even the most basic things. That's of course true for more senior people, but I find that it is as true for the generation who graduates today. They can do incredible things with their smartphone that I didn't suspect was possible, but wouldn't know where to start to code something.
Until this skill gap changes dramatically, and that everyone gets out of high school with basic knowledge in programming, like they have basic knowledge of maths, biology, physics or history, this gap will never close.
Surely we shouldn't rush to anthropomorphize neural networks, but we'd ignoring the obvious if we didn't at least note that neural networks do seem to share some structural similarities with our own brains, at least at a very low level, and that they seem to do well with a lot of pattern-recognition problems that we've traditionally considered to be co-incident with brains rather than logical systems. The article not…
It may be that distinctions like the one you're describing here are useful to make, but I don't think this claim refutes the possibility of ML "fitting a particular piece within a larger, yet unarticulated model."
I think the assertion is more that our current ways of representing elements of human experience are necessarily very lossy - or that there's some aspect of the situation that you can't describe/implement in terms of models of neural nets.
As someone primarily interested in interpretation of deep models, I strongly resonate with this warning against anthropomorphization of neural networks. Deep learning isn't special; deep models tend to be more accurate than other methods, but fundamentally they aren't much closer to working like the human brain than e.g. gradient boosting models. I think a lot of the issue stems from layman explanations of neural net…
Well, what we've done so far is emulate maybe 1 mm^3 of brain matter - some isolated, very specialized functional blocks in the greater architecture of the brain. They behave as expected - are experts on very narrow topics, but of course fail to integrate their functioning with a larger body of knowledge, because that body just isn't there (yet).
The strength of the human mind is that is has this profusion of little subject matter experts all over the place, covering an enormous array of topics - and then it has an intricate superstructure that integrates the outputs of these narrow expert machines, tweaks their functioning, even subtly alters their inputs, providing coherence to the global output according to the capabilities of the whole system.
We're still far from that complex high level architecture.
There's quite a few others but these were the most readily available papers.
Are deep nets AGI? No, but they're a lot better than Mr.Chollet gives them credit for.
Earlier quoted context omitted.
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 m…
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.
As someone primarily interested in interpretation of deep models, I strongly resonate with this warning against anthropomorphization of neural networks. Deep learning isn't special; deep models tend to be more accurate than other methods, but fundamentally they aren't much closer to working like the human brain than e.g. gradient boosting models. I think a lot of the issue stems from layman explanations of neural net…
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 m…
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.
Recurrent models do not simply map from one vector space to another and could very much be interpreted as reasoning about their environment. Of course they are significantly more difficult to train and backprop through time seems a bit of a hack.
Earlier quoted context omitted.
It isn't anthropomorphizing. There are undeniable architectural similarities between ANN's and biological neural networks. We don't understand either very well yet but the parts we do understand have led to a lot of cross pollination. I don't think computational intelligence will ever match biological networks detail by detail due to the different substrates and resource usage tradeoffs, and they don't need to match.…
This is exactly my point - the danger of "anthropomorphization" lies in taking the brain analogy too far. That is, there shouldn't necessarily be a link between research in neuroscience and advances that make deep learning models more accurate. The tasks are completely different (human learning vs. minimizing a loss function), and it's important for researchers in both fields - neuroscience and AI - to keep that in m…