61–70 of 119 posts
This is suspiciously close "data science" and "machine learning" experts. Can't we just be honest and say that most of these are applied statistics jobs with a specialty in large volumes of data? Or is "statistics" just not fashionable enough nowadays?
I often heard the Big Data guys hype that there's no sampling in Big Data, you have the whole data, so it's not exactly statistics.
It is a very interestimg field, but as a self-taught programmer I'm used to learning by building things, and it's hard for me to come up with some project that would be practically useful and yet doable.
Does anyone have any ideas?
This is suspiciously close "data science" and "machine learning" experts. Can't we just be honest and say that most of these are applied statistics jobs with a specialty in large volumes of data? Or is "statistics" just not fashionable enough nowadays?
The big thing that prevents me from getting into AI is the lack of practical projects that I can build. It is a very interestimg field, but as a self-taught programmer I'm used to learning by building things, and it's hard for me to come up with some project that would be practically useful and yet doable. Does anyone have any ideas?
This is suspiciously close "data science" and "machine learning" experts. Can't we just be honest and say that most of these are applied statistics jobs with a specialty in large volumes of data? Or is "statistics" just not fashionable enough nowadays?
The new machine learning is about building layers of components on top of each other, very much like circuits seen in EE. The "circuit" components being used are no longer well defined mathematical pieces built from the bottom up using ideal assumptions, but less well understood, somewhat black-box newer components that were built from the top down. Far more like a type of engineering than a type of statistics.
If you haven't been seeing all the latest Arvix papers, you're really missing out. It's evolved to look sharply different than statistics now.
Earlier quoted context omitted.
What do you mean it doesn't exist? I work in that field, it exists.
What do you mean it exists? How do you definite AI? Can your project reason with you? Or is it simply an Input-Output type of program? Just because you use natural language with it instead of punch cards doesn't mean it is intelligent. People tried to do AI in the 60s to 90s era. It is dubbed symbolic AI. It didn't work out. A good chance that it never will. Today machine learning algos and a bunch of automated stati…
Also, it's more tricky than you'd think to narrow down what counts as intelligence. There aren't really any hard lines between an I/O program and an intelligent agent, even though they seem pretty far apart.
The big thing that prevents me from getting into AI is the lack of practical projects that I can build. It is a very interestimg field, but as a self-taught programmer I'm used to learning by building things, and it's hard for me to come up with some project that would be practically useful and yet doable. Does anyone have any ideas?
Game AIs, Stock market bots, character recognition, adaptations to game of life, computer art wherein you get an AI to paint or to draw and can seed it values. There's lots of cool stuff you can hack away at. Even training a neural net to recognise a '3' is quite interesting.
The big thing that prevents me from getting into AI is the lack of practical projects that I can build. It is a very interestimg field, but as a self-taught programmer I'm used to learning by building things, and it's hard for me to come up with some project that would be practically useful and yet doable. Does anyone have any ideas?
The state of hobbyist robotics is very hardware-centric. I not yet discovered something appropriate for use by AI programmers.
So I decided to step back and work on theory. I did this for several years and recently finished. Hobbyist robots still aren't ready to implement my ideas so now what do I do? I started working on a software architecture based on my high-level research. But how to test it? So now I am working on software to simulate an environment for my AI to interact with because I don't have the time or skill to build a real robot.
I honestly don't think this is a bad thing because I would imagine that when we start to work on "real" AI (not this nonsense that passes for it today) testing behaviors in a simulated environment before deployment to hardware would save a huge amount of time (and physical damage.)
Now I am hoping that once I have taken my virtual habitat as far as it can go, hardware will be available for me to apply what I have been working on. My hopes are low.
Earlier quoted context omitted.
What do you mean it exists? How do you definite AI? Can your project reason with you? Or is it simply an Input-Output type of program? Just because you use natural language with it instead of punch cards doesn't mean it is intelligent. People tried to do AI in the 60s to 90s era. It is dubbed symbolic AI. It didn't work out. A good chance that it never will. Today machine learning algos and a bunch of automated stati…
I tend to agree that AI today is a long way from what the founders of the subject imagined – it's become something more like "Applied Computer Science". But what's now called "Artificial General Intelligence" isn't dead, and people are still working on it. Also, it's more tricky than you'd think to narrow down what counts as intelligence. There aren't really any hard lines between an I/O program and an intelligent ag…
Earlier quoted context omitted.
I often heard the Big Data guys hype that there's no sampling in Big Data, you have the whole data, so it's not exactly statistics.
I've heard this too and it's a great way to demonstrate you don't really know what statistics is :) Statistics is not (just) opinion polling, there's a lot more to it than estimating observable properties of a population. If you're trying to make decisions, predictions or estimates which involve any uncertainty at all (and in my experience big data almost always is), then it's definitely within the purview of statist…
Once you hit millions of rows, it's not humanly possible to survey the data. All you can do is make assertions about the data's structure / buckets it will fall into. You then try to disprove that assertion, or establish an error bounds on it. You will never see all the data, only the results of assumptions you've made about it.