• Add a Comment
  • Edit
  • More Actions v
  • Quarantine this Entry

Comments (5)

1 sexypast commented Permalink

This is a very good analysis about the human input in machines and that machines can only operate so much as the information you input into it. <div>&nbsp;</div> Machines cannot function alone. <div>&nbsp;</div> John, in your view, do you think machines are better than humans? If yes, why? In what areas? How? <div>&nbsp;</div> I will like to get a reply to this because it will help me understand a lot better and help fuel my curiosity in machine learning and development. <div>&nbsp;</div> Thank you. <div>&nbsp;</div> Sunday

2 John M. Boyer commented Permalink

Hi Sunday, <br /> Machine learning today involves analyzing a potentially large amount of data to develop predictive analytics like regression models, neural nets or hidden Markov models, or to develop prescriptive analytics that can help find support for 'next best actions'. These machine algorithms absolutely can be better than humans trusting their gut instinct alone. It's actually better when the human is there to interpret the results and make a decision, but it remains true that the human decision is usually going to be better off with the machine intelligence helping out. <br /> If you have any doubt about this, then attempt to defeat the "James Blog" entry I posted in November, then see how much better you are if you use the "expert advisor" button. <br /> But is this "machine is better than human". Not really, since it was human work that directed the creation of the machine learning or that wrote the code of the machine intelligent algorithm anyway. If you're asking "will machines be better than us once we can get them to perform true cognition at the human level?", well that's really impossible to speculate since noone has come close to doing that. For example, we don't know whether a mechanized capability for human-level cognition would necessarily lose the extremely fast calculation capability of today's machines. We seem to be hotwired for representation of frames of context on so many levels and with so much capacity for variation, and that kind of wiring may well be doggedly slow at arithmetic. Such a mechanized cognition would then need and benefit from today's "expert advisor" machine learning/intelligence algorithms just as much as we do. <br /> Netting it out, making real progress on machine learning today requires seeing it for what it is: an algorithmic adjunct that improves human insight and decision-making by optimizing rote tasks of increasing complexity that humans aren't particularly fast at doing for various reasons (processing speed, access to data, limitations of the individual, etc.). Every new innovation in this area seems like we're touching a human-only domain at first, but we're really not. We're really just making it easier for a human to do something they used to find hard to do for themselves.

3 FPXA_Atri_Sharma commented Permalink

Hi sir, <div>&nbsp;</div> Thank you for the blog post.It is very good! <div>&nbsp;</div> I had a few additions to the above article. <div>&nbsp;</div> First, can we use normal curve instead of straight line because normal curve can be used to model more real world scenarios as compared to straight lines? <div>&nbsp;</div> Also, since mlr does not include any probabilistic model, I think that it would be inaccurate as compared to bayseian models? Could you please highlight some points there? <div>&nbsp;</div> Regards, <br /> Atri

4 John M. Boyer commented Permalink

Hi Atri, Thanks for commenting, and yes you're quite right that there are a number of other techniques that can provide better modeling than MLR for lots of real-world phenomena. In fact, MLR often gets misused because the user assumes a linear relationship where there isn't one. Although my original hope for this post was to mention a few machine learning techniques, not just MLR, I had to defer that discussion so that my post would be a more manageable length and be about *one* thing. It's about the machine learning vs. just machine intelligence. MLR turns out to be a great example here because the learning from real-world samples really does imbue relatively a lot of intelligence to what is otherwise a very dumb calculation. However, the fact that there exist multiple machine learning methods that are often more powerful in various problem domains serves to strengthen a separate point about today's machine learning methods. That point is that they tend to have very limited domains and do not represent true generalized learning capability. I've been on the analogous point about machine intelligence, and so I'd say you have anticipated a point that will be upcoming on this blog in the near future :-)

5 OlivierPhilip commented Permalink

Hi, A lot of model used in supervised machine learning (when the training data are provided with a corresponding dependent variable, Y) are linear: Each parameter (the C's) associated with a feature (predictor variable, the X's) are multiplied and all the products are summed. This result is often used as an input to a function to change the respond range (sigmoid function to get a ]0, 1[ range for instance). To get rid of the linear dependency between parameters and features without having to change all the learning process (and redo all the learning theory), mapping of features is used. Any combination of features is mapped through functions. The only limitation is the original space of the functions that need to include all the possible outcome of the combination of features. As an example, let's take a one dimension data set with an obvious quadratic relation between X and Y. Without mapping, Y=C1.X + C0 will, most of the time, not lead to good prediction. A simple mapping f(X) = X.X will give much better results.

Add a Comment Add a Comment