I don't quite understand why people want to dismiss examples of machine learning as valid techniques for understanding the human environment.. It's not as if th

I don't quite understand why people want to dismiss examples of machine learning as valid techniques for understanding the human environment.. It's not as if the human brain was built and guided from nothing, many of the same adaptive principles are as present in our minds as they are in other mammals and equally so from where all the branches divide. Even tiny organisms. And we seems to center the brain at the core of humans intelligence, when there's a range of chemical and metabolic coordination going that might bypass the brain entirely. It's efficient, failure resistant models that matter. We're talking about accelerated learning, finding the models that work out of all those many iterations that fail. You can model it, decompile the results and try to understand and emulate what makes things seem real, but we don't even need to analyze it, because case by case it changes and it's circumstance makes things very different. 'Many ways to skin a cat'. I think the challenge of the future is finding the general API that can negotiate all the things and make all the parts communicate, the kernel if you want. We can determine optimum speech algorithms, babel communication, create seeing eyes that recognize objects, optimize forms that can negotiate physical terrain, work out what is meant in human expression, but it's not until all these units work together that the 'AI' will seem seamless in human terms. All of those parts have discreet forms, they generate a lineage of algorithms from iterations based on code, languages often derived from need. A Lisp might be the best way of interpreting language, a Haskell might be work best for defining strict biomechanics and area physics. Different abstractions are better for the results they are designed to intuit. But when we are to create the ultimate neural net, the composite of all these machine languages that are constantly required to optimize beyond human intelligible understanding, what will be using? What structure will state 'this works good enough' to not bother with the computation any more - in the familiar context of why don't our eyes have faster frame rate, need better detail, or need us to see into UV. What regulates such a machine, and how does a machine understand failure without guidance? I like to think of these questions when I see rough examples posited around potentials in machine learning. Getting one human system sorted is one thing, communicating the results to other sub-systems an optimize concurrent results is another. The data model is too huge to even comprehend! I'm just excited that these things exist, that there are individuals, research groups and companies looking at the what makes us 'us'. It might help us unlock the features of the brain and evolution.. Used for commercial gain - who cares, just a small cog, with revenue to continue development. Just going to add my favourite example of machine learning, not because it's 'best' but because it's so dynamic that you feel the wonder. http://www.goatstream.com/research/papers/SA2013/ http://www.goatstream.com/research/papers/SA2013/