One of the leading proponents of man-machine integration is Gary Kasparov.
It is ironic, given that his defeat by Big Blue was one of the landmark moments of artificial intelligence progress. But he managed to turn the tables on his opponent beyond the chessboard and push for the idea of Freestyle Chess, where you are allowed to compete on human only, machine only, or mixed teams. In these competitions we are able to see that both man and machine only teams are at a disadvantage when faced with the mixed teams. This goes to show that neither side has an absolute advantage, neither side is truly superior, and we can Augment Human Intelligence. (A.H.I.?)
What are the practical implications of taking this view point? How would it change the way we relate with the machines? How does it change the future of work in the 21st century? What about art? Science?
In this blog I'll talk about various subjects around working with market data. Getting the data, transforming it, making it available for analysis and extracting knowledge from it is the name of the game. We will get the job done using a fully open source stack. I hope to give you a good idea of what it takes to put together the technology stack to work with fundamental and technical market data.
Showing posts with label augmented intelligence. Show all posts
Showing posts with label augmented intelligence. Show all posts
Sunday, May 13, 2018
Sunday, May 6, 2018
towards augmented intelligence
I think we're going to see a shift towards "Augmented Intelligence" where the machine works more like a tool than as a replacement for people. In a symbiotic process. As bio-tech, virtual reality and nerve-machine interfaces evolve, this symbiotic relation will become much tighter. We are going to see cyborgs way before we see artificial general intelligence, of the kind you see in terminator movies.
With all the advances in machine learning, neural nets, deep learning, in the last few years. We've seen machines become really good at tasks where we can infer from millions of examples. But they aren't good at tasks which require decisions based on a unique set of conditions, and where every instance is unique and must be considered on its own. Us humans are quite the opposite, we are great at judging unique situations, but really bad at inferring from large populations. Statistical thinking doesn't come naturally to us. I believe that the easiest way to bridge this gap is by having machine and person work together, each playing to its strengths.
What do you think?
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