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I'm in favor of changing the terminology from AI and ML to something along the lines of 'prediction model' so that the idea of machines 'thinking' is replaced w
by shreyshnaccount 5y ago
I'm in favor of changing the terminology from AI and ML to something along the lines of 'prediction model' so that the idea of machines 'thinking' is replaced with them 'predicting'. it's just easier for our mushy meat brains to think that AI and ML means that it'll lead to general AI or as I like to call it 'general purpose decision maker'. it's all about the language!
- coddle-hark 5y agoI like the term “data driven algorithm“. It makes it clear to everyone involved that what we’re doing is just adjusting an algorithm based on the data we have. No-one in their right minds would confuse that with building a true “A.I.”.
- spockz 5y agoWhat about “data derived algorithm”? The algorithm itself isn’t really driven by data after it has been designed anymore.
- skohan 5y agoI mean if we want to be really accurate, we could say something like "highly dimensional data-derived function"
- shreyshnaccount 5y agowhy stop at that? 'high dimensional matrix parameterised data derived non linear function optimisation and unique hypothesis generation' just rolls off the tongue doesn't it xD
- tw04 5y agoTo be frank: that very much does not make it clear to everyone involved. If you told the average Joe you had a “data driven algorithm” instead of “AI” you would likely get a blank stare in return.
- deleted 5y ago[deleted]
- shreyshnaccount 5y agoconfusion is better than wrongful understanding?
- falcor84 5y agoI'm sorry to say that I don't see any clear line separating "data driven algorithms" from the embodied minds that we are.
- gmadsen 5y agowhy? we don't understand the architecture, but the brain certainly uses electrical signals in an algorithmic way
- zoomablemind 5y agoIn not so long past, there was another popular expression - "computer-aided ...", which was quite fit for the practical use (like CAD for design, CAT for translation etc) Perhaps, CAI for inference or insight would express it more fairly. Alternatively, AI could've stood for 'automated inference', but sure it's all too late to rebrand. We humans still not clear about nature of our own intelligence, yet already claimed being able to manufacture it.
- skohan 5y agoI think inference isn't the right term either. I think current ML is more like automated inductive reasoning.
- aidenn0 5y agoAutomated inductive reasoning sounds a lot like artificial intelligence to me...
- skohan 5y agoIdk maybe it's semantics, inference to me sounds more like a logical leap is happening, whereas in my mind the simplest form of inductive reasoning is just expecting a pattern to repeat itself.
- aidenn0 5y agoExpecting a pattern to repeat itself may not be sufficient to count as intelligence, but general purpose pattern recognition certainly seems to fit the bill.
- bississippi 5y agoComputer Aided Pattern Recognition sounds reasonable in setting public expectations.
- kzrdude 5y agoML seems to be an ok term to me? It's the "intelligence" part in AI that needs a disclaimer.
- MR4D 5y agoI propose “heuristic optimization”.
- dgb23 5y agoI like this the most, but you can also generate things, which isn’t implied by optimization strongly.
- esfandia 5y agoWe already have "Pattern Recognition", not sure why it got absorbed by Machine Learning (the two terms seemed to co-exist with some overlap on what they covered), and then ML got absorbed by AI.
- jstx1 5y agoML is still widely used and is much more common than AI as a term. So I wouldn't say that it has been absorbed by AI but their use sometimes overlaps depending on the target audience.
- akomtu 5y agoIirc, predictive coding is a well known branch of math that's said to be the next big step towards AI.
- JohnJamesRambo 5y agoDo I think or predict?
- quickthrower2 5y agoI predict therefore I will be
- piokoch 5y agoWhen I have encountered for the first time ML term I decided to learn what is that new great stuff. To my great surprise this was a typical old new thing called "statistical inference" in the days when I was working as a statistician. There were a few new things, like ignoring model, choosing right variables, whatever was available was thrown into the equation, if it was clear that such "model" is over-fitted, there were some methods to overcome this by adding some random coefficients to the model that were smoothing it a little. So, the naming is there... could be modified by adding some clarification that we don't care that much about understanding model we plan to use.