61–70 of 285 posts
Earlier quoted context omitted.
I 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.”.
To 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.
> I am an AI skeptic. I am baffled by anyone who isn’t. I don’t see any path from continuous improvements to the (admittedly impressive) ‘machine learning’ field that leads to a general AI - I share the skepticism towards any progress towards 'general AI' - I don't think that we're remotely close or even on the right path in any way. - That doesn't make me a skeptic towards the current state of machine learning thoug…
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!
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.
> It’s not sorcery, it’s “magic” – in the sense of being a parlor trick, something that seems baffling until you learn the underlying method, whereupon it becomes banal. I think part of the problem is the belief that human or animal intelligence is somehow more mystical. People who think like this will see an ML implementation solve a problem better and/or faster than a human and counter "well, it's just using statis…
>simple machines. Ooof. Premed dropout here, so admittedly not an expert in human biology but this is a wild statement. A neuron is simple in the same way a transistor is simply a silicon sandwich doped with metals. A parlor trick is something that once you understand, is straightforward to implement on your own. Are you arguing that anyone now or in the foreseeable future could simply recreate the abilities of a hum…
There's a bias toward the marvel of human intelligence that causes some people to dismiss ML for the same underlying reasons we don't try to put a square peg in a round hole after infancy.
Side note: disagree all you like but starting a rebuttal with "oof" is the kind of dismissive language that lets people know you'll be taking a very reductionist approach in your reply.
The term “AGI” is also good, “artificial general intelligence” describes long term goals.
Earlier quoted context omitted.
What is quantitative in understanding the meaning of a name? We don't know that the brain runs on "numbers" (and no, it's no just like a "computer"). To respond to your edit: That is not brown... there is a whole science of color perception, have a look.
It’s not a computer, but it is quantified.
I see no path from "observation" to "model" that does not involve an arbitrary (aesthetic? Nonrational, human-necessitating?) choice. This would suggest that "general" AI is impossible. ON THE OTHER HAND There is a variety of general AI, called an "optimizer". It starts with something better than a void. Maybe that's the path we should be looking at.
Well, human thinking relies on prior models/filters for understanding the world as well so that would invalidate us as having general intelligence too?
Via aesthetics etc.
Or, in the case of the optimizer, I think the human equivalent would be desire.
Earlier quoted context omitted.
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!
I 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.”.
Earlier quoted context omitted.
First group. You’re observing that they aren’t doing a perfect job, which is true, but my grouping isn’t related to perfection of results.
> The deepmind team etc type of group who actually know what they’re doing and the boundaries of what they are working with. You claim that they "know what they are doing and the boundaries of what they are working with" -- and yet they recklessly make public a racist vision product?
Earlier quoted context omitted.
> But I also don't think the engineers working in this space are as out to lunch as the author seems to imply. Are you at all close to this space? It sounds you may be underestimating corporate politics and the lack of rigour and ethical thought with which these systems are applied. The example Cory puts on policing -- and the many other examples you can find in Evgeny Morozov's book or "The End of Trust" -- are soli…
My first thought was that I'm not the target audience of this article. I'm a ML practitioner. This seems more like an overstated opinionated wake up call to mgmt and sales people. Is not it?
I’d also point out that not all models are “theory-free”, as he describes it. I specifically do work in areas where we combine “theory” and machine learning, and it works very well.
And finally, his point about comprehension does not really fly for me. There is no magical comprehension circuit in our brain. It’s all done via biological processes we can study and emulate. Will that end up being a scaled up version of current neural nets? Will it need to arise from embodied cognition in robots? Will it be something else? I don’t know, but it’s certainly not magic, and we’ll get there eventually. Whether that’s 10 years or 1000, who knows.
Are current paradigms going to lead to AGI? Frankly, I’d just be guessing if I even tried to answer that. My gut instinct is no, but again, that’s just a guess. Can current methods evolve into better constrained systems with more generalizable results and measurable fairness? Absolutely.