> The Turk, also known as the Mechanical Turk or Automaton Chess Player (German: Schachtürke, "chess Turk"; Hungarian: A Török), was a fake chess-playing machine constructed in the late 18th century. From 1770 until its destruction by fire in 1854 it was exhibited by various owners as an automaton, though it was eventually revealed to be an elaborate hoax.[1]
The BS-Industrial Complex of Phony A.I.
191–200 of 384 posts
Re: The BS-Industrial Complex of Phony A.I.
#192Getting ready for the next AI winter.... this is a cyclic phenomena.
Hopefully we don't take decades again for a simple but important change like changing tanh to relu activations.
Re: The BS-Industrial Complex of Phony A.I.
#193At the Biennale in Venice (one of the most important art shows there is) I saw a work which looked like this: There was a metal frame holding two glas plates with ventian sediment inbetween (sand, soil, mud). In the center there was another metal frame which formed a hole. There also were to PCB boards with ATMEGA micro controllers. In the text the artist claimed she controlled the biome of the soil with an AI using…
Anyone who read science fiction as a youth, must surely cringe at the way AI is used. Its a barely disguised synonym of magic.
Re: The BS-Industrial Complex of Phony A.I.
#194>> Deep learning algorithms have proven to be better than humans at spotting lung
cancer, a development that if applied at scale could save more than 30,000
patients per year.
It's not easy to scale deep learning because deep neural nets have a very strong tendency to overfit to their training dataset and are very bad at generalising outside their training dataset.
In a medical context this means that, while a particular deep learning image classifier might be very good at recognising cancer in images of patients' scans collected from a specific hospital, the same classifier will be much worse in the same task on images from a different hospital (or even from a different department in the same hospital).
To overcome this limitation, the only thing anyone knows that works to some extent is to train deep neural nets with a lot of data. If you can't avoid overfitting, at least you can try to overfit to a big enough sample that most common kinds of instances in your domain of interest will be included in it.
So basically to scale a diagnostic system based on deep neural net image classification to the nation level one would have to train a deep learning image classifier with the data from all hospitals in that nation.
This is not an easy task, to say the least. It's not undoable, but it's not as simple as having someone at Hospital X download a pretrained model in Tensorflow and train its last few layers on some CT scans.
Re: The BS-Industrial Complex of Phony A.I.
#195Earlier quoted context omitted.
The DotA bot is a very specific subproblem. Look at the open worm project for progress towards AGI
There are broadly two approaches to AGI. Behavior-driven or biology-driven. That's a cool biology-driven project, but for now, OpenAI Five is much closer to AGI than OpenWorm is.
Do you care to cite your assertions? The dichotomy you are referring to doesn't exist. Do you have a reliable source that says that there are two broad approaches to AGI, behavior driven and biology driven?
https://en.m.wikipedia.org/wiki/Embodied_cognition
There is widespread consensus that embodiment is significant for AGI. Modern approaches to AGI tend to be biologically inspired.
OpenAI five is not even remotely in the same class as an AGI system.
Re: The BS-Industrial Complex of Phony A.I.
#196Is Medium for pay now? They told me to sign up to get "one more free story".
Re: The BS-Industrial Complex of Phony A.I.
#197>> Deep learning algorithms have proven to be better than humans at spotting lung cancer, a development that if applied at scale could save more than 30,000 patients per year. It's not easy to scale deep learning because deep neural nets have a very strong tendency to overfit to their training dataset and are very bad at generalising outside their training dataset. In a medical context this means that, while a partic…
This statement is false, as recently demonstrated by DeepMind on retinal scans. Not only did they generalize outside of the training dataset but they were able to use the features learned by the model on an entirely different type of scanning device.
https://www.nature.com/articles/s41591-018-0107-6.epdf?autho...
"Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device."
Re: The BS-Industrial Complex of Phony A.I.
#198At the Biennale in Venice (one of the most important art shows there is) I saw a work which looked like this: There was a metal frame holding two glas plates with ventian sediment inbetween (sand, soil, mud). In the center there was another metal frame which formed a hole. There also were to PCB boards with ATMEGA micro controllers. In the text the artist claimed she controlled the biome of the soil with an AI using…
>> Accidentally (?) she managed to create the best representation of AI I have seen in art: all that counts is that you call it AI even if it is a simple algorithm.
Backpropagation, which most researchers will agree is an AI algorithm, is a "simple algorithm".
So are many other AI algorithms, some of which are simple enough to be understood so well that most people don't recognise them as AI anymore: search algorithms like depth- breadth- or best-first search, game-playing algorithms like alpha-beta minimax, gradient descent/ hill climb, are the examples that readily come to mind.
I think the above article and your comment are assuming that, for an algorithm to be "AI" it must be very complicated and difficult to understand. This is common enough to have a name: "the AI effect". A few years down the line I bet people will say that "this is not AI, it's just deep learning".
There's no reason for AI algorithms to be complicated. Very simple algorithms can create enormous complexity, even infinite complexity. The state of deterministic systems with even a couple of parameters can become impossible to predict after a small number of steps if they have the chaos property. Language seems to be the application of a finite set of rules on a finite vocabulary to produce an infinite set of utterances. Complexity arises from very simple sources, in nature.
Re: The BS-Industrial Complex of Phony A.I.
#199Earlier quoted context omitted.
Not even an ant. If AGI is a human then what we have is the equivalent of synthetic RNA molecules.
What? Ants don’t have general intelligence, and even ant colonies’ decision making (= simple swarm intelligence) is readily replicable in a programmed system, and has been, for a while. I don’t think a gradual scale is very helpful because I don’t think that the progression from current-generation AI to AGI is going to be gradual (it will require at least one paradigm shift). That said, if you want to compare AI prog…
If you are referring to the open worm project, then the conclusions you have drawn are exactly the opposite of what I have drawn.
As I understand it open worm is a hodge podge of statistical and numerical methods to try and replicate the sensorimotor behavior of c elegans. Open worm is neither complete, accurate nor elegant, despite knowing c elegans connectome and having mapped the some 900 cells in the worms body.
Re: The BS-Industrial Complex of Phony A.I.
#200Earlier quoted context omitted.
Surprisingly, recognizing a bird is much harder when you count rare species and running birds. Thus, sparse data. Does it recognize penguins too? AIs today still fail at it. Some folks were trying to train one to match endangered species and they had to pull mighty tricks to have some 70% accuracy. I think it was here on HN some time ago, but can't recall a link.
And yet, average humans can classify even fewer species.
Also after extensively studying ornithology?