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How to recognize AI snake oil [pdf]

cs.princeton.edu

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Re: How to recognize AI snake oil [pdf]

#11

Post by nabla9 »

cellular wrote: post #5

It seems like at least half the tech people I talk to work with AI now... no matter their field.

Any type of heuristic search = AI. hand-made decision tree or lookup table = AI, Naive bayes = AI, KNN = AI. Uses numpy = AI. One employee in mobile app startup has graduate thesis in ML = AI startup. Approximate string matching in SQL query = AI. Not clear what the product is going to be = AI.

"Information is not knowledge. Knowledge is not wisdom. Wisdom is not truth. Truth is not beauty. Beauty is not blockchain. Blockchain is not AI. AI is THE BEST.”

Re: How to recognize AI snake oil [pdf]

#14

Post by buboard »

avip wrote: post #10

My company is sourcing AI from MTurk. It's actually cheaper than running fat GPU model training instances. The network learns fast and adapts well to changes in inputs. I envision the sticker "human inside" strapped on our algorithms.

You should emphasize that this is Organic AI. It's low carbon and overall greener.

Re: How to recognize AI snake oil [pdf]

#15

Post by formalsystem »

Over the years my heuristic has turned into: "Did the team formulate their problem as a supervised learning problem?" - If not it's probably BS.

In longform if anyone is interested https://medium.com/@marksaroufim/can-deep-learning-solve-my-...

EDIT: I would consider autoencoders, word2vec, Reinforcement Learning examples of turning a different problem into a supervised learning problem

EDIT 2: Social functions like happiness, emotion and fairness are difficult to state - you can't have a supervised learning problem without a loss function

Re: How to recognize AI snake oil [pdf]

#16

Post by Tistel »

I worked at a place that was selling ML powered science instrument output analysis. It did not work at all (fake it till you make it is normal, was told). So there was a person in the loop (machine output -> internet -> person doing it manually pretending to be machine -> internet -> report app). The joke was “organic neural net.” Theranos of the North! ML is a great and powerful pattern matcher (talking about NN not first order logic systems) right now, but, I fear we are going into another AI winter with all the over promising.

Re: How to recognize AI snake oil [pdf]

#17

Post by Isamu »

Excellent takeaways:

>AI excels at some tasks, but can’t predict social outcomes.

>We must resist the enormous commercial interests that aim to obfuscate this fact.

>In most cases, manual scoring rules are just as accurate, far more transparent, and worth considering.

Re: How to recognize AI snake oil [pdf]

#18

Post by mumblemumble »

buboard wrote: post #14
avip wrote: post #10

My company is sourcing AI from MTurk. It's actually cheaper than running fat GPU model training instances. The network learns fast and adapts well to changes in inputs. I envision the sticker "human inside" strapped on our algorithms.

You should emphasize that this is Organic AI. It's low carbon and overall greener.

Or keep calling it AI, and concede that AI stands for "actual intelligence" if someone asks you directly.

Re: How to recognize AI snake oil [pdf]

#19

Post by jnwatson »

nabla9 wrote: post #11
cellular wrote: post #5

It seems like at least half the tech people I talk to work with AI now... no matter their field.

Any type of heuristic search = AI. hand-made decision tree or lookup table = AI, Naive bayes = AI, KNN = AI. Uses numpy = AI. One employee in mobile app startup has graduate thesis in ML = AI startup. Approximate string matching in SQL query = AI. Not clear what the product is going to be = AI. "Information is not knowledge. Knowledge is not wisdom. Wisdom is not truth. Truth is not beauty. Beauty is not blockchain.…

In my space (cybersecurity) I’ve heard “more than 2 joins” (in an SQL context) is ML.
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