Since I specialize in this, let me just say the challenge here is socializing it. The base line at typical companies with resources people care about do not use a lot of ML yet. The enterprise is easily 20 years behind R&D. Gap is increasing.https://twitter.com/yoavgo/status/969623317429805056 …
-
No amount of AutoML from google and every other AI vendor can fix that. To give you an idea: Intel is still what is bought 90% of the time. GPUs are still niche outside of HPC, small teams at companies, and academia.
-
-
Only if you work at google :). It took microsoft 10 years to get to a stage where they understood enterprise. It will take google a long time before they get to that point. In the mean time we are making ASICs and publishing. (Great keep moving the field forward!)
-
two points for me. Small companies do not need any AutoML o magical black boxed stuff they cannot understand of afford. They just need enterprise products suited on their needs (
#dataset) and that work.#MachineLearning or not, those product must integrate in their pipeline.
End of conversation
New conversation -
-
-
Seriously try getting on the phone and having meetings with actual people making these decisions. Outside the west coast, you might be in for a big surprise :). It's a solvable problem sure but overnight? It's going to take some massive changes at the top for that to happen.
Thanks. Twitter will use this to make your timeline better. UndoUndo
-
-
-
My bet is on "fear", fear to get distrupted away by the 3 person startup downtown. Having been working with old economy companies, I am seeing change ... although limited to linear and logistic regression ... but its happening
-
about 25% of US public companies will be distrupted away if they don't changepic.twitter.com/xw7uncUfBq
End of conversation
New conversation -
Loading seems to be taking a while.
Twitter may be over capacity or experiencing a momentary hiccup. Try again or visit Twitter Status for more information.