What is truly fascinating is that Neil Laurence needed the Covid crisis to make this observation.A.I. can’t solve this: The coronavirus could be highlighting just how overhyped the industry is
https://www.cnbc.com/2020/04/29/ai-has-limited-role-coronavirus-pandemic.html?__source=sharebar|linkedin&par=sharebar
“It’s fascinating how quiet it is,” said Neil Lawrence, the former director of machine learning at Amazon Cambridge.
“This (pandemic) is showing what bulls--t most AI hype is. It’s great and it will be useful one day but it’s not surprising in a pandemic that we fall back on tried and tested techniques.”
Those techniques include good, old-fashioned statistical techniques and mathematical models. The latter is used to create epidemiological models, which predict how a disease will spread through a population. Right now, these are far more useful than fields of AI like reinforcement learning and natural-language processing.
// In fairness, it is not a law of gravity that AI should be good at everything. Maybe stick to statistics?
Maybe you should consider the same approach than the one used for the curse of dimensionality : just do the best possible with your training data and see if it is enough.Stupid question: I want to do ML application but I have no (or not enough) training data; is this possible?
Of course, AI wasn't around in 2001 when the ODE model were used to predict [$]2e^6[$] deaths during the foot-and-mouth disease.What is truly fascinating is that Neil Laurence needed the Covid crisis to make this observation.A.I. can’t solve this: The coronavirus could be highlighting just how overhyped the industry is
https://www.cnbc.com/2020/04/29/ai-has-limited-role-coronavirus-pandemic.html?__source=sharebar|linkedin&par=sharebar
“It’s fascinating how quiet it is,” said Neil Lawrence, the former director of machine learning at Amazon Cambridge.
“This (pandemic) is showing what bulls--t most AI hype is. It’s great and it will be useful one day but it’s not surprising in a pandemic that we fall back on tried and tested techniques.”
Those techniques include good, old-fashioned statistical techniques and mathematical models. The latter is used to create epidemiological models, which predict how a disease will spread through a population. Right now, these are far more useful than fields of AI like reinforcement learning and natural-language processing.
// In fairness, it is not a law of gravity that AI should be good at everything. Maybe stick to statistics?
I think that classic AI is not built for these kinds of problems.Maybe you should consider the same approach than the one used for the curse of dimensionality : just do the best possible with your training data and see if it is enough.Stupid question: I want to do ML application but I have no (or not enough) training data; is this possible?
I don't think so. I'll write to Paul to have newsI think that classic AI is not built for these kinds of problems.Maybe you should consider the same approach than the one used for the curse of dimensionality : just do the best possible with your training data and see if it is enough.Stupid question: I want to do ML application but I have no (or not enough) training data; is this possible?
BTW have you already published your article?