title: "Assembled Thoughts on Lifelong Learning and Robotics" abstract: > author:
- family: Lawrence given: Neil D. gscholar: r3SJcvoAAAAJ institute: University of Cambridge twitter: lawrennd url: http://inverseprobability.com date: 2020-09-04 venue: Naples or Zoom transition: None
\include{talk-macros.tex}
\section{Introduction}
https://sites.google.com/view/ll4lhri2020
\include{_ml/includes/empirical-effectiveness-of-deep-learning.md}
\include{_ml/includes/new-methods-required.md}
\include{_ml/includes/massively-missing-data.md}
\notes{Machine learning involves taking data and combining it with a model in order to make a prediction. The data consist of measurements recorded about the world around us. A model consists of our assumptions about how the data is likely to interrelate, typical assumptions include smoothness. Our assumptions reflect some undelying belief about the regularities of the universe that we expect to hold across a range of data sets.} $$ \text{data} + \text{model} \rightarrow \text{prediction} $$ \notes{From my perspective, the model is where all the innovation in machine learning goes. The etymology of the data indicates that it is given (although in some cases, such as active learning, we have a choice as to how it is gotten), our main control is over the model. This is the key to making good predictions. The model is a mathematical abstraction of the regularities of the universe that we believe underly the data as collected. If the model is chosen well we will be able to interpolate the data and precit likely values of future data points. If it is chosen badly our predictions will be overconfident and wrong.}
\include{_ml/includes/model-vs-algorithm.md}
\include{_ml/includes/is-my-model-useful.md}
\include{_ml/includes/big-data-health-motivation.md}
\include{_ml/includes/not-useful-model.md}
\include{_ml/includes/big-data-consistency.md}
\include{_ml/includes/parameter-bottleneck.md}
\include{_ml/includes/non-parametric-challenge.md}
\include{_ml/includes/multivariate-gaussian-closure.md}
\include{_ml/includes/making-parameters-non-parametric.md}
\include{_ml/includes/making-parameters-non-parametric-illustration.md}
\section{Conclusions}
\thanks
\references