Improve readme (#443)

The improved readme taken from my previous tutorial PR.

README.md

@@ -8,14 +8,24 @@
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-A domain-specific language and backend for probabilistic programming languages, used by [Turing.jl](https://github.com/TuringLang/Turing.jl).
+*A domain-specific language and backend for probabilistic programming, used by [Turing.jl](https://github.com/TuringLang/Turing.jl).*
+
+DynamicPPL is the part of Turing.jl that deals with defining, running, and manipulating models. DynamicPPL provides:
+
+  - General-purpose probabilistic programming with an intuitive syntax.
+  - The `@model` syntax and macro for easily specifying probabilistic generative models.
+  - A tracing data-structure for tracking random variables in dynamic probabilistic models.
+  - A rich contextual dispatch system allowing for tailored behaviour during model execution.
+  - A user-friendly syntax for probabilistic queries.
+
+Information on how to use the DynamicPPL frontend to build Bayesian models can be found on the [Turing website](https://turing.ml/). Tutorials explaining how to use the backend can be found [alongside the documentation](https://turinglang.github.io/DynamicPPL.jl/stable/). More information can be found in our paper [DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models](https://arxiv.org/pdf/2002.02702.pdf).
 
 ## Do you want to contribute?
 
-If you feel you have some relevant skills and are interested in contributing then please do get in touch and open an issue on Github.
+If you feel you have some relevant skills and are interested in contributing, please get in touch! You can find us in the #turing channel on the [Julia Slack](https://julialang.org/slack/) or [Discourse](discourse.julialang.org). If you're having any problems, please open a Github issue, even if the problem seems small (like help figuring out an error message). Every issue you open helps us improve the library!
 
 ### Contributor's Guide