grakn VS Serpent.AI

TypeDB : A Strongly-typed Database

How does it compare to, say grakn (renamed https://vaticle.com/, I think?), or draph (https://dgraph.io/), or Ontotext's GraphDB (https://www.ontotext.com/products/graphdb/), or Datomic?

Edge database looks pretty freakin awesome. It basically seems to re-write SQL and Graph databases together to create some new-ish programming language. It takes care of all the problems GraphQL has, and seems to be built separately but on top of postgres. It is really something unique, beautiful, and powerful. They don't have a security layer yet or a cloud hosting environment, but both are in the works. However, postgres still suffers from the scalable problems we all know. If you like unique fetching and strong typing, also check out TypeDB. It doesn't make its own list number because there is not cloud version, middleware, etc. However, worth checking out.

If you find yourself limited by triplestores, there's also a new growing area of development in knowledge engines, which allow edges-of-edges, entailed relations, hypergraph relations, and more of the power you'd get from full logic programming. TypeDB (recently renamed from Grakn) is an example of that type of database.

In the story to follow, David presents how his team at Roche was able to identify potential novel targets that were not identified by Open Targets as highly ranked. This was made possible with TypeDB, which his team used to store the relevant data and then find underlying biological evidence for those new targets.

{ stdenv, lib, openjdk,typedbHome ? "~/.typedb_home", fetchzip}: let typedbVersion = "2.1.1"; typedbDirLinux = "typedb-all-linux-${typedbVersion}"; typedbDirMac = "typedb-all-mac-${typedbVersion}"; typedbDirWindows = "typedb-all-windows-${typedbVersion}"; typedbDir = if stdenv.hostPlatform.isWindows then typedbDirWindows else if stdenv.isDarwin then typedbDirMac else typedbDirLinux; linuxSrc = builtins.fetchTarball { url = "https://github.com/vaticle/typedb/releases/download/2.1.1/typedb-all-linux-2.1.1.tar.gz"; sha256 = "15nwm2dr68p67c2xcqigs66gd679j1zr72gqv7qxgvflwyvyz8fb"; }; windowsSrc = fetchzip { url = "https://github.com/vaticle/typedb/releases/download/2.1.1/typedb-all-windows-2.1.1.zip"; sha256 = "0vd66gfshkg697z07nhy957mwqzlli4r4pmn67hx58n9mkg024kq"; }; macSrc = fetchzip { url = "https://github.com/vaticle/typedb/releases/download/2.1.1/typedb-all-mac-2.1.1.zip"; sha256 = "16hlfy6kh2rnvcralz206q13mghb0rv8wazpg6q3h324p5rdys54"; }; srcFolder = if stdenv.hostPlatform.isWindows then windowsSrc else if stdenv.isDarwin then macSrc else linuxSrc ; javaPatch = '' 20c20 < JAVA_BIN=java --- > JAVA_BIN=${openjdk}/bin/java ''; in stdenv.mkDerivation rec { pname = "typedb"; version = typedbVersion; src = srcFolder; phases = [ "installPhase" ]; buildDepends = [ openjdk ]; installPhase = '' echo "here" # added for debugging ls -lah # " echo "--" # " #patch before install echo "${javaPatch}" > typedb_java.patch patch ./${typedbDir}/typedb typedb_java.patch mkdir $out cp -r ./${typedbDir} $out # add a wrapper script to $out that will move typedb to $typedb # this is necessary because typedb needs a writable environment echo " # on the first start copy everything to typedbHome if [ ! -f ${typedbHome}/typedb ]; then mkdir -p ${typedbHome}; cp -r $out/${typedbDir}/* ${typedbHome}; # correct permissions so that typedb and the user can write there chmod -R u+rw ${typedbHome} chmod u+x ${typedbHome}/typedb fi; ${typedbHome}/typedb \$@; " > $out/typedb chmod +x $out/typedb ''; doCheck = true; meta = with lib; { description = "TypeDB is a distributed knowledge graph: a logical database to organise large and complex networks of data as one body of knowledge."; longDescription = '' TypeDB is a distributed knowledge graph: a logical database to organise large and complex networks of data as one body of knowledge. TypeDB provides the knowledge engineering tools for developers to easily leverage the power of Knowledge Representation and Automated Reasoning when building complex systems. Ultimately, TypeDB serves as the knowledge-base foundation for intelligent systems. ''; homepage = "https://www.grakn.ai/"; license = licenses.gpl3Plus; platforms = platforms.all; maintainers = [ maintainers.haskie ]; }; }

grakn.ai : The Database for AI

Give the open-source Grakn a look as well (full transparency: I work there), it is an abstraction over a built in-house hypergraph storage engine and persisted layer using RocksDB. It's a logical database with a reasoning engine at the database level. https://github.com/graknlabs/grakn

A: I am. While serpent.ai attempted to get an AI to play Isaac, the project hasn't been updated in years.

You don't need a whole team nowadays. Amazon has services that let you train your own neural nets with a little bit of knowledge. Then there are tools like SerpentAI that let your AI interface with games (don't know if it works with Blizzard games, but it works with Steam).

i cant. is there any way https://github.com/SerpentAI/SerpentAI would work. the game is entirely mouse movements.

Hey! So, I'd like to implement NEAT machine learning to train an agent to play Donkey Kong Country, but there doesn't seem to be much in the way of tutorials/examples for Serpent.AI (like, its weirdly dead given how powerful it seems to be and github page is full of dead links) so I wanted to see if any of you fine folk would recommend for/against its use or that of an alternative. Any other advice also appreciated.

Serpent AI : Game Agent Framework. Helping you create AIs / Bots to play any game you own! BETA