IBM hopper med på vognen og open sourcer system til kunstig intelligens

IBM har lanceret sit machine learning-system SystemML som open source i kølvandet på lignende træk fra både Facebook og Google.

Det er åbenbart blevet populært blandt it-giganterne at frigive deres machine learning-software, som kan bruges til at udvikle selvlærende systemer.

Først frigav Facebook machine learning-systemet Torch, og siden hen fulgte Google trop med systemet TensorFlow, som blandt andet står bag de avancerede algoritme, som søgemaskinen bruger til at forudsige, hvad du vil søge på, mens du skriver.

Nu har IBM så lanceret SystemML som open source, hvilket gør valgmulighederne endnu større for virksomheder, der vil forsøge sig med machine learning.

Platformen er tilgængelig på Github.

Silicon Angle skriver:

The SystemML technology emerged from IBM’s development of Watson, and integrates closely with another Apache project, Spark. SystemML helps Watson to keep up to date by providing a language that directly exposes the capabilities of the artificial intelligence so data scientists can harvest it. Queries are written in syntax modeled after the popular R statistical programming framework, before being executed according to the most efficient mode of operation for the specific workload and operational characteristics of a Spark cluster.

“SystemML provides declarative large-scale machine learning (ML) that aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single node, in-memory computations, to distributed computations on Apache Hadoop and Apache Spark. ML algorithms are expressed in a R or Python syntax, that includes linear algebra primitives, statistical functions, and ML-specific constructs. This high-level language significantly increases the productivity of data scientists as it provides (1) full flexibility in expressing custom analytics, and (2) data independence from the underlying input formats and physical data representations. Automatic optimization according to data characteristics such as distribution on the disk file system, and sparsity as well as processing characteristics in the distributed environment like number of nodes, CPU, memory per node, ensures both efficiency and scalability.”

IBM said it would be donating SystemML to The Apache Foundation back in June this year, and the project has already hit a significant number of milestones since then, including more than 320 patches including APIs, Data Ingestion, Optimizations and Additional Algorithms. There have also been more than 90 contributions to the Apache Spark project from IBM’s engineers, aimed at making Machine Learning compatible with Spark.

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