Goals Include:

A community-driven alternative to the PyTorch and Tensorflow ecosystems, that can work with them based on a collection of interoperable projects and standards for doing deep learning. This requires auto-differentiation, graph construction and optional lazy evaluation, and support for GPUs and distributed and sparse arrays across libraries.
Ability to use OpenTensors for other domains than deep learning - general data science and scientific computing. This is equally important to deep learning, there are many applications for differentiable computing and in need of the performance and flexibility that lazy evaluation and GPUs provide.
Building a community of companies, projects and people to reduce duplication of effort and to accelerate the development of the Python AI, Data Science and scientific computing ecosystem.

Achieving these goals will require equal amounts of innovation and community and consensus building. The capacity of community projects to engage with faster-paced deep learning frameworks, and define or adopt extension points and API alignments, needs to be increased.

Innovation will consist of a mix of new libraries (e.g. for auto-differentiation), accelerating existing projects (e.g. pydata/sparse for sparse arrays) and making choices between or bridging of competing technologies (e.g. MLIR, TVM IR or Weld IR as the intermediate representation to target).

Quansight is aiming to bring companies and community projects together, and coordinate a concerted effort to build the OpenTensors community, standards and tools, and reference implementations.