This article provides an overview of the Collective Knowledge technology (CK or cKnowledge). CK attempts to make it easier to reproduce ML&systems research, deploy ML models in production, and adapt them to continuously changing data sets, models, research techniques, software, and hardware. The CK concept is to decompose complex systems and ad-hoc research projects into reusable sub-components with unified APIs, CLI, and JSON meta description. Such components can be connected into portable workflows using DevOps principles combined with reusable automation actions, software detection plugins, meta packages, and exposed optimization parameters. CK workflows can automatically plug in different models, data and tools from different vendors while building, running and benchmarking research code in a unified way across diverse platforms and environments. Such workflows also help to perform whole system optimization, reproduce results, and compare them using public or private scoreboards on the CK platform (https://cKnowledge.io). For example, the modular CK approach was successfully validated with industrial partners to automatically co-design and optimize software, hardware, and machine learning models for reproducible and efficient object detection in terms of speed, accuracy, energy, size, and other characteristics. The long-term goal is to simplify and accelerate the development and deployment of ML models and systems by helping researchers and practitioners to share and reuse their knowledge, experience, best practices, artifacts, and techniques using open CK APIs.

Deep learning and unsupervised feature learning have shown great promise in many practical applications. State-of-the-art performance has been reported in several domains, ranging from speech recognition and image recognition to text processing and beyond. It's also been observed that increasing the scale of deep learning--with respect to numbers of training examples, model parameters, or both--can drastically improve accuracy. These results have led to a surge of interest in scaling up the training and inference algorithms used for these models and in improving optimization techniques for both. The use of GPUs is a significant advance in recent years that makes the training of modestly-sized deep networks practical. A known limitation of the GPU approach is that the training speed-up is small when the model doesn't fit in a GPU's memory (typically less than 6 gigabytes).

Designers and entrepreneurs have been fascinated with AI and conversational design for years. So I wasn't surprised when a potential client asked me about AI: "We're building an AI assistant for enterprises, and it needs to combine conversational design with machine learning." I said, "Of course we can design it, but it will take a lot of user research." Designing an AI application, particularly one with a voice interface, is a huge project. Even with the popularity of Alexa, Google Assistant and Siri, there's still a great deal of ambiguity about how to build this type of technology.

Good advice on how NOT to be an academic when you finish your PhD is pretty thin on the ground. Many supervisors have never done anything else, and/or are not well enough connected with industry to know what is'hot'. Careers centres at universities tend to shape their offerings around the huge undergraduate cohort, who have very different needs. If you want to leave academia at the end of you PhD it's likely you will face some kind of career transition. While we train astrophysicists, we don't have any astrophysics companies in Australia.