The discussion has been open for a while now. We know we want Machine Learning (ML) to be able to drive the Artificial Intelligence (AI) and smart automation that we demand in tomorrow’s software -- but how do we build, train and further educate the ML brains we seek to now bring forward?
Computer brains are built with models that are exposed to datasets, which are then trained, tested and enriched over time as they learn what’s right, what’s wrong and ultimately what is altogether culturally and ethically appropriate or not.
Inside Microsoft’s ML brain
As already noted on Forbes, Microsoft used its sometimes physical sometime virtual Microsoft Connect(); event this year to detail the next stage of development in its cloud platform’s Machine Learning services. The Microsoft Azure Machine Learning service is a cloud service that enables developers and data scientists to build, train and deploy machine learning models. The software will reside inside Microsoft’s AI and ML portfolio, which includes the firm’s Cognitive Services brand and the wider Azure ML Studio.
So why this update? Because the trend (indeed, the need) among software application developers seeking to put ML ‘smartness’ into our apps is real, yet the task of doing so is laborsome, very complex and requires a lot of backend grunt work.
What developers are looking for -- and this is not a Microsoft message, this is what the whole industry is talking about -- are routes to be able to abstract that backend complexity and (in the most non-technical terms) plug in chunks of additional ML intelligence into the apps they are trying to build.
According to Microsoft’s Connect(); event ‘book of news’ story compendium (yes, that’s now a thing, really) documentation, “Azure Machine Learning service eliminates the heavy lifting of end-to-end machine learning workflows and can reduce time to production from weeks to hours. It enables data scientists to automate model selection and tuning, increase productivity with DevOps for machine learning -- and easily deploy models to the cloud and the edge."
How was this brain built?
The advancements that now come forward in this part of Microsoft’s ML brain play result in part from experimentation carried out by Nicolo Fusi, who works in the automated machine learning research team at Microsoft Research.
Working on gene editing experiments, Fusi found that trying to work out which ML model to use was too complex and, quite simply, just not a good use of time. His team instead focused on developing an AI capability that could automatically perform the data transformation (categorizing which data is relevant to an intelligence task, how it should be parsed and filed… and in what order of importance it should be ranked), the model selection for the job (what data rules govern what happens inside the AI brain) and the hyperparameter tuning (constraints, weights or learning rates to generalize different data patterns) part of AI development. View More
