Intensix raises $8.3M for machine-learning tech to head off ICU complications Intensix, which is harnessing machine learning in the ICU, reeled in an $8.3 million Series A. The funds are pegged for the expansion of its sales and marketing ops in North America as well as further development of its predictive analytics platform. The Intensix platform applies machine learning to the early detection of life-threatening complications in intensive care. A proliferation of structured and unstructured ICU data--from vital signs to historical and demographic data--goes into the system, is run through a set of models and results in predictions, CEO Gal Salomon said. ICU staff and management could use these predictions to head off deterioration before it happens. Studies have shown that the tech could potentially save lives, reduce the length of hospital stays and lower costs, the company said in a statement.

Yesterday, during DLD Conference, Demis Hassabis, founder and CEO of DeepMind, discussed AlphaGo beating Go-champion with machine learning, and what the collaboration between humans and machines will be bringing in the future. He highlighted the importance of staying creative and follow our own intuitions when working with AI, forecasting 10 exciting years ahead of us. We also hosted our own panel on "Fixing Education for the A.I. age" where our panelists discussed the shifts in education that will push towards a computational knowledge economy. Conrad Wolfram introduced us to the need to teach students the skills to solve problems with the support of computers, instead of learning how to do specific things (particularly in maths) ourselves. Jurgen Schmidhuber instead observed how we are only thinking of short-term improvements in the education system, while we should be looking decades ahead.

Image Synthesis from Yahoo's open_nsfwWarning: This post contains abstract depictions of nudity and may be unsuitable for the workplaceYahoo's recently open sourced neural network, open_nsfw, is a fine tuned Residual Network which scores images on a scale of to on its suitability for use in the workplace. In the documentation, Yahoo notes "Defining NSFW material is subjective and the task of identifying these images is non-trivial. Moreover, what may be objectionable in one context can be suitable in another." What makes an image NSFW, according to Yahoo? Like Google's Deep Dream, this visualization trick works by maximally activating certain neurons of the classifier.