Welcome to our weekly selection of digital innovation news. Based on our opinionated always changing automated token based selection algorithm we present some top innovation news to get you thinking, debating and collaboration on making our world better.
1 Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease
We observed that data-driven models used on an extended dataset can outperform conventional models for prognosis, without data preprocessing or imputing missing values. We then used Cox models, random forests and elastic net regression on an extended dataset with 586 variables to build prognostic models and identify novel prognostic factors without prior expert input. Electronic health records (EHR) provide large quantities of data for such models, but conventional epidemiological approaches require significant researcher time to implement. by Andrew J. Steele, Spiros C. Denaxas, Anoop D. Shah, Harry Hemingway, Nicholas M. Luscombe Prognostic modelling is important in clinical practice and epidemiology for patient management and research. We also found that data-driven models allow identification of novel prognostic variables; that the absence of values for particular variables carries meaning, and can have significant implications for prognosis; and that variables often have a nonlinear association with mortality, which discretised Cox models and random forests can elucidate.
(PLOS ONE)
2 Model can more naturally detect depression in conversations
In the majority of tests, the researchers’ model outperformed nearly all other models. With text, the model can accurately detect depression using an average of seven question-answer sequences. As the sequences accumulated, the model extracted speech patterns that emerged for people with or without depression. Machine- learning models, for instance, have been developed that can detect words and intonations of speech that may indicate depression. In a paper being presented at the Interspeech conference, MIT researchers detail a neural-network model that can be unleashed on raw text and audio data from interviews to discover speech patterns indicative of depression.
3 Distributed Auto-ML with TPOT with Dask
TPOT is an automated machine learning library for Python. It does some feature engineering and hyper-parameter optimization for you. TPOT uses genetic algorithms to evaluate which models are performing well .
(Anaconda)
4 Bradley M. Kuhn: Challenges in Maintaining A Big Tent for Software Freedom
The simplicity and beauty of copyleft is that it takes away someone’s software freedom only at the moment when they take away someone else’s software freedom; copyleft ensures that is the only reason your software freedom should be lost. It means we have a big tent for software freedom, and we sometimes stand under it with people whose behavior we despise. Speaking out from our microphone built from our principled stand on software freedom, we can make an impact that denying software freedom to others never could. However, taking away software freedom from bad actors, while it seems like a panacea to other societal ills, will simply backfire. Fortunately, software freedom is already successful enough that we can do at least a little bit of that now.
5 The New Science of Seeing Around Corners
The answer is that there are too many of these light rays traveling in too many different directions. Using conventional recording equipment, even iPhones, in broad daylight, Bouman and company filmed a building corner’s “penumbra”: the shadowy area that is illuminated by a subset of the light rays coming from the hidden region around the corner. A set of light rays that strikes the wall in the first video frame is briefly blocked by the arm in the next. The window was acting as a pinhole camera — the simplest kind of camera, in which light rays pass through a small opening and form an inverted image on the other side. Like pinholes and pinspecks, edges and corners also restrict the passage of light rays.
6 Research Headlines – Empowering citizens through science
An EU-funded project is organising a multitude of events across Europe to actively involve citizens in science. The aim is to motivate people from all walks of life to explore and create solutions for a more sustainable future.
7 There’s Always a Time Lag (With a Price Tag)
They failed to appreciate that quality technologies demanded a new organizational structure — teams — and a new leadership philosophy — empowerment. This time lag usually has a very high price tag. But when it comes to AI, CEOs and other experts seem to be escaping this standard. At one conference, a senior partner of a global consultancy urged his audience not to fear AI. Technologies require “one to three decades” for the “plateauing of adoption,” he said. As with prior technologies, leaders will face profound challenges much earlier.
8 MIT-created programming language Julia 1.0 debuts
After years of tinkering, the dynamic programming language Julia 1.0 was officially released to the public during JuliaCon, an annual conference of Julia users held recently in London. Edelman is director of the Julia Lab at MIT and one of the co-creators of the language at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL). Julia is the only high-level dynamic programming language in the “petaflop club,” having achieved 1.5 petaflop/s using 1.3 million threads, 650,000 cores and 9,300 Knights Landing (KNL) nodes to catalogue 188 million stars, galaxies, and other astronomical objects in 14.6 minutes on the world’s sixth-most powerful supercomputer. At MIT, Julia users and developers include professors Steven Johnson, Juan Pablo Vielma, Gilbert Strang, Robin Deits, Twan Koolen, and Robert Moss. It is used at more than 700 universities and research institutions and by companies such as Aviva, BlackRock, Capital One, and Netflix.
9 Introducing a New Framework for Flexible and Reproducible Reinforcement Learning Research
Research Scientist, Google Brain Team Reinforcement learning (RL) research has seen a number of significant advances over the past few years. Today we’re introducing a new Tensorflow-based framework that aims to provide flexibility, stability, and reproducibility for new and experienced RL researchers alike. We are already actively using it for our research and finding it is giving us the flexibility to iterate quickly over many ideas. Further, reproducing the results from existing frameworks is often too time consuming, which can lead to scientific reproducibility issues down the line. This release also includes a set of colabs that clarify how to use our framework. Ease of Use Clarity and simplicity are two key considerations in the design of this framework.
10 Let’s be Transparent
This year, we’re continuing the tradition by releasing the Firefox Public Data Report. This report expands on the hardware report by adding data on how Firefox desktop users are using the browser and the web. We collect non- sensitive data from the Firefox desktop browsers’ Telemetry system, which sends us data on the browser’s performance, hardware, usage and customizations. Two years ago, we released the Firefox Hardware Report to share with the public the state of desktop hardware.
The Radical Open Innovation weekly overview is a brief overview of innovation news on Digital Innovation and Management Innovation from all over the world. Your input for our next edition is welcome! Send it to [info] at [bm-support]dot[org]