In 2018 we are all living in a world where almost everything is becoming connected, whether it’s the power grid, network, phone system, our cars, or the appliances that heat our home or chill our food. As this Internet of Things (IoT) continues to proliferate. This growing class of cloud-connected devices – 9 billion of which ship every year – run tiny MCU chips that will power everything from kitchen appliances and toys to industrial equipment on factory floors. This next wave of connected devices is in increasingly intelligent and connected. They will improve daily life in countless ways, but if they’re not secure, they will make people, communities and countries vulnerable to attack in more ways than ever before.
As s result of this the Threat and security risks expand exponentially. At this year RSA conference in San Francisco, Microsoft announced new offerings to take security more squarely to where it needs to go and where it has not effectively gone before – the edge.
The Azure Sphere Services are a new services and features that will better harden not only our intelligent cloud but also the billions of connected devices that live on its edge.
I’ve spent many years referencing Wikipedia’s list of cognitive biases whenever I have a hunch that a certain type of thinking is an official bias but I can’t recall the name or details. It’s been an invaluable reference for helping me identify the hidden flaws in my own thinking. Nothing else I’ve come across seems to be both as comprehensive and as succinct.
However, honestly, the Wikipedia page is a bit of a tangled mess. Despite trying to absorb the information of this page many times over the years, very little of it seems to stick. I often scan it and feel like I’m not able to find the bias I’m looking for, and then quickly forget what I’ve learned. I think this has to do with how the page has organically evolved over the years. Today, it groups 175 biases into vague categories (decision-making biases, social biases, memory errors, etc) that don’t really feel mutually exclusive to me, and then lists them alphabetically within categories. There are duplicates a-plenty, and many similar biases with different names, scattered willy-nilly.
I’ve taken some time over the last four weeks (I’m on paternity leave) to try to more deeply absorb and understand this list, and to try to come up with a simpler, clearer organizing structure to hang these biases off of. Reading deeply about various biases has given my brain something to chew on while I bounce little Louie to sleep.
Source: Cognitive bias cheat sheet
What seems like common sense isn’t common practice, says Rowena Murray who shares her top tips for getting published
The Windows Azure for Research project facilitates and accelerates scholarly and scientific research by enabling researchers to use the power of Windows Azure to perform big data computations in the cloud.
Windows Azure Research Award Program
Microsoft Research is soliciting proposals for the use of Windows Azure in research. We welcome research proposals from any branch of scholarly activity. To qualify, applicants must be affiliated with an academic institution or non-profit research laboratory. In addition to individual investigator projects, we are interested in projects that will support access to services and data of value to a collaboration or community. Winning proposals will be awarded large allocations of Windows Azure storage and compute resources for a period of one year.
We will periodically announce additional special-opportunity RFPs on specific cloud research topics. These topics will include community research data services, streaming instrument data to the cloud, machine learning in the cloud, large-scale image analysis, environmental science, astronomy, genomics, and urban science.
Your proposal should not exceed three pages in length. It should include resource requirement estimates (number of core, storage requirements, and so forth) for your project.
This article from Will Thalhimer, particularly in relation to the limitations of lab-based rather than practical reserach, is excellent. It encapsulates exactly why my own research is based on my actual work and not some contrived set of lab-based circumstances. It is let down only by its fondness for exclamation marks…
It also reminds me of my mantra, “Despite what they would have you believe, no one knows anything.”
In the learning field, research insights can help practitioners (trainers, teachers, instructional designers, elearning developers) build more effective learning interventions. Unfortunately, some practitioners look at the flaws and limitations in the research and reject research entirely. This article, by noted research-translator, Will Thalheimer, PhD, provides insights into balancing research limitations and benefits—by examining the workplace learning field.