We are pleased to announce the findings of our recent survey and should like to thank all those who gave their opinions on the impact of artificial intelligence on the facilities management (FM) and real estate sectors (RE). For practical purposes, artificial intelligence (AI) covers developments in software and hardware that include, but which are not limited to, smart applications, cognitive frameworks, robotics, actroids and drones. A summary of the findings is outlined below. The age distribution of respondents placed 52% in the 51-67 age group, with 32% in the 36-50 age group and 8% in the 26-35 age group. Our first question asked for an opinion on the impact that AI will have had on the FM and RE sectors 5 years from now. Just 4% considered that the impact would be negligible.

Some very intelligent people – including Stephen Hawking, Elon Musk and Bill Gates – seem to have been seduced by the idea that because computers are becoming ever faster calculating devices that at some point relatively soon we will reach and pass a "singularity" at which computers will become "more intelligent" than humans. Some are terrified that a society of intelligent computers will (perhaps violently) replace the human race, echoing films such as the Terminator; others – very controversially – see the development of such technologies as an opportunity to evolve into a "post-human" species. Already, some prominent technologists including Tim O'Reilly are arguing that we should replace current models of public services, not just in infrastructure but in human services such as social care and education, with "algorithmic regulation". Algorithmic regulation proposes that the role of human decision-makers and policy-makers should be replaced by automated systems that compare the outcomes of public services to desired objectives through the measurement of data, and make automatic adjustments to address any discrepancies. Not only does that approach cede far too much control over people's lives to technology; it fundamentally misunderstands what technology is capable of doing. For both ethical and scientific reasons, in human domains technology should support us taking decisions about our lives, it should not take them for us. At the MIT Sloan Initiative on the Digital Economy last week I got a chance to discuss to discuss some of these issues with Andy McAfee and Erik Brynjolfsson, authors of "The Second Machine Age", recently highlighted by Bloomberg as one of the top books of 2014. Andy and Erik compare the current transformation of our world by digital technology to the last great transformation, the Industrial Revolution.

Stumbling through overwhelming aisles of wines, it can be daunting to find the perfect match to that asparagus-heavy dish. There are pairing tables, books and websites filled with descriptions and ratings, but they aren't quite an exact science. Seeking out and buying a wine, only to realize you've gone the wrong direction, is something most people experience at least once. What makes pairing and choosing wine easier? It might sound unromantic, but several wineries already use data extensively during the growing and planning processes.

Too often, Machine Learning is used synonymously with Data Science. Before I knew what both of these terms were, I simply thought that Data Science was just some new faddish word for Machine Learning. Over time though, I've come to appreciate the real differences in these terms. I've always wondered how misconceptions like these endure for so long -- my current working hypothesis: people are deathly afraid of looking stupid. Too afraid of asking someone "what is machine learning?

Data Scientists enjoy one of the top-paying jobs, with an average salary of 120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists in the tech industry - and prepare you for a move into this hot career path. This comprehensive course includes 68 lectures spanning almost 9 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I'll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn't. The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers.

Summary: I learn best with toy code that I can play with. This tutorial teaches gradient descent via a very simple toy example, a short python implementation. Followup Post: I intend to write a followup post to this one adding popular features leveraged by state-of-the-art approaches (likely Dropout, DropConnect, and Momentum). Feel free to follow if you'd be interested in reading more and thanks for all the feedback! In Part 1, I laid out the basis for backpropagation in a simple neural network. Backpropagation allowed us to measure how each weight in the network contributed to the overall error. This ultimately allowed us to change these weights using a different algorithm, Gradient Descent.

Just like nuclear physics, machine learning, AI, and data science can be used either for the better of for the worse. You can make either useful energy or terrible bombs using nuclear fission. The same applies to machine learning, and in my example below, it gets even worse than Hiroshima or Nagasaki. Here I am discussing a potential use of machine learning in military operations. The scenario below is entirely hypothetical.

Atlas, the humanoid robot created by Alphabet (GOOGL, Tech30) company Boston Dynamics, can open doors, balance while walking through the snow, place objects on a shelf and pick itself up after being knocked down. The new version of Atlas is smaller and more nimble than its predecessor. It's fully mobile too -- the previous version had to be tethered to a computer. Atlas was created to perform disaster recovery in places unsafe for humans, such as damaged nuclear power plants. The robot made its debut in 2013 during a competition held by the Defense Advanced Research Projects Agency. The new version of Atlas is a result of seven computer research teams from around the world who were contracted to develop software to give Atlas a better brain.

Google's artificial intelligence (AI) ethics board, established when Google acquired London AI startup DeepMind in 2014, remains one of the biggest mysteries in tech, with both Google and DeepMind refusing to reveal who sits on it. Google set up the board at DeepMind's request after the cofounders of the 400 million research-intensive AI lab said they would only agree to the acquisition if Google promised to look into the ethics of the technology it was buying into. Business Insider asked Google once again who is on its AI ethics board and what they do but it declined to comment. A number of AI experts told Business Insider that it's important to have an open debate about the ethics of AI given the potential impact it's going to have on all of our lives. Artificial intelligence is the field of building computer systems that understand and learn from observations without the need to be explicitly programmed, as defined by Nathan Benaich, an AI investor at venture capital firm Playfair Capital.

I am from Kashmir, a region prone to earthquakes and floods. When I was 17 years old, in 2005, 70,000 people lost their lives in an earthquake in my hometown. This event compelled me to study engineering and specifically in 2005, start performing earthquake engineering research. Then, in 2014, a combination of two events on different sides of the world inspired the creation of One Concern. In 2014, during a break from graduate school at Stanford, I was visiting my parents in Kashmir when a large flood engulfed the state.