When we offered CS231n (Deep Learning class) at Stanford, we intentionally designed the programming assignments to include explicit calculations involved in backpropagation on the lowest level. The students had to implement the forward and the backward pass of each layer in raw numpy. This is seemingly a perfectly sensible appeal - if you're never going to write backward passes once the class is over, why practice writing them? Are we just torturing the students for our own amusement? Some easy answers could make arguments along the lines of "it's worth knowing what's under the hood as an intellectual curiosity", or perhaps "you might want to improve on the core algorithm later", but there is a much stronger and practical argument, which I wanted to devote a whole post to: In other words, it is easy to fall into the trap of abstracting away the learning process -- believing that you can simply stack arbitrary layers together and backprop will "magically make them work" on your data.

Financial Portfolio theories are one of the important achievements in financial economics in the last XX Century. One such theory goes by the designation of Markowitz Portfolio Theory or Modern Portfolio Theory, named after Nobel Prize in Economic Sciences winner Harry Markowitz. We read the Wikipedia entry for this theory and we can immediately confirm it as a mathematical and statistical theory at its core. And if it is mathematical and statistical at its core it is well positioned to be improved and enhanced by an algorithmic, computational approach. And that is the case with our paper's proposal: it is another one software approach to Portfolio Theory that turns the problem of finding the best efficient frontier predicted by the theory into a mathematical optimization problem, but from the new machine learning/deep learning perspective.

Real-time machine learning has access to a continuous flow of transactional data, but what it really needs in order to be effective is a continuous flow of labeled transactional data, and accurate labeling introduces latency. During this year, I heard and read a lot about real-time machine learning. People usually provide this appealing business scenario when discussing credit card fraud detection systems. They say that they can continuously update credit card fraud detection model in real-time (See "What is Apache Spark?", "…real-time use cases…" and "Real time machine learning"). It looks fantastic but not realistic to me.

This will reduce attrition, hiring costs and the effort.

With an increase in automation and the advent of artificial intelligence (AI), there are expected to be significant changes in the job market. The White House has released a report in the ways AI will affect the economy over the coming years and decades. The pace and direction of the development of AI will decide what sectors are affected and how soon. At a minimum, and in the near term future, drivers and cashiers are going to be replaced by machines. AI is going to sooner or later replace millions of jobs, and affect the livelihoods of these workers.

We hear and read in the popular media about Artificial Intelligence (AI) all the time. We have movies about them. We hear about Elon Musk and Stephen Hawking warning us about AI's apocalyptic consequences. We hear from the World Economics forum about AI's effect on taking away our jobs. We here about how disruptive AI will be for businesses.

Or Meaningless New research offers insights into what gives work meaning -- as well as into common management mistakes that can leave employees feeling that their work is meaningless. GE's Big Bet on Data and Analytics This case study focuses on GE's "industrial internet" strategy. Aligning the Organization for Its Digital Future In this report, MIT Sloan Management Review and Deloitte explore how digitally savvy executives are aligning their people, processes, and culture with an eye toward long-term digital success. Data Sharing and Analytics Drive Success with IoT This MIT Sloan Management Review study concluded that obtaining business value from the internet of things depends on companies' willingness to share data with other organizations. Beyond the Hype: The Hard Work Behind Analytics Success This report by MIT Sloan Management Review and SAS found that few companies have a strategic plan for analytics or are executing a strategy for what they hope to achieve with analytics.

Would you have sex with a robot? Would a robot have the right to say no to such a union? These were just a few of the questions being asked at the second Love and Sex with Robots conference hastily rearranged at Goldsmiths University in London after the government in Malaysia - the original location - banned it. It has proved controversial, not only to countries with conservative views. There were no representatives from the sex industry in attendance and no sex robots on display, leading some to question the point of the event.

The report anticipates economic effects across several fronts. AI, like any new technology, is key to growth because it increases output without requiring increases in labor or capital. "In the last decade, despite technology's positive push, measured productivity growth has slowed in 30 of the 31 advanced economies, slowing in the United States from an average annual growth rate of 2.5% in the decade after 1995 to only 1.0% growth in the decade after 2005," the report states. Any increase in aggregate productivity from adopting artificial intelligence would be a welcomed change. But the resultant job automation is causing alarm.

Artificial intelligence (AI) had a coming out party of sorts in 2016. Even though it has been in development for decades, this year, with the perfect combination of cheap computing power and access to increasing amounts of data, it seems AI's time has come. Its first foray in business has been directed at making salespeople more efficient at every level of the sales workflow. If you think about it, it makes sense to start with the part of the company that drives revenue. Certainly the vendors recognize that, says Alan Lepofsky, an analyst at Constellation Research, who is working on the impact of AI on work.