The Professional Programmer

I recently read an article focused on Richard Branson's views about flexible work arrangements, specifically the outdated idea of the 5-day work week (Richard Branson is the founder of Virgin Group).

I'm going to say this right up front: I'm not a fan of Facebook. In fact, I've turned down offers to interview with them before, because I am not a fan of the platform, how it is used by people, and how it monetizes the resulting breadcrumb trail from that use. As you might guess, I don't have a Facebook account. I was deleting my Facebook account before that was a cool thing to do (2015).

People always want to know what the leaders of technology companies have to say on all sorts of topics. MSNBC realized this and has a limited-run series called " Revolution " where they speak to different technology CEOs. Tonight's was Apple CEO Tim Cook (the first one was Google CEO Sundar Pachai and YouTube CEO Susan Wojcicki). It was a serendipitously-timed interview, coming on the heels of revelations about potentially ALL of Facebook's user profiles being inappropriately (if not unlawfully) being made available to groups like the now-infamous Cambridge Analytica (check out my thoughts on Mark Zuckerberg's Senate testimony here ). Mr. Cook spoke about several different subjects in the course of the 1 hour special, focusing mostly on the role technology plays in the evolution of employment and education. But he also dipped his toes into data, privacy, policy, and politics.

I recently retweeted an interesting article in the Economist that talks about Otto's incorporation of AI into its core business and how they allow AI models to automatically order product in anticipation of demand:

Today, a great piece by Dr. Clay Johnston (Dean of the UT Dell Medical School) popped up in my email called Today's Doctors Are an Endangered Species . It's a great blog post that I recommend everyone take a look at. In it, Dr. Johnston talks about how AI is going to eventually be a full replacement for the way physicians practice medicine today, but that it doesn't necessarily mean the end of human-practiced medicine. In fact, physicians' ability to communicate with patients will become more crucial than ever, as the more tedious jobs of diagnosis and prescription move into the realm of AI.

The Washington Post published an article yesterday (Feb. 5, 2018) about the rapid progress being made in the area of quantum computing, and how this will likely effect fields such as cryptography, and the dependent IT security field. The article also talks about quantum computing's potential "in [improving] weather forecasting, financial analysis, logistical planning, the search for Earth-like planets, and drug discovery."

( Engadget ) Google is trying its hand at flight delay prediction, saying they will now flag flights that they feel have at least an 80% chance of being delayed. I'm sure frequent flyers will find this useful. For the casual traveler, the second item mentioned might be more interesting: You can now check flight amenities, such as whether a rate includes seat selection or overhead bin access. Of course, as a Southwest traveler I rarely worry about such things... I've personally used Google Flights  for years when selecting business travel or making sure that Southwest's fares beat out my company's preferred carriers. It's nice to see that AI is bringing even more functionality to an already good product. https://www.engadget.com/2018/01/31/google-flights-predicts-delays/

[caption id="" align="alignleft" width="343"] The "Snowflake" mobile coprocessor for Deep Learning from Purdue-affiliated startup FWDNXT (via HPCWire).[/caption] A Purdue affiliated startup called FWDNXT is trying to design low-power mobile deep learning. Eugenio Culurciello, an associate professor at Purdue, says that the "Snowflake" mobile coprocessor "is able to achieve a computational efficiency of more than 91 percent on entire convolutional neural networks..." I'm not exactly sure what he means by 91% computational efficiency. One of the biggest problems with deep learning right now is the last of agreed-upon benchmarks and means of measuring performance. This could mean 91% of peak theoretical efficiency for the chip in terms of single-precision FLOPs, or 91% of theoretical peak performance on this particular network architecture. Unfortunately, there just isn't any way to know. The original article is posted ...