Lifeboat Foundation

Here’s an interesting thought experiment. Pretend that you took all words at their literal interpretation, all of the time, wherever you went, and acted upon those words strictly and accordingly. For example, suppose you are driving in your car and perchance see a billboard that is touting the message that you should eat at Joe’s Pizzeria, accessible at the next exit up ahead. The normal course of events would be that you would consult your stomach to ascertain whether you are hungry. Furthermore, if you were hungry, the next question is whether you want pizza. Upon deciding that maybe you do want pizza, the next aspect would be whether you want to take the upcoming exit and eat at Joe’s Pizzeria since you might have in mind some other pizza eatery instead. But, none of those sensible and reasonable ideas rattle around in your noggin. We have agreed that you are going to take everything in a meticulously literal way. By gosh, the billboard instructed you to go eat at Joe’s Pizzeria, so that’s what you are going to do. Come heck or high water, you will take the next exit and you will drive straight to that pizzeria and you will order yourself a juicy hot pizza. This might work out okay and you’ll be happy that you obediently abided by the wording of the billboard. Perhaps though this side trip has made you late for work. Your boss won’t especially appreciate that you opted to be tardy because you just had to get a slice of pizza. Ouch, your boss fires you the moment you proffer such a lame excuse.

Full Story:

Existing AI and Machine Learning is stuck at doing literal interpretation and lacks any common-sense, which bodes for great concerns and especially when it comes to the advent of self-driving cars.

Continue reading “Strict Literal Interpretation Is A Hardheaded Drawback Of Machine Learning And Likewise Bad For AI Self-Driving Cars” »

Over the years, much has been said about artificial intelligence (AI) and the healthcare industry. Much of it has been focused on two extremes. On one hand, there’s the fairly mature use of neural networks for radiological analysis. On the other, there’s the focus on fraud management. Those have become “must have’s” in my perspective. It’s filling the middle ground that interests me. Medical insurance is, as patients, providers, and payors all can agree, is often convoluted and complex. There’s a business problem in making processes more efficient, and the foolishly named robotic process automation (RPA) is only a step in the right direction. More robust AI can help all three stakeholder groups address their needs in managing medical insurance. The general medical insurance industry does deal with radiology and images. However, that’s typically in specialties. In the dental industry, radiology is a regular tool, using x-rays to understand tooth and gum conditions and then to document work that has been done. The basics of AI and radiology have been covered, in this column and many other places, so this article isn’t going to cover the concepts, it’s important to realize how important that analysis is in dental care.

Full Story:

In this case, it’s increasing the accuracy and speed of dental insurance processing, resulting in better medical control, improved financial outcomes for providers and payors, and improved care and customer service for the patient.

Continue reading “Artificial Intelligence And Dental Insurance — It Is More Than A Focus On Radiology And Fraud” »

Ceramic Bodies is a collection of four porcelain vases that fit together like a three-dimensional puzzle.

Designer Jörg Hugo studied architecture before opening his own design studio. Calling it Studio Jörg Hugo, his work largely explores “the relationship between materiality, form, and space,” as he describes on his personal website. Relying on either digital or analog design methods and production techniques, Hugo creates timeless pieces that completely reinterpret how we interact with space and material. One of his most recent projects, Ceramic Bodies, comprises a collection of four porcelain vases that almost appear to melt into each other like a three-dimensional puzzle.

Designer: Jörg Hugo

Water flows more easily through narrower carbon nanotubes than larger ones and we have struggled to explain why. Now, one team has an answer: it may all be due to quantum friction.

Friction in its standard, classical sense is well understood by most people. The greater the degree of contact between two things moving past one another, the greater the energy needed to overcome friction. A narrow pipe has a larger wall relative to its cross-sectional area than a wider pipe, so you would expect the frictional forces experienced by water inside the smaller pipe to be proportionally greater. This means the water should flow less easily.

But carbon nanotubes don’t obey this rule. These are made of thin layers of graphite rolled into tubes just a few nanometres wide – and the narrower the diameter, the easier it is for water to flow through them.

Two teams have shown how quantum approaches can solve problems faster than classical computers, bringing physics and computer science closer together.