Learning how to challenge fake news

Vox featured an author who teaches fifth graders. This teacher showed her students how to investigate whether or not what an article was saying could be backed up by objective evidence. They quickly became very accomplished fact-checkers!

Here was a summary of the plan.

I was determined to change the way I help my students critically analyze the information they were finding on the internet

To make sure I wouldn’t have any student in the same situation as Andy ever again, I started asking my students to examine seven different elements of a news article. If the information checks out on each of these points, it has a high likelihood of being accurate. Still, passing the test is not a guarantee that it’s fact.

1. Copyright: I always ask students to check the bottom of the webpage to see if the information has been submitted for ownership.
2. Verification with multiple sources: Students must double check the information on a few different web pages. Like in a trial, the more corroborating witnesses, the more likely the truth will be discovered.
3. Credibility of source, such as between History.com versus a random unknown source: I tell them to check if the source has been recently created. Sources that have been around for a while can show reliability over time and be tested by hindsight, whereas recently created sources don’t carry much of a track record.
4. Date published: I always ask them to check how recently the page was updated to see how current the information is and whether anything has changed.
5. Author's expertise and background with the subject: Students should check if the author is someone who has dedicated time and effort to learning this subject. For example, a university professor typically has increased credibility versus a hobbyist.
6. Does it match your prior knowledge: I ask them if the information matches up with what they have learned before
7. Does it seem realistic: I tell students to use their common sense. Does something seem authentic or probable?

Here is a link to the full article:  Vox: I taught my 5th-graders how to spot fake news

More about Pareto: why the best get the most

This article examines why the top 1% seems to accumulate the lion's share of the benefits.

The 1 Percent Rule: Why a Few People Get Most of the Rewards

Pareto is famous for the 80/20 rule. Scientists now attribute this effect to something called "accumulated advantage." The article gives an example based on trees in the Amazon.

Imagine two plants growing side by side. Each day they will compete for sunlight and soil. If one plant can grow just a little bit faster than the other, then it can stretch taller, catch more sunlight, and soak up more rain. The next day, this additional energy allows the plant to grow even more. This pattern continues until the stronger plant crowds the other out and takes the lion’s share of sunlight, soil, and nutrients.

From this advantageous position, the winning plant has a better ability to spread seeds and reproduce, which gives the species an even bigger footprint in the next generation. This process gets repeated again and again until the plants that are slightly better than the competition dominate the entire forest.

Scientists refer to this effect as “accumulative advantage.” What begins as a small advantage gets bigger over time. One plant only needs a slight edge in the beginning to crowd out the competition and take over the entire forest.

What hospitals waste

Excellent ProPublica article on the waste in healthcare.

Link to the full article:  ProPublica: What Hospitals Waste

Talk to experts and many agree that waste would be a good place to start. In 2012 the National Academy of Medicine estimated the U.S. health care system squandered $765 billion a year, more than the entire budget of the Defense Department. Dr. Mark Smith, who chaired the committee that authored the report, said the waste is “crowding out” spending on critical infrastructure needs, like better roads and public transportation. The annual waste, the report estimated, could have paid for the insurance coverage of 150 million American workers — both the employer and employee contributions.

UCSF reviewed all the preference cards for each surgeon, which specify how the operating room should be set up before each operation. The hospital now makes sure the set-up doesn’t include supplies that aren’t actually needed, preventing a significant amount of the waste.

In a separate study in the December edition of JAMA Surgery, Zygourakis and her colleagues showed each UCSF surgeon his or her direct costs per procedure in comparison to other surgeons in the institution. Most doctors were unaware of operating-room costs. Then they gave them an incentive: Their departments would get a bonus if they reduced costs by at least 5 percent.

The median surgical supply costs dropped by 6.5 percent in the group of surgeons who participated — a savings of about $836,000 over one year — while the control group’s costs increased by almost 7.5 percent.

The secret sauce in AI: Reinforcement Learning

This article discusses how computers learned to perform complex tasks like playing the game of Go. The breakthroughs did not come from programming. The real growth came when the computer was able to use trial and error to improve its performance.

I’m watching the driving simulation at the biggest artificial-intelligence conference of the year, held in Barcelona this past December. What’s most amazing is that the software governing the cars’ behavior wasn’t programmed in the conventional sense at all. It learned how to merge, slickly and safely, simply by practicing. During training, the control software performed the maneuver over and over, altering its instructions a little with each attempt. Most of the time the merging happened way too slowly and cars interfered with each other. But whenever the merge went smoothly, the system would learn to favor the behavior that led up to it.

This approach, known as reinforcement learning, is largely how AlphaGo, a computer developed by a subsidiary of Alphabet called DeepMind, mastered the impossibly complex board game Go and beat one of the best human players in the world in a high-profile match last year. Now reinforcement learning may soon inject greater intelligence into much more than games. In addition to improving self-driving cars, the technology can get a robot to grasp objects it has never seen before, and it can figure out the optimal configuration for the equipment in a data center.....

That view changed dramatically in March 2016, however. That’s when AlphaGo, a program trained using reinforcement learning, destroyed one of the best Go players of all time, South Korea’s Lee Sedol. The feat was astonishing, because it is virtually impossible to build a good Go-playing program with conventional programming. Not only is the game extremely complex, but even accomplished Go players may struggle to say why certain moves are good or bad, so the principles of the game are difficult to write into code. Most AI researchers had expected that it would take a decade for a computer to play the game as well as an expert human.
....
Reinforcement learning works because researchers figured out how to get a computer to calculate the value that should be assigned to, say, each right or wrong turn that a rat might make on its way out of its maze. Each value is stored in a large table, and the computer updates all these values as it learns. For large and complicated tasks, this becomes computationally impractical. In recent years, however, deep learning has proved an extremely efficient way to recognize patterns in data, whether the data refers to the turns in a maze, the positions on a Go board, or the pixels shown on screen during a computer game.
....

The article goes on to cite self-driving cars as a good application of this technology. It enables "good sequences of decisions" to perform complex maneuvers like negotiating roundabouts.

Here is a link to the full article:  MIT Technology Review: Reinforcement learning

Superbugs can migrate up pipes!

Via Ars Technica, research indicates that splashy sinks can aid in the transmission of superbugs in healthcare facilities.

It started with research into an outbreak in Canada that infected 36 people and killed 12. They knew it came from the sinks, but could not figure out how. No amount of cleaning or disinfection seemed to wipe out the bacteria.

Now, with a new study published in Applied and Environmental Microbiology, researchers may finally have an answer to superbugs’ sink-dwelling skills: They survive in P-traps and can quickly climb pipes. More specifically, researchers at the University of Virginia found that bacteria can happily colonize a sink’s P-trap and then sneak back up the pipe and into the drain by forming a protective, creeping film, called a biofilm, on the plumbing. Once they get to the drain, they only need a burst of water to scatter up into the sink and surrounding, touchable surfaces.

It will be interesting to see how this type of analysis changes sink design moving forward.

Here is the link to the full article:

Superbugs in P-traps