Summary of “The Netflix Prize: How a $1 Million Coding Contest Changed Streaming”

The contest didn’t just catch the attention of college students with time to kill: An hour east of Princeton, in Middletown, New Jersey, the Netflix Prize announcement caught the eye of Chris Volinsky, head of a statistics research group at AT&T, and his team, who regularly read blogs to see what was going on in the emerging data science world.
“This was before ‘Big Data,'” he tells me, and therefore a Big Deal.
He pulled his group together and asked who wanted to poke around at the data set.
He didn’t know the contest would stretch on for years.
Hobbyists, academics, and professionals weren’t just drawn to the contest by the potential payday.
The revelations were just as enticing; because the winners would retain ownership of their work, a contestant like Volinsky could also pitch management at AT&T on devoting time and resources to the project.
Most importantly, the data was just plain interesting: an unruly mess of insights into taste, behavior, and pre-streaming viewer psychology.
As Chris Volinsky put it, “Everyone likes movies.”

The orginal article.

Summary of “AI is changing how we do science. Get a glimpse”

Their field lends itself to AI and machine-learning algorithms because nearly every experiment centers on finding subtle spatial patterns in the countless, similar readouts of complex particle detectors-just the sort of thing at which AI excels.
Particle physicists strive to understand the inner workings of the universe by smashing subatomic particles together with enormous energies to blast out exotic new bits of matter.
In 2012, for example, teams working with the world’s largest proton collider, the Large Hadron Collider in Switzerland, discovered the long-predicted Higgs boson, the fleeting particle that is the linchpin to physicists’ explanation of how all other fundamental particles get their mass.
At the LHC, a Higgs boson emerges from roughly one out of every 1 billion proton collisions, and within a billionth of a picosecond it decays into other particles, such as a pair of photons or a quartet of particles called muons.
Physicists still rely mainly on their understanding of the underlying physics to figure out how to search data for signs of new particles and phenomena.
Troyanskaya combined hundreds of data sets on which genes are active in specific human cells, how proteins interact, and where transcription factor binding sites and other key genome features are located.
To train the program-a deep-learning system-Zhou exposed it to data collected by the Encyclopedia of DNA Elements and Roadmap Epigenomics, two projects that cataloged how tens of thousands of noncoding DNA sites affect neighboring genes.
Like master chefs who start with a vision of the finished dish and then work out how to make it, many chemists start with the final structure of a molecule they want to make, and then think about how to assemble it.

The orginal article.

Summary of “Psychologists Shouldn’t Cite That Famous Hungry Judge Study”

As Glöckner notes, one surprising aspect of this study is the magnitude of the effect: “A drop of favorable decisions from 65% in the first trial to 5% in the last trial as observed in DLA is equivalent to an odds ratio of 35 or a standardized mean difference of d = 1.96.”.
This study might seem to be a convincing illustration of such an effect.
That’s the effect size in the hungry judges study.
If hunger had an effect on our mental resources of this magnitude, our society would fall into minor chaos every day at 11:45 a.m. Or at the very least, our society would have organized itself around this incredibly strong effect of mental depletion.
The first is the effect that a jury’s final verdict is likely to be the verdict a majority initially favored, which 13 studies show has an effect size of r = 0.63, or d = 1.62.
In their entire database, some effect sizes that come close to d = 2 are the findings that personality traits are stable over time, people who deviate from a group are rejected from that group, or that leaders have charisma.
There are simply no plausible psychological effects that are strong enough to cause the data pattern in the hungry judges study.
It is up to authors to interpret the effect size in their study, and to show the mechanism through which an effect that is impossibly large, becomes plausible.

The orginal article.

Summary of “How economics became a religion”

We follow an even more powerful religion, around which we have oriented our lives: economics.
At the end of the 20th century, amid an economic boom that saw the western economies become richer than humanity had ever known, economics seemed to have conquered the globe.
The hubris in economics came not from a moral failing among economists, but from a false conviction: the belief that theirs was a science.
The American Economic Association, to which Robert Lucas gave his address, was created in 1885, just when economics was starting to define itself as a distinct discipline.
Such responses served to remind practitioners of the taboos of economics: a gentle nudge to a young academic that such shibboleths might not sound so good before a tenure committee.
If you think describing economics as a religion debunks it, you’re wrong.
Paradoxically as economics becomes more truly scientific, it will become less of a science.
This is an edited extract from Twilight of the Money Gods: Economics as a Religion and How it all Went Wrong by John Rapley, published by Simon & Schuster on 13 July at £20.

The orginal article.

Summary of “Everybody lies: how Google search reveals our darkest secrets”

The power in Google data is that people tell the giant search engine things they might not tell anyone else.
Men conduct more searches for how to make their penises bigger than how to tune a guitar, make an omelette, or change a tyre.
Google search data can give us a minute-by-minute peek into such eruptions of hate-fuelled rage.
The top Google search in California with the word “Muslims” in it at the time was “Kill Muslims”.
Just about every negative search we could think to test regarding Muslims shot up during and after Obama’s speech, and just about every positive search we could think to test declined.
We might look at how racist searches change after a black quarterback is drafted in a city, or how sexist searches change after a woman is elected to office.
Google search data and other wellsprings of truth on the internet give us an unprecedented look into the darkest corners of the human psyche.
What would your search records reveal about you?They could definitely tell I’m a hypochondriac because I’m waking up in the middle of the night doing Google searches about my health.

The orginal article.

Summary of “You need to encrypt all your data. This is how it’s done”

In case you don’t know it, encryption is the science of modifying data to prevent intruders from making sense of it.
As I’ve argued before, encryption is your last line of defense, the one thing that can protect your data when all else goes wrong.
In case you find it too cumbersome to manually encrypt and decrypt your files, you can use tools such as Boxcryptor or Whisply, which integrate with most popular cloud services and add an easy-to-use layer of encryption.
Another alternative is to use an encrypted storage service such as SpiderOak One, Tresorit or Cryptobox, which have end-to-end encryption incorporated into their service.
Most desktop and mobile operating systems support full-disk encryption, a feature that will encrypt everything on your phone, computer or flash drive.
Newer Android devices also come with device encryption enabled out of the box, but with the variety of devices available out there, you might want to verify to make sure yours is encrypted.
Encryption is not a complete security solution and it doesn’t obviate the need for basic security measures such as keeping your operating system and software up to date with the latest security patches.
Encryption is definitely one of your best friends in the hostile world of digital information, connected devices and online services.

The orginal article.

Summary of “Frog evolution linked to dinosaur asteroid strike”

The huge diversity of frogs we see today is mainly a consequence of the asteroid strike that killed off the dinosaurs, a study suggests.
A new analysis shows that frog populations exploded after the extinction event 66 million years ago.
The new study shows that three major lineages of modern frogs – which together comprise about 88% of living frog species – appeared almost simultaneously.
The scientists sampled a core set of 95 genes from the DNA of 156 frog species.
Using frog fossils to provide “Ground truth” for the genetic data, the researchers were able to add a timeline to their family tree.
The three biggest frog groups – the hyloidea, microhylidae and the natatanura – all trace their origins to an expansion that occurred after 66 million years ago.
Another author, David Blackburn, from the Florida Museum of Natural History, explained: “Frogs have been around for well over 200 million years, but this study shows it wasn’t until the extinction of the dinosaurs that we had this burst of frog diversity that resulted in the vast majority of frogs we see today.”
The researchers point out that none of the frog lineages that originate before the extinction and survive through the asteroid impact happen to be adapted to living in trees.

The orginal article.

Summary of “A Reality Check for IBM’s AI Ambitions”

“Watson is a joke,” Chamath Palihapitiya, an influential tech investor who founded the VC firm Social Capital, said on CNBC in May. However, most of the criticism of Watson, even from M.D. Anderson, doesn’t seem rooted in any particular flaw in the technology.
It still seems likely that Watson Health will be a leader in applying AI to health care’s woes.
In 2015, the Washington Post quoted an IBM Watson manager describing how Watson was busy establishing a “Collective intelligence model between machine and man.” The Post said that the computer system was “Training alongside doctors to do what they can’t.”
To really help doctors get better outcomes for patients Watson will need to find correlations between what it reads in health records and what Tang calls “All the social determinants of health.” Those factors include whether patients are drug-free, avoiding the wrong foods, breathing clean air, and on and on.
Even M.D. Anderson, despite the fate of the Watson project, is continuing a large program that began around the same time, focused on gathering 1,700 types of clinical data on every patient who walks in its doors.
Y Futreal, the scientist who runs the program, says combining that patient information with research data will be crucial for the sorts of capabilities that systems like Watson could provide.
On the drug-discovery front, Watson Health is working with the Barrow Neurological Institute, where Watson helped find five genes linked to ALS that had never before been associated with the disease, and with the Ontario Brain Institute, where Watson identified 21 promising potential drug candidates.
Will Watson eventually make a difference in improving health outcomes and lowering costs? Probably, says Stephen Kraus, a partner at the VC firm Bessemer Venture Partners who focuses on health care and has invested in AI health-care startups.

The orginal article.

Summary of “How Analytics Are Used in the NFL”

SMARTER FOOTBALL WEEK: A series examining the cerebral side of the sport, including technology, analytics, how a brainy linebacker prepares and just what goes into a typical NFL play.
So here’s where you start: The reason there isn’t an NFL team ignoring analytics is because analytics has been done in football since Paul Brown came along.
How is that possible? Well, as Jaguars SVP of football technology and analytics Tony Khan-the son of owner Shahid Khan-explains it: “The adoption rate is far behind other sports.” More than three-quarters of NFL teams employ either a director of analytics or have a full-blown analytics department.
“So let’s say you’re playing Cincinnati, and you want to look at their tendencies when they’re in base personnel. You might wind up with 40 snaps out of 280. And then you’ll make a judgment. Well, of those 40, how many were on third down? How many came on second down?”.
HOW PRO FOOTBALL FOCUS CAME TO BE: In 2015, Jenny Vrentas told the story of how an Englishman who never played the game abandoned a profitable business to run an NFL advanced stats website.
“There are very, very few examples of an NFL player who produced a lot of sacks that wasn’t able to run a 10 time around 1.6,” says Banner, who established an analytics department in Philly in 1995.
Along those lines, ex-Browns GM Phil Savage used to send his scouts out for school visits after they were in-house for training camp with the warning: Now, remember, you were just watching NFL players.
As Kelly’s teams used it, individual profiles were built on players to provide coaches a roadmap for how hard guys were working, how far they could be pushed, and when they were at risk to suffer soft-tissue injuries.

The orginal article.

Summary of “How to Integrate Data and Analytics into Every Part of Your Organization”

The stakes are high, with International Data Corporation estimating that global business investments in D&A will surpass $200 billion a year by 2020.
D&A should be the pulse of the organization, incorporated into all key decisions across sales, marketing, supply chain, customer experience, and other core functions.
What’s the best way to build effective D&A capabilities? Start by developing a strategy across the entire enterprise that includes a clear understanding of what you hope to accomplish and how success will be measured.
One of the major American sports leagues is a good example of an organization that is making the most of its D&A function, applying it in scheduling to reduce expenses, for example, reducing the need for teams to fly from city to city for games on back-to-back nights.
Some organizations have D&A capabilities spread across functions, or rely on a few data scientists to provide insights.
In our experience, companies that build a D&A capability meeting their business needs have teams of data and software engineers who are skilled in the use of big data and data scientists who are wholly focused on a D&A initiative.
While structures vary, the team should be seamlessly integrated with the company’s existing providers and consumers of D&A, operating in cohesion with non-D&A colleagues – people who really understand both the business challenges and how the business works – to set and work toward realistic and relevant strategic goals.
In an age where data is created on a scale far beyond the human mind’s ability to process it, business leaders need D&A they can trust to inform their most important decisions – not just to reduce costs but also to achieve growth.

The orginal article.