Summary of “Here’s How Cornell Scientist Brian Wansink Turned Shoddy Data Into Viral Studies About How We Eat”

More than three years later, Wansink would publicly praise Siğirci for being “The grad student who never said ‘no.'” The unpaid visiting scholar from Turkey was dogged, Wansink wrote on his blog in November 2016.
Her tenacity ultimately turned the buffet experiment into four published studies about pizza eating, all cowritten with Wansink and widely covered in the press.
Over the last 14 months, critics the world over have pored through more than 50 of his old studies and compiled “The Wansink Dossier,” a list of errors and inconsistencies that suggests he aggressively manipulated data.
Now, interviews with a former lab member and a trove of previously undisclosed emails show that, year after year, Wansink and his collaborators at the Cornell Food and Brand Lab have turned shoddy data into headline-friendly eating lessons that they could feed to the masses.
The correspondence shows, for example, how Wansink coached Siğirci to knead the pizza data.
The newly uncovered emails – obtained through records requests to New Mexico State University, which employs Wansink’s longtime collaborator Collin Payne – reveal two published studies that were based on shoddy data and have so far received no public scrutiny.
Wansink said his lab’s data is “Heavily scrutinized,” and that’s “What exploratory research is all about.”
In 2013, Werle and Wansink were discussing a different study about whether describing a walk as fun, such as by framing it as a scenic stroll rather than a form of exercise, influenced how much the walkers would want to eat afterward.

The orginal article.

Summary of “The GANfather: The man who’s given machines the gift of imagination”

The goal of GANs is to give machines something akin to an imagination.
When future historians of technology look back, they’re likely to see GANs as a big step toward creating machines with a human-like consciousness.
Yann LeCun, Facebook’s chief AI scientist, has called GANs “The coolest idea in deep learning in the last 20 years.” Another AI luminary, Andrew Ng, the former chief scientist of China’s Baidu, says GANs represent “a significant and fundamental advance” that’s inspired a growing global community of researchers.
In one widely publicized example last year, researchers at Nvidia, a chip company heavily invested in AI, trained a GAN to generate pictures of imaginary celebrities by studying real ones.
Once it’s been trained on a lot of dog photos, a GAN can generate a convincing fake image of a dog that has, say, a different pattern of spots; but it can’t conceive of an entirely new animal.
Researchers at Yale University and Lawrence Berkeley National Laboratory have developed a GAN that, after training on existing simulation data, learns to generate pretty accurate predictions of how a particular particle will behave, and does it much faster.
Hany Farid, who studies digital forensics at Dartmouth College, is working on better ways to spot fake videos, such as detecting slight changes in the color of faces caused by inhaling and exhaling that GANs find hard to mimic precisely.
Researchers are already highlighting the risk of “Black box” attacks, in which GANs are used to figure out the machine-learning models with which plenty of security programs spot malware.

The orginal article.

Summary of “Tech companies should stop pretending AI won’t destroy jobs”

First, China has a huge army of young people coming into AI. Over the past decade, the number of AI publications by Chinese authors has doubled.
In China, shared bicycles generate 30 terabytes of sensor data in their 50 million paid rides per day-that’s roughly 300 times the data being generated in the US. Third, Chinese AI companies have passed the copycat phase.
The rise of China as an AI superpower isn’t a big deal just for China.
The competition between the US and China has sparked intense advances in AI that will be impossible to stop anywhere.
As my Uber driver in Cambridge has already intuited, AI will displace a large number of jobs, which will cause social discontent.
Some people argue that it will take longer than we think before jobs disappear, since many jobs will be only partially replaced, and companies will try to redeploy those displaced internally.
“Take the extra money made by AI and distribute it to the people who lost their jobs,” they say.
We need to find the jobs that AI can’t do and train people to do them.

The orginal article.

Summary of “Why cops won’t need a warrant to pull the data off your autonomous car”

Instantly, Jaeger realized that the promise of AVs to not only be safer for those inside the car, but it may also, potentially, be a way for law enforcement to collect data and information about everything else around it.
Under current law, all of that data can be obtained relatively easily by federal law enforcement.
If the companies don’t want to play ball, such data can be accessed with a mere court order under the Stored Communications Act of 1986.
The case involves phone company records and a string of robberies in Michigan around 2010, but Carpenter essentially asks, does law enforcement need a warrant to be able to access potentially intimate location information? Or, as the government has claimed, is such data easily accessible to law enforcement under the increasingly-anachronistic third-party doctrine?
“There is nothing to stop a federal agency from requesting location data from an autonomous vehicle maker,” Catherine Crump, a law professor at the University of California, Berkeley, told Ars.
California does have a relatively new law, known as the California Electronic Communications Privacy Act, which does impose a warrant requirement to access location data.
So state and local authorities in the Golden State, at least, would likely have to clear the warrant hurdle before getting at such data.
As of today, there appears to be no publicly-known instance of law enforcement going to an AV manufacturer, with a warrant or otherwise, to obtain data.

The orginal article.

Summary of “Lasers Reveal a Maya Civilization So Dense It Blew Experts’ Minds”

The lasers are only the first step, he added, noting that he and archaeologists still had to trek through jungles to verify the data while contending with thick undergrowth, poisonous snakes, swarms of killer bees and the odd scorpion.
The lidar technology essentially allows researchers to spot bumps in the landscape.
The Maya culture was known for its sophisticated approach to agriculture, arts and astronomy.
The peak era for the civilization, which some archaeologists refer to as the Classic Period, is generally considered to have lasted from around A.D. 250 to 900.The total population at that time was once estimated to be a few million, said Diane Davies, an archaeologist and Maya specialist based in the United Kingdom.
In light of the new lidar data, she said it could now be closer to 10 million.
Dr. Davies was not involved in the lidar project but considered it “Really big, sensational news.” She said the data should encourage people not only to re-evaluate Maya civilization, but also to learn from it.
“To have such a large number of people living at such a high level for such a long period of time, it really proves the fact that these people were highly developed, and also quite environmentally conscientious,” she said.
He added that in addition to changing people’s perception of the Maya culture, lidar represented “a sea change” in the field of archaeology.

The orginal article.

Summary of “Strava Heatmap shows that fitness trackers represent a privacy threat”

Just how big of a problem was brought into stark relief Monday, when a Twitter user pointed out that the Strava global heatmap – an online, interactive map of activity by people who use the Strava mobile app or have a Fitbit or Jawbone – inadvertently revealed the location of military bases overseas.
To make matters worse, Wired reported it’s also possible to take data publicly available via Strava’s API and see the names of individuals tied to specific running routes.
People have hard time comprehending what a searchable database of many people’s data reveal, etc.
Adam Harvey, perhaps best known for creating CV Dazzle and Hyperface, pointed out that the digital traces we leave online via fitness tracking apps like Strava give others power over us.
Strava helps illustrate fact that if anyone gained access to all of Google’s data they would rule the world.
While the U.S. government is likely not about to drone you, there exist other threats to your privacy – both present and future – that are only exacerbated by apps like Strava and devices like Fitbit.
Sure, you can denote your runs as “Private” on the aforementioned app, but even having done that, your data is still being uploaded to Strava’s servers – it’s just not actively shared with every creep who decides to poke around publicly available datasets like the heatmap.
For starters, try deleting the Strava app from your smartphone or tossing that Fitbit in your drawer and leaving it there.

The orginal article.

Summary of “how DIY ​​rebels ​are working to ​replace the tech giants”

These people often talk in withering terms about Big Tech titans such as Mark Zuckerberg, and pay glowing tribute to Edward Snowden.
In the last few months, they have started working with people in the Belgian city of Ghent – or, in Flemish, Gent – where the authorities own their own internet domain, complete with.
Using the blueprint of Heartbeat, they want to create a new kind of internet they call the indienet – in which people control their data, are not tracked and each own an equal space online.
“I want to be able to be in a society where I have control over my information, and other people do as well. Being a woman in technology, you can see how hideously unequal things are and how people building these systems don’t care about anyone other than themselves. I think we have to have technology that serves everybody – not just rich, straight, white guys.”
It has been in its new home for three months: 10 people work here, with three in a newly opened office in Chennai, India, and others working remotely in Australia, Slovakia, Spain and China.
“There’s a big server, and people connect to it. That used to be the way companies work; now, they’ve done the same thing to the internet. Which is remarkably stupid, because they are central points of failure. They’re points of attack. There are passwords on them: stuff gets stolen.” He goes on: “And as the internet was starting, it was clear to me straight away that it would centralise around several large companies and they would basically control the world.”
There is a community of around 7,000 interested people already working on services that will work on the Safe network, including alternatives to platforms such as Facebook and YouTube.
One big question hangs over Irvine’s concept of a decentralised internet: given what we know about what some people use technology for, the encrypted information stored on people’s devices will include fragments of nasty, illegal stuff, won’t it?

The orginal article.

Summary of “Strava Heatmap shows that fitness trackers represent a privacy threat”

Just how big of a problem was brought into stark relief Monday, when a Twitter user pointed out that the Strava global heatmap – an online, interactive map of activity by people who use the Strava mobile app or have a Fitbit or Jawbone – inadvertently revealed the location of military bases overseas.
To make matters worse, Wired reported it’s also possible to take data publicly available via Strava’s API and see the names of individuals tied to specific running routes.
People have hard time comprehending what a searchable database of many people’s data reveal, etc.
Adam Harvey, perhaps best known for creating CV Dazzle and Hyperface, pointed out that the digital traces we leave online via fitness tracking apps like Strava give others power over us.
Strava helps illustrate fact that if anyone gained access to all of Google’s data they would rule the world.
While the U.S. government is likely not about to drone you, there exist other threats to your privacy – both present and future – that are only exacerbated by apps like Strava and devices like Fitbit.
Sure, you can denote your runs as “Private” on the aforementioned app, but even having done that, your data is still being uploaded to Strava’s servers – it’s just not actively shared with every creep who decides to poke around publicly available datasets like the heatmap.
For starters, try deleting the Strava app from your smartphone or tossing that Fitbit in your drawer and leaving it there.

The orginal article.

Summary of “Job One for Quantum Computers: Boost Artificial Intelligence”

In one demonstration last year, Alejandro Perdomo-Ortiz, a researcher at NASA’s Quantum Artificial Intelligence Lab, and his team exposed a D-Wave system to images of handwritten digits.
“State preparation – putting classical data into a quantum state – is completely shunned, and I think this is one of the most important parts,” said Maria Schuld, a researcher at the quantum-computing startup Xanadu and one of the first people to receive a doctorate in quantum machine learning.
Lloyd and his colleagues have proposed a quantum RAM that uses photons, but no one has an analogous contraption for superconducting qubits or trapped ions, the technologies found in the leading quantum computers.
Finally, how do you get your data out? That means measuring the quantum state of the machine, and not only does a measurement return only a single number at a time, drawn at random, it collapses the whole state, wiping out the rest of the data before you even have a chance to retrieve it.
“Given a big enough and fast enough quantum computer, we could revolutionize many areas of machine learning.” And in the course of using the systems, computer scientists might solve the theoretical puzzle of whether they are inherently faster, and for what.
“This is why I started to work the other way around and think: If have this quantum computer already – these small-scale ones – what machine-learning model actually can it generally implement? Maybe it is a model that has not been invented yet.” If physicists want to impress machine-learning experts, they’ll need to do more than just make quantum versions of existing models.
Quantum machine learning is similarly embodied – but in a richer world than ours.
It is not obvious that quantum physics could ever be harnessed for computation, since the distinctive effects of quantum physics are so well hidden from us.

The orginal article.

Summary of “Google is using 46 billion data points to predict a hospital patient’s future”

Some of Google’s top AI researchers are trying to predict your medical outcome as soon as you’re admitted to the hospital.
To conduct the study, Google obtained de-identified data of 216,221 adults, with more than 46 billion data points between them.
The data span 11 combined years at two hospitals, University of California San Francisco Medical Center and University of Chicago Medicine.
The biggest challenge for AI researchers looking to train their algorithms on electronic health records, the source of the data, is the vast, disparate, and poorly-labelled pieces of data contained in a patient’s file, the researchers write.
In addition to data points from tests, written notes have traditionally been difficult for automated systems to comprehend; each doctor and nurse writes differently and can take different styles of notes.
To compensate for this, the Google approach relies on three complex deep neural networks that learn from all the data and work out which bits are most impactful to final outcomes.
After analyzing thousands of patients, the system identified which words and events associated closest with outcomes, and learned to pay less attention to what it determined to be extraneous data.
Google heavy-hitters like Quoc Le, credited with creating recurrent neural networks used for predictions based on time, and Jeff Dean, a legend at the company for his work on Google’s server infrastructure, are both on the paper, as well as Greg Corrado, a director at the company involved in high-profile projects like translation and its Smart Reply feature.

The orginal article.