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DAILY DOSE: Sex is all the rage among scientists; College rankings are a scam.


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Sex is a hot topic in scientific research and not for reasons you might think. Per Nature

“Over the past decade or so, a growing list of funders and publishers, including the US National Institutes of Health (NIH) and the European Union, have been asking researchers to include two sexes in their work with cells and animal models.

Two major catalysts motivated these policies. One was a growing recognition that sex-based differences, often related to hormone profiles or genes on sex chromosomes, can influence responses to drugs and other treatments. The other was the realization that including two sexes can increase the rigour of scientific inquiry, enhance reproducibility and open up questions for scientific pursuit.

When studies do include two sexes, the results can be important for health. For example, sex is known to affect people’s responses to common drugs, including some antibiotics, and the risk of cardiovascular disease seems to rise at a lower blood pressure in women than in men.”

This makes so much sense that it’s amazing it’s taken this long to realize and implement changes.


As a rule of thumb, it’s probably not a good idea to keep wild animals as pets. It can often have tragic consequences. Per the Associated Press,

“A man who may have been keeping a wild kangaroo as a pet was killed by the animal in southwest Australia, police said Tuesday. It was reportedly the first fatal attack by a kangaroo in Australia since 1936.

A relative found the 77-year-old man with “serious injuries” on his property Sunday in semirural Redmond, 400 kilometers (250 miles) southeast of the Western Australia state capital Perth.

It was believed he had been attacked earlier in the day by the kangaroo, which police shot dead because it was preventing paramedics from reaching the injured man, police said.”

It’s sad news all around. For the man. For the kangaroo.


Rankings. Grades based on test taking. Awards prizes. They’re all pretty dumb but people love them because they provide a straightforward, lowest common denominator way of quantifying quality. There’s beginning to be some blowback against popular University rankings, particularly in light of Columbia University’s admission that it gamed the system to achieve a number 2 ranking last year. Per CNN,

“…experts say that the rankings offer a narrow view of what success should look like for students seeking higher education, especially as costs climb.

“In 2022, higher education should measure what matters, not just what’s become tradition to measure,” said Mamie Voight, president and CEO of the Institute for Higher Education Policy, a nonprofit research and advocacy group.

“​​We should recognize and celebrate institutions that help their students achieve social and economic mobility. That mobility boost — especially for students who have historically been left behind by higher education — is what should qualify as ‘prestigious,’ not the test scores of students when they arrive at college or the number of people an institution turns away.”

These institutions are not likely to be the ones to claim the spotlight in rankings, experts say.”

Will anything change in the short term? Doubt it. In the long term? Doubt that as well.


On the Tech front… We’re all drowning in oceans of unread messages. Emails. Text messages. Facebook. Twitter. Blah blah blah. One type of messaging sticks out though. A recent article in The Guardian bemoaned the tyranny of belonging to WhatsApp groups,

“…it is the WhatsApp messages, specifically the WhatsApp group chats, that terrorise me the most. If I were a woman of courage, I would simply exit these chats as soon as I am added to them; but I feel the weight of social obligation, and so I remain.

I am not the only person to feel this way. Last month, WhatsApp bowed to public pressure, and announced that users will be able to exit groups invisibly, without notifying other members of their decision. (The new policy has yet to be implemented, however.) The conflict-avoidant among us rejoiced: now, finally, we can slink out of groups without being perceived as rude. But 11 years after the instant messaging app introduced a group chat feature, will we ever truly escape the tyranny of the WhatsApp group?”



It’s no secret that for all the nifty and mundane things artificial intelligence can do at this point, it’s pretty easy to outwit it once you move away from its narrow comfort zone/area of expertise. And sometimes, even within that space, it’s not too hard to find some random exception that throws the whole system into a downward-spiraling tizzy. An article in the Proceedings of the National Academy of Sciences investigates the state of AI/Deep Learning and alternatives to current models and begins by making the case that, while deep learning is useful, it is also deeply flawed.

“Deep learning is good at emulating this kind of automatic response, which is why its most successful applications tend to include tasks in which perception and recognition skills are paramount, such as photo editing and voice recognition. The basic idea, which dates back to the first brain-inspired neural networks in the 1940s, is to process incoming data via a web of neuron-like nodes linked by synapse-like connections. This network is trained by asking the system to process a given library of inputs while adjusting the data flow through each connection until the network produces satisfactory outputs.

Early neural networks were small and limited in what they could do. But their capabilities and speed have improved dramatically since the deep-learning era began around 2010, thanks to hardware and software advances that have allowed the networks to reach gargantuan scales. DeepMind’s Gopher natural language system, for example, was trained on some 2 trillion English words while tweaking 280 billion parameters in its connections.

Yet deep learning’s success has highlighted its greatest weakness: the ease with which a network can be confounded by input not covered in its training.

Humans can be confounded by strange situations, too. But that’s when we start looking for solutions with system-2 thinking: a far slower, more difficult mode of cognition that dominates our consciousness and is anything but automatic. “Maybe you’re driving in a new city where the traffic laws are different, like you have to drive on the left and not on the right,” says Yoshua Bengio, an AI researcher at the University of Montreal, Canada. You can’t just go by habit, he says. You have to pay attention, remember the new traffic laws, and decide where to steer next, all at once.

Compared with system-1 thinking, system-2 constitutes a tiny fraction of what’s going on in our brains. But because it’s the only kind of cognition we’re aware of, most of AI’s founders in the 1940s and 1950s assumed that it was all machine intelligence needed. This inspired the symbol-processing paradigm that dominated AI for its first half-century until the deep-learning revolution of the past decade. The idea was to give the computer some kind of model of the problem at hand, in the form of a data structure analogous to the mental models of the world that we build in our conscious minds, then look for a solution to the problem by manipulating the model with algorithms that mimic our system-2 reasoning.”

If/when quantum computing really comes into its own, it will be interesting to see how AI paradigms change with the exponential increase in processing power.

Thanks for reading. Let’s be careful out there.

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