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The first reports of a new virus emerging in Wuhan came around Christmas 2019. By early January, there were 27 cases in the Chinese city. Within a couple of weeks, airports in Asia were screening all passengers who passed through their doors. Weeks on, UK airports still were not despite three direct flights a week from Wuhan to Heathrow.

Screening is ineffective as a method to stop the spread of a virus such as Covid-19, said one scientist.

We should have done it at least for the Wuhan flights, said another. It wouldnt have been a great imposition.

We left our doors open, said a public health expert, with barely controlled fury. And it contributed substantially to the rapid growth of the virus in the UK.

Such a back and forth was the defining feature of BBC Twos Lockdown 1.0: Following the Science? It is the latest contribution to what I suspect will become a string of more-or-less excellent documentaries about the UKs handling of the coronavirus pandemic. (The first was Channel 4s The Country That Beat the Virus, which compared and contrasted South Koreas response to the advent of Covid with ours no spoilers, but we dont come out of it well.)

Lockdown 1.0 also intertwined, though less overtly, two narratives. One involved the evolving amount and quality of the data that the scientists modellers, virologists, epidemiologists were receiving and, therefore, the predictions and other information they could obtain from it and hand on to groups such as the Scientific Advisory Group for Emergencies (Sage) and then to government ministers. The other narrative strand involved those ministers and what Professor Anthony Costello, a former director of maternal, child and adolescent health at the World Health Organization, described as the managerial issue how to test, how to gather the results, how to lock down, how to trace, how to isolate, and how to scale all that up quickly. Again, no spoilers but

What we might call, in bleak reference to earlier, happier, altogether easier times, the science bit was perhaps the more illuminating. The managerial issue is generally writ larger on our screens and in our newspapers as it is happening. The beavering away with numbers and in laboratories, less so. The science bit, perhaps tellingly, was also where most of the back-and-forth took place. The managerial response did not admit much nuance.

There was evident frustration, for example, among many of the modellers who comprised the Scientific Pandemic Influenza Group on Modelling (SPI-M) about the fact that only Imperial College as an established collaborative centre with the WHO had access to what was, at the beginning, the best (though by no means perfect) data, coming out of China. Dr Ian Hall from Manchester University, deputy chair of SPI-M, noted: The public may be surprised to hear we were using data from Wikipedia very early on but it really was the only data publicly available.

Juxtaposed with them was Professor Graham Medley of the London School of Hygiene and Tropical Medicine and chair of SPI-M. Its not as simple as saying: Make it all publicly available. People own it; it includes patient information.

Costello professed himself pretty shocked by the situation. The whole point of science and scientists in these circumstances, he said, was to share findings openly.

There were many more moments and insights that complicated in a good way, by giving more detail, by exposing more structures, by expanding vistas the view we take from headlines, social media or, if we are committed, articles. The uncomfortable truth is that there are often no good choices, and that it can be irremediably unclear which choice is the best of a bad lot. Likewise, the bitter fact is that people cannot always be made to follow the best paths and, therefore, impositions on them must be watered down if they are to have any effect.

The data scientists made the nearest thing to an unequivocal error with the modelling they did around the risk in care homes, by failing to understand the movement of staff between homes. Should they have known about agency workers? If not, who should have told them?

Still, we are not yet at the stage of apportioning blame although there will surely be many documentaries to come that will deal with exactly that. By the end of this one, it was possible to feel both better and worse about the state we are in. There are people out there, definitely, who have our ignorant little backs. But the search for an overarching synthesising intelligence some kind of prime minister who could gather up all the reins, say continues.

Excerpt from:
Lockdown 1.0: Following the Science? review Wikipedia, Wuhan and worrying mistakes - The Guardian

NBA fans can do a lot of crazy things to tease rival teams and players. Some of them take things to the next level, using different tactics to troll their opponents. Myles Turner has been the last victim of these trolls, as his Wikipedia page was hacked recently.

The 24-year-old saw his official Wikipedia page altered by some hackers that put him in the Boston Celtics. The NBA offseason just started but Turner hasnt been part of any rumors in the last couple of days. Hes under contract with the Indiana Pacers until 2023 and the team hasnt shown any signs of wanting to trade him, even less to the Boston Celtics.

The person or people behind the attack had a lot of fun messing with Turners information. They took things off the court and wrote some crazy things about Turner. They even said he was a WNBA player and used a pic of Adolf Hitler to change Turners.

NBA Twitter noticed this activity and was quick to report it. Turner later found out about the whole thing and reacted on Twitter; he found these little changes funny and everything went back to normal in a matter of hours. He had his laugh, the hackers, too, and now its time to move on.

The Pacers have been mentioned in several reports in recent days thanks to Victor Oladipo and his future in the league. The combo guard doesnt have trade value and the team needs to figure out what to do next since the relationship between the player and his teammates isnt the best. Oladipo reportedly asked rivals if he could join them in front of teammates, something that caused trouble in the Pacers locker room.

More here:
Myles Turner's Wikipedia Is Hacked: 'He Is Playing For The Boston Celtics' - Fadeaway World

Pooja Jadhav | (Pic: Tata Motors)

Wikipedia is everything. But when it comes to Indian languages there is a whole lot of distance to cover. Pooja Jadhav, who has a background in Computer Science, learnt 2D design, 3D design, communication and management and a whole lot more, is going that distance. The 25-year-old became a Computer Lab Assistant at Vigyan Ashram and began to mentor other students. Not just this, they also bagged a project with Wikipedia to convert articles to Marathi and now, she is the Senior Regional Contributor at Marathi Wikipedia. "As a part of T20, we had also learnt about presentation skills and personality development. Putting these skills to good use, I have travelled to other cities for the Wikipedia training and this has instilled a lot of confidence in me," says the youngster.

Pooja also took up a Fab Academy course last year and as a part of her final year project, she chose to work on a display that shows the reserve water levels of the dam in Pabal and also tells you how much longer it can be used. But this is just one feather in her cap, her immense contribution towards Marathi Wikipedia is laudable. "I have contributed over 400 articles and edited over 4,000 articles; scanned over 40,000 pages and uploaded it on Wikimedia and uploaded over 400 pictures on Wikimedia Commons as well," she lists. Astounding! She is even training other girls to contribute to Marathi Wikipedia and one such youngster is Komal.

"Pooja tai taught us all kinds of skills from photoshop to the basic knowledge of computers and from PowerPoint to video editing," shares the 21-year-old who is currently focussed on contributing to Marathi Wikipedia as much as she can. "When you Google anything, the first search that shows up is Wikipedia and I work for it. I feel so proud," she shares. Komal gave her BCom exams this year and is planning to pursue MCom soon.

A pilot project sowed half a decade ago, the Tata Motors Vigyan Ashram programme, T20, is the reason for all this. It has been helmed by Tata Motors and implemented by NGO Vigyan Ashram, who back in 2016-2017decided that an exclusive batch of 20 girls would be enrolled in Diploma in Basic Rural Technology at the Vigyan Ashram centre in Pabal, a village in the Pune district of Maharashtra. There is nothing more heart-warming than looking at young girls who are owning their futures and contributing towards a larger cause as well.

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Meet the 25-year-old contributor to Marathi Wikipedia who is inspiring others to chip in too - EdexLive

Wikipedia is the go-to place to get fast, quick facts on someone or something. Its highly editable, so theres always a sense of caution you need to have while reading, but for the most part, the general stuff is typically true. And while High School Musical was big, its a bit odd that one of the stars has the fifth-most translated Wikipedia page right now.

RELATED:A New High School Musical 3 Theory Suggests 1 Character Died at the Beginning of the Movie

In 2013, a project named Pantheon collected data about the Wikipedia pages in the world. The project was from the MIT Media Lab and according to BuzzFeed it was working collaboratively to quantify, analyze, measure and visualize global culture.

What does this mean? If you go to their site even today, seven years later users can filter through pages. So, one can pick a country, the birthdate range, and occupation of the people theyre searching for. There are then columns of information, with one being how many translated Wikipedia pages there are for this person.

The most-translated page goes to President Ronald Reagan with 250 language pages, with Jesus Christ right behind at 246. Then comes Michael Jackson and President Barack Obama with 233 and 230 pages, respectively. And in fifth place? Mr. Corbin Bleu, Disney Channel star known for his role as Chad in 2006s High School Musical.

Its truly a wild find and in 2013 he was at number 3. Even though hes gone down a few pegs, he still has a massive amount of language pages on Wikipedia, with 216. To put this into perspective, the next living person on the list, under Bleu, is President Donald Trump with 205.

And its not even Zac Efron, Vanessa Hudgens, or Ashley Tisdale. Efron only has 86 translated pages, Hudgens has 69, and Tisdale has 61. Thats not to say Bleu didnt leave an impact on the world as Chad, but still. He has more pages than Shakespeare and Leonardo da Vinci.

BuzzFeed actually got ahold of Bleu in 2013 and told him about the find and he was also very shocked.

What? Bleu said. Holy sh*t! Really? I wonder why that is! Are that many people looking me up? What the hell! Thats amazing. Thats ridiculous, actually. That is unnecessary, but I will definitely put that on my resume.

Diving into why that is, in 2013 no one really knew. Everyone was really just as baffled by it as Bleu was. But in 2019, it seems like Reddit came up with an equally complex answer.

Insider reported that Reddit was on the case. Someone posted this fact in the r/UnresolvedMysteries subreddit and an answer was found within hours.

According to Reddit user u/Lithide (whos now deleted) Wikipedia user Zimmer610, AKA Chace Watson from (presumably) Saudi Arabia made them all. Theyre apparently a polyglot, Corbin-superfan.

I actually think theres a dedicated fan of Corbin Bleu from Saudi Arabia who wanted to make sure there were Wikipedia articles for their idol in every language possible and also spent a few dozen hours working on the Arabic-language article, Lithide wrote.

One of the original Reddit posts updates also noted that they might have had a run-in with Wikipedia authorities in doing their translations. Allegedly they were banned from the English Wikipedia and Wikipedia Commons. Apparently, the Arabic page for Bleu is a featured Arabic Wikipedia page because its so well-done.

Its a whole, complicated find on Reddit with many layers and a lot of detective work. There also doesnt seem to be a known motive yet. But, if one is looking for a good distraction in the year 2020, this mystery (solved or not) is a great thing to dive into.

RELATED: Which Original High School Musical Cast Members Gave Their Blessing For The New Series?

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This 'High School Musical' Star Has 1 of The Most-Translated Wikipedia Pages, More Than Shakespeare and Donald Trump - Showbiz Cheat Sheet

AI models that can parse both language and visual input also have very practical uses. If we want to build robotic assistants, for example, they need computer vision to navigate the world and language to communicate about it to humans.

But combining both types of AI is easier said than done. It isnt as simple as stapling together an existing language model with an existing object recognition system. It requires training a new model from scratch with a data set that includes text and images, otherwise known as a visual-language data set.

The most common approach for curating such a data set is to compile a collection of images with descriptive captions. A picture like the one below, for example, would be captioned An orange cat sits in the suitcase ready to be packed. This differs from typical image data sets, which would label the same picture with only one noun, like cat. A visual-language data set can therefore teach an AI model not just how to recognize objects but how they relate to and act on one other, using verbs and prepositions.

But you can see why this data curation process would take forever. This is why the visual-language data sets that exist are so puny. A popular text-only data set like English Wikipedia (which indeed includes nearly all the English-language Wikipedia entries) might contain nearly 3 billion words. A visual-language data set like Microsoft Common Objects in Context, or MS COCO, contains only 7 million. Its simply not enough data to train an AI model for anything useful.

Vokenization gets around this problem, using unsupervised learning methods to scale the tiny amount ofdata in MS COCO to the size of English Wikipedia. The resultant visual-language model outperforms state-of-the-art models in some of the hardest tests used to evaluate AI language comprehension today.

You dont beat state of the art on these tests by just trying a little bit, says Thomas Wolf, the cofounder and chief science officer of the natural-language processing startup Hugging Face, who was not part of the research. This is not a toy test. This is why this is super exciting.

Lets first sort out some terminology. What on earth is a voken?

In AI speak, the words that are used to train language models are known as tokens. So the UNC researchers decided to call the image associated with each token in their visual-language model a voken. Vokenizer is what they call the algorithm that finds vokens for each token, and vokenization is what they call the whole process.

The point of this isnt just to show how much AI researchers love making up words. (They really do.) It also helps break down the basic idea behind vokenization. Instead of starting with an image data set and manually writing sentences to serve as captionsa very slow processthe UNC researchers started with a language data set and used unsupervised learning to match each word with a relevant image (more on this later). This is a highly scalable process.

The unsupervised learning technique, here, is ultimately the contribution of the paper. How do you actually find a relevant image for each word?

Lets go back for a moment to GPT-3. GPT-3 is part of a family of language models known as transformers, which represented a major breakthrough in applying unsupervised learning to natural-language processing when the first one was introduced in 2017. Transformers learn the patterns of human language by observing how words are used in context and then creating a mathematical representation of each word, known as a word embedding, based on that context. The embedding for the word cat might show, for example, that it is frequently used around the words meow and orange but less often around the words bark or blue.

This is how transformers approximate the meanings of words, and how GPT-3 can write such human-like sentences. It relies in part on these embeddings to tell it how to assemble words into sentences, and sentences into paragraphs.

Theres a parallel technique that can also be used for images. Instead of scanning text for word usage patterns, it scans images for visual patterns. It tabulates how often a cat, say, appears on a bed versus on a tree, and creates a cat embedding with this contextual information.

The insight of the UNC researchers was that they should use both embedding techniques on MS COCO. They converted the images into visual embeddings and the captions into word embeddings. Whats really neat about these embeddings is that they can then be graphed in a three-dimensional space, and you can literally see how they are related to one another. Visual embeddings that are closely related to word embeddings will appear closer in the graph. In other words, the visual cat embedding should (in theory) overlap with the text-based cat embedding. Pretty cool.

You can see where this is going. Once the embeddings are all graphed and compared and related to one another, its easy to start matching images (vokens) with words (tokens). And remember, because the images and words are matched based on their embeddings, theyre also matched based on context. This is useful when one word can have totally different meanings. The technique successfully handles that by finding different vokens for each instance of the word.

For example:

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This could lead to the next big breakthrough in common sense AI - MIT Technology Review