Human-generated knowledge bases like Wikipedia have a recall problem. First, there are the articles that should be there but are entirely missing. The unknown unknowns.

Consider Joelle Pineau, the Canadian roboticist bringing scientific rigor to artificial intelligence and who directs Facebook’s new AI Research lab in Montreal. Or Miriam Adelson, an actively publishing addiction treatment researcher who happens to be a billionaire by marriage and a major funder of her own field. Or Evelyn Wang, the new head of MIT’s revered MechE department whose accomplishments include a device that generates drinkable water from sunlight and desert air. When I wrote this a few days ago, none of them had articles on English Wikipedia, though they should by any measure of notability.

(Pineau is up now thanks to my friend and fellow science crusader Jess Wade who created an article just hours after I told her about Pineau’s absence. And if the internet is in a good mood, someone will create articles for the other two soon after this post goes live.)

But I didn’t discover those people on my own. I used a machine learning system we’re building at Primer. It discovered and described them for me. It does this much as a human would, if a human could read 500 million news articles, 39 million scientific papers, all of Wikipedia, and then write 70,000 biographical summaries of scientists.

[…]

We are publicly releasing free-licensed data about scientists that we’ve been generating along the way, starting with 30,000 computer scientists. Only 15% of them are known to Wikipedia. The data set includes 1 million news sentences that quote or describe the scientists, metadata for the source articles, a mapping to their published work in the Semantic Scholar Open Research Corpus, and mappings to their Wikipedia and Wikidata entries. We will revise and add to that data as we go. (Many thanks to Oren Etzioni and AI2 for data and feedback.) Our aim is to help the open data research community build better tools for maintaining Wikipedia and Wikidata, starting with scientific content.

Fluid Knowledge

We trained Quicksilver’s models on 30,000 English Wikipedia articles about scientists, their Wikidata entries, and over 3 million sentences from news documents describing them and their work. Then we fed in the names and affiliations of 200,000 authors of scientific papers.

In the morning we found 40,000 people missing from Wikipedia who have a similar distribution of news coverage as those who do have articles. Quicksilver doubled the number of scientists potentially eligible for a Wikipedia article overnight.

It also revealed the second flavor of the recall problem that plagues human-generated knowledge bases: information decay. For most of those 30,000 scientists who are on English Wikipedia, Quicksilver identified relevant information that was missing from their articles.

Source: Primer | Machine-Generated Knowledge Bases

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A recent scientific survey off the coast of Sulawesi Island in Indonesia suggests that some shallow water corals may be less vulnerable to global warming than previously thought.

Between 2014 and 2017, the world’s reefs endured the worst coral bleaching event in history, as the cyclical El Niño climate event combined with anthropogenic warming to cause unprecedented increases in water temperature.

But the June survey, funded by Microsoft co-founder Paul Allen’s family foundation, found the Sulawesi reefs were surprisingly healthy.

In fact the reefs hadn’t appeared to decline significantly in condition than when they were originally surveyed in 2014 – a surprise for British scientist Dr Emma Kennedy, who led the research team.

A combination of 360-degree imaging tech and Artificial Intelligence (AI) allowed scientists to gather and analyse more than 56,000 images of shallow water reefs. Over the course of a six-week voyage, the team deployed underwater scooters fitted with 360 degree cameras that allowed them to photograph up to 1.5 miles of reef per dive, covering a total of 1487 square miles in total.

Researchers at the University of Queensland in Australia then used cutting edge AI software to handle the normally laborious process of identifying and cataloguing the reef imagery. Using the latest Deep Learning tech, they ‘taught’ the AI how to detect patterns in the complex contours and textures of the reef imagery and thus recognise different types of coral and other reef invertebrates.

Once the AI had shown between 400 and 600 images, it was able to process images autonomously. Says Dr Kennedy, “the use of AI to rapidly analyse photographs of coral has vastly improved the efficiency of what we do — what would take a coral reef scientist 10 to 15 minutes now takes the machine a few seconds.”

Source: AI identifies heat-resistant coral reefs in Indonesia | Environment | The Guardian

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Description

Sketch2Code is a solution that uses AI to transform a handwritten user interface design from a picture to a valid HTML markup code.

Process flow

The process of transformation of a handwritten image to HTML this solution implements is detailed as follows:

1. The user uploads an image through the website.
2. A custom vision model predicts what HTML elements are present in the image and their location.
3. A handwritten text recognition service reads the text inside the predicted elements.
4. A layout algorithm uses the spatial information from all the bounding boxes of the predicted elements to generate a grid structure that accommodates all.
5. An HTML generation engine uses all these pieces of information to generate an HTML markup code reflecting the result.
6. <A href=”https://github.com/Microsoft/ailab/tree/master/Sketch2Code”>Sketch2Code Github</a>

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A group of researchers from Aalto University and the University of Padua found this out when they tested seven state-of-the-art models used to detect hate speech. All of them failed to recognize foul language when subtle changes were made, according to a paper [PDF] on arXiv.

Adversarial examples can be created automatically by using algorithms to misspell certain words, swap characters for numbers or add random spaces between words or attach innocuous words such as ‘love’ in sentences.

The models failed to pick up on adversarial examples and successfully evaded detection. These tricks wouldn’t fool humans, but machine learning models are easily blindsighted. They can’t readily adapt to new information beyond what’s been spoonfed to them during the training process.

“They perform well only when tested on the same type of data they were trained on. Based on these results, we argue that for successful hate speech detection, model architecture is less important than the type of data and labeling criteria. We further show that all proposed detection techniques are brittle against adversaries who can (automatically) insert typos, change word boundaries or add innocuous words to the original hate speech,” the paper’s abstract states.

Source: AI sucks at stopping online trolls spewing toxic comments • The Register

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Google is putting an artificial intelligence system in charge of its data center cooling after the system proved it could cut energy use.

Now Google and its AI company DeepMind are taking the project further; instead of recommendations being implemented by human staff, the AI system is directly controlling cooling in the data centers that run services including Google Search, Gmail and YouTube.

“This first-of-its-kind cloud-based control system is now safely delivering energy savings in multiple Google data centers,” Google said.

Data centers use vast amount of energy and as the demand for cloud computing rises even small tweaks to areas like cooling can produce significant time and cost savings. Google’s decision to use its own DeepMind-created system is also a good plug for its AI business.

Every five minutes, the AI pulls a snapshot of the data center cooling system from thousands of sensors. This data is fed into deep neural networks, which predict how different choices will affect future energy consumption.

The AI system then identifies tweaks that could reduce energy consumption, which are then sent back to the data center, checked by the local control system and implemented.

Google said giving the AI more responsibility came at the request of its data center operators who said that implementing the recommendations from the AI system required too much effort and supervision.

“We wanted to achieve energy savings with less operator overhead. Automating the system enabled us to implement more granular actions at greater frequency, while making fewer mistakes,” said Google data center operator Dan Fuenffinger.

Source: ​Google just put an AI in charge of keeping its data centers cool | ZDNet

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AI is being deployed by those who set and mark exams to reduce fraud — which remains overall a small problem — and to create far greater efficiencies in preparation and marking, and to help improve teaching and studying. From a report, which may be paywalled: From traditional paper-based exam and textbook producers such as Pearson, to digital-native companies such as Coursera, online tools and artificial intelligence are being developed to reduce costs and enhance learning. For years, multiple-choice tests have allowed scanners to score results without human intervention. Now technology is coming directly into the exam hall. Coursera has patented a system to take images of students and verify their identity against scanned documents. There are plagiarism detectors that can scan essay answers and search the web — or the work of other students — to identify copying. Webcams can monitor exam locations to spot malpractice. Even when students are working, they provide clues that can be used to clamp down on cheats. They leave electronic “fingerprints” such as keyboard pressure, speed and even writing style. Emily Glassberg Sands, Cousera’s head of data science, says: “We can validate their keystroke signatures. It’s difficult to prepare for someone hell-bent on cheating, but we are trying every way possible.”

Source: How AI Can Spot Exam Cheats and Raise Standards – Slashdot

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Artificial intelligent software has been trained to detect and flag up clickbait headlines.

And here at El Reg we say thank God Larry Wall for that. What the internet needs right now is software to highlight and expunge dodgy article titles about space alien immigrants, faked moon landings, and the like.

Machine-learning eggheads continue to push the boundaries of natural language processing, and have crafted a model that can, supposedly, detect how clickbait-y a headline really is.

The system uses a convolutional neural network that converts the words in a submitted article title into vectors. These numbers are fed into a long-short-term memory network that spits out a score based on the headline’s clickbait strength. About eight times out of ten it agreed with humans on whether a title was clickbaity or not, we’re told.

The trouble is, what exactly is a clickbait headline? It’s a tough question. The AI’s team – from the International Institute of Information Technology in Hyderabad, the Manipal Institute of Technology, and Birla Institute of Technology, in India – decided to rely on the venerable Merriam-Webster dictionary to define clickbait.

Source: Oi, clickbait cop bot, jam this in your neural net: Hot new AI threatens to DESTROY web journos • The Register

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