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"A smartphone application called Peek, Portable Eye Examination Kit, utilizes the phone's camera and can conduct visual and color field tests, lens imaging for cataracts, and retinal imaging, among other tests to detect sight impairments and diseases."

"From a computer science perspective, learning the behaviour of a single user is tough. This is the small data problem; unlike big data, where patterns and trends easily emerge, individual human beings can be unpredictable and can change behaviour, which is not helpful for pattern-hunting algorithms."

Great article - worth a read through...

"Now, the number of combinations of these columns grows exponentially with the number of columns. So if you have many, many columns-and we do in modern databases-you'll get up into millions and millions of attributes for each person. Now, if I start allowing myself to look at all of the combinations of these features-if you live in Beijing, and you ride bike to work, and you work in a certain job, and are a certain age-what's the probability you will have a certain disease or you will like my advertisement? Now I'm getting combinations of millions of attributes, and the number of such combinations is exponential; it gets to be the size of the number of atoms in the universe."

"The really significant thing about AlphaGo is that it (and its creators) cannot explain its moves. And yet it plays a very difficult game expertly. So it's displaying a capability eerily similar to what we call intuition - "knowledge obtained without conscious reasoning". Up to now, we have regarded that as an exclusively human prerogative. It's what Newton was on about when he wrote "Hypotheses non fingo" in the second edition of his Principia: "I don't make hypotheses," he's saying, "I just know.""

"Expert Moshe Vardi told the American Association for the Advancement of Science (AAAS): "We are approaching a time when machines will be able to outperform humans at almost any task. "I believe that society needs to confront this question before it is upon us: if machines are capable of doing almost any work humans can do, what will humans do?""

" Critics have pointed out that the old computing adage "garbage in, garbage out" also applies to ML. If the data from which a machine "learns" is biased, then the outputs will reflect those biases. And this could become generalised: we may have created a technology that - however good it is at recommending films you might like - may actually morph into a powerful amplifier of social, economic and cultural inequalities."

"His team trained a machine learning algorithm to spot words and phrases associated with bullying on social media site AskFM, which allows users to ask and answer questions. It managed to detect and block almost two-thirds of insults within almost 114,000 posts in English and was more accurate than a simple keyword search. Still, it did struggle with sarcastic remarks."

" The observations made at Facebook are the latest in a long line of similar cases. In each instance, an AI being monitored by humans has diverged from its training in English to develop its own language. The resulting phrases appear to be nonsensical gibberish to humans but contain semantic meaning when interpreted by AI "agents." "

"At its core, the researchers' "Planning with Uncertain Specifications" (PUnS) system gives robots the human-like planning ability to simultaneously weigh many ambiguous - and potentially contradictory - requirements to reach an end goal. In doing so, the system always chooses the most likely action to take, based on a "belief" about some probable specifications for the task it is supposed to perform."

"Raza wasn't the only one in her class who felt concerned about new levels of surveillance. Another student in the class, who did not want to be named, said that in addition to privacy worries, they were concerned that they didn't even have enough RAM to run the Proctorio software. Worse, the tool's facial detection algorithm seemed to struggle to recognize them, so they needed to sit in the full light of the window to better expose the contours of their face, in their view an indication that the system might be biased. "

"When the field of AI believes it is neutral, it both fails to notice biased data and builds systems that sanctify the status quo and advance the interests of the powerful. What is needed is a field that exposes and critiques systems that concentrate power, while co-creating new systems with impacted communities: AI by and for the people."

"Qoves founder Shafee Hassan claimed to MIT Technology Review that beauty scoring is widespread; social media platforms use it to identify attractive faces and give them more attention."