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"The Wealth of Nations, by Adam Smith, should be a required reading for every head of state. What took 17 years to be written and transformed the whole world, takes only a few days to read it. Right in the first page Smith asserts that the wealth of "every nation… [is] regulated by two different circumstances; first by the skill, dexterity, and judgment with which its labour is generally applied; and, secondly, by the proportion between the number of those who are employed in useful labour,""

"That is why we must demand transparency here, especially in the case of technology that uses human-like interfaces such as language. For any automated system, we need to know what it was trained to do, what training data was used, who chose that data and for what purpose. In the words of AI researchers Timnit Gebru and Margaret Mitchell, mimicking human behaviour is a "bright line" - a clear boundary not to be crossed - in computer software development. We treat interactions with things we perceive as human or human-like differently. With systems such as LaMDA we see their potential perils and the urgent need to design systems in ways that don't abuse our empathy or trust."

"As such, there are certainly some sinister ways technology like this could be used-and the faces don't need to be attractive, they just need to look real. Any circumstances where it would be useful to have fake people-like profile photos for dummy social media accounts used to manipulate online discourse-are a ready target for technological treachery."

""As expected, we found evidence of a performance improvement over generations due to social learning," the researchers wrote. "Adding an algorithm with a different problem-solving bias than humans temporarily improved human performance but improvements were not sustained in following generations. While humans did copy solutions from the algorithm, they appeared to do so at a lower rate than they copied other humans' solutions with comparable performance." Brinkmann told Motherboard that while they were surprised superior solutions weren't more commonly adopted, this was in line with other research suggesting human biases in decision-making persist despite social learning. Still, the team is optimistic that future research can yield insight into how to amend this."

"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 underlying theory is that art evolves "through small alterations to a known style that produce a new one," which, as Ian Bogost (previously) points out, is "a convenient take, given that any machine-learning technique has to base its work on a specific training set.""

"In October 2016, algorithms reacted to negative news headlines about Brexit negotiations by sending the pound down 6% against the dollar in under two minutes, before recovering almost immediately. Knowing which particular headline, or which particular algorithm, caused the crash is next to impossible. When one haywire algorithm started placing and cancelling orders that ate up 4% of all traffic in US stocks in October 2012, one commentator was moved to comment wryly that "the motive of the algorithm is still unclear"."

"LeCun argues that even self-supervised learning and learnings from neurobiology won't be enough to achieve artificial general intelligence (AGI), or the hypothetical intelligence of a machine with the capacity to understand or learn from any task. That's because intelligence - even human intelligence - is very specialized, he says. "AGI does not exist - there is no such thing as general intelligence," said LeCun. "We can talk about rat-level intelligence, cat-level intelligence, dog-level intelligence, or human-level intelligence, but not artificial general intelligence.""

"a word that does not exist; it was invented, defined and used by a machine learning algorithm. "

"Robots, artificial intelligence and smart speakers will ease the burden on doctors and give them more time with patients, according to an NHS report on the pending technological "revolution" in healthcare. Developments in the ability to sequence individuals' genomes - the entirety of their genetic data - will also spur on advances, according to the review published on Monday."

"But researchers have approximately zero clues as to why it's so good at it, and it doesn't help that the AI essentially taught itself to make these predictions. According to one researcher involved in the project, "We can build these models, but we don't know how they work.""

"Personalized pricing, which is also known as price discrimination or price optimization, depending on whether you're an economist or an online marketer, is a growing trend. According to a recent Deloitte and Salesforce report, 40% of brands that currently use AI to personalize the customer experience have used it to tailor pricing and promotions in real time. "

" The machine's doing the prediction, making the distinct role of judgment in decision making clearer. So as the value of human prediction falls, the value of human judgment goes up because AI doesn't do judgment-it can only make predictions and then hand them off to a human to use his or her judgment to determine what to do with those predictions."

"But maybe I'm wrong. Because, if we believe tech gurus at least, the Trolley Problem is about to become of huge real-world importance. Human beings might not find themselves in all that many Trolley Problem-style scenarios over the course of their lives, but soon we're going to start seeing self-driving cars on our streets, and they're going to have to make these judgments all the time."

"With 850 million children worldwide shut out of schools, tech evangelists claim now is the time for AI education. But as the technology's power grows, so too do the dangers that come with it"

"Common-sense reasoning - the ability to make mundane inferences using basic knowledge about the world, like the fact that "matches" plus "logs" usually equals "fire" - has resisted AI researchers' efforts for decades."

"The researchers programmed a robot called Pepper, made by SoftBank Robotics, with the ability to vocalise its thought processes. This means the robot is no longer a "black box" and its underlying decision-making is more transparent to the user."

"On inbuilt bias in algorithms, Sharkey said: "There are so many biases happening now, from job interviews to welfare to determining who should get bail and who should go to jail. It is quite clear that we really have to stop using decision algorithms, and I am someone who has always been very light on regulation and always believed that it stifles innovation."