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Greece used AI to curb COVID: what other nations can learn

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Greece's decision to deploy machine learning in pandemic surveillance will be much-studied around the world.Credit: Konstantinos Tsakalidis/Bloomberg/Getty A few months into the COVID-19 pandemic, operations researcher Kimon Drakopoulos e-mailed both the Greek prime minister and the head of the country's COVID-19 scientific task force to ask if they needed any extra advice. Drakopoulos works in data science at the University of Southern California in Los Angeles, and is originally from Greece. To his surprise, he received a reply from Prime Minister Kyriakos Mitsotakis within hours. The European Union was asking member states, many of which had implemented widespread lockdowns in March, to allow non-essential travel to recommence from July 2020, and the Greek government needed help in deciding when and how to reopen borders. Greece, like many other countries, lacked the capacity to test all travellers, particularly those not displaying symptoms.


Making machines that make robots, and robots that make themselves

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After a summer of billionaires in space, many people have begun to wonder when they will get their turn. The cost of entering space is currently too high for the average citizen, but the work of PhD candidate Martin Nisser may help change that. His work on self-assembling robots could be key to reducing the costs that help determine the price of a ticket. Nisser's fascination with engineering has been a consistent theme throughout a life filled with change. Born to Swedish parents, he spent a decade in Greece before moving to the UAE, and eventually to Scotland for his undergraduate degree.


Artificial intelligence vs. neurophysiology: Why the difference matters

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All the sessions from Transform 2021 are available on-demand now. On the temple of Apollo in Delphi (Greece), it was written: "Cognosce te Ipsum" (Know thy self). It is important to remember these words for everyone who wants to create artificial intelligence. I continue my series of articles about the nature of human intelligence and the future of artificial intelligence systems. This article is a continuation of the article titled "Symbiosis Instead of Evolution -- A New Idea about the Nature of Human Intelligence." In the previous article, after analyzing the minimum response time to a simple incoming signal, we found that the human brain with a high degree of probability may turn out to be a binary system, consisting of two functional schemes of response to excitation: reflex and intellectual.


Are robots the solution to the ageing population crisis?

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Across Europe, consistently low birth rates and rising life expectancy are transforming the continent's age demographic. The coming years will see older people make up a much larger demographic in Europe, a phenomenon that is already being felt in countries like Italy, Greece and Germany. The prospect presents enormous challenges to policymakers tasked with meeting the healthcare needs of an ageing population, particularly in a situation where there may be fewer working-age people to support them. As a result, policymakers in Europe are turning their attention to tech solutions and robotics in the hope that they can help make up some of this shortfall. French start-up Kompaï Robotics presented their latest innovation at the VivaTech conference in Paris - a robot that has been specifically designed for healthcare providers.


A Never-Before-Seen Type of Signal Has Been Detected in The Human Brain

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Scientists have discovered a unique form of cell messaging occurring in the human brain that's not been seen before. Excitingly, the discovery hints that our brains might be even more powerful units of computation than we realized. Early last year, researchers from institutes in Germany and Greece reported a mechanism in the brain's outer cortical cells that produces a novel'graded' signal all on its own, one that could provide individual neurons with another way to carry out their logical functions. By measuring the electrical activity in sections of tissue removed during surgery on epileptic patients and analysing their structure using fluorescent microscopy, the neurologists found individual cells in the cortex used not just the usual sodium ions to'fire', but calcium as well. This combination of positively charged ions kicked off waves of voltage that had never been seen before, referred to as a calcium-mediated dendritic action potentials, or dCaAPs.


Clearview AI's facial recognition tech comes under fire in Europe

Engadget

Privacy groups in Europe have filed complaints against Clearview AI for allegedly breaking privacy laws by scraping billions of photos from social media sites like Facebook, Bloomberg has reported. Watchdog groups like Privacy International have taken legal action against the company in Austria, France, Greece, Italy and the UK, telling regulators that the practices "are incredibly invasive and dangerous." "Extracting our unique facial features or even sharing them with the police and other companies goes far beyond what we could ever expect as online users," Privacy International's Ioannis Kouvakas told Bloomberg. Clearview has been controversial since it was first revealed. The company has an immense database of faces taken from social media and uses AI to compare those to images from security cameras or other sources.


Cybersecurity Researchers Build a Better 'Canary Trap'

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During World War II, British intelligence agents planted false documents on a corpse to fool Nazi Germany into preparing for an assault on Greece. "Operation Mincemeat" was a success, and covered the actual Allied invasion of Sicily. The "canary trap" technique in espionage spreads multiple versions of false documents to conceal a secret. Canary traps can be used to sniff out information leaks, or as in WWII, to create distractions that hide valuable information. WE-FORGE, a new data protection system designed in the Department of Computer Science, uses artificial intelligence to build on the canary trap concept.


Seeing stones: pandemic reveals Palantir's troubling reach in Europe

The Guardian

The 24 March, 2020 will be remembered by some for the news that Prince Charles tested positive for Covid and was isolating in Scotland. In Athens it was memorable as the day the traffic went silent. Twenty-four hours into a hard lockdown, Greeks were acclimatising to a new reality in which they had to send an SMS to the government in order to leave the house. As well as millions of text messages, the Greek government faced extraordinary dilemmas. The European Union's most vulnerable economy, its oldest population along with Italy, and one of its weakest health systems faced the first wave of a pandemic that overwhelmed richer countries with fewer pensioners and stronger health provision. One Greek who did go into the office that day was Kyriakos Pierrakakis, the minister for digital transformation, whose signature was inked in blue on an agreement with the US technology company, Palantir. The deal, which would not be revealed to the public for another nine months, gave one of the world's most controversial tech companies access to vast amounts of personal data while offering its software to help Greece weather the Covid storm. The zero-cost agreement was not registered on the public procurement system, neither did the Greek government carry out a data impact assessment – the mandated check to see whether an agreement might violate privacy laws. The questions that emerge in pandemic Greece echo those from across Europe during Covid and show Palantir extending into sectors from health to policing, aviation to commerce and even academia.


How Artificial Intelligence Can Slow the Spread of COVID-19

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A new machine learning approach to COVID-19 testing has produced encouraging results in Greece. The technology, named Eva, dynamically used recent testing results collected at the Greek border to detect and limit the importation of asymptomatic COVID-19 cases among arriving international passengers between August and November 2020, which helped contain the number of cases and deaths in the country. The findings of the project are explained in a paper titled "Deploying an Artificial Intelligence System for COVID-19 Testing at the Greek Border," authored by Hamsa Bastani, a Wharton professor of operations, information and decisions and affiliated faculty at Analytics at Wharton; Kimon Drakopoulos and Vishal Gupta from the University of Southern California; Jon Vlachogiannis from investment advisory firm Agent Risk; Christos Hadjicristodoulou from the University of Thessaly; and Pagona Lagiou, Gkikas Magiorkinis, Dimitrios Paraskevis and Sotirios Tsiodras from the University of Athens. The analysis showed that Eva on average identified 1.85 times more asymptomatic, infected travelers than what conventional, random surveillance testing would have achieved. During the peak travel season of August and September, the detection of infection rates was up to two to four times higher than random testing.


How Artificial Intelligence Can Slow the Spread of COVID-19 - Knowledge@Wharton

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A new machine learning approach to COVID-19 testing has produced encouraging results in Greece. The technology, named Eva, dynamically used recent testing results collected at the Greek border to detect and limit the importation of asymptomatic COVID-19 cases among arriving international passengers between August and November 2020, which helped contain the number of cases and deaths in the country. The findings of the project are explained in a paper titled "Deploying an Artificial Intelligence System for COVID-19 Testing at the Greek Border," authored by Hamsa Bastani, a Wharton professor of operations, information and decisions and affiliated faculty at Analytics at Wharton; Kimon Drakopoulos and Vishal Gupta from the University of Southern California; Jon Vlachogiannis from investment advisory firm Agent Risk; Christos Hadjicristodoulou from the University of Thessaly; and Pagona Lagiou, Gkikas Magiorkinis, Dimitrios Paraskevis and Sotirios Tsiodras from the University of Athens. The analysis showed that Eva on average identified 1.85 times more asymptomatic, infected travelers than what conventional, random surveillance testing would have achieved. During the peak travel season of August and September, the detection of infection rates was up to two to four times higher than random testing.