global battle
The Global Battle to Regulate AI Is Just Beginning
Dan Nechita has spent the past year shuttling back and forth between Brussels and Strasbourg. As the head of cabinet (essentially chief of staff) for one of the two rapporteurs leading negotiations over the EU's proposed new AI law, he's helped hammer out compromises between those who want the technology to be tightly regulated and those who believe innovation needs more space to evolve. The discussions have, Nechita says, been "long and tedious." First there were debates about how to define AI--what it was that Europe was even regulating. "That was a very, very, very long discussion," Nechita says.
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An expert explains: 3 ways AI can help in the global battle against cancer
This rings an alarm bell as our health systems are overburdened and fragile. The pandemic has further exposed how the response of health systems to the chronic diseases can take the back seat with the onset of new and emerging infections. A report by the Indian Institute of Health Management and Research reveals that India has only over 1,250 oncologists currently, against the estimated need for 5,000. The lack of trained manpower for mass screening programs and required infrastructure portrays further apathy. India has about 27 dedicated cancer hospitals and an additional 300 general or multi-specialty ones providing care to cancer patients.
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AI Is Transforming The Global Battle Against Human Trafficking - Pioneering Minds
The U.S. Department of Homeland Security defines trafficking referred to as modern-day slavery, as a crime that involves the use of force, fraud, or coercion to obtain some type of labor or commercial sex act. AI and ML, have the power to analyze more than just financial activity. The current anti-money laundering (AML) environment relies on simple transaction-monitoring rules to detect human trafficking, and it simply does not have the capacity to consider, weight and examine the necessary number of inputs. The problem with it now is that it produces a lot of false positives, so the real issues are lost in the weeds. If you use more machine learning, you can program more variables and machines can use the information it has and then teach itself how to better identify patterns.
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