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Interesting article discussing how ai is being used in medical diagnoses

Australian farms generate huge volumes of agricultural data. Examples include the types of crops being grown, crop yields, livestock numbers and locations, types of fertilisers and pesticides being used, soil types, rainfall and more. This data is typically collected through the use of digital farming machinery and buildings featuring robotics and digital technologies, artificial intelligence, and devices connected to the internet ("internet of things", or IoT). But a recent review from the Australian Bureau of Statistics and the Australian Bureau of Agricultural and Resource Economics highlights the patchy and fragmented nature of existing government and industry approaches to agricultural data. What that means is Australian farmers are currently not adequately protected from their farm data being collected and used without their knowledge or consent.

The Supreme Court does not compute. Or at least some of its members would rather not. The justices, the most powerful jurists in the land, seem to have a reluctance - even an allergy - to taking math and statistics seriously. For decades, the court has struggled with quantitative evidence of all kinds in a wide variety of cases. Sometimes justices ignore this evidence. Sometimes they misinterpret it. And sometimes they cast it aside in order to hold on to more traditional legal arguments. (And, yes, sometimes they also listen to the numbers.) Yet the world itself is becoming more computationally driven, and some of those computations will need to be adjudicated before long. Some major artificial intelligence case will likely come across the court's desk in the next decade, for example. By voicing an unwillingness to engage with data-driven empiricism, justices - and thus the court - are at risk of making decisions without fully grappling with the evidence. This problem was on full display earlier this month, when the Supreme Court heard arguments in Gill v. Whitford, a case that will determine the future of partisan gerrymandering - and the contours of American democracy along with it. As my colleague Galen Druke has reported, the case hinges on math: Is there a way to measure a map's partisan bias and to create a standard for when a gerrymandered map infringes on voters' rights?

Social media algorithms, artificial intelligence, and our own genetics are among the factors influencing us beyond our awareness. This raises an ancient question: do we have control over our own lives? This article is part of The Conversation's series on the science of free will.

A series of short articles, with videos and browser addons "investigating algorithmic injustice and the formulas that influence our lives."

Machines are besting humans in more and more tasks; thanks to technology, fewer Americans make more stuff in less time. But today economists debate not whether machines are changing the workplace and making us more efficient - they certainly are - but whether the result is a net loss of jobs. The figures above may look dire. But compare the number of manufacturing jobs and total jobs in the chart below. Since 1990, the total non-farm workforce has grown 33 percent, more than accounting for the manufacturing jobs lost.

Have you ever you looked for a product online and then been recommended the exact thing you need to complement it? Or have you been thinking about a particular purchase, only to receive an email with that product on sale? All of this may give you a slightly spooky feeling, but what you're really experiencing is the result of complex algorithms used to predict, and in some cases, even influence your behaviour.

Algorithms are increasingly making decisions that affect ordinary people's lives. One example of this is so-called "algorithmic lending", with some companies claiming to have reduced the time it takes to approve a home loan to mere minutes. But can computers become better judges of financial risk than human bank tellers? Some computer scientists and data analysts certainly think so.