• Copyright Law Makes Artificial Intelligence Bias Worse.

    Much of the data used to train algorithms is protected by copyright restrictions in the United States, and courts haven't yet decided whether training an AI amounts to infringement. Due to potential legal implications, major AI companies keep the data they use to train their products a secret, preventing journalists and academics from uncovering biases, as well as stifling competition.

    The most prominent AI corporations, like DeepMind, Facebook, Google, Apple, IBM, and Microsoft often don't release the underlying datasets on which the algorithms they create are trained, according to a forthcoming article about how copyright law affects AI bias to be published in Washington Law Review.

  • Big data analytics spot illegal opioid sales on Twitter.

    Rogue pharmacies using Twitter to peddle powerful painkillers and counterfeit drugs can be identified by big data analytics, a new study has revealed.

    Due to be published in the November issue of the American Journal of Public Health, the study by researchers at the University of California San Diego successfully used a methodology that identified tweets with specific keywords that were advertising the sale of opioid drugs directly to consumers in violation of US federal law.

    Using big data, cloud computing, machine learning and web forensic evaluation, the researchers were able to filter tweets using keywords including Percocet, Vicodin, OxyContin, as well as fentanyl, isolate those that were related to the marketing of opioids and analyse posts with hyperlinks to external websites.

  • 'We can't compete': why universities are losing their best AI scientists. This one hits close to home: I left academia for industry. Yes, salary was a factor (not the only one, or the most important one, but it wasn't ignored).

    It was the case of the missing PhD student.

    As another academic year got under way at Imperial College London, a senior professor was bemused at the absence of one of her students. He had worked in her lab for three years and had one more left to complete his studies. But he had stopped coming in.

    Eventually, the professor called him. He had left for a six-figure salary at Apple.

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