• Intelligent to a Fault: When AI Screws Up, You Might Still Be to Blame.

    Artificial intelligence is already making significant inroads in taking over mundane, time-consuming tasks many humans would rather not do. The responsibilities and consequences of handing over work to AI vary greatly, though; some autonomous systems recommend music or movies; others recommend sentences in court. Even more advanced AI systems will increasingly control vehicles on crowded city streets, raising questions about safety—and about liability, when the inevitable accidents occur.

  • The Machine Learning Reproducibility Crisis.

    I was recently chatting to a friend whose startup’s machine learning models were so disorganized it was causing serious problems as his team tried to build on each other’s work and share it with clients. Even the original author sometimes couldn’t train the same model and get similar results! He was hoping that I had a solution I could recommend, but I had to admit that I struggle with the same problems in my own work. It’s hard to explain to people who haven’t worked with machine learning, but we’re still back in the dark ages when it comes to tracking changes and rebuilding models from scratch. It’s so bad it sometimes feels like stepping back in time to when we coded without source control.

  • Getting Value from Machine Learning Isn’t About Fancier Algorithms — It’s About Making It Easier to Use.

    Machine learning can drive tangible business value for a wide range of industries — but only if it is actually put to use. Despite the many machine learning discoveries being made by academics, new research papers showing what is possible, and an increasing amount of data available, companies are struggling to deploy machine learning to solve real business problems. In short, the gap for most companies isn’t that machine learning doesn’t work, but that they struggle to actually use it.


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