As a result, research on diagnostic uses of AI has stayed narrow in scope and applicability. You can’t deploy a breast cancer detection model around the world when it’s only been trained on a few thousand patients from the same hospital.
Current state-of-the-art algorithms require immense amounts of data to learn—in most cases, the more data the better. Hospitals and research institutions need to combine their data reserves if they want a pool of data that is large and diverse enough to be useful. But especially in the US and the UK, the idea of centralizing reams of sensitive medical information in the hands of tech companies has repeatedly—and unsurprisingly—proved intensely unpopular.
AI in cybersecurity is a hot topic in the infosec world, as Machine Learning (ML) algorithms become increasingly complex. AI in cybersecurity is being applied to or considered for nearly every field application you can imagine. If a team of humans can do it, then AI can do it […]