Over the past decade, the exploitation of personal data has been at the forefront of public concerns about digital technologies. However, as more and more businesses use AI, the debate is entering a new phase.
All of us make mistakes and (try to) learn from our experiences. Machine learning is pretty similar, but unlike us, machines can learn and improve automatically. There exist many types of ML techniques, all with different learning methods. What are those?
Traditional methods for detecting money laundering have become outdated in our global, web-based economy. That’s why we need to add ML to AML: it gives us a unique opportunity to take anti-money laundering efforts to the next level.
The capabilities of artificial intelligence cannot be overestimated. The technology has made its presence felt in a variety of industries, the insurance sector being no exception. But how exactly is AI set to help insurance companies make the grade?
Fraud attacks have grown in sophistication. The concept behind using machine learning in fraud detection presupposes using algorithms that detect patterns in financial operations and decide whether a given transaction is fraudulent.
AI is becoming a universal tool for game design. Despite video game artificial intelligence still being in its infancy, game companies have already started to recognize attractive benefits such as enhanced player experience and cost reduction.
Machine learning models are expected to evolve and become more precise in the data-driven business world as more data is collected. MLOps, the unification of machine learning workflows and DevOps principles, makes sure that expectation is met.
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