Unite.AI

OCTOBER 23, 2023

Identity theft , in which impersonated victims lose money due to credit card fraud or become entangled in fraudulent loans, is a growing threat today. With the advancement of AI, our online personas are not only vulnerable to theft but also to replication and manipulation. ” It is a frightening prospect, right?

Metadata 189

Metadata AI AI Artificial Intelligence 189

IBM Journey to AI blog

OCTOBER 16, 2023

Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). ML classification algorithms are also used to label events as fraud, classify phishing attacks and more. Machine learning in financial transactions ML and deep learning are widely used in banking, for example, in fraud detection.

Machine Learning 124

Machine Learning ML Algorithm Chatbots 124

Towards AI

FEBRUARY 20, 2024

You might be using machine learning algorithms from everything you see on OTT or everything you shop online. K-means) Applications of Machine Learning Here are just a few of the countless applications of machine learning: Face recognition: Used to unlock smartphones, tag people in photos, and even track criminals.

Machine Learning 83

Machine Learning ML Neural Network Algorithm 83

IBM Journey to AI blog

FEBRUARY 14, 2024

Promote cross- and up-selling Recommendation engines use consumer behavior data and AI algorithms to help discover data trends to be used in the development of more effective up-selling and cross-selling strategies, resulting in more useful add-on recommendations for customers during checkout for online retailers.

Computer Vision 208

Computer Vision Automation Robotics AI 208

NVIDIA

JULY 13, 2023

Advanced AI applications have the potential to help the industry better prevent fraud and transform every aspect of banking, from portfolio planning and risk management to compliance and automation. Another common application is in recommender systems that power personalized banking experiences, marketing optimization and investment guidance.

Generative AI 98

Generative AI AI AI Natural Language Processing 98

Sebastian Ruder

AUGUST 23, 2021

Key examples of these trends are a transition from a focus on core linguistic tasks such as part-of-speech tagging and dependency parsing to tasks that are closer to the real-world such as goal-oriented dialogue and open-domain question answering ( Kwiatkowski et al., 2021) frame this as an online learning problem in the context of MT.

NLP 52

NLP Natural Language Processing ML Deep Learning 52

AWS Machine Learning Blog

JANUARY 9, 2023

Use cases such as fraud detection, product recommendations, and traffic prediction are examples where milliseconds matter and are critical for business success. In this case, you might want an online inference option that is able to automatically provision and scale compute capacity based on the volume of inference requests.

ML 72

ML Auto-complete Auto-classification Deep Learning 72