Remove Data Quality Remove Machine Learning Remove Magazine
article thumbnail

How to collect voice data for machine learning

Becoming Human

Machine learning and artificial intelligence have revolutionized our interactions with technology, mainly through speech recognition systems. At the core of these advancements lies voice data, a crucial component for training algorithms to understand and respond to human speech.

article thumbnail

Going beyond AI assistants: Examples from Amazon.com reinventing industries with generative AI

Flipboard

Non-conversational applications offer unique advantages such as higher latency tolerance, batch processing, and caching, but their autonomous nature requires stronger guardrails and exhaustive quality assurance compared to conversational applications, which benefit from real-time user feedback and supervision. Puneet Sahni is Sr.

AI 150
professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Top Use Cases of AI in the Banking Sector

Becoming Human

AI Chatbots The banking sector has started to use AI and ML (machine learning) significantly, with chatbots being one of the most popular applications. On the other hand, conversational AI that acts as a personal assistant can help with data input without the requirement of typing everything manually.

AI 84
article thumbnail

10 Years Later: Who’s the GOAT of Data Catalogs?

Alation

March 2015: Alation emerges from stealth mode to launch the first official data catalog to empower people in enterprises to easily find, understand, govern and use data for informed decision making that supports the business. May 2016: Alation named a Gartner Cool Vendor in their Data Integration and Data Quality, 2016 report.

article thumbnail

5 Key Open-Source Datasets for Named Entity Recognition

Becoming Human

NLP is a branch of artificial intelligence (AI) that aims to teach machines how to understand, interpret, and generate human language. It’s crucial in various AI and machine learning (ML) applications. These datasets act as training data for machine learning models. Disadvantages 1.Data