There are three critical areas where companies most often go wrong: data preparation and training, choosing tools and specialists and timing and planning.

AI safety tests found to rely on 'obvious' trigger words; with easy rephrasing, models labeled 'reasonably safe' suddenly fail, with attacks succeeding up to 98% of the time. New corporate research ...

Plotly announces major update to AI-native data analytics platform Plotly Studio, turning data into production-ready ...

Introduction The proliferation of deepfake technology, synthetic media generated using advanced artificial intelligence techniques, has emerged as a ...

Speechify's Voice AI Research Lab Launches SIMBA 3.0 Voice Model to Power Next Generation of Voice AI SIMBA 3.0 represents a major step forward in production voice AI. It is built voice-first for ...

Physical AI is not merely a product feature. It is an architectural shift. When intelligence lives next to the phenomenon it observes, we gain what the cloud alone cannot consistently provide: low ...

AI isn’t killing tech jobs — it’s changing them, favoring pros who pair data and cloud savvy with curiosity, empathy and ...

AI tools are fundamentally changing software development. Investing in foundational knowledge and deep expertise secures your career long-term.

It’s time to return to Tunisia, the Mediterranean’s most intriguing country - As tourists slowly return to the North African ...

A REST API (short for Representational State Transfer Application Programming Interface) is a way two separate pieces of ...

Stacker compiled data on the top feature-length films from the past 100 years, crowning a champion for each year using Metacritic and IMDb data.

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...