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 ...

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

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

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

Use the vitals package with ellmer to evaluate and compare the accuracy of LLMs, including writing evals to test local models ...

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 ...

AI-powered platform adds Identification Support for 1M+ Football Cards to 4M Baseball Catalog; adds $199.95 Ultra tier.

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 ...

Physical AI is not merely a product feature. It is an architectural shift. The question before us is simple: Will the world of Physical AI be built by a few thousand engineers, or by millions of ...