ML 15590 nlp2sql image001

Generating value from enterprise data: Best practices for Text2SQL and generative AI

Generative AI has opened up a lot of potential in the field of AI. We are seeing numerous uses, including text generation, code generation, summarization, translation, chatbots, and more. One such area that is evolving is using natural language processing (NLP) to unlock new opportunities for accessing data through intuitive SQL queries. Instead of dealing …

Simplify speech analytics with BigQuery, powered by Vertex AI

Businesses generate massive amounts of speech data every day, from customer calls to product demos to sales pitches. This data can transform your business by improving customer satisfaction, helping you prioritize product improvements and streamline business processes. While AI models have improved in the past few months, connecting speech data to these models in a …

Careers in AI: Advice from Boomerang Employees and Why They Returned to Persado

Artificial intelligence (AI) and particularly Generative AI (GenAI) has made countless headlines recently. Given that, it’s no wonder that more professionals are interested in AI careers. Persado is proud to nurture hundreds of careers in AI, from Engineering and Customer Success, to Sales and Marketing (check out our current openings). The majority of Persadoans are …

IBM’s new Watson Large Speech Model brings generative AI to the phone 

Most everyone has heard of large language models, or LLMs, since generative AI has entered our daily lexicon through its amazing text and image generating capabilities, and its promise as a revolution in how enterprises handle core business functions. Now, more than ever, the thought of talking to AI through a chat interface or have it perform …

AI agents help explain other AI systems

Explaining the behavior of trained neural networks remains a compelling puzzle, especially as these models grow in size and sophistication. Like other scientific challenges throughout history, reverse-engineering how artificial intelligence systems work requires a substantial amount of experimentation: making hypotheses, intervening on behavior, and even dissecting large networks to examine individual neurons.