LLMs
LLMs “tend to fail” when faced with the “messy data, ambiguous language, and nuanced complexities of actual data analysis."
From a home-grown, 3b parameter on-device model to the use of its "Talaria" tool, Apple is innovating quietly on AI. Details? Well...
Microsoft's engineers won't need to retrain just yet, although Gates does believe that AI will generate "so much extra productivity" that it will "surprise people and require us to rethink.”
"I started talking about this tipping point four years ago, and you've seen it continue to play out in our results since then.”
"We must build frontier data which is always pushing the boundaries of AI capabilities towards complex reasoning, agents, multimodality, and more."
Communicating RAG/ML is hard. Engineering, legal and communications need to sit down and thrash this out in a growing number of companies.
"To develop AI/ML models, our systems analyse Customer Data (e.g. messages, content and files) submitted to Slack..."
"For product experiences that tolerate such a level of errors, building with generative AI is refreshingly straightforward. But it also creates unattainable expectations..."
That's the sound of a lifeline being thrown...