#llm

#llm

Amazon's AI-powered Alexa is on its way to more homes soon. The upgraded assistant will be available straight out of the box with new hardware announced today. New Echo devices will come with Alexa Plus straight out of the box; it'll also be available to those who buy one of the new FireTV. Alexa Plus is still an early access program and only available in the US, but including it on the new hardware marks an important step forward for Amazon's overdue assistant overhaul.

According to internal sources at Apple, the company has built a ChatGPT-like app for iPhones that helps the engineering team develop the next-generation Siri, powered by LLM. Reportedly, the AI division is using the internal app to test new Siri features, including the ability to search through personal information like songs, emails, photos, videos, etc. It's also capable of executing in-app actions, such as editing photos.

Even if you love to code, there probably are times when you'd rather ask a question like, "What topics generated the highest reader interest this year?" than write an SQL query with phrases like STRFTIME('%&Y', Date) = STRFTIME('%Y', 'now'). And, if your data set has dozens of columns, it's nice to be able to avoid looking up or remembering the exact name of each one.

If you ask a large language model (LLM) like ChatGPT or Google Gemini to solve your customers' pain points, it will give you an answer based on the easiest-to-verify information. That often includes published articles, consistent founder commentaries, structured product pages and other third-party references. If those answers do not include your brand, these learning models default to featuring your competitors.

GenAI has the ability to search, summarize, analyze, and make conclusions that search engines cannot, requiring a level of judgment that can lead to errors.

Imagine a fat, frustrating, overbearing, overcunning boss, and world domination. While it commands humans left and right. For 'our own benefit'. Yeah. Right.

The TnT-LLM framework improves taxonomy generation and text classification by combining a zero-shot approach for taxonomy creation with pseudo-labeling from LLM outputs.