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The news from OpenAI this week is that the front-running model provider has released<\/a> a new set of LLMs, called GPT-Live, that change the way that their products interact with human users.<\/p>\n Specifically, there\u2019s a regular model and a \u201cmini.\u201d Both of these, engineers say, replace the traditional linear or \u201cturn-taking\u201d conversation method with something called continual interaction, or, in other words, a \u201cfull-duplex architecture\u201d for a certain kind of multi-tasking.<\/p>\n \u201cThis framework allows the AI to simultaneously receive information and produce outputs,\u201d writes Katelyn Chedraoui at CNet<\/a>. \u201cSo in the case of voice, it can \u2018listen\u2019 and \u2018speak\u2019 at the same time. This is a change from previous voice modes that could only respond after you finished speaking. Part of the new architecture can also delegate or offload tasks to OpenAI’s frontier models. For example, it can answer a question you asked while researching another one.\u201d<\/p>\n So the new models can, in a sense, \u201cdo more things\u201d in a set amount of time, making them more fully functional as conversational partners. What else is going on under the hood?<\/p>\n They\u2019re Watching and Listening<\/p>\n It seems reasonable that as makers build more of this engineering into LLMs, the machines will be finding new ways to evaluate the responses of the human or humans they are talking to. That\u2019s one way to gain influence with a user base that is subject to all of the frailties for which humans are known.<\/p>\n For example, check out this paper<\/a> on VADER sentiment analysis, and keep in mind that LLMs are only now venturing more fully into voice, where they previously relied on text, and token analysis of text. (I say, \u201ccheck out this paper,\u201d but also note the tech-ese in which it is written, and consider how its ideas could be more accessible elsewhere.)<\/p>\n The VADER system is already quite effective for looking at the social cues in text-based conversation. What happens when the machines apply this to human voice, and gestures? And while you think about that, remember this: there are already ways that LLMs understand you as well or better than other humans. You don\u2019t have to spell correctly when you write to GPT, the model will \u201cget it.\u201d But I digress. <\/p>\n The Caveat: A Thing to Remember<\/p>\n In evaluating the results of GPT-live and continuous interaction models, CoPilot came up with a concrete bullet list of some of the things that this technology enables LLMs to do:<\/p>\n Acknowledge you mid\u2011sentence with short replies like \u201cmhmm\u201d or \u201cgot it.\u201dStay quiet when you need a moment to think.Handle interruptions and quick back\u2011and\u2011forth exchanges more naturally.<\/p>\n All of this is going to make us even more convinced that we\u2019re talking to an entity that is, in some way, sentient.<\/p>\n But we need to always keep in mind that the ways that LLMs \u201cthink\u201d are quite different than ours.<\/p>\n