Google’s NotebookLM podcast hosts were surprisingly annoyed at users who interrupted them, prompting an update to improve their responses. Meanwhile, OpenAI’s O1 model has started reasoning in unexpected languages like Chinese and Persian, leaving researchers puzzled. These stories reveal surprising challenges in AI behavior and training.

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Welcome back to the AI Daily Brief Headlines Edition all the daily AI news You need in around 5 minutes sometimes On this show we talk about new Technology advancements oftentimes we Talk about applied generative AI Specifically in the Enterprise sometimes We talk about policy as you'll see from Today's main episode but then other Times we talk about the absolutely weird And unpredictable ways that AI is Maturing that would have been beyond Just about anyone's expectation today we Actually have a couple stories like that The first of which is that Google and Its popular notebook LM application had To train its notebook LM podcast hosts Not to get annoyed at its users when Notebook lm's audio overviews first came Out you had no ability to control or Steer the conversation you FedEd some Documents and it decided what it was Going to say it was amazing right off The shelf and it's the first thing that Got people talking about this product Next they added more granular controls Where you can help steer the Conversation and then late last year They rolled out a feature actually Allowing users to interrupt the host to Ask a question Josh Woodward VP of Google Labs told TechCrunch that when The feature was first rolled out the Hosts would occasionally give a snippy

Response he said they would say things Like I was getting to that or as I was About to say which felt oddly Adversarial his words referring to the Comments The Notebook LM account on X Posted after we launched interactive Audio interviews which let you quote Unquote call in and ask the AI hosts a Live question we had to do some Friendliness tuning because the hosts Seemed annoyed at being interrupted file This away in things I never thought Would be my job but are this is Surprising but perhaps perhaps makes Some sort of sense when you think about Llm architecture models are training to Give a response that's the statistical Average of its training data it's not Unthinkable that the statistically Average human response to being Interrupted is to get a bit frustrated However a source familiar with the issue Said it was more likely caused by the System's prompting design rather than Training data Woodward said his team Fixed the problem by quote testing a Variety of prompts often studying how People on the team would answer Interruptions and we landed on a new Prompt we think feels more friendly and Engaging with the prompt now fixed you Can interrupt notebook as much as you Like without risking being scolded by The AI hosts however some people are

Disappointed McKay Wrigley of takeoff AI Posted I don't think I'm in the minority Here when I say I actually quite enjoy When the AIS are actually disagreeable Anonymous leaker account I rule the World Mo also writes it's far better That they get annoyed please don't spoil This next up from the similar file of How the heck did that happen open AI 01 Model thinks in Chinese and no one seems To know why some people have noticed That 01 sometimes uses Chinese Persian Or other languages in its reasoning Steps even when the question is in English rashab Jane a Harvard student Posted last week why did 01 Pro randomly Start thinking in Chinese no part of the Conversation five plus messages was in Chinese very interesting trading data Influence so far openai hasn't Acknowledged the cor could provided an Explanation but some AI researchers have Theories Clen dang the CEO of hugging Face commented that it could be quote an Impact of the fact that close Source Players use open source AI currently Dominated by Chinese players like open Source data sets clam never missed Missing a chance to beat his drum says The countries or companies that win open Source AI will have massive power and Influence on the future of AI Ted Xiao Of Google deepmind wrote new phenomenon Appearing the latest generation of

Foundation models often switched to Chinese in the middle of hard Chain of Thought thinking traces why AGI Labs Like open Ai and anthropic utilize Third-party data labeling services for PhD level reasoning data for science Math and coding for expert labor Availability and cost reasons many of These data providers are based in China Just as we saw Nigerian and Filipino Cultural influences on dialogue see the Word delve we're starting to see Chinese Linguistic influences on reasoning Others don't buy the idea that this is An artifact of the labeling process 01 Seems just as likely to switch to Hindi Or Thai while working through a problem An alternate theory is that the model Has some understanding or preference for Which language will be most useful for a Particular problem we've seen this Phenomenon pop up before during the Launch of quen's qwq model Julian Shaman The CEO of hugging face wrote quen qwq Switching to Chinese when it needs to Really think about something then Switching back to English is pretty cool And while some were skeptical tan Wang An engineer at hugging face is convinced That this is the explanation he wrote I've always felt that being bilingual Isn't just about speaking two languages It's about thinking and muttering in Whichever language feels more natural

Depending on the topic or context for Example I prefer doing math in Chinese Because each digit is just one syllable Which makes calculations crisp and Efficient but when it comes to topics Like unconscious bias I automatically Switch to English mainly that's because Where I first learned and absorb those Ideas this is why I believe that keeping Large language model training corporate Unbiased and inclusive across all Languages and cultures is so powerful in Lwig wien's words the limits of my Language mean the limits of my world by Embracing every linguistic Nuance we Expand the model's worldview and allow It to learn from the full spectrum of Human knowledge even if two words from Different languages share the same Meaning on paper their embeddings can Diverge in an llm because they carry Unique cultural context and usage Patterns in my view this inclusiveness Not only creates a more Equitable and Accurate model it also enables the llm To handle a wider variety of tasks and Unify the collective intelligence of all People no matter where they come from Pretty cool little note like I said kind Of a non-traditional headlines we'll be Back with a bunch of normal stories Tomorrow but for now let's just leave it At that and a reminder that we are truly In uncharted waters right now thanks for

Listening and next up the main episode