AI Doesn't Know What It Doesn't Know

Researchers asked 44 different AI models to pick any word at random, and 41 percent of them chose the same one: 'serendipity.' This is not a fun trivia fact. It means that when you use AI to brainstorm or generate 'random' ideas, you are likely getting the same suggestions as everyone else using a different tool.

State-of-the-art language models were tested on Korean Braille and largely failed, which means a technology that bills itself as a universal assistant is quietly leaving blind users behind. If your organization is thinking about AI for accessibility, this is a good moment to actually test that assumption before announcing it.

A new paper tackles one of the more practical problems in using AI for predictions: the model does not just need to give you an answer, it needs to tell you how much to trust that answer. Right now, most models are confidently wrong at a rate that would get a human analyst fired.

Researchers built a graph-based system to track how disinformation narratives move between Russian and Ukrainian Telegram channels. The practical upshot is that the same false story can be translated, reworded, and re-shared in ways that make it nearly invisible to simple keyword filters, which is exactly why this kind of structural detection matters.

A study found that most language models blend two very different things: what experts believe and what the model itself 'believes,' even when you ask them to separate those clearly. If you are using AI to research a contested topic, it may be quietly mixing citations with opinion without flagging the difference.

A team is building language models that are deliberately frozen in time, trained only on data available before a given date, so that financial and social-science researchers can backtest ideas without the model cheating by knowing how things turned out. It is a small idea with wide implications for anyone who wants to use AI to study the past honestly.