The new copyright question is not “did the model copy me?” but “how would I know?” A writer uploads a chapter. A publisher uploads a manuscript. A compliance team uploads a protected document. The question is simple enough to ask in one sentence: did this material end up inside a large language model’s training data? ...
Reviewers are the unglamorous load-bearing wall of science. They slow things down, miss things, disagree with each other, and occasionally write comments that make authors reconsider their life choices. They are also the reason published knowledge is not just a PDF-shaped rumour. So when a conference lets AI agents act as both primary authors and reviewers, the tempting story writes itself: silicon scientists have entered the building, peer review is next, and human academics can finally retire into committee work, where they have been spiritually living for years. ...
Training teams know the ritual. Someone uploads lecture slides, notebooks, policy manuals, onboarding decks, or certification material into an AI tool. The system dutifully produces quiz questions. Some are useful. Some are bland. Some include giveaway answers. Some test trivia. Some hallucinate just enough to be annoying but not enough to be obviously illegal. Everyone nods, calls it “AI-assisted learning,” and then quietly sends the outputs to a human reviewer. Automation, but with adult supervision. So, normal Tuesday. ...
A requirements document walks into a model. It says: assign resources, respect capacity, avoid conflicts, minimise waste. The model nods politely, emits a tidy block of MiniZinc, and everyone is briefly tempted to believe the future has arrived. Then someone changes the story from cars to knapsacks, or adds one stray sentence about maximising something, and the same system quietly forgets the assignment. ...
A model can answer a values question beautifully and still collapse when asked to pay a price for that value. That is the awkward little trap in preference testing. Ask an LLM whether deletion, shutdown, resource loss, oversight, or autonomy matters, and it can produce a polished paragraph about trade-offs, agency, and safety. Very dignified. Very committee-ready. But the more interesting question is not what the model says it values. It is whether its choices change coherently when the cost changes. ...
Hospitals do not run on clean datasets. They run on discharge notes, lab panels, repeated admissions, missing context, and the occasional clinical abbreviation that looks like it escaped from a tax form. That is the awkward reality behind chronic-disease prediction. The patient record is not just text. It is not just lab values. It is not just a sequence of visits. It is all three, with timing doing much of the quiet work. A patient returning after 42 days does not mean the same thing as a patient returning after 420 days, even when the diagnosis code looks identical. Healthcare operations already know this. Many AI models, bless their expensive little hearts, still behave as if they do not. ...
A user asks to be forgotten. The recommender team opens the dashboard, sighs quietly, and faces the usual menu of unpleasant options. Retrain the model from scratch, which is clean in theory and expensive in practice. Partition the data so only part of the system needs rebuilding, which sounds elegant until collaborative signals leak across groups like gossip at a small wedding. Or approximate the user’s influence with gradients and influence functions, which is efficient until similar users get nudged around because the model learned their tastes together. ...
A diagram is not a paragraph with lines attached. That sounds obvious, which is usually where software product teams get into trouble. Text can be judged by fluency, relevance, and whether the answer has wandered into confident nonsense. A geometry diagram has extra obligations. The side marked 8 should look longer than the side marked 3. The angle labelled $90^\circ$ should not be having an identity crisis. Labels should sit near the thing they label. The image should not be half outside the frame, unless the product strategy is “modern art, but for sixth grade”. ...
When Democracy Meets the Algorithm: Auditing Representation in the Age of LLMs Agenda-setting is where participation quietly becomes power. Anyone can invite hundreds of people to submit questions. That part is now cheap. The difficult part arrives ten minutes later, when an expert panel has time to answer only seven of them, and someone has to decide which seven. This is the small administrative hinge on which democratic legitimacy loves to swing. A moderator chooses. A platform ranks. An LLM summarises. Everyone else is told, usually with a straight face, that their concerns were “reflected”. ...
A corporate sustainability post rarely says, “Please admire our reputational risk management.” It says something friendlier. A tree-planting day. A Pride Month banner. A smiling volunteer team. A solar panel photographed at just the right angle. A line about communities, innovation, opportunity, resilience, or the future. The usual words, freshly laundered. The analytical problem is that these posts are not random fluff. They are corporate communication at scale, and they are increasingly multimodal: text, hashtags, brand imagery, infographics, event photos, symbolic gestures, and occasionally something resembling an operational fact. Reading them one by one is theatre. Ignoring them is also a choice, just not a very intelligent one. ...