How Bad Is Plagiarism, Really? From ancient Rome to the era of A.I., people have prized originality, but the line where influence ends and cribbing begins is notoriously blurry. One pleasing facet of plagiarism is that, in the eyes of the law, it doesn't exist. I could come over later, bring a few beers, and we could, you know, get down to some serious humanizing. Hard to resist, these days, given what's at stake. For students with assignments to complete, who have already vanquished their desolation by asking ChatGPT to compose an essay on their behalf, a humanizer is an A.I. tool that takes what has been produced, puts it through a further digital mill, and makes it sound as if it had emerged from a verifiable person. Among the companies that offer such tools are StealthWriter, HIX AI, and QuillBot. Anyone who has buttered and blitzed a mountain of mashed potatoes into a purée will understand.

Uncertainty estimation is crucial for evaluating Large Language Models (LLMs), particularly in high-stakes domains where incorrect answers result in significant consequences. Numerous approaches consider this problem, while focusing on a specific type of uncertainty, ignoring others. We investigate what estimates, specifically token-wise entropy and model-as-judge (MASJ), would work for multiple-choice question-answering tasks for different question topics. Our experiments consider three LLMs: Phi-4, Mistral, and Qwen of different sizes from 1.5B to 72B and $14$ topics. While MASJ performs similarly to a random error predictor, the response entropy predicts model error in knowledge-dependent domains and serves as an effective indicator of question difficulty: for biology ROC AUC is $0.73$. This correlation vanishes for the reasoning-dependent domain: for math questions ROC-AUC is $0.55$. More principally, we found out that the entropy measure required a reasoning amount. Thus, data-uncertainty related entropy should be integrated within uncertainty estimates frameworks, while MASJ requires refinement. Moreover, existing MMLU-Pro samples are biased, and should balance required amount of reasoning for different subdomains to provide a more fair assessment of LLMs performance.

The developer of a popular app which transforms users' faces to predict how they will look as older people has insisted they are not accessing users' photographs without permission. FaceApp, which was launched by a Russian developer in 2017, uses artificial intelligence allowing people to see how they would look with different hair colour, eye colour or as a different gender. The app has topped download charts again this week, after users homed in on its ageing filter, which has since been used by dozens of celebrities and prominent figures to picture how they will supposedly look in several decades' time. This surge of interest has in turn created concerns that FaceApp is systematically harvesting users' images. People who upload their image to the app transfer the picture to a server controlled by the developer, with the photograph processing done remotely, rather than on their phone. These concerns have been heightened by growing awareness of online privacy issues in recent years and the fact that the developer is based in Russia, where many high-profile online misinformation campaigns have been based, in addition to a loosely-phrased privacy policy.

In 2018, blockchain and artificial intelligence (AI) continue to be two of the technologies generating the most buzz--and yet, for the former, not all of that buzz was positive. Blockchain, especially cryptocurrencies like bitcoin, were battered heavily this past year. Bitcoin had topped $17,000 early in 2018 and is now worth less than a quarter of that. That is the bad news. The good news, however, is that the trend for AI is much more positive, with the technology gaining significant momentum consistently for the last few years.

Falls are the leading cause of injury among people 65 and older. Approximately 9,500 deaths in older Americans are associated with trips or stumbles each year, and on average, folks between the ages of 65 to 69 suffer a hip fracture one out of every 200 falls. More worryingly, as a result, 20 to 30 percent experience moderate to severe complications that can cause disability. Cherry Labs, a Cupertino startup founded in 2016 by entrepreneurs Max Goncharov, Stas Veretennikov, and Nick Davidov, aims to prevent those sorts of injuries with an artificially intelligent (AI) in-home system -- Cherry Home -- that's able to detect and track users with vision sensors and microphones. It today announced a $5.2 million funding round led by GSR Ventures, which it says will fuel a pilot program set to kick off in the coming weeks with TheraCare, a caregiving service, and TriCura, a tech platform that uses mobile apps to capture and share information among families, caregivers, and agencies. Cherry Home officially launched in October, and is currently testing with 15 families in the Bay Area.

FaceApp has removed a number of racially themed photo filters after being accused of racism. The app, which uses artificial intelligence to edit pictures, this week launched a number of "ethnicity change filters". They claimed to show users what they'd look like if they were Caucasian, Black, Asian or Indian. FaceApp has attracted fierce criticism for launching the filters, with some users claiming they were racist, and encouraged users to "black up" digitally. Responding to the backlash, FaceApp founder and CEO, Yaroslav Goncharov, said, "The ethnicity change filters have been designed to be equal in all aspects. "They don't have any positive or negative connotations associated with them.

An array of ethnic filters on the photo-editing app, FaceApp, has stirred backlash as users decry the options for facial manipulation as racist. The selfie-editing app, FaceApp, was updated earlier this month with four new filters: Asian, Black, Caucasian and Indian. The filters immediately drew criticism on Twitter by users who made comparisons to blackface and yellowface racial stereotypes. In addition to these blatantly racial face filters – which change everything from hair color to skin tone to eye color – other FaceApp users noted earlier this year that the "hot" filter consistently lightens people's skin color. "#FaceApp has a new feature where you can see yourself #CaucasianLiving. Look how privileged I look!" one of the app's users commented on Twitter.

The creator of an app which changes your selfies using artificial intelligence has apologised because its "hot" filter automatically lightened people's skin. FaceApp is touted as an app which uses "neural networks" to change facial characteristics, adding smiles or making users look older or younger. But users noticed one of the options, initially labelled as "hot" made people look whiter. So I downloaded this app and decided to pick the "hot" filter not knowing that it would make me white. Yaroslav Goncharov, the creator and CEO of FaceApp, apologised for the feature, which he said was a side-effect of the "neural network".