Group items tagged

GREAT website with lots of debate re Privacy issues...

"Computers once performed facial recognition rather imprecisely, by identifying people's facial features and measuring the distances among them - a crude method that did not reliably result in matches. But recently, the technology has improved significantly, because of advances in artificial intelligence. A.I. software can analyze countless photos of people's faces and learn to make impressive predictions about which images are of the same person; the more faces it inspects, the better it gets. Clearview is deploying this approach using billions of photos from the public internet. By testing legal and ethical limits around the collection and use of those images, it has become the front-runner in the field. "

"Getty points to images produced by Stable Diffusion which contain its copyright watermark, suggesting that Stable Diffusion has ingested and is reproducing copyrighted material without permission (Stability AI has not yet commented publicly on the lawsuit). The same level of evidence is harder to come by when examining ChatGPT's text output, but there is no doubt that it has been trained on copyrighted material. OpenAI will be hoping that its text generation is covered by "fair use", a provision in copyright law that allows limited use of copyrighted material for "transformative" purposes. That idea will probably one day be tested in court."

"Doctors' appointments, job applications, personal banking, key services and more are today mostly managed online. While the UK government details its plans for a digital future to transform public services, one in seven Britons are forced to live without the internet. This film is voiced by three individuals experiencing digital exclusion, revealing how varied and complex the repercussions can be. Through enacted scenes from their lives, it makes visible the expanding digital divide - an issue too often unseen or ignored by policy makers, businesses and society at large. "

"AI models will inevitably have to make value judgments. If we want those judgments to be congruent with our own values (which is, after all, the central goal of AI alignment research) then we need to have ways of testing which values a model expresses in the real world. Our method provides a new, data-focused method of doing this, and of seeing where we might've succeeded-or indeed failed-at aligning our models' behavior."