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"On Tuesday, security firm Cleafy reported that TeaBot was back. This time, the trojan spread through a malicious app called QR Code & Barcode Scanner, which as the name suggests, allowed users to interact with QR codes and barcodes. The app had more than 10,000 installations before Cleafy researchers notified Google of the fraudulent activity and Google removed it."

"Although disinformation has been extensively discussed as a powerful weapon employed by state and non-state actors, especially given the quick rise of AI tools capable of generating fabricated texts, sounds, and moving or still images,"

"But ChatGPT and similar programs are, by design, unlimited in what they can "learn" (which is to say, memorize); they are incapable of distinguishing the possible from the impossible. Unlike humans, for example, who are endowed with a universal grammar that limits the languages we can learn to those with a certain kind of almost mathematical elegance, these programs learn humanly possible and humanly impossible languages with equal facility. Whereas humans are limited in the kinds of explanations we can rationally conjecture, machine learning systems can learn both that the earth is flat and that the earth is round. They trade merely in probabilities that change over time."

The Canadian office of primary commissioner stated the changes in the privacy policies, especially after 9-11 when the privacy laws were looked past to concentrate on the gathering of information to reduce terrorism in the US and Canada with no real results. However, this problem has been tackled in Canada and stated that they "believe that Canadians deserve federal privacy laws based on rights. The incorporation of a rights-based framework in our privacy laws would help support responsible innovation and foster trust in government, giving people confidence to fully participate in the digital age."

Interoperability in the healthcare system is the gateway to a successful healthcare information exchange. It has excellent benefits like it improves clinical decision-making and reduces time wasting. However, even though there are many attempts to implement the policy, interoperability still has barriers that stop it from success. There are still some third parties that use this for financial gains. Also, the people struggle with the technical and practical challenges of the protocol. Plus, privacy and trust are not truly gained in terms of the privacy and security of those data exchanges.

"In the future, it may be possible to guard against this kind of photo misuse through technical means. For example, future AI image generators might be required by law to embed invisible watermarks into their outputs so that they can be read later, and people will know they're fakes. But people will need to be able to read the watermarks easily (and be educated on how they work) for that to have any effect. Even so, will it matter if an embarrassing fake photo of a kid shared with an entire school has an invisible watermark? The damage will have already been done."

"In the fall of 2020, gig workers in Venezuela posted a series of images to online forums where they gathered to talk shop. The photos were mundane, if sometimes intimate, household scenes captured from low angles-including some you really wouldn't want shared on the Internet. In one particularly revealing shot, a young woman in a lavender T-shirt sits on the toilet, her shorts pulled down to mid-thigh. The images were not taken by a person, but by development versions of iRobot's Roomba J7 series robot vacuum. They were then sent to Scale AI, a startup that contracts workers around the world to label audio, photo, and video data used to train artificial intelligence."

"Applications of these systems have been plagued by persistent inaccuracies in their output; these are often called "AI hallucinations". We argue that these falsehoods, and the overall activity of large language models, is better understood as bullshit in the sense explored by Frankfurt (On Bullshit, Princeton, 2005): the models are in an important way indifferent to the truth of their outputs. We distinguish two ways in which the models can be said to be bullshitters, and argue that they clearly meet at least one of these definitions. We further argue that describing AI misrepresentations as bullshit is both a more useful and more accurate way of predicting and discussing the behaviour of these systems."