Beyond mistakes or nonsense, deliberately bad information being injected into AI search summaries is leading people down potentially harmful paths. These days, rather than showing you the traditional list of links when you run a search query, Google is intent on throwing up AI Overviews instead: synthesized summaries of information scraped off the web, with some word-prediction magic added, and packaged together in a way to sound as accurate and reliable as possible. We've written before about some of the problems with these AI Overviews, which regularly contain mistakes or nonsense, and of course rip off the work of the human writers who actually know the answers to the questions you're putting into Google. There's another problem though--these AI answers can actually be dangerous. As with every other new technology through history, scams are now making their way into AI Overviews as well, apparently injecting Google's AI answers with fraudulent phone numbers that you shouldn't trust.

When you purchase through links in our articles, we may earn a small commission. Here's how to get rid of them Are you tired of AI summaries in Google Search? Want the search results to look like they used to? Here are some tricks you can use. Unless you live under a rock, you've probably seen that Google Search has been showing "AI Overviews" at the top of its search results.

Using a swearword in your Google search can stop the AI answer. Artificial intelligence is more than Trump deepfakes of Tilly the actor. It's used in smartphones, customer service, healthcare - even legal cases. Is it possible to avoid? Using a swearword in your Google search can stop that annoying AI overview from popping up.

When online search engines first appeared, they seemed miraculous. It is a truth near-universally acknowledged that search is in the dumps, corroded by spam and ads. Big players like Google are insistent that AI is the savior of search, despite many early attempts to integrate AI ending in disaster. Recently, I got an email promoting another new AI search engine--but this one has a notably quirky approach to answering questions. Called Pearl, it's coming out of beta this week.

There's no arguing that AI still has quite a few unreliable moments, but one would hope that at least its evaluations would be accurate. However, last week Google allegedly instructed contract workers evaluating Gemini not to skip any prompts, regardless of their expertise, TechCrunch reports based on internal guidance it viewed. Google shared a preview of Gemini 2.0 earlier this month. Google reportedly instructed GlobalLogic, an outsourcing firm whose contractors evaluate AI-generated output, not to have reviewers skip prompts outside of their expertise. Previously, contractors could choose to skip any prompt that fell far out of their expertise -- such as asking a doctor about laws.

As anyone who so much as glanced at the internet in the past few weeks probably noticed, Google's sweeping AI upgrade to its search engine had a rocky start. Within days of the company launching AI-generated answers to search queries called AI Overviews, the feature was widely mocked for producing wrong and sometimes bonkers answers, like recommendations to eat rocks or make pizza with glue. New data from search engine optimization firm BrightEdge suggests that Google has significantly reduced how often it is showing people AI Overviews since the feature launched, and had in fact already substantially curbed the feature prior to the outpouring of criticism. The company has been tracking the appearance of Google's AI answers on results for a list of tens of thousands of sample searches since the feature was first offered as a beta test last year. When AI Overviews rolled out to logged-in US users in English after Google's I/O conference on May 14, BrightEdge saw the AI-generated answers on just under 27 percent of queries it tracked.

The field of eXplainable artificial intelligence (XAI) has produced a plethora of methods (e.g., saliency-maps) to gain insight into artificial intelligence (AI) models, and has exploded with the rise of deep learning (DL). However, human-participant studies question the efficacy of these methods, particularly when the AI output is wrong. In this study, we collected and analyzed 156 human-generated text and saliency-based explanations collected in a question-answering task (N=40) and compared them empirically to state-of-the-art XAI explanations (integrated gradients, conservative LRP, and ChatGPT) in a human-participant study (N=136). Our findings show that participants found human saliency maps to be more helpful in explaining AI answers than machine saliency maps, but performance negatively correlated with trust in the AI model and explanations. This finding hints at the dilemma of AI errors in explanation, where helpful explanations can lead to lower task performance when they support wrong AI predictions.

NEW YORK, April 13 (Reuters) - Face it, we could all use a little help with our money. So who better to ask for personal finance advice than a couple of the most powerful chatbots on the planet? Both OpenAI's ChatGPT and Google's Bard are dominating headlines recently, for their generative capabilities and vast storehouses of information. Each has far more processing power than, say, any individual personal finance writer (ahem). What is one great business idea?

ChatGPT is a large language model. That means it's very good at stringing together words in ways that humans tend to use them. It's able to construct sentences that are grammatically correct and sound natural, for the most part, because it's been trained on language. Because it's good at stringing together words, it's able to take your prompt and generate words in a grammatically correct way that's similar to what it's seen before. But that's all that it's doing: generating words and making sure it sounds natural. It doesn't have any built-in fact checking capabilities, and the manual limitations that OpenAI placed can be fairly easily worked around.

Researchers have created a method to help workers collaborate with artificial intelligence systems. Researchers have created a method to help workers collaborate with artificial intelligence systems. In a busy hospital, a radiologist is using an artificial intelligence system to help her diagnose medical conditions based on patients' X-ray images. Using the AI system can help her make faster diagnoses, but how does she know when to trust the AI's predictions? Instead, she may rely on her expertise, a confidence level provided by the system itself, or an explanation of how the algorithm made its prediction -- which may look convincing but still be wrong -- to make an estimation.