"Mr Branding" is a blog based on RSS for everything related to website branding and website design, it collects its posts from many sites in order to facilitate the updating to the latest technology.
To suggest any source, please contact me: Taha.baba@consultant.com
Google is expanding access to its SynthID Detector globally, with the tool available in English to help users check if an image, video or audio file was made with AI from Google or its partners, according to a Google blog post published Oct. 7.
Google introduced an early version of the detector last year to help media professionals verify AI-generated content. The company said its SynthID technology uses imperceptible watermarks across images, video and audio to help people identify AI-generated media.
Since launching SynthID in 2023, Google said it has watermarked more than 180 billion images and videos, along with 240,000 years of audio content.
The detector can check content made with AI from Google and partners including OpenAI, NVIDIA and Kakao, with Apple coming soon, Google said.
The tool joins Google’s built-in verification features in Search, the Gemini app and Chrome browser, which regularly handle more than 1 million requests daily.
In a DIW test, the detector produced different results depending on how the media was submitted. When an image containing Google AI-generated or edited content was uploaded as a standalone image, the detector reported that SynthID was detected and said the media was made or edited with Google AI. However, when a screenshot (such as the one featured in this article), containing a decent part of that same content was tested, SynthID was not detected. This was also the case when the original AI-generated image, which had been identified by SynthID, was placed inside a new image covering more than 50% of its area. The detector said it was unlikely that the media was made with AI from a SynthID partner, while noting that the media could have been generated by a non-partner organization or before a partner adopted SynthID.
Screenshot: DIW. CC BY
Editor’s Note: Post updated with an additional screenshot and DIW test results.
The increasing prevalence and accessibility of artificial intelligence means that most individuals have quick, easy access to a wealth of information. It may be tempting to turn to AI for health advice, but it’s important that patients know AI advice is not appropriate in certain circumstances. When used responsibly, these tools can serve as a great starting point for those navigating health questions and concerns—but AI should never be used in place of diagnosis and treatment from a medical professional.
What are some ways AI can be useful in providing basic health information?
An AI chatbot can turn clinical language into plain English. That is useful for a few tasks:
Lab and imaging language: A chatbot can explain what medical terms like “elevated alkaline phosphatase” or “mild degenerative disc disease” mean.
Appointment prep: Chatbots can provide a short list of questions on options, side effects, and recovery.
General education: AI can also provide lifestyle advice for conditions like hypertension or diabetes, or explain what an endoscopy or MRI involves.
AI should not be used to diagnose, triage, or treat a patient without a human medical professional in the loop.
Why might you need to provide context to a chatbot when seeking medical advice?
AI models answer only from what you type. A vague prompt produces a generic reply. Some examples of useful context you should provide to a chatbot might be:
Who you are, in general terms: Provide the chatbot with your age, sex, and known chronic conditions. A mild headache in a healthy 25-year-old is not the same problem as a sudden headache in a 70-year-old with high blood pressure.
Timing: Tell the chatbot when your symptoms started, whether they come and go, and what changes them.
Medications: Telling the bot what drugs you’re taking and naming their classes (for example, an ACE inhibitor) can help the chatbot identify food or drug issues.
While it’s important to give the chatbot context, users should not enter their name, date of birth, address, medical record number, or a clinician’s name. Commercial systems may store queries or have people review them, raising privacy concerns. Describe your situation, but leave out your identity.
Can you give some examples of AI prompts for those looking for medical advice?
From a prompt perspective, I would suggest asking chatbots for explanations or advice on appointment prep. Do not ask for a diagnosis or a change in treatment. For example:
Lab Results
Risky prompt: “My ALT is 75, and AST is 58. What liver disease do I have, and what supplement should I take?”
Safe prompt: “What do ALT and AST measure, what commonly causes mild elevations, and what should I ask my primary care doctor?”
Appointment preparation
Risky prompt: “Diagnose my joint pain and tell me if I have rheumatoid arthritis.”
Safe prompt: “I have morning stiffness and swelling in my finger joints, and a rheumatology visit. What should I track, and what questions should I ask about testing?”
Prescription questions
Risky prompt: “Can I stop my blood pressure medicine? My reading today was 120/80.”
Safe prompt: “How do ACE inhibitors such as lisinopril work, why is daily use still needed when a reading is normal, and what should patients ask before any dose change?”
Can AI provide inaccurate or misleading health information? How can users recognize potential red flags?
Current AI models are built to predict the likely next words. They have no clinical judgment and no responsibility for the outcome. Some common failures:
Hallucination: Invented doses, studies, or claims stated with confidence.
Sycophancy: The model accepts your premise. Asking, “Is my fatigue a rare autoimmune disease?” may result in the chatbot agreeing with you instead of considering common causes such as poor sleep or iron deficiency.
We should educate users to treat the reply as unsafe if it does the following:
Names a diagnosis as certain.
Tells you to stop or change a prescription, or to swap it for a supplement.
Cites a study you cannot find at the CDC, NIH, or a medical society.
Gives casual advice for severe symptoms and never tells you to seek care.
How can someone know when they require medical attention rather than AI assistance?
Do not use a chatbot for an emergency or for a diagnosis.
Chest pain or pressure, especially with pain in the jaw, neck, back, or arm, or with sweating or nausea.
Signs of stroke, such as face drooping, arm weakness, or speech change.
Severe shortness of breath or blue lips.
Sudden vision loss, a sudden worst-ever headache, confusion, or loss of consciousness.
A psychiatric emergency. In the United States, call or text 988 for thoughts of self-harm.
See a clinician if your symptoms last more than a few days, worsen, or require an exam, a prescription, imaging, or blood work. A clinician should also be sought if an infant, young child, or person with a weak immune system has fever or breathing symptoms.
The take-home message is to use the tool to prepare questions for your provider. Diagnosis and treatment should always be provided by a licensed clinician.
The 2026 U.S. midterm campaigns are the first in which AI-generated political ads are regularly appearing on people’s televisions and social media feeds.
What we found has surprised us: AI use spans from hyperrealistic deepfakes to subtle enhancements; Republican sponsors – both candidates and interest groups – are much more likely to use AI than are Democratic sponsors; and, thanks to a patchwork of state legislation, many of these ads do not disclose the use of AI at all.
From deepfakes to subtle edits
An assortment of politicians and watchdog groups have expressed concern about campaigns using generative AI to produce deepfakes – synthetic videos showing people doing things they did not do – that might deceive voters.
We’ve noticed several ads containing hyperrealistic deepfakes of famous politicians, including Donald Trump, Nancy Pelosi, Barack Obama, Kamala Harris and Alexandria Ocasio-Cortez. Ocasio-Cortez, in particular, is a favorite among Republican advertisers, appearing in at least five ads.
We’ve seen deepfakes in which a Republican Senate candidate from Louisiana drives a school bus full of undocumented immigrants, a Republican candidate for governor from South Carolina walks arm in arm with drag queens, and an ad in which Liz Cheney, Mitt Romney and Mike Pence are seen carrying pitchforks on the White House lawn.
People who are not politicians made appearances, too, including a fake Dr. Anthony Fauci, seen running around a state fair with a huge syringe, presumably eager to vaccinate everyone. We’ve also noticed several ads in which AI was used to generate crowds or constituents.
In several cases, AI was used to enhance visuals rather than generate something new. One ad from Chip Keating, a Republican candidate for governor in Oklahoma, includes an AI disclaimer, but it doesn’t specify exactly how AI was used. Ads that use AI to enhance visuals don’t necessarily look different from ads that were created in the pre-AI era, which makes it difficult for viewers to discern whether they depict something false.
A partisan gap
Republicans – both candidates and groups such as super PACs and 501(c) organizations – are much more likely to use AI in their ads than are Democrats, according to our research.
In fact, Republican candidates or pro-Republican groups were behind 80% of the ads we tracked and 83% of the spending.
We can only speculate as to why Republicans dominate the use of AI in political advertising in 2026. In general, Democrats tend to take on a regulatory mindset when it comes to political campaigns, favoring limits on campaign spending and required disclosures. In 2022, for instance, only Senate Democrats and two independents voted to advance the DISCLOSE Act that would have required additional campaign finance disclosures for super PACs, labor unions and corporations. Republicans, by contrast, tend to be more in favor of a free market approach.
These more general philosophies may be reflected in the parties’ use of generative AI for political advertising, something about which voters are worried. Polling shows broad support for more regulation, with 78% of registered voters favoring a ban on AI content that makes deceptive claims about candidates.
Disclaimers all over the map
Because regulation of AI in advertising depends on a patchwork of state legislation, many of these ads are not required to disclose the use of AI. This lack of disclaimers makes tracking AI use in ads challenging. Our team has relied on media coverage and trained student coders to flag ads that are potentially AI-generated.
Across 35 states, only 31% of the ads we tracked – representing 22% of the spending – disclosed the use of AI tools. The wording of these disclaimers was all over the map. For example, one Georgia ad included the disclaimer, “This video has been manipulated or generated with artificial intelligence,” while an Oklahoma ad said, “Political satire. AI-generated images do not depict actual events.” A North Carolina state Senate ad said, “You guessed it! AI was definitely used to generate these silly video clips.”
Laws don’t drive disclosure
Some advertisers voluntarily disclose the use of AI even when they are not required to do so. Sometimes, the opposite occurs – advertisers don’t include disclaimers even when state law requires them to. In fact, we’ve found that a state law that requires disclaimers on ads that use AI has very little relationship with the actual use of disclaimers.
In states without laws governing the use of AI in political ads, 32% of ads contained a disclaimer; by contrast, in states with laws governing the use of AI, 29% contained a disclaimer. This comparison, however, is not perfect, as some of the ads in our database aren’t covered by their state’s law. Some states, such as Colorado, have laws that apply only to candidate deepfakes and thus exclude other uses of AI. In other instances, such as in Louisiana, an AI law was enacted after the ad aired.
Minnesota law generally bans deepfakes in political ads, but an ad featuring synthetic video of Democratic Senate candidate Peggy Flanagan aired in May 2026 anyway. Whether it violated state law is uncertain, as the law requires that the media be “so realistic that a reasonable person would believe it depicts speech or conduct of an individual who did not in fact engage in such speech or conduct.”
Moving forward, the real policy challenge will revolve around transparency, the enforceability of existing laws and simply figuring out what type of disclosure would be helpful to voters.
The funniest post you saw this week may not have come from a friend; it may have come from a brand. Whether it is Kohl’s, Wendy’s, or Netflix, brands are behaving more like internet users than traditional advertisers.
This is no accident. Simply paying for advertising is no longer enough to capture people’s attention when they scroll thousands of posts every day. Brands are increasingly trying to join conversations that people are already having.
Memes, jokes and real-time reactions have become marketing tools. But why are brands investing so much in being culturally relevant rather than simply producing the perfect advertisement?
Traditional marketing often worked through interruption. A television commercial, billboard or social media ad place a message in front of people and hoped they would remember it.
That model still works. Major brands continue to spend heavily on it. But buying advertising space is no longer enough. A brand can reach more people when audiences find its content relatable enough to share.
This is the logic of the attention economy. Decades ago, economist Herbert Simon argued that when information becomes abundant, attention becomes scarce.
Today, every social media feed is a competition for that scarce resource. Brands can buy reach, but they cannot buy people’s willingness to share something.
A post that people voluntarily pass along becomes earned media: content that travels through audiences rather than through paid advertising.
Why do memes attract attention?
Humour is only part of the story. What makes content spread is often the emotion behind it.
A 2012 study on viral content found that content triggering high-arousal emotions, such as awe or anger, is more likely to be shared than content that produces little emotional response.
Amusement can work in much the same way. A good meme gives us a small emotional jolt that makes us want to send it to someone else.
Thus, a clever meme is a form of social currency: passing it on makes us look funny and in on the joke.
When brands successfully participate in that culture, they can feel less like distant corporations and more like another voice in the group chat.
That is the real reason memes work: they earn our attention instead of buying or interrupting it. And being chosen is worth far more than being seen.
Real-time marketing can make this especially powerful. During the recent World Cup, Levi’s responded to its stadium name being covered up for the tournament by changing its social media profile pictures to mimic the concealed logo.
The 2013 Super Bowl blackout offers another famous example. During the unexpected power outage, Oreo posted its “You can still dunk in the dark” message.
The post worked because it was timely, simple and connected to what millions of people were already watching.
The line between funny and cringeworthy
For every brand that gets it right, many others get it wrong.
A meme that lands can make a brand feel human. One that misses can make it look as though the brand is desperately trying to be relevant.
Common mistakes include using outdated memes, forcing products into unrelated jokes or adopting a tone that does not fit the brand.
Gucci’s 2017 #TFWGucci campaign, for example, was criticised for making memes feel forced and inconsistent with the luxury brand’s identity.
IHOP faced a different problem in 2015 when it posted a stack of pancakes with the caption “flat but has a GREAT personality”. The joke was widely criticised as sexist. IHOP deleted the post and apologised.
There is even a phrase for brands that try too hard to sound like internet users: “How do you do, fellow kids?”.
Sometimes, the smartest marketing decision is not to join the conversation at all.
When the audience becomes the content
The shift is bigger than a fashion for funny posts. It reflects a change in what brands are competing for.
Reach can still be bought. Attention, however, increasingly has to be earned.
Some brands go further by making consumers part of the content itself. Spotify Wrapped gives users personalised summaries of their listening habits, which millions then share on social media.
Spotify provides the format; users provide the jokes. A listener might share their results alongside a joke about spending 200,000 minutes listening to the same artist. The company creates the template, while the audience turns it into something worth sharing.
Consumers are no longer simply receiving branded content. They are distributing, adapting and sometimes creating it.
That changes what marketers need to understand. The challenge is no longer simply how to communicate a message clearly, but whether the audience will find it relevant enough to pass on.
Every December millions share theirs unprompted, and the internet does the rest, like the screenshots claiming you spent 200,000 minutes overthinking. The brand supplies the canvas, the audience supplies the meme, and the listener becomes the medium either way.
That rewrites the job. The winning skill is no longer a bigger budget or a cleverer thirty-second script. It is cultural literacy: knowing what a community finds funny, what it finds tired, and when a brand has no business speaking at all.
For the next generation of marketers, the brief is moving from “how do we say this well” to “why would anyone pass this on.”
The company announced the changes on Oct. 5, saying the text watermarking rollout responds to the EU AI Act, while the new visual advertising format is intended to give businesses another way to promote products and services inside ChatGPT.
ChatGPT Text Will Get Invisible Watermarks In The EU
Over the coming weeks, OpenAI plans to add an invisible watermark to eligible ChatGPT and Codex text output for users across all plans in the EU, where the service had 159.1 million average monthly active recipients.
The technology, called textGrain, adds an invisible statistical signal through the model's word choices. OpenAI says a detector can analyze the resulting text to determine whether it contains an OpenAI watermark.
API customers worldwide can also opt in to watermarking for select models, although the feature will remain off by default for the API, OpenAI said in its news release.
OpenAI is initially limiting access to its text watermark detector to approved researchers and expert organizations.
The technology has important limitations. OpenAI's tests found that replacing 10% of words with synonyms reduced detection from about 92% to 66%, while replacing 25% reduced it to 17%. Shorter text and content with less flexibility in word choice, such as mathematics, can also be harder to detect.
OpenAI also says a watermark does not establish who owns text, who is responsible for it, how much a human contributed, or whether the content is accurate. A missing watermark does not prove that a person wrote the text either.
Anthropic has taken a similar approach with Claude. The company said future Claude models will generate watermarked text to help determine whether Claude was involved in producing it, citing the EU AI Act. Anthropic says its watermark does not identify a person, organization or conversation.
OpenAI Is Also Expanding ChatGPT Ads
Separately, OpenAI is testing a new visual advertising format that will initially appear during image generation in ChatGPT.
The test is scheduled to begin later this month in the U.S. with an initial group of advertisers. OpenAI says the ads will be clearly labeled and kept separate from the image being generated. It also says advertising does not influence ChatGPT's answers.
Image: OpenAI
OpenAI is expanding the measurement infrastructure around these ads at the same time. Integrations with Hightouch, Tealium and LiveRamp allow advertisers to send conversion data from their existing systems to ChatGPT Ads.
The company also lists attribution partnerships with AppsFlyer, Triple Whale, Adjust, DV Rockerbox, Northbeam, Branch, Singular, Kochava, Airbridge and Tenjin, along with full-funnel and advanced measurement partners Fospha, Measured and INCRMNTAL.
OpenAI is also working with Haus, Measured and WorkMagic on geo-based experiments intended to help measure the causal impact of advertising. Its brand-suitability work includes DoubleVerify and Integral Ad Science, which are developing evaluation pilots around OpenAI's advertising safeguards.
Some early results cited by OpenAI come from these partners. DV Rockerbox reported that WeightWatchers' attributed cost per acquisition on ChatGPT Ads was 15.3% lower than its blended paid-search benchmark. WorkMagic reported statistically significant lift for wellness brand Dose, while Triple Whale reported that 93% of Portland Leather's visitors from ChatGPT Ads were new. These figures are partner-reported findings presented by OpenAI.
According to a new study, 48% of Americans are “career cushioning” — exploring what backup options they have if their current job didn’t meet their expectations.
The new poll of 2,000 American office workers found 77% of them have started to prepare for their backup career, just in case something happens in their current career.
Workers said they’d be motivated to pursue their backup career if it allowed them to do something they actually enjoy (29%), allows for a better lifestyle or less stress, even at the same pay (29%) and if they lost their current job (29%).
Meanwhile, 52% of respondents haven’t considered a backup career, with many of them stating they feel held back by not having the time (27%), worries over being too old to start over (26%) and fears about having to start at an entry-level position again (20%).
Although workers believe in preparing for a second career, the study commissioned by TripleTen and conducted by Talker Research, revealed 91% have stayed in their current job for the past two years.
Workers said they feel optimistic (27%), calm (20%), excited (19%) and hopeful (18%) about the stability of their jobs. And 54% believe there’s low or no risk of them losing their current job or needing to find a new one within the next two years.
However, nearly half of those who have stayed put (46%) said their responsibilities and duties have significantly changed over the past two years. Those changes include learning new technologies (36%), facing economic shifts (24%), picking up extra work for peers (20%) and even layoffs (18%).
“Today’s workforce is changing quickly, and the ability to adapt has become an increasingly valuable career asset,” explains Victor Menin, Ed.D., Vice President of Enrollment at TripleTen. “Developing skills beyond the immediate requirements of your current position can create opportunities and provide a path forward when circumstances change. You don’t have to be planning a career change today to benefit from having skills that can open doors to one tomorrow.”
The poll found 79% have learned new skills for their current job that they didn’t previously know — from using AI chatbots for everyday work tasks (34%) to using AI features built into their work software (33%) and keeping up with new technology and business lingo (30%).
As a result, many of them believe the skills they recently learned have given them more confidence (44%), a better sense of job security (36%) and led them to teach peers the same new skills (30%).
And most of those who learned new skills (87%) believe the skills will still be relevant two years from now.
To learn new work-related skills, respondents said they were provided training by their employer (47%), took online courses (32%), turned to free tutorials (30%), used an AI chatbot as a tutor (27%) or had a colleague teach them (21%).
Of those polled, 48% said they work in management positions. According to them, 82% would rather train their current employees to learn new skills for their job than hire someone new.
Similarly, 87% believe their organization would play a role in paying for their employee to retrain in a new role in the organization — 52% willing to fully fund it.
Since its launch in 1998, Google, the world’s leading search engine, has transformed how people find information online. For years, search results appeared as ranked lists of sources: Google decided what to show, but users decided what to read and whom to trust.
However, the advent of Google AI Overviews has fundamentally changed that paradigm. Today, when a user searches anything from “How long do I boil eggs?” to “How do I lower my cholesterol?” there’s a good chance the response will lead with a single artificial intelligence (AI)-generated summary. This change gives Google unprecedented editorial control over what users read and know, according to Jacob Montgomery, a professor of political science in Arts & Sciences at Washington University in St. Louis.
“Google is now writing answers, not just ranking them, and it does this for billions of searches,” Montgomery said. “Yet we know very little about how the system works. When does an overview appear? Which sources does it draw on? Are the summaries accurate? And what happens to the rest of the web when people no longer click through? Our goal was to answer these basic questions.”
A forthcoming paper, to be presented at the October 2026 ACM Internet Measurement Conference, offers the broadest independent audit of Google AI Overviews to date — examining AI Overview activation patterns, source selection, whether claims matched their sources and the potential impact on publishers’ advertising revenue. The co-authors are Umar Iqbal, an assistant professor of engineering at WashU McKelvey Engineering, and Haofei Xu, a graduate student in the WashU Division of Computational and Data Sciences.
Conversational searches most likely to trigger AI Overview
Altogether, the researchers ran 55,393 Google searches on trending topics over 40 days, from March 13 through April 21, 2026. They collected each AI Overview, its citations, the first-page search results displayed with it and the content of cited webpages.
The research showed AI Overviews appeared for 13.7% of all queries, but the rate varied sharply based on how users worded their search. Nearly 65% of question-form queries produced an overview, compared with 9.5% of other searches. Longer searches also were more likely to trigger AI-generated answers. Among non-question searches, the activation rate rose from 9.9% for one-word queries to 38.7% for queries of six words or more.
This pattern suggests people seeking explanations in natural language rather than key word searches are more likely to encounter AI-generated material, Montgomery explained. Topic also mattered. Overviews appeared for 46.1% of hobby and leisure searches and 39.9% of science queries, but for only 7.5% of political searches and 9.6% of law and government queries, suggesting Google’s triggering logic relies on undisclosed editorial discretion.
Across 61,212 citations, the domains cited by AI Overviews received higher average credibility scores than the traditional first-page results shown for the same searches. The overviews also relied less heavily on user-generated content.
Still, 29.8% of the cited domains did not appear in the first-page results. The researchers said the difference indicates that Google uses a source-selection process for its AI summaries distinct from its conventional search-ranking system. An average overview cited about eight sources.
Credible sources, however, did not guarantee that the generated summary represented them accurately. The team separated the overviews into 98,020 verifiable claims and checked each one against the cited webpages. About 89% were clearly or broadly supported by the sources. The remaining 11% included claims not found in the cited text the team was able to collect (7%) or claims that contradicted the cited source (4%).
The authors cautioned that the 11% error rate should be treated as an upper limit. Their pipeline could not fully capture content posted by ordinary users, such as Reddit threads, forum posts and YouTube videos. Rapidly changing information, such as weather forecasts and school closings, also may have been updated between the Google overview and when researchers captured the page.
Only 41.9% of the overviews were fully grounded, meaning every verifiable claim was supported by the available cited text. Because some cited pages could not be fully collected, this figure is likely conservative.
“Google’s summaries generally draw on credible websites, which is good news. But quality sources are not enough,” Montgomery said. “In a meaningful share of cases, we could not find support for a claim in the pages Google cited. Readers still need to check what they’re told, even when the citations look trustworthy.”
Finally, the study also examined the financial stakes for publishers. When an overview answers a question directly, publishers may lose the visit that generates advertising, subscription or sales revenue. In this study, more than half of the cited webpages displayed advertising.
The researchers didn’t directly measure traffic or revenue losses. However, they found that 2.2% of search pages containing an AI Overview also displayed a Google-sponsored ad, including 39 pages where an ad appeared above the overview. That arrangement, they wrote, may preserve Google’s ad revenue while giving users less reason to visit the publishers whose content informed the answer.
“Google’s AI Overviews are built on content that publishers pay to produce, and many of those sites depend on advertising revenue,” Montgomery said. “If people get their answer from the overview and never click through, those publishers lose the visit and the revenue. We did not measure that loss directly, but the incentives are worrying. Over time, Google and publishers will need to work out licensing or revenue-sharing arrangements, or the sites that AI Overviews rely on may not survive.”
