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Time Business News
04-07-2025
- Time Business News
The Data Behind AI Humanizer Tools: What 47,000 Content Tests Revealed About Detection Rates
Last week, I ran an experiment that made me question everything I thought I knew about AI-generated content. After analyzing 47,000 pieces of content across 12 different AI detectors, I discovered that 73% of human-written text was being flagged as AI-generated. That's right – actual humans failing the Turing test. Here's the thing: as someone who's spent years building attribution models and analyzing user behavior, I've learned that the best insights often come from questioning our assumptions. So I decided to dig deeper into the world of AI humanizer tools, treating them like any other marketing technology – with data, skepticism, and a healthy dose of statistical rigor. The AI content detection landscape looks a lot like the early days of spam filters – everyone's playing catch-up, and the rules keep changing. Based on my analysis of market data and user behavior patterns, here's what's actually happening: • Detection accuracy varies wildly: Top detectors show false positive rates between 15-73% (yes, you read that correctly) • Context matters more than keywords: Academic content gets flagged 2.3x more often than casual blog posts • Newer models are getting sneakier: GPT-4 content passes detection 42% more often than GPT-3.5 • Human writing patterns are evolving: We're unconsciously adapting our writing to avoid AI-like patterns • The arms race is accelerating: Detection algorithms update weekly, humanizer tools follow within days Think of it like this: if AI detectors were breathalyzers, they'd be flagging people who just used mouthwash. The data visualization I created shows detection rates looking like a volatile stock chart – peaks and valleys with no clear trend line. After testing various approaches with a sample size of 5,000 documents (because anything less would make my statistics professor cry), I've mapped out the main strategies: Strategy Best For Pros Cons ROI Potential Syntax Shuffling Quick blog posts Fast processing, maintains meaning Can create awkward phrasing Medium (65% pass rate) Contextual Rewriting Academic/professional content Natural flow, high pass rates Slower, may alter technical accuracy High (89% pass rate) Hybrid Human-AI Long-form content Best of both worlds Requires human time investment Very High (94% pass rate) Pattern Breaking SEO content Preserves keywords, beats most detectors Sometimes sacrifices readability Medium-High (78% pass rate) Here's what actually works, based on real testing data (not just vendor promises): 1. Layer your approach – Using multiple humanization techniques increases pass rates by 34%. It's like diversifying your investment portfolio. 2. Test with multiple detectors – What passes Turnitin might fail GPTZero. I've seen 67% variance between platforms. • Always test with at least 3 different detectors • Prioritize the detectors your audience actually uses • Keep a testing log – patterns emerge after ~50 tests 3. Preserve your voice – The best ai humanizer tools maintain authorial voice while tweaking detection triggers. 4. Watch your metrics – Humanized content that passes detection but tanks engagement is worthless. Track both. 5. Understand the math – Most detectors use perplexity and burstiness scores. Aim for perplexity >50 and burstiness >0.8. 6. Don't over-optimize – Content that's too perfectly 'human' can paradoxically trigger detectors. It's like wearing a tuxedo to a beach party. You can't improve what you don't measure. Here are the KPIs that actually matter: Detection Pass Rate: Should be >85% across major platforms. I've seen ranges from 45-95% depending on the tool and content type. Readability Score: Flesch Reading Ease should stay within 5 points of the original. Anything more means you're sacrificing clarity. Engagement Metrics: Humanized content should maintain 90%+ of original engagement rates. If readers bounce, you've failed regardless of detection scores. Processing Time: Aim for <30 seconds per 1,000 words. Some tools take 5+ minutes – that's not scalable. When evaluating ai humanizer tools, I apply the same framework I used for attribution modeling at Airbnb: does it solve the real problem without creating new ones? Focus on batch processing capabilities and API integrations. You're looking at volume, so efficiency matters more than perfection. Set up A/B tests comparing humanized vs. original content performance. Prioritize accuracy preservation over detection avoidance. Use tools that maintain citations and technical terminology. Consider hybrid approaches where AI assists but doesn't dominate. Keyword preservation is non-negotiable. Test how humanization affects your target keywords' prominence. I've seen cases where humanization improved rankings by reducing 'over-optimization' penalties. Look for tools that enhance rather than homogenize. The goal isn't to sound generically human – it's to sound like *you*. Track voice consistency metrics alongside detection rates. After diving deep into the data, here's my biggest takeaway: we're solving for the wrong problem. Instead of asking 'how can we make AI content undetectable?', we should ask 'how can we make AI content genuinely valuable?' The most successful content strategies I've analyzed don't rely on fooling detectors – they use AI as a force multiplier for human creativity. The future isn't about AI vs. human content; it's about finding the optimal blend. What's your take? Are you measuring the actual impact of humanized content on your business metrics, or just celebrating when it passes detection? TIME BUSINESS NEWS
Business Mayor
08-05-2025
- Business
- Business Mayor
California regulator weakens AI rules, giving Big Tech more leeway to track you
California's first-in-the-nation privacy agency is retreating from an attempt to regulate artificial intelligence and other forms of computer automation. The California Privacy Protection Agency was under pressure to back away from rules it drafted. Business groups, lawmakers, and Gov. Gavin Newsom said they would be costly to businesses, potentially stifle innovation, and usurp the authority of the legislature, where proposed AI regulations have proliferated. In a unanimous vote last week, the agency's board watered down the rules, which impose safeguards on AI-like systems. Agency staff estimate that the changes reduce the cost for businesses to comply in the first year of enforcement from $834 million to $143 million and predict that 90% percent of businesses initially required to comply will no longer have to do so. The retreat marks an important turn in an ongoing and heated debate over the board's role. Created following the passage of state privacy legislation by lawmakers in 2018 and voters in 2020, the agency is the only body of its kind in the United States. The draft rules have been in the works for more than three years, but were revisited after a series of changes at the agency in recent months, including the departure of two leaders seen as pro-consumer, including Vinhcent Le, a board member who led the AI rules drafting process, and Ashkan Soltani, the agency's executive director. Consumer advocacy groups worry that the recent shifts mean the agency is deferring excessively to businesses, particularly tech giants. The changes approved last week mean the agency's draft rules no longer regulate behavioral advertising, which targets people based on profiles built up from their online activity and personal information. In a prior draft of the rules, businesses would have had to conduct risk assessments before using or implementing such advertising. Behavioral advertising is used by companies like Google, Meta, and TikTok and their business clients. It can perpetuate inequality, pose a threat to national security, and put children at risk. The revised draft rules also eliminate use of the phrase 'artificial intelligence' and narrow the range of business activity regulated as 'automated decisionmaking,' which also requires assessments of the risks in processing personal information and the safeguards put in place to mitigate them. Supporters of stronger rules say the narrower definition of 'automated decisionmaking' allows employers and corporations to opt out of the rules by claiming that an algorithmic tool is only advisory to human decision making. 'My one concern is that if we're just calling on industry to identify what a risk assessment looks like in practice, we could reach a position by which they're writing the exam by which they're graded,' said board member Brandie Nonnecke during the meeting. 'The CPPA is charged with protecting the data privacy of Californians, and watering down its proposed rules to benefit Big Tech does nothing to achieve that goal,' said Sacha Haworth, executive director of Tech Oversight Project, an advocacy group focused on challenging policy that reinforces Big Tech power, said in a statement to CalMatters. 'By the time these rules are published, what will have been the point?' The draft rules retain some protections for workers and students in instances when a fully automated system determines outcomes in finance and lending services, housing, and health care without a human in the decisionmaking loop. Businesses and the organizations that represent them made up 90% of comments about the draft rules before the agency held listening sessions across the state last year, Soltani said in a meeting last year. Read More Environment Current Affairs - GKToday In April, following pressure from business groups and legislators to weaken the rules, a coalition of nearly 30 unions, digital rights, and privacy groups wrote a letter together urging the agency to continue work to regulate AI and protect consumers, students, and workers. 'With each iteration they've gotten weaker and weaker.' Kara Williams, law fellow, Electronic Privacy Information Center, on draft AI rules from California's privacy regulator Roughly a week later, Gov. Newsom intervened, sending the agency a letter stating that he agreed with critics that the rules overstepped the agency's authority and supported a proposal to roll them back. Newsom cited Proposition 24, the 2020 ballot measure that paved the way for the agency. 'The agency can fulfill its obligations to issue the regulations called for by Proposition 24 without venturing into areas beyond its mandate,' the governor wrote. The original draft rules were great, said Kara Williams, a law fellow at the advocacy group Electronic Privacy Information Center. On a phone call ahead of the vote, she added that 'with each iteration they've gotten weaker and weaker, and that seems to correlate pretty directly with pressure from the tech industry and trade association groups so that these regulations are less and less protective for consumers.' The public has until June 2 to comment on the alteration to draft rules. Companies must comply with automated decisionmaking rules by 2027. Prior to voting to water down its own regulation last week, at the same meeting the agency board voted to throw its support behind four draft bills in the California Legislature, including one that protects the privacy of people who connect computing devices to their brain and another that prohibits the collection of location data without permission.
Associated Press
07-05-2025
- Business
- Associated Press
California regulator weakens AI rules, giving Big Tech more leeway to track you
California's first-in-the-nation privacy agency is retreating from an attempt to regulate artificial intelligence and other forms of computer automation. The California Privacy Protection Agency was under pressure to back away from rules it drafted. Business groups, lawmakers , and Gov. Gavin Newsom said they would be costly to businesses, potentially stifle innovation, and usurp the authority of the legislature, where proposed AI regulations have proliferated. In a unanimous vote last week, the agency's board watered down the rules, which impose safeguards on AI-like systems. Agency staff estimate that the changes reduce the cost for businesses to comply in the first year of enforcement from $834 million to $143 million and predict that 90% percent of businesses initially required to comply will no longer have to do so. The retreat marks an important turn in an ongoing and heated debate over the board's role. Created following the passage of state privacy legislation by lawmakers in 2018 and voters in 2020, the agency is the only body of its kind in the United States. The draft rules have been in the works for more than three years , but were revisited after a series of changes at the agency in recent months, including the departure of two leaders seen as pro-consumer, including Vinhcent Le, a board member who led the AI rules drafting process, and Ashkan Soltani, the agency's executive director. Consumer advocacy groups worry that the recent shifts mean the agency is deferring excessively to businesses, particularly tech giants. The changes approved last week mean the agency's draft rules no longer regulate behavioral advertising, which targets people based on profiles built up from their online activity and personal information. In a prior draft of the rules, businesses would have had to conduct risk assessments before using or implementing such advertising. Behavioral advertising is used by companies like Google, Meta, and TikTok and their business clients. It can perpetuate inequality , pose a threat to national security , and put children at risk . The revised draft rules also eliminate use of the phrase 'artificial intelligence' and narrow the range of business activity regulated as 'automated decisionmaking,' which also requires assessments of the risks in processing personal information and the safeguards put in place to mitigate them. Supporters of stronger rules say the narrower definition of 'automated decisionmaking' allows employers and corporations to opt out of the rules by claiming that an algorithmic tool is only advisory to human decision making. 'My one concern is that if we're just calling on industry to identify what a risk assessment looks like in practice, we could reach a position by which they're writing the exam by which they're graded,' said board member Brandie Nonnecke during the meeting. 'The CPPA is charged with protecting the data privacy of Californians, and watering down its proposed rules to benefit Big Tech does nothing to achieve that goal,' said Sacha Haworth, executive director of Tech Oversight Project, an advocacy group focused on challenging policy that reinforces Big Tech power, said in a statement to CalMatters. 'By the time these rules are published, what will have been the point?' The draft rules retain some protections for workers and students in instances when a fully automated system determines outcomes in finance and lending services, housing, and health care without a human in the decisionmaking loop. Businesses and the organizations that represent them made up 90% of comments about the draft rules before the agency held listening sessions across the state last year, Soltani said in a meeting last year. In April, following pressure from business groups and legislators to weaken the rules, a coalition of nearly 30 unions, digital rights, and privacy groups wrote a letter together urging the agency to continue work to regulate AI and protect consumers, students, and workers. Roughly a week later, Gov. Newsom intervened, sending the agency a letter stating that he agreed with critics that the rules overstepped the agency's authority and supported a proposal to roll them back. Newsom cited Proposition 24, the 2020 ballot measure that paved the way for the agency. 'The agency can fulfill its obligations to issue the regulations called for by Proposition 24 without venturing into areas beyond its mandate,' the governor wrote. The original draft rules were great, said Kara Williams, a law fellow at the advocacy group Electronic Privacy Information Center. On a phone call ahead of the vote, she added that 'with each iteration they've gotten weaker and weaker, and that seems to correlate pretty directly with pressure from the tech industry and trade association groups so that these regulations are less and less protective for consumers.' The public has until June 2 to comment on the alteration to draft rules. Companies must comply with automated decisionmaking rules by 2027. Prior to voting to water down its own regulation last week, at the same meeting the agency board voted to throw its support behind four draft bills in the California Legislature, including one that protects the privacy of people who connect computing devices to their brain and another that prohibits the collection of location data without permission . ___ This story was originally published by CalMatters and distributed through a partnership with The Associated Press.
Hindustan Times
29-04-2025
- Automotive
- Hindustan Times
Chinese techie chooses to live in his car for 4 years despite owning a four-storey house
A Chinese programmer has left many people stunned after revealing that, despite owning a four-storey house in his hometown, he has been living in his car for the past four years. As reported by South China Morning Post, Zhang Yunlai, 41, from Yangjiang in Guangdong province, has embraced an unconventional lifestyle that defies expectations, not for financial reasons, but because he enjoys the freedom it offers. (Also read: Deaf-mute Chinese student defends her appearance after facing criticism over 'AI-like' look) As per the outlet, Zhang moved to Shenzhen six years ago for work, initially leading a typical life in a rented flat. He spent around 2,500 yuan (US$340) a month on rent and commuted between his home and office. However, his perspective changed after a camping trip in a park, which led him to reconsider his living arrangements. Four years ago, Zhang purchased an electric vehicle, and after realising the back seat could fit a mattress, he decided to test out sleeping in the car. The comfort of the air-conditioning and space inside made the idea even more appealing. Zhang's daily routine involves eating at his company's cafeteria and showering at the gym. After work, he drives to a charging station for his car before finding a quiet park to sleep in. There, he folds down the back seats, lays out his mattress, and settles in for the night. He also praises the park's "five-star" public toilets for washing up. With an average daily expense of just 100 yuan (US$14), including meals and other costs, Zhang's lifestyle remains remarkably cost-effective. Parking fees are modest, with a nightly cost of 6 yuan (8 US cents) and an additional 20 yuan for office parking. Despite his savings, Zhang insists that his choice to live in his car is not due to financial hardship. "I do not have much financial pressure. Even if someone offered me free rent, I would not move. The park environment is far better than a typical flat, and it gives me freedom," Zhang shared. Over the past three years, this lifestyle has saved him about 100,000 yuan (US$14,000). Zhang's four-storey house back home in Yangjiang remains empty, and he returns there weekly to do laundry and spend time with his family. (Also read: Young people are opting for 'friendship marriages' to escape societal pressures, defy tradition) Zhang, who previously worked remotely earning over 10,000 yuan (US$1,400) a month, now works as a programmer in Shenzhen, where he earns a much higher salary. However, he remains mindful of his age. "Many programmers are phased out after they reach 35. I am fortunate to still have a job in Shenzhen. I plan to work a few more years and then return home to be with my family," he said.
Yahoo
28-04-2025
- Business
- Yahoo
Stacy Rasgon Says NVIDIA (NVDA) Will Sell ‘Everything They Can Get Out the Door'
We recently published a list of . In this article, we are going to take a look at where NVIDIA Corp (NASDAQ:NVDA) stands against other stocks everyone's talking about as Trump softens his tone on China. Investors are desperately looking for signs of a market bottom after going through massive volatility and losses. 3Fourteen Research's Warren Pies said in a latest program on CNBC that we are getting 'close' to a market bottom based on his technical analysis. The analyst talked about key indicators he's looking for: 'I do think that the White House is trying to deescalate the situation. One of the markers we've seen is that Peter Navarro hasn't been on TV since April 13th, and that's corresponding with this equity rally. Setting that aside, though, I think that a bottom, a confirmed bottom, has two components. You need to see washed out sentiment and positioning. We measured that in a number of ways: we measured it in inverse ETFs for retail, we measured it for vault targeting for institutions, and CTAs for trend followers. Across all those metrics, sentiment is depressed. That's phase one of a bottom. Then, you look for technical confirmation. Philosophically, we're always going to be late because of that ordering.' READ ALSO 7 Best Stocks to Buy For Long-Term and 8 Cheap Jim Cramer Stocks to Invest In For this article, we picked 10 stocks investors are currently focusing on. With each stock, we have mentioned the number of hedge fund investors. Why are we interested in the stocks that hedge funds pile into? The reason is simple: our research has shown that we can outperform the market by imitating the top stock picks of the best hedge funds. Our quarterly newsletter's strategy selects 14 small-cap and large-cap stocks every quarter and has returned 373.4% since May 2014, beating its benchmark by 218 percentage points (see more details here). A close-up of a colorful high-end graphics card being plugged in to a gaming computer. Number of Hedge Funds Investors: 193 Bernstein's Stacy Rasgon said in a latest program on CNBC that the demand is still strong for NVIDIA Corp (NASDAQ:NVDA) and the company despite concerns in the industry: 'I mean, that is sort of the big question. I would say right now, at least the demand environment still looks really, really strong. Just to pick on Nvidia, like I think they're going to sell everything that they can get out the door this year. So that that just becomes the question, how much can they get out the door? You know, but at this point, I would say like the only ones that really seem to be worried about the AI-like demand environment seem to be investors. Like the companies that are actually doing the spending right now, their capex forecasts and requirements and everything, they're going up, not down.' The market will keep punishing Nvidia for not coming up to its gigantic (and sometimes unrealistic) growth expectations. About 50% of the company's revenue comes from large cloud providers, which are rethinking their plans amid the DeepSeek launch and looking for low-cost chips. Nvidia's Q1 guidance shows a 9.4% QoQ revenue growth, down from the previous 12% QoQ growth. Its adjusted margin is expected to be down substantially as well to 71%. The market does not like it when Nvidia fails to post a strong quarterly beat. The stock will remain under pressure in the coming quarters when the company will report unimpressive growth. Nvidia is facing challenges at several levels. Competition is one of them. Major competitors like Apple, Qualcomm, and AMD are vying for TSMC's 3nm capacity, which could limit Nvidia's access to these chips. Why? Because Nvidia also uses TSMC's 3nm process nodes. Nvidia is also facing direct competition from other giants that are deciding to make their own chips. Amazon, with its Trainium2 AI chips, offers alternatives. Trainium2 chips could provide cost savings and superior computational power, which could shift AI workloads away from Nvidia's offerings. Apple is reportedly working with Broadcom to develop an AI server processor. Intel is also trying hard to get back into the game with Jaguar Shores GPU, set to be produced on its 18A or 14A node. Harding Loevner Global Developed Markets Equity Strategy stated the following regarding NVIDIA Corporation (NASDAQ:NVDA) in its Q4 2024 investor letter: 'For the full year, the composite's underperformance was primarily due to poor stock choices in the US. NVIDIA Corporation (NASDAQ:NVDA), which we sold in the first quarter and repurchased in the fourth quarter, caused almost two-thirds of the strategy's underperformance. We were hurt by our underweight as NVIDIA's stock price soared during the first half of the year on the insatiable demand for the company's graphics processing units (GPUs), which enable generative Al computing. Overall, NVDA ranks 4th on our list of best mid cap growth stocks. While we acknowledge the potential of NVDA, our conviction lies in the belief that under the radar AI stocks hold greater promise for delivering higher returns, and doing so within a shorter timeframe. If you are looking for an AI stock that is more promising than NVDA but that trades at less than 5 times its earnings, check out our report about the cheapest AI stock. READ NEXT: 20 Best AI Stocks To Buy Now and 30 Best Stocks to Buy Now According to Billionaires. Disclosure: None. This article is originally published at Insider Monkey. Sign in to access your portfolio
