MAROKO133 Breaking ai: This new AI technique creates ‘digital twin’ consumers, and it coul

📌 MAROKO133 Eksklusif ai: This new AI technique creates ‘digital twin’ consumers,

A new research paper quietly published last week outlines a breakthrough method that allows large language models (LLMs) to simulate human consumer behavior with startling accuracy, a development that could reshape the multi-billion-dollar market research industry. The technique promises to create armies of synthetic consumers who can provide not just realistic product ratings, but also the qualitative reasoning behind them, at a scale and speed currently unattainable.

For years, companies have sought to use AI for market research, but have been stymied by a fundamental flaw: when asked to provide a numerical rating on a scale of 1 to 5, LLMs produce unrealistic and poorly distributed responses. A new paper, "LLMs Reproduce Human Purchase Intent via Semantic Similarity Elicitation of Likert Ratings," submitted to the pre-print server arXiv on October 9th proposes an elegant solution that sidesteps this problem entirely.

The international team of researchers, led by Benjamin F. Maier, developed a method they call semantic similarity rating (SSR). Instead of asking an LLM for a number, SSR prompts the model for a rich, textual opinion on a product. This text is then converted into a numerical vector — an "embedding" — and its similarity is measured against a set of pre-defined reference statements. For example, a response of "I would absolutely buy this, it's exactly what I'm looking for" would be semantically closer to the reference statement for a "5" rating than to the statement for a "1."

The results are striking. Tested against a massive real-world dataset from a leading personal care corporation — comprising 57 product surveys and 9,300 human responses — the SSR method achieved 90% of human test-retest reliability. Crucially, the distribution of AI-generated ratings was statistically almost indistinguishable from the human panel. The authors state, "This framework enables scalable consumer research simulations while preserving traditional survey metrics and interpretability."

A timely solution as AI threatens survey integrity

This development arrives at a critical time, as the integrity of traditional online survey panels is increasingly under threat from AI. A 2024 analysis from the Stanford Graduate School of Business highlighted a growing problem of human survey-takers using chatbots to generate their answers. These AI-generated responses were found to be "suspiciously nice," overly verbose, and lacking the "snark" and authenticity of genuine human feedback, leading to what researchers called a "homogenization" of data that could mask serious issues like discrimination or product flaws.

Maier's research offers a starkly different approach: instead of fighting to purge contaminated data, it creates a controlled environment for generating high-fidelity synthetic data from the ground up.

"What we're seeing is a pivot from defense to offense," said one analyst not affiliated with the study. "The Stanford paper showed the chaos of uncontrolled AI polluting human datasets. This new paper shows the order and utility of controlled AI creating its own datasets. For a Chief Data Officer, this is the difference between cleaning a contaminated well and tapping into a fresh spring."

From text to intent: The technical leap behind the synthetic consumer

The technical validity of the new method hinges on the quality of the text embeddings, a concept explored in a 2022 paper in EPJ Data Science. That research argued for a rigorous "construct validity" framework to ensure that text embeddings — the numerical representations of text — truly "measure what they are supposed to." 

The success of the SSR method suggests its embeddings effectively capture the nuances of purchase intent. For this new technique to be widely adopted, enterprises will need to be confident that the underlying models are not just generating plausible text, but are mapping that text to scores in a way that is robust and meaningful.

The approach also represents a significant leap from prior research, which has largely focused on using text embeddings to analyze and predict ratings from existing online reviews. A 2022 study, for example, evaluated the performance of models like BERT and word2vec in predicting review scores on retail sites, finding that newer models like BERT performed better for general use. The new research moves beyond analyzing existing data to generating novel, predictive insights before a product even hits the market.

The dawn of the digital focus group

For technical decision-makers, the implications are profound. The ability to spin up a "digital twin" of a target consumer segment and test product concepts, ad copy, or packaging variations in a matter of hours could drastically accelerate innovation cycles. 

As the paper notes, these synthetic respondents also provide "rich qualitative feedback explaining their ratings," offering a treasure trove of data for product development that is both scalable and interpretable. While the era of human-only focus groups is far from over, this research provides the most compelling evidence yet that their synthetic counterparts are ready for business.

But the business case extends beyond speed and scale. Consider the economics: a traditional survey panel for a national product launch might cost tens of thousands of dollars and take weeks to field. An SSR-based simulation could deliver comparable insights in a fraction of the time, at a fraction of the cost, and with the ability to iterate instantly based on findings. For companies in fast-moving consumer goods categories — where the window between concept and shelf can determine market leadership — this velocity advantage could be decisive.

There are, of course, caveats. The method was validated on personal care products; its performance on complex B2B purchasing decisions, luxury goods, or culturally specific products remains unproven. And while the paper demonstrates that SSR can replicate aggregate human behavior, it does not claim to predict individual consumer choices. The technique works at the population level, not the person level — a distinction that matters greatly for applications like personalized marketing.

Yet even with these limitations, the research is a watershed. While the era of human-only focus groups is far from over, this paper provides the most compelling evidence yet that their synthetic counterparts are ready for business. The question is no longer whether AI can simulate consumer sentiment, but whether enterprises can move fast enough to capitalize on it before their competitors do.

🔗 Sumber: venturebeat.com

📌 MAROKO133 Eksklusif ai: Pixel 10 Pro Fold explodes during JerryRigEverything ben

Google’s Pixel 10 Pro Fold became the first smartphone to explode during a JerryRigEverything durability test, marking a shocking moment in YouTuber Zack Nelson’s decade-long history of stress-testing devices.

Known for pushing phones to their limits, Nelson found Google’s latest foldable to be structurally weak and dangerously unstable.

The test began routinely. The cover display, protected by Gorilla Glass Victus 2, showed scratches at level six on the Mohs scale and deeper grooves at level seven, which is standard.

The inner screen, however, remained fragile, showing marks even from a fingernail.

Nelson noted that while the soft polymer layer scratches easily, it stays mostly safe when folded.

Things escalated during the dust-resistance test. Google promotes the Pixel 10 Pro Fold as the first foldable phone with an IP68 rating for dust and water resistance. But Nelson’s sand test told a different story.

After pouring sand onto the phone, he found that the inner screen held up fine, but the hinge did not.

He noted that a grinding noise was heard as fine particles lodged inside.

Nelson questioned how the device could justify its IP rating and called the hinge design misleading.

Catastrophic bend test and explosion

The real disaster came during the bend test. Google claims its redesigned hinge can endure a decade’s worth of folds inward.

Yet when Nelson applied force in the opposite direction, the phone snapped in half, not at the hinge but along the antenna lines on the left side.

He had warned Google to relocate these weak points after the Pixel Fold and Pixel 9 Pro Fold failed in the same spot.

When Nelson tried to bend the damaged device further, the battery caught fire. “The battery decides it’s had enough,” he said.

Smoke billowed from the broken frame as the Pixel 10 Pro Fold became the first phone in his series to ignite on camera.

Nelson later explained that the explosion happened after the antenna lines pinched the battery layers together, causing a short circuit.

The resulting thermal reaction unleashed the battery’s full energy in one violent burst.

He compared Google’s decision to reuse the same flawed antenna design to “Darth Vader building a third Death Star with the same exhaust port.”

Google’s durability claims questioned

Despite the destruction, Nelson continued his teardown. He discovered that the rumored under-display sensor was not a hidden camera but a proximity and light sensor.

His verdict was blunt: “By far the weakest folding phone I’ve ever tested.”

The YouTuber accused Google of overhyping its hardware durability. “Having the audacity to call the Pixel 10 Fold extremely durable… is an insult to tech enthusiasts everywhere,” he said.

The Pixel 10 Pro Fold’s failure highlights a recurring issue.

For the third year in a row, Google’s foldable design has broken at the same structural weak point.

With this latest incident ending in an explosion, the company faces growing scrutiny over whether it is truly learning from past mistakes.

🔗 Sumber: interestingengineering.com