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AtScale Finds Semantic Layer Boosts AI Accuracy on Enterprise Data by Up to 2.4x
A more powerful AI model isn’t necessarily the answer to unreliable business analytics. New research from AtScale found that leading AI models answered fewer than 40% of business questions correctly when querying databases directly. But when AtScale added its semantic layer, accuracy improved by 1.7x to 2.4x across all seven models tested. In one test, […]
13News Now
13News Now (WVEC) is made up of storytellers, innovators, content creators, and idea generators. We stand for passion. Passion for our people, passion in our product, and above all- passion in our community. We value honesty, accuracy, and solutions. We serve as the local ABC affiliate for Hampton Roads. We strive for interaction with our audience and are constantly working on our presentation so that you have the best experience when consuming our products. Whether on air, online, or on the go- we are everywhere you go. Digitally we lead the market by more than a million actions over our nearest competitor. Join the conversation with us, help us tell the Stories of Now. About our parent company TEGNA- TEGNA Inc. (NYSE: TGNA) is an innovative media company that serves the greater good of our communities. With 46 television stations in 38 markets, TEGNA delivers relevant content and information to consumers across platforms.
13News Now
13News Now (WVEC) is made up of storytellers, innovators, content creators, and idea generators. We stand for passion. Passion for our people, passion in our product, and above all- passion in our community. We value honesty, accuracy, and solutions. We serve as the local ABC affiliate for Hampton Roads. We strive for interaction with our audience and are constantly working on our presentation so that you have the best experience when consuming our products. Whether on air, online, or on the go- we are everywhere you go. Digitally we lead the market by more than a million actions over our nearest competitor. Join the conversation with us, help us tell the Stories of Now. About our parent company TEGNA- TEGNA Inc. (NYSE: TGNA) is an innovative media company that serves the greater good of our communities. With 46 television stations in 38 markets, TEGNA delivers relevant content and information to consumers across platforms.
Don't Trust AI For Blood Pressure Monitor Recommendations, Study Warns
By Dennis Thompson HealthDay ReporterFRIDAY, Oct. 9, 2026 (HealthDay News) — Want to buy an accurate blood pressure monitor? Don’t turn to AI for help, a new study warns. Three of four leading AI-powered search tools frequently recommended home BP monitors that haven’t been validated for accuracy,
Psychic AI can read minds — and recreate our thoughts with frightening accuracy: 'Sci-fi can come true'
Is it total recall?
New AI Detector Spots Machine-Written Science Papers With 91.5% Accuracy
Researchers in Jordan have developed a hybrid deep learning detector that distinguishes human-written scientific text from output generated by ChatGPT, Gemini, and LLaMA-3 with 91.5 percent accuracy.
Stages SB200 Indoor Smartbike In-Depth Review: Redemption?
In this review I cover whether the SB200 is a worthwhile upgrade over the existing SB20, as well as everything you need to know from ride feel to accuracy.
How to safely use AI for health advice
Many patients use AI for health advice, but doctors warn about accuracy. Learn how to use these tools safely.
How to safely use AI for health advice
Many patients use AI for health advice, but doctors warn about accuracy. Learn how to use these tools safely.
Apple's Health Age Test Doesn't Hold Back (Oof)
We test Apple's updated Health app tools in iOS 27.2 to evaluate fitness tracking metrics and age assessment accuracy.
A fact-checked guide to Trump's talking points and falsehoods at midterm rallies
President Donald Trump is hitting the campaign trail to support Republican candidates in toss-up races that will determine which party controls Congress for the final two years of his presidency. Here’s a list of more than two dozen go-to Trump statements and how they square for accuracy.
Evan McPherson Earns Recognition for Red-Hot Start to 2026
Perfect accuracy from distance and a historic 20-kick streak have secured September honors for Cincinnati’s specialist, placing him one swing away from etching his name into franchise history.
An Old Discovery At Pompeii Is Helping Scientists To Put Dates To Other Big Events In History With Incredible Accuracy
Pliny the Younger witnessed the event, and described it in two letters. They may help us date other, unrelated disasters from our history.
3 Reasons ‘Sleep on It’ Is Actually Pretty Good Advice, According to Science
After a long day or a stressful situation, you might hear the advice of “just sleep it off” or “you’ll feel better in the morning.” And while it might seem like an invalidating, oversimplified recommendation, it has some accuracy: often, our bodies and minds are better after a good night’s rest. According to Josef Newton, […]
New soil test could reveal how soil microbes help feed plants
Scientists have streamlined a method for measuring DNA-bound phosphorus in soil, making it cheaper and easier while preserving its accuracy. The technique could reveal how soil microbes help recycle phosphorus and ultimately support more sustainable fertilizer and nutrient management.
KFF Women’s Health Survey Explores How Women Navigate Contraceptive Information
Many reproductive-age women seek health information through online sources, where they may encounter false or misleading information about contraception, according to the 2026 KFF Women’s Health Survey. And an Ohio health department’s new AI policy offers a rare example of a health-specific policy addressing accuracy and public trust.
Why politicians should pay more attention to the polls (reprise)
Welcome to the 231st edition of The Week in Polls (TWIP), a polling newsletter that always welcomes new readers. So please don’t keep TWIP a secret; share it with your friends and colleagues: Share The Week in Polls With all the stories swirling around Reform, JL Partners and political polling, I am finally tearing myself away from talking about the regulation of polling this week - and instead re-running a previous piece reminding us why the solution to problems with polls isn’t simply to ban or ignore them. Rather, we need them to play a vital role in making our politics - and politicians - better. This is followed by summaries of the latest national voting intention polls, seat projections from MRPs and the most recent party leader ratings. Subscribers who pay for the full edition can also read ten insights from the latest polls and analyses, including: A trio of organisations and a pair of professors have been casting their eyes over the Liberal Democrats ahead of the party’s autumn conference; What impact did the summer heatwave have on views on climate change? and Which wins out between chocolate and cheese? If you are not yet a paid subscriber, you can read all ten by starting a free trial now: Get FREE 7-day trial For more polling news as it comes in ahead of next week’s edition, you can also follow The Week in Polls on Bluesky. Picking up on my doubt last week about whether or not 35% of British train travellers have tried to order a takeaway for delivery to a railway station or carriage, thank you to reader Iain, who has been in touch to say he has done just that… in India. Another quick pair of updates, this time on the controversies around Reform and polling. Politico has a further scoop - “Farage aide caught in donor sting attended Egypt retreat with JL Partners” - and Ian Dunt has written about the problems with polling too: “Seeing the response of many polling experts in the last few weeks suggests that they are nervous about what is happening and keen to mitigate against it. Seeing the response of journalists, which has been basically non-existent, suggests the precise opposite.” My usual reminder as well: please do hit reply if you’ve spotted a poll, focus group or new piece of poll-based research that you think should be in contention for coverage in a future edition. Thank you! And with that, on with the show. How politicians get public opinion wrong I have written before about the benefits that come from widespread availability of high-quality political polling. We need more polls and more good coverage of polls. However, what I’ve not dug into before is the research into how accurately politicians estimate public opinion on issues. Thankfully we have academic research to tell us. First, the bad news: We present results from a study of 866 politicians in four countries.1 Politicians were asked to estimate the percentage of public support for various policy proposals. Comparing more than 10,000 estimations with actual levels of public support, we conclude that politicians are quite inaccurate estimators of people’s preferences. They make large errors and even regularly misperceive what a majority of the voters wants. Politicians are hardly better at estimating public preferences than ordinary citizens. Walgrave, S., Jansen, A., Sevenans, J., Soontjens, K., Pilet, J.B., Brack, N., Varone, F., Helfer, L., Vliegenthart, R., van der Meer, T., Breunig, C., Bailer, S., Sheffer, L. & Loewen, P.J. (2023a), “Inaccurate Politicians: Elected Representatives’ Estimations of Public Opinion in Four Countries”, The Journal of Politics, 85(1): 209-222. The bad news is not quite as bad as that makes it sound because, when asked whether the majority of public opinion was for or against various policies, politicians picked the wrong option ‘only’ 29% of the time. However, this included getting it wrong even when public opinion is far from evenly split on an issue: They do not only get it wrong when the public is divided and hard to read (e.g., when there is a 51%:49% distribution). Seventy percent of the misplacements occur for policies with a relatively clear distribution of at least 60% (dis)agreeing citizens. Moreover, the public scored only slightly worse than politicians, picking the wrong option 2.7 out of 8 times on average, compared with 2.3 times for politicians. Turning to the other measure of accuracy (not which side is in the lead but what the percentage support is for each side), politicians on average were 18 percentage points out in their guesses. Again, this was only slightly better than the public, whose error came in at 21 percentage points.2 Further research, using the same mix of countries, gives a little more comfort about how (in)accurate perceptions are: We show that politicians have a better understanding of public opinion when they think the issue matters to voters. Further, when an issue is personally important to politicians they more accurately estimate their party supporters’ opinions. The results confirm that politicians hold more accurate perceptions of voters’ preferences when they think it is important to do so but not necessarily when the issues actually are important to voters. Butler, C., Walgrave, S., Soontjens, K. & Loewen, P.J. (2024), “Politicians are better at estimating public opinion when they think it is more salient”, Party Politics: 1–13. Even so, it’s not a good picture. The remedy for this? Politicians should spend more time reading the polls. So forward this newsletter to someone you know… Share For a more general argument against banning polls, see my 2024 piece. Is “Let Bartlet Be Bartlet” good advice or not? The latest episode of my Political Fictions podcast heads across the Atlantic for a dip into one of the political TV classics: Mark and Cory talk about a giant of American political drama for the first time this week, as they tackle The West Wing. They talk about Let Bartlet Be Bartlet, the nineteenth episode of series one. In it, Mark wonders if The West Wing is actually the epitome of the “Boris Johnson school of politics”, Cory outmanoeuvres Mark into confirming we will record a Christmas Special about Love, Actually and they debate whether the central message of this episode has been misunderstood. Cory’s email newsletter is Paperback Rioter and Mark has a family of email newsletters. Subscribe to our newsletter for some additional bonus content and to get alerted whenever a new episode comes out. Our theme tune is “Monkeys Spinning Monkeys” by Kevin MacLeod/incompetech.com and licensed under the Creative Commons: By Attribution 4.0 License. You can also listen to our episode on Apple Podcasts, Spotify, the web or YouTube. Elsewhere from me… [ A Lord's Eye View How to fix the problem of money in our politics Welcome to my latest update on work in the House of Lords. This time I am taking a look at how our political system is regulated, and the protections we need to keep democracy safe from becoming simply a contest to see who has the biggest bank account… Read more 5 days ago · 3 likes · 2 comments · Mark Pack ](https://lordseyeview.substack.com/p/how-to-fix-the-problem-of-money-in?utm_source=substack&utm_campaign=post_embed&utm_medium=web&embedding_publication_id=1269283) If you spot a factual error… Borrowing an idea from Stuart Ritchie and others, if you spot a factual error in an edition of this newsletter, I will give you a free 12-month subscription to the paid-for version (or an extra 12 months on your existing one). Factual errors qualify, but grammatical errors or disagreements over interpretation do not, and my decision is final. Messages about both of those are always welcome, though. Voting intention and leadership ratings Here are the latest voting intention polls from different pollsters. It is all pretty static at the moment, with a small and consistent Labour lead in nearly all the polls. The table is also online here, along with explanations about what is included. It is updated regularly throughout the week as new polls are released. Next are the seat projections from MRPs, also sorted by fieldwork dates, listing those which are based on dedicated fieldwork (rather than models based on averages of other polls). Because such MRPs are published infrequently, some of the entries below are quite old. Finally, here is a summary of leadership ratings, with a graph from PollCheck: Detailed numbers from the latest polls are online here, and both that and the graph are updated regularly throughout the week as new data comes out. For historical figures on both voting intention and leader approval stretching back to the 1930s, including Parliamentary by-election polls, see PollBase. Catch up on the previous two editions [ ](https://theweekinpolls.substack.com/p/a-crisis-point-for-political-polling) [ A crisis point for political polling Sep 13 ](https://theweekinpolls.substack.com/p/a-crisis-point-for-political-polling) [ Read full story ](https://theweekinpolls.substack.com/p/a-crisis-point-for-political-polling) [ ](https://theweekinpolls.substack.com/p/extra-questions-about-the-polls-in) [ Extra questions about the polls in Reform’s funding scandal Sep 6 ](https://theweekinpolls.substack.com/p/extra-questions-about-the-polls-in) [ Read full story ](https://theweekinpolls.substack.com/p/extra-questions-about-the-polls-in) My privacy policy and other legal information are available here. Links to purchase books or other items are usually affiliate links which pay a commission for each sale. Quotes from social media messages are occasionally lightly edited for punctuation and clarity. If you are subscribed to other email lists of mine, please note that unsubscribing from this one will not automatically remove you from the others. If you wish to be removed from all my lists, simply reply to this email to let me know. A quintet of Lib Dem polling analyses, and 9 other polling insights you shouldn’t miss The following ten findings from the most recent polls and analyses are only for subscribers who pay for the full edition, but you can sign up for a free trial to read them straight away. Ahead of the start of Liberal Democrat conference, we have a trio of organisations and a pair of professors with pieces out about Ed Davey’s party:
An Ohio University researcher looks at how hearing loss affects our ability to isolate and locate sounds
ATHENS, Ohio (WOUB) — Creatures with two ears, such as ourselves, can isolate sounds and pinpoint their location with a high degree of accuracy, even in an environment filled with competing noises.
Engrams Embedding Entendre: Codesign for Efficient DRAM/SSD Offloading
Engram extends standard token embeddings with learned multi-token lookups. Recurring local patterns retrieve vectors directly, reducing the need to reconstruct them through attention and feed-forward layers. With Engram model architecture optimization, it allows for lower HBM capacity to be needed for models at the same quality. This does not mean there won’t be an insane demand for HBM but it just means that model architecture will continue to innovate around constraints. This model architecture design is naturally codesigned for parameter offloading: each token accesses a few embedding rows whose addresses depend on token IDs, not hidden states. The runtime can prefetch those rows from host DRAM while earlier layers compute, keeping the table outside HBM without transferring entire weight matrices. Our Memory model contains our latest estimates of quarter by quarter HBM, DRAM, & NAND supply and demand. Offloading frees HBM for model weights and KV cache, potentially supporting larger batches or more concurrent sessions. When DRAM becomes the next constraint, NVMe offers another tier. Recommendation systems already cache frequently or recently accessed embedding rows in faster memory while backing colder rows with SSDs. After NVIDIA roadmap had to change due to massively despec’ing Rubin Ultra from 1024GB to now ~200GB of HBM per chip, model architecture optimizations like emgram maybe helpful. Our DeepSeek-V4.1-Flash configuration uses roughly 189 GiB of memory for Engram. We replace it with a memory-mapped (mmap) file and measure serving performance with offload to SSD. Later on in our report, we will show our Engram offloading experiments along with the official InferenceX agentic inference serving results on Engram models like DeepSeekv4.1 Flash across all 6 NVIDIA GPU SKUs along with MI355X. Unsurprisingly, the CUDA Moat is still mogging MI355X on the ultra popular DeepSeekV4.1 Flash model. We also show how even on high capacity HBM SKUs, offloading emgrams to DRAM could result in even better performance for most of the pareto than keeping the emgram in HBM. Our benchmark has been widely reproduced, validated and/or supported by almost every major buyer of compute from Google Cloud to Microsoft Azure to Oracle, to Meta and many more. Furthermore, it has the support of the ML community including from vLLM, LMCache, SGLang, PyTorch, Huggingface and the support of major labs like OpenAI, MiniMax, ZAI, Qwen, Moonshot Kimi, etc. Star the InferenceX GitHub repository if you find the open-source benchmark and data useful!. InferenceX is the only inference benchmark in the world to have TPUv7, Jalapeño, Nvidia Rubin NVL72, AMD, and soon, SambaNova and Trainium. Due to how realistic AgentX scenario is to real world agentic inference workloads, AMD has committed to collaborating on MI455X UALoE72 too. DeepSeek did not release the original paper’s two trained Engram models. We replicated its setup on fineweb-edu using the released code and training hyperparameters, at an estimated 6E18 FLOPs per run. We observed the same U-shape scaling: Engram improved performance over pure MoE baselines. We also reproduced DeepSeek’s results, where earlier-layer representations with Engram resembled those of later layers. Like the original Engram paper, it is possible to probe Engram’s gate scores to see what n-grams DeepSeek-V4.1-Flash makes the most use of. Our gate scan finds names, code fragments, relational phrasing, and boilerplate. These examples prioritize interesting-ness over gate strength. One unexpected result was Wright : Ace Attorney. These examples suggest learned memory optimizes the training objective, not a judgment of which facts deserve storage. Licenses, bibliography fragments, API scaffolding, and website furniture can provide prediction shortcuts, so the value of additional Engram capacity may depend on what survives data preparation. This does not show that table capacity is “wasted”: the evaluation-corpus scan establishes neither training exposure nor the capacity occupied by each category. For offloading, strong gates do not identify cache-hot rows. Low gates do not automatically save reads either: computing the gate requires the retrieved key and undoes the performance gain of a fused kernel. Skipping reads would require a separate usefulness predictor before retrieval. In the original paper’s inference-time ablation, factual-knowledge benchmarks retained just 29–44% of their original performance, while reading comprehension retained 81–93%. This is due to the training–inference mismatch. The resulting degradation therefore measures this trained model’s dependence on Engram, not the performance difference between models trained with and without it. In our ablations, suppressing Engram worsens token likelihood across all evaluated domains, especially encyclopedia text and several code corpora. Surprisingly, GSM8K accuracy stays within measured run-to-run variation and removing Engram has no effect. Engram is not a detachable dictionary beside an unchanged MoE. Removing it changes downstream features and expert selection. We tested whether rerouting hurts or compensates by holding tokens fixed in a teacher-forced experiment on CRUXEval, a code-reasoning benchmark of small Python functions where the model predicts a function’s output from its code and an input, and scoring the reference answer. Removing Engram raised answer loss from 0.2848 to 0.3093 bits/token. Forcing the ablated model to use the original Engram-on expert choices made it worse still, at 0.3375 bits/token. Rerouting partially compensates for the missing memory. Memory features and expert selection work together, rather than following a clean “memory stores facts; experts reason” division. On the same CRUXeval, removing Engram during either phase reduced accuracy and increased generated tokens; removing it throughout produced the largest changes. Keeping Engram for prefill leads to more correct answers than keeping for decode likely due to semantically richer KV cache transferred to decode workers, allowing it to mitigate some of the performance loss. Engram’s table is large, but each lookup is small. DeepSeek-V4.1-Flash requests 24 rows at each of two Engram layers, about 12.4 KiB per processed token position across the model, or 3.1 KiB per GPU when split across four GPUs. Currently as of Day 7 since Model Release, MI355X is still 2-4x worse performance per dollar compared to B200 even when normalized by Mi355X’s lower TCO. Our full total cost of ownership breakdown comes from our AI Cloud TCO Model along with monthly market surveys of over 100+ gpu clouds & gpu cloud customers. On the Day 0 release of DeepSeekv4.1 Flash, NVIDIA vLLM works out of the box with zero issues across all 6 SKUs: H100, H200, B200, B300, GB200, GB300! This was thanks to the amazing work by the NVIDIA & Interact teams! In comparison, AMD vLLM did not work on day 0 for DeepSeekv4.1 Flash.Source: SemiAnalysis InferenceX AMD’s vLLM documentation points to using vllm/vllm-openai-rocm:deepseekv41-flash-0909, but from hour 0 of the model release to hour 23, AMD has not publicly released the image. AMD claims, “SPEED IS THE MOAT,” yet it has still not released it by the 23rd hour. We wish that, going forward, the AMD team has a better process for hour 0 model releases. Eventually when they did publicly release for “day 0” image support, performance-wise, it is currently up to 14.8x worse perf per dollar than H200 and up to 42x worse perf per dollar than B200/B300. The power of the CUDA MOAT is NVIDIA’s collaboration with its massive 6 million-developer community ecosystem including most of the vLLM & SGLang & Tokenspeed maintainers which means that CUDA is optimized on day 0. Overall, AMD did make significant improvements but the performance per dollar is still currently 2-4x worse than B200. AgentX Engram DRAM Offloading Improving Performance The HBM and DRAM offload use the same GPU kernel to select and dequantize rows. With HBM, it reads GPU memory; with Unified Virtual Addressing (UVA), it reads pinned host memory directly. Both support full decode graphs. Moving the table into HBM accelerates only the sparse lookup, leaving decoder computation and communication unchanged, resulting in little overall benefit while consuming memory otherwise available to KV cache. Another benefit of Engram offloaded to DRAM which means you can reduce the communication overhead by using less HBM GPUs per replica. For example, when enabling Engram offloading on B300, we are able to switch from TP4 to now TP2 which improves the pareto curve by up to 1.6x. When iso-model quality, less HBM is required as the engrams could be offloaded to host DRAM. Thus HBM bandwidth matters way more than HBM capacity. For inference workloads where memory bandwidth matters the most, 4-hi HBM provides the best $/bandwidth and therefore lowest cost per token. If China continues to make more and more revolutionary model architecture innovations, soon it could potentially 0Hi HBM stacks. [ ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) [ Long Live the Short King: Why 4-hi HBM Wins ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) Myron Xie, Bryan Shan, and 3 others · Sep 13 [ Read full story ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) Moreover, on B300 and week-0 stack, moving the Engram table back to HBM did not improve results and stay within run-to-run variance. This is a result of the work optimizing DRAM offload, such as async, and overlap. SSD Offloading On B200, we created a unoptimized vLLM fork and stored the Engram tables in memory-mapped files on local SSDs. File backing lets the OS reclaim table pages when other applications need RAM. Pages already cached in memory can be served without reading the SSD again. Furthermore, note that we were unable to turn on GDS The unoptimized SSD implementation changes how rows reach the GPU. It copies row IDs to the CPU, deduplicates them, gathers the requested rows into pinned buffers, copies those rows back to the GPU and dequantizes them. This work runs between segments of the GPU execution graph. Native UVA performs row selection and dequantization directly on the GPU, avoiding the CPU round trip. A warm filesystem cache removes physical SSD reads, but leaves the coordination, row gathering and transfers. This is why a file already cached in RAM can still perform worse than a pinned DRAM table. The comparison measures the whole serving path; it does not separate the time spent on each of these operations. B200 DRAM dominates both measured SSD serving curves in total tokens per dollar and P90 interactivity. Near 125 tokens/s/user, DRAM delivers 121 million total tokens per dollar versus 52 million for SSD. For production serving, SSD offloading is likely not worth the tradeoff. On the B200 configurations we measured, SSD offloading loses on both measures: every observed SSD point has a DRAM alternative that delivers higher P90 interactivity and more total tokens per dollar. The only points where Cheaper storage does not automatically produce a cheaper inference service. Moving Engram to SSD leaves the same four expensive GPUs and the rest of the server in place. Reclaiming RAM only creates an economic benefit if it enables a cheaper server configuration or additional useful capacity. The current unoptimized path provides neither benefit, and the filesystem cache still consumes RAM when table pages are resident. Next we will look at the mechanisms and specific implementation of ngrams from DeepSeekv4.1 Flash, LongCat, Qwen3.8 Flash Next.
A machine learning system that identifies cancer cells based on how they scatter light
Cytology tests are widely used for cancer screening, but diagnosis can be challenging when tumor and non-tumor cells look very similar. Researchers from Japan developed a machine learning system that identifies tumor cells based on their white-light scattering spectra. The approach achieved high diagnostic accuracy for mesothelioma and showed potential for distinguishing several other tumor types, offering a promising approach for enhancing cytological diagnosis.
Knowtex launches frontier healthcare AI lab
Artificial intelligence company Knowtex launched a frontier AI lab for healthcare, aimed at developing and rigorously evaluating clinical AI tools for accuracy, auditability and clinician trust.
Rashee Rice manages a touchdown in Week 1
With Patrick Mahomes struggling with his accuracy, Rashee Rice was targeted just twice in Week 1, catching both for 19 yards and a score.
13News Now
13News Now (WVEC) is made up of storytellers, innovators, content creators, and idea generators. We stand for passion. Passion for our people, passion in our product, and above all- passion in our community. We value honesty, accuracy, and solutions. We serve as the local ABC affiliate for Hampton Roads. We strive for interaction with our audience and are constantly working on our presentation so that you have the best experience when consuming our products. Whether on air, online, or on the go- we are everywhere you go. Digitally we lead the market by more than a million actions over our nearest competitor. Join the conversation with us, help us tell the Stories of Now. About our parent company TEGNA- TEGNA Inc. (NYSE: TGNA) is an innovative media company that serves the greater good of our communities. With 46 television stations in 38 markets, TEGNA delivers relevant content and information to consumers across platforms.
Correcting False Health Claims and Navigating AI-Generated Information
Corrections can improve the accuracy of people’s beliefs, but their impact may depend on reaching people before a claim has already spread widely. And research on AI-generated search results suggests that people may be less likely to visit the original sources behind AI-generated answers.
Galaxy S27 Pro and S27 Ultra to get major display upgrade
Samsung’s Galaxy S27 Pro and S27 Ultra are reportedly set to receive M16 OLED panels, promising improved brightness, color accuracy, efficiency, durability and new privacy features. The standard S27 models, however, are expected to use older M14 technology.
OpenAI's ChatGPT for Financial Services boosts data for bankers
OpenAI announced ChatGPT for Financial Services on Thursday, bringing more data under the hood of the AI tool and adding new accuracy checks.
KAIST identifies cause of artifacts in battery nanoscale analysis, paving the way for more reliable measurements
- A research team led by Professors Seungbum Hong and Jong Min Yuk from the Department of Materials Science and Engineering and Professor Nam-Soon Choi from the Department of Chemical and Biomolecular Engineering has identified the cause of a measurement artifact in nanoscale battery analysis that can make surface topography appear to represent ion movement. - The team has developed a method to reduce these artificial signals by smoothing rough battery-material surfaces, potentially improving the accuracy of analyses for next-generation batteries, including conventional lithium-ion batteries and emerging technologies such as sodium-ion and solid-state batteries.
Australian science prize awarded to researcher studying memory accuracy of domestic violence survivors
Helen Paterson’s work on advancing how police and courts understand memory is recognised in prestigious Eureka prizes
How a former ARPA-H director's startup is tackling AI's ‘dumb problems’
In this edition of STAT's AI Prognosis: A startup tackling the 'dumb problems' at the intersection of AI and bio, accuracy of AI scribes, and more.
Bayer's investigational PET tracer shows high accuracy for cardiac amyloidosis
The imaging agent could offer a new approach for diagnosing stiff heart syndrome, according to data presented during the European Society of Cardiology Congress.
In translating the world’s biggest entertainment, accuracy isn’t everything
A Final Fantasy veteran says some debates over localization can overlook the nuances of the craft.
In translating the world’s biggest entertainment, accuracy isn’t everything
A Final Fantasy veteran says some debates over localization can overlook the nuances of the craft.
In translating the world's biggest entertainment, accuracy isn't everything
A Final Fantasy veteran says some debates over localization can overlook the nuances of the craft.
How health systems are embracing chatbots to query and summarize patient records
Several large health systems are rolling out AI chatbots to query patient records, saying they save time and increase diagnostic accuracy.
Did life-friendly planets really form just after the Big Bang? Science stirs debate - Futura-Sciences
Standard models of galactic evolution indicated that the universe needed several billion years after the Big Bang to produce enough heavy elements for rocky planets. Vorobyov and co-author Daniel Whalen at the University of Portsmouth tested the accuracy of the time scale using computational models. Their team found evidence that...
Estée Lauder Companies (EL) Expands Color Science Research For Shade Matching
Estée Lauder Companies (NYSE: EL) announced a research collaboration with the University of Leeds and Dr. Kaida Xiao focused on advanced color science for complexion products. The partnership aims to improve shade matching accuracy in luxury foundations and related complexion lines using visual perception science. The work is expected to inform future formula development and product testing in the prestige cosmetics segment. For investors tracking how advanced data and perception science...
Feeling sick? Some Minnesotans are asking AI what to do
Patients are turning to AI chatbots for help diagnosing symptoms and navigating the health care system—even as questions about accuracy and privacy persist.
At 25, the big shift: why we are sure humans heat the planet - Futura-Sciences
The Early Hints: From Wild Theories to Alarming Accuracy The idea of planetary warming isn’t new—although it once sounded more like science fiction than established fact. Back in the 1960s, a handful of pioneering researchers began suggesting that greenhouse gas emissions from human activity could warm the climate. At the...
The Government's Earthquake Warning System Has a 30% Accuracy Rate. Scientists Found Something Better
Researchers tested a new technique that uses fiber optic cables to detect earthquakes. It’s twice as precise as what the government currently uses.
“Cognitive Spoofing”: How AI Fakes Expertise, And Why It’s A Huge Problem In Healthcare
AI systems have to be pressure-tested in real-world clinical settings to ensure correct reasoning, accuracy and reliability.
Clinical chatbots are taking medicine by storm. Should doctors trust them?
As LLMs compete for doctors' attention, some developers say the science of benchmarking their AI tools for safety and accuracy is flawed.
Turning AI Plant Science Into a Commercial Tool: Heritable Agriculture’s Next Chapter
Heritable says its platform can match plant genetics to geography with 10-meter field-level accuracy, helping users predict how varieties of nearly any crop could perform in specific locations around the world.
NVIDIA’s Synthetic Video Detector Spots Fake News & AI-Generated Content With 92% Accuracy, Analyzing 1080p Footage In Just 22ms
NVIDIA introduces the Synthetic Video Detector NIM microservice, achieving up to 92% accuracy in identifying Fake News.
AI advice made people three times less accurate but twice as confident, researchers found
A study found that access to AI advice collapsed people's willingness to say "I don't know" from 44% to 3%, while accuracy dropped from 27% to 9%.
Vikings Receive Stern Warning on Depth Chart Battle
Splitting practice reps risks stalling Kyler Murray’s transition to Kevin O’Connell’s system, giving J.J. McCarthy a tactical edge despite last season's struggles and glaring accuracy concerns.
Oops, Scientists May Have Miscalculated Our Global Warming Timeline
A study suggests Earth surpassed the 1.5°C warming threshold in 2020, sparking debate among scientists about the accuracy of global climate data.
Mesa businesses struggle as Val Vista Drive roadwork drags on
This story was written and reported by a journalist and revised with the assistance of AI. Our editorial team verifies all reporting on all platforms for fairness and accuracy.
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AI Is Changing How Customers Choose Your Business
As AI increasingly mediates how customers research, evaluate, and choose suppliers, competitive advantage is shifting from direct customer understanding to managing AI-shaped interactions. Drawing on the experiences of three small-to-medium sized businesses they worked with, the authors show how firms can adapt. A manufacturer improved sales efficiency by screening AI-generated inquiries before investing engineering time in quotations. A boutique hotel learned to monitor how AI tools described its business and revised public content to improve accuracy and differentiation. A B2B software company replaced periodic customer reviews with continuous monitoring of customer feedback and AI-generated market perceptions. Across all three cases, the lesson is that firms must treat AI as a new intermediary in the customer relationship. Winning companies will not necessarily spend the most on technology; they will build systems for continuously listening, interpreting signals, and adapting strategy in markets where both customers and the information they rely on are increasingly AI-mediated.
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Generative AI chest X-ray models offer new approach to radiology reporting and quality improvement
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New data shows consumers dropped health coverage after Republicans let subsidies expire
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Millions of exploding stars could soon reveal dark energy's secrets
A new AI-powered framework could transform how astronomers measure the expansion of the Universe. By analyzing images of Type Ia supernovae and modeling their environments in unprecedented detail, researchers can estimate cosmic distances with near-spectroscopic accuracy. The technique is designed for the flood of data expected from the upcoming Vera C. Rubin Observatory and may greatly improve our understanding of dark energy.
Making Our Own Luck
We are cross-posting a piece that former NIH official Kris Willis wrote for Macroscience. Kris is now the President and Founder of the Woodley Park Institute, for which I’m a board member. Picking winners or hedging bets? Federal science agencies have long wrestled with the question of how to ensure their billions of dollars of grants and contracts result in maximum benefits for Americans. On the surface, it might seem that a goal-oriented, interventionist style of management would be the best policy: prioritize our most pressing problems and distribute funding accordingly.This top-down strategy creates a unique hazard, though: bad choices can send resources in the wrong direction, resulting in slow progress that impedes the delivery of tangible benefits to citizens. Moreover, if decision makers pursue an applied advance when the underlying scientific principles remain poorly understood, they risk costly failures and a loss of support for the research enterprise at large. Examples include Nixon’s 1971 War on Cancer, undertaken on the premise that a cure could be achieved in less than a decade, the drive to build an operational commercial fusion plant by the mid-1990s, and the National Plan to Address Alzheimer’s Disease, which failed to produce the effective treatments and prevention strategies promised by 2025. The contrasting laissez-faire approach is to hedge your bets. If the path to an advance is impossible to predict, then the wisest choice might be to distribute funding to as widely as possible. The main risk of this strategy is that spreading funding thinly may limit the resources available to the most promising research, resulting in the same negative outcomes as the interventionist approach: limited support for the most promising topics, slow progress, and delayed returns. In reality, at science agencies like the National Science Foundation and National Institutes of Health, program managers navigate between these two extremes, setting some funds aside to target high-priority areas and using the rest to cover the widest possible range of meritorious proposals. This middle path seems like the most rational way to proceed, but it still requires making choices. How much should be set aside for high priority topics? What should those topics be? How should we define merit? Expert opinion can provide a helpful guide, but leading experts can have remarkably different opinions about what matters and what should be prioritized. Moreover, experts and administrators alike can be risk averse, giving an edge to high-profile, well-established concepts. What’s really needed is a data-driven framework to help guide decision-making, one that doesn’t simply repackage prestige or incumbency. Recognizing breakthroughs years in advance Many breakthroughs fail to attract funding or recognition at the earliest stages of development. Katalin Karikó, for example, spent years struggling to win grants for work on mRNA that would eventually win her and Drew Weissman a Nobel Prize. Similar stories can be told about Jim Allison’s groundbreaking studies leading to the development of cancer immunotherapy, Stan Pruisner’s demonstration that prions can self-replicate without DNA, or Robin Warren and Barry Marshall’s proof that Helicobacter pylori, not stress, causes ulcers. Yet at some point, the scientific community recognizes and responds to transformative breakthroughs like these. How does that change happen? And what if science funders could identify the areas that are on the cusp of a breakthrough? Those questions led my collaborators and me to study how scientists responded to past breakthroughs. Based on patterns in that data, we developed a means of predicting future breakthroughs, using biomedicine as a proof of concept. It’s worth taking a moment to describe how our approach works. Briefly, our prediction process begins by grouping the 18 million or so peer-reviewed papers in PubMed, the authoritative database of biomedical research, into roughly 50,000 unique topics. A small subset of these topics are so widely acclaimed as breakthroughs that they have been recognized with a major prize like a Nobel or a Lasker Award. We theorized that studying the development of these outliers over time, especially before they gained acclaim, was the key to identifying future instances of them. After defining topics, we chose 21 representative breakthroughs and asked what they looked like before they won prestigious prizes. To answer that question, we re-ran our topic-mapping algorithm repeatedly, winding back the clock one year each time, stringing together the years to follow the progress of each field as it grew and advanced. We found that in each case, the years leading up to a discovery followed a pattern: a burst of papers on a new, rapidly evolving topic, many of which quickly became influential. These characteristics—the percent of papers that are new, the percentage of papers that are brought in from other topics, and the influence of each individual paper—can all be measured separately for any given topic. Together, they act as a predictive signal that is detectable an average of five years before a transformative discovery is made, and up to thirty years before the discovery receives a major prize. Scanning the current research landscape for topics that display this signal allows us to predict what work will likely produce a future breakthrough. Each year we analyzed includes four or five signals, a number that appears to have remained constant across a twenty year time frame. In their classic work on the sociology of science, Bruno Latour and Steve Woolgar established that the desire to participate in a major discovery is an important motivator for scientists as they consider whether they should take up (or abandon) a research problem. Although further work is needed, the simplest explanation for our results is that this behavior is widespread enough to detect at scale. A foundational advance, or something like it, draws the attention of scientists and causes them to change the direction of their research. They vote with their feet, staking their own reputation and careers by publishing on and citing the new idea. That upswell of interest is the foundation of our breakthrough signal. What kind of research does our model identify as a breakthrough? After building a model based on the patterns in our 21 preselected breakthroughs, we tested it by identifying all early signals of discovery from 1994 through 1997. Of the 18 signals we found in that time period, 17 can be traced forward in time to a breakthrough. Examples include super-resolution fluorescence microscopy (see the image above), the directed evolution of proteins and enzymes (Chemistry Nobel, 2018), and sequencing the human genome (National Medal of Science, 2008). The discovery of the role of leptin signaling in obesity is a particularly interesting case study. Around 1950, scientists noticed the existence of a type of lab mouse that suffered from a unique inherited form of obesity. When given the freedom to choose their own diet, these mice ate more than normal and preferred food that was high in fat. Researchers had few clues as to why the animals overate; early experiments pointed to the existence of a soluble ob factor found circulating in the bloodstream that regulated appetite, but its identity was unknown. In 1994, Jeffrey Friedman and colleagues identified and sequenced the ob gene, noting that the protein it encoded appeared to fit the profile of a circulating factor. A year later, a trio of high-profile publications demonstrated that ob was a hormone that regulated body weight and fat deposition by regulating appetite. One of these was co-authored by Friedman, who christened it leptin from the ancient Greek word for thin. Using our model, the data from 1996 make it clear this advance would become a breakthrough. Scientists had flocked to study the biology of the new hormone, generating the flurry of new papers and citations required to produce a signal. One of these demonstrated that serum leptin concentrations reflected the amount of adipose tissue in the human body, providing clinical validation of the earlier mouse studies. Before the identification of leptin, scientists were unable to point to a specific molecule that controlled appetite and adiposity, so that it was impossible to rule out non-physiological causes of obesity; its characterization fundamentally changed our concept of weight gain. In 2010, Friedman and fellow pioneer Douglas Coleman were awarded a Lasker for the discovery. Our work also identifies breakthroughs in clinical practice and behavioral and social sciences, even though these areas are less likely to draw the attention of major prize committees. One example that falls into this category is the introduction of endovascular aneurysm repair, a minimally invasive surgical procedure for the treatment of abdominal aortic aneurysm, a leading cause of death among Americans over the age of 65. Relative to the previous standard of care, it reduces in-hospital mortality by almost four-fold. In 2017, Timothy Chuter received the American College of Surgeons’ prestigious Jacobsen Innovation award for his multiple refinements to the technique. Another under-recognized breakthrough identified by our model is the development of standardized survey instruments to assess the quality of life for HIV+ patients. The antiretroviral cocktails introduced in the mid-1990s reduced AIDS mortality, but treatment came with serious side effects. The new evaluations showed that patients whose disease progressed had worse physical functioning than Americans with other chronic diseases, while those who were asymptomatic maintained a health-related quality of life on par with the overall US population. This evidence made it possible for patients and activists to argue that even in the absence of a total cure, and in spite of the side effects, alleviation of symptoms was a worthwhile priority for clinicians. My colleagues and I have failed to identify any significant recognition for the physicians and health policy experts who pioneered this advance, although in 2016, researchers called for the World Health Organization to set targets for good health-related quality of life as part of its framework to end the AIDS pandemic. From forecasting to funding How should science funders use our predictions? Any serious discussion of what we should do with this new capability needs to begin with an understanding of its limitations. We can’t (and shouldn’t) invest all of our resources in the small number of topics predicted to produce breakthroughs. Innovative new fields are born from existing ones; they rarely arise de novo. If funders don’t maintain a diverse portfolio, the breakthroughs of tomorrow will have no antecedents. Think of this as eating the seed corn, or killing the goose that lays the golden egg. There are also multiple goals of funding beyond basic scientific discovery, including translating discoveries into clinical practice, developing new technologies, supporting economic development, and training new scientists. The investments needed to accomplish these goals are likely different from those required to support emerging breakthroughs. Finally, we must be aware that we may not capture every instance of a breakthrough. The concept is fuzzy, and not every significant discovery is recognized with a major award. Further, breakthroughs are rare, meaning that our dataset is, by necessity, small. This makes formal estimates of accuracy challenging. Although our overall success rate appears to be high, any one signal may turn out to be a false positive. We found one apparent example: the development of the third generation COX-2 inhibitors, Vioxx and Celebrex, which were much celebrated in the early 2000s as effective but non-addictive pain relievers, meets all the algorithmic criteria of a breakthrough. Five years after it received FDA approval, researchers demonstrated that Vioxx was associated with serious cardiovascular complications, and the manufacturer voluntarily pulled the drug from the market. Celebrex remains available, although like other NSAIDs, it carries an FDA warning for increased cardiovascular risk. Decision makers who were overly focused on the breakthrough potential of this research might have wasted substantial resources nurturing an area that ultimately proved to be a failure—but so would those who relied on expert opinion at the time. That said, the potential benefits of funding the next mRNA vaccine or cancer immunotherapy years earlier than we might have are large. When we can’t assume the outcome of an action, it’s worth conducting an experiment. Can targeted support for areas that are poised to produce a breakthrough increase the return on scientific investment without cannibalizing future advances? With the necessary administrative infrastructure in place, conducting this experiment would be straightforward. First, identify all the breakthrough signals between 2018 and 2023, select half at random, and commit to a decade of investment. For biomedicine, a six year window should yield about 30 topics with the potential to produce a breakthrough. Providing strong support to half of these might be accomplished for $300 million a year. That’s roughly half of the recent annual budget of the NIH Common Fund or around 20% of the fiscal year 2025 budget for ARPA-H. After five years, then again at ten, compare to see which group — the one that was actively managed, or the control — produced more breakthroughs, and on what timescale. Although we used biomedicine as an example, the same experiment could just as well be run for physics, materials science, or any other discipline. Any attempt to undertake such an experiment should keep three points top of mind. First, we can predict the topic of future breakthroughs, but the people and vision for how to move the work forward are still important. In practice, this means success requires active program managers with the expertise to develop the science and the authority to do so, including by recruiting investigators. Second, we would be wise to learn from the failures of past attempts to fund transformative research. High among these are funding modestly repackaged work, confusing the genuinely novel with the merely unfamiliar, and relying too much on incumbent investigators. All of these risks can be mitigated by thoroughly and intelligently integrating high quality data into portfolio management. Finally, we need to have patience. Even with strong funding support, producing a breakthrough takes time. If supporting breakthroughs is the best way to nurture the birth of new fields, we also need to accurately describe and properly manage the other two stages, so that we invest wisely throughout the productive lifetime of ideas and divest once they reach the point of diminishing returns. We would benefit from more study of all of these phases, especially the transition from one to another. More research could answer questions like: When does a breakthrough become an established field? How do the availability of funding, the size and characteristics of the available workforce, and the accumulation of evidence that disagrees with prevailing models affect a field’s decline? Exactly how do old ideas give birth to new ones, and can we speed up the process? The more we understand about the dynamics of scientific progress, the better we can maximize the odds that our curiosity leads to results. I’d call that finding a way to make our own luck.
The Grand Seiko Evolution 9 Spring Drive U.F.A. Models Get Serious Fan-Requested Updates
✓ Grand Seiko Evolution 9 gets five Spring Drive U.F.A. models ✓ New 9RB2 accuracy, materials, and clasp ✓ See them here
AI and brain-computer interface allow speechless ALS patient to work a full-time job
The hardware isn't new, but a UC Davis research team's machine learning-powered method of translating brain activity in an ALS patient into sentences with 92% accuracy is
Scientists found a way to explain bird flocks that “defy” Newton’s third law
Physicists have solved a long-standing problem involving systems that appear to violate Newton’s third law, such as bird flocks and bacterial swarms. By adding carefully designed “imaginary partners” to their models, they can now simulate these complex systems with unprecedented accuracy.
iPhone 17's 8GB Limit Costs It These Two Siri AI Features in iOS 27
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A classic brain test exposed AI's biggest weakness
Researchers gave top AI models a classic attention test used in psychology and found a major flaw. While the models could correctly name colors in short lists, their performance deteriorated sharply as the task became longer and more complex. Some leading systems fell from over 90% accuracy to nearly complete failure.
Why Model Flows Are the Key for Reproducibility in AI for Science
Reproducibility is absolutely critical in science, but it’s a troublesome characteristic when it comes to AI. Frontier models developed by Big AI may deliver superior accuracy and reasoning capabilities, but they do so largely as black boxes with little regard for reproducibility. If AI is going to turbo-charge scientific productivity, it must do so without […]
Finding Nemo: Scientists Use AI To Catch Illegal Marine Wildlife Traffickers
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The Wearable Showdown: Oura Ring 5 vs. Fitbit Air vs. Whoop MG vs. Apple Watch
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Best Look at Foldable iPhone Design Revealed, May Only Come in White
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How prediction markets could forecast the future of science
Online prediction markets are taking bets on everything from climate change to quantum computing. But researchers question their accuracy
Scientists warn Trump plan to axe US ocean monitoring system will leave world ‘flying blind’
Experts say dismantling the ocean observation system will ‘severely degrade’ the accuracy of weather predictions
Satellite science: FSU research increases accuracy of high-resolution ocean surface measurements
Florida State University research published today in Science Advances demonstrates a new framework for predicting the motion of kilometer-scale underwater
When used in the ‘wild’ conditions of the real world, large language AI stumbles
Asking medical questions of AI with language spoken “in the wild”—meaning with consumer-level LLM prompts—brings back answers with decidedly mediocre accuracy—76%—a new study shows.
Fix fragmented HR systems to limit payroll errors and compliance risks
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Scientists trained an AI model using an IBM quantum computer — and it answered questions correctly that the base model couldn't
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Replacing RAG with bash cut AI retrieval costs 30%
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Garmin Forerunner 70: Everything That’s New & Changed!
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NASA’s C-20A Flights Advance Understanding of Earthquake Dynamics
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Business Office Leader Improved Financial Systems and Oversight at Chico State
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PSMA PET imaging’s proliferation produces uptick in aggressive treatment for prostate cancer
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The merits and pitfalls of metaphor use in science communication
Effective science communication is a powerful but underrated tool. Metaphors, a common literary device, draw connections between unlike things and are often conceptually, innately understood. In science and science communication, these figures of speech act as a means of structuring concepts to provide the basis for further exploration. The use of metaphors in science can be a catalyst for change, invention, and, unfortunately, misunderstanding. Metaphors should be used responsibly when attempting to make complex scientific concepts easily understandable, and should weigh the accuracy and accessibility of information in a favorable balance.
Reduce restaurant labor costs without cutting staff
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California's race for secretary of state shows partisan divide over how to count ballots
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Civil-Military Relations under the Trump Administration (Part I) -- Politicians and Military Leaders | List of Articles | International Information Network Analysis
IINA provides ongoing analysis of international affairs both by region—such as North America, China, and Europe—and by such topics as human security, nontraditional security threats, and cyber security. The articles on this site, written by experts at the Sasakawa Peace Foundation and guest contributors, are carefully selected for their objectivity, accuracy, timeliness, and relevance for Japan.
AI just discovered new physics in the fourth state of matter
Physicists have taken a major step toward using AI not just to analyze data, but to uncover entirely new laws of nature. By combining a specially designed neural network with precise 3D tracking of particles in a dusty plasma—a strange “fourth state of matter” found from space to wildfires—the team revealed hidden patterns in how particles interact. Their model captured complex, one-way (non-reciprocal) forces with over 99% accuracy and even overturned long-held assumptions about how these forces behave.
Companies Expand AI Health Offerings, Even as Accuracy Questions Remain — The Monitor
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How Al Gore Politicized Climate Science
An Inconvenient Truth turns 20 next month. In the 2006 documentary, former vice president Al Gore argued that human-driven climate change was an urgent, civilization-threatening crisis. The film became a cultural phenomenon, winning two Academy Awards and making climate change one of the defining political issues of our time. I am sure that there will be many retrospectives seeking to relitigate the scientific claims in the film. But the far more important anniversary story is not about the accuracy of any of Gore’s individual claims but rather about what the film helped to unleash in the scientific community: bringing partisan politics into the institutions of science. Gore did not simply make a film about climate change: He implored the scientific community to join him in overt climate advocacy. The fuel that Gore added to the fire of pathological politicization of the climate science community is the most important legacy of An Inconvenient Truth. Almost three years after the film’s release, Gore took the stage at the 2009 annual meeting of the American Association for the Advancement of Science (AAAS)—the country’s largest and most prominent scientific organization—and delivered something much closer to a revival sermon than a scientific lecture. Gore told the assembled scientists that they could no longer “in good conscience accept this division between the work you do and the civilization in which you live.” His directive was clear:
Volcanoes Send Tiny Warning Signals Before Eruptions. These Scientists Are Decoding Them
The team behind "Jerk," a novel tool that predicted eruptions with 92% accuracy for a French volcano, discusses its work and implications.
Matthew Deemer advancing research on wrist worn devices
With growing concerns about the accuracy of wrist-worn health devices, a Slippery Rock University student is charting a…
How five UF business students faced 7,000 international competitors and took home first place
From mid-January to mid-March, the five of us competed in the Bridgewater x Metaculus Forecasting Contest — an international tournament where participants assign probabilities to macroeconomic and geopolitical questions and get scored on relative accuracy.
Building Custom Atomistic Simulation Workflows for Chemistry and Materials Science with NVIDIA ALCHEMI Toolkit
For decades, computational chemistry has faced a tug-of-war between accuracy and speed. Ab initio methods like density functional theory (DFT) provide high fidelity but are computationally expensive…
NYSE Content Update: OpenLoop Health Debuts At-Home Sleep Apnea Test with 98% Accuracy
The New York Stock Exchange (NYSE) provides a daily pre-market update directly from the NYSE Trading Floor. Access today's NYSE Pre-market update for market insights before trading begins.
Novo Nordisk CEO: This is where science meets speed and scale
Maziar Mike Doustdar, CEO of Novo Nordisk, says AI partnership with OpenAI will speed drug discovery and delivery, boost accuracy and strengthen confidence in Wegovy despite rising competition.
UConn Health Offers New Level of Precision in Cancer Care
High-tech radiation therapy machine enhances accuracy, safety
“AI polls” are fake polls
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
Analysis finds Google AI Overviews is wrong 10 percent of the time
Is 90 percent accuracy good enough for a search robot?
Institute of Industrial Science, The University of Tokyo and George Mason University scientists mine past air temperature forecasts, lower cost with greater subseasonal prediction accuracy
Researchers at Institute of Industrial Science, The University of Tokyo and George Mason University‘s College of Science have developed a new method that improves air temperature forecasts one to five weeks in advance—without requiring additional model simulations.
AI breakthrough cuts energy use by 100x while boosting accuracy
AI is consuming staggering amounts of energy—already over 10% of U.S. electricity—and the demand is only accelerating. Now, researchers have unveiled a radically more efficient approach that could slash AI energy use by up to 100× while actually improving accuracy. By combining neural networks with human-like symbolic reasoning, their system helps robots think more logically instead of relying on brute-force trial and error.
How Scientifically Accurate Is ‘Project Hail Mary’? Experts Weigh In
Based on hard science fiction, a genre that prioritizes scientific accuracy, the blockbuster gets a lot right but misses a few things, experts say.
Navigating the social media minefield
Scientists and researchers seeking to announce promising findings, exchange ideas or contribute to policy discussions outside of academic circles may find social media an expedient tool.But increasingly, scientists and other professionals are discovering that online platforms can be a double-edged sword: while sharing their work and expertise is crucial for visibility and meeting funder expectations, it also exposes them to baseless criticism and even accusations of spreading false information."It is getting harder and harder for scientists, and researchers in general, to convey their findings without facing attacks on their legitimacy and accuracy of their research," says Valère Ndior, a professor of public law at the University of Western Brittany in Brest, France, who has spent more than a decade studying misinformation on social media. Researchers attempting to challenge deceptive online information may themselves be accused of political bias or not being objective in their research, he explains, adding that some scientists disengage because they feel overwhelmed and unable to sustain meaningful dialogue. "It is quite sad to see in some instances that researchers end up leaving social media altogether because they feel like the pressure is getting too intense or feel threatened," he says.Ndior, a past president of the Francophone Network of International Law, is also involved in European efforts to make science and research findings more accessible to the public. As an ambassador for the Science Comes to Town (SCTT) project, he has participated in recent discussions on public perceptions of science and online misinformation. The European Commission-funded initiative connects Brest and two other cities — Kiel in Germany and Split in Croatia — and aims to strengthen ties between communities, universities and research institutions to promote science awareness and cooperation across Europe.Coordinated campaigns around contentious topicsNdior draws a clear distinction between critical thinking and thoughtless criticism. Yet he sees online discussions being increasingly shaped by suspicious behaviour and coordinated information campaigns — including "raids" aimed at dominating discussions and "astroturfing," where small groups act as larger movements to sway debates. Tracking these patterns has become harder because some platforms have restricted data or charge for it, despite provisions of the European Union's Digital Securities Act aimed at ensuring researchers can have access to such data. According to Ndior, "platforms became more reluctant from the moment access to data became a legal requirement rather than a voluntary initiative."Unfounded claims and scepticism about science existed long before Internet chat groups. But the noticeable rise in online hostility has intensified around contentious topics, such as the coronavirus pandemic and climate change, alongside the weakening of gatekeepers to moderate content.For Ndior, dubious claims about pandemic countermeasures and vaccine safety highlighted the struggle between "balancing freedom of expression and the protection of public health and order." Efforts to remove social media content deemed false or risky for public health triggered accusations of censorship and the silencing of legitimate discussion, and in some cases, legal challenges.Concerns about attacks on the credibility of scientists and researchers have generated a flurry of academic papers in recent years and prompted professional communities to address ways to counter online attacks. Such unease extends to the generation that has grown up with digital media. Ndior points out that his students, some of whom research artificial intelligence regulation, hate speech, online surveillance and other controversial topics, hesitate to share their work or viewpoints online. "Many of them don't want to expose themselves too much because they don't feel ready to be faced with the backlash of other people criticising their research," he explains.Safer harbours for engagementLegal challenges against false or defamatory posts are not necessarily a solution — especially given the trans-jurisdictional nature of the Internet. Lawsuits also take time and money, and if they fail, can be counterproductive. Instead, Ndior advises scientists and researchers to be selective in how and where they communicate. Professional online platforms and moderated discussion spaces offer a better environment for engagement. He also highlights the value of more traditional formats, such as radio interviews and in-person discussions at schools, libraries and science fairs, which encourage dialogue with the public. "Personal contact makes a big difference," he says, while acknowledging that these approaches lack the global reach of social media.Ndior’s interest in digital law and online misinformation stems from his time as a doctoral student in international law in the early days of social media. He began analysing posts from governments and heads of state to determine if they could be considered official and thus used as evidence in litigation. His work attracted media attention and led to conference and speaking invitations. "This was not part of my plan," he says, "but this is what happens as a researcher — your interest is sparked by a certain event or phenomenon, and it suddenly becomes your focus."Ndior believes SCTT provides "a double benefit" for researchers, enabling them to connect with peers and interact with the public. "There is a wide community of people who are not in academia who are critical thinkers looking for opportunities to ask questions, to get information and contribute their ideas,” he says. "I really value that kind of feedback."Science|Business, which connects policymakers, academics and industry through its news reporting, analysis and events, is an SCTT partner.
Navigating the social media minefield
Scientists and researchers seeking to announce promising findings, exchange ideas or contribute to policy discussions outside of academic circles may find social media an expedient tool.But increasingly, scientists and other professionals are discovering that online platforms can be a double-edged sword: while sharing their work and expertise is crucial for visibility and meeting funder expectations, it also exposes them to baseless criticism and even accusations of spreading false information."It is getting harder and harder for scientists, and researchers in general, to convey their findings without facing attacks on their legitimacy and accuracy of their research," says Valère Ndior, a professor of public law at the University of Western Brittany in Brest, France, who has spent more than a decade studying misinformation on social media. Researchers attempting to challenge deceptive online information may themselves be accused of political bias or not being objective in their research, he explains, adding that some scientists disengage because they feel overwhelmed and unable to sustain meaningful dialogue. "It is quite sad to see in some instances that researchers end up leaving social media altogether because they feel like the pressure is getting too intense or feel threatened," he says.Ndior, a past president of the Francophone Network of International Law, is also involved in European efforts to make science and research findings more accessible to the public. As an ambassador for the Science Comes to Town (SCTT) project, he has participated in recent discussions on public perceptions of science and online misinformation. The European Commission-funded initiative connects Brest and two other cities — Kiel in Germany and Split in Croatia — and aims to strengthen ties between communities, universities and research institutions to promote science awareness and cooperation across Europe.Coordinated campaigns around contentious topicsNdior draws a clear distinction between critical thinking and thoughtless criticism. Yet he sees online discussions being increasingly shaped by suspicious behaviour and coordinated information campaigns — including "raids" aimed at dominating discussions and "astroturfing," where small groups act as larger movements to sway debates. Tracking these patterns has become harder because some platforms have restricted data or charge for it, despite provisions of the European Union's Digital Securities Act aimed at ensuring researchers can have access to such data. According to Ndior, "platforms became more reluctant from the moment access to data became a legal requirement rather than a voluntary initiative."Unfounded claims and scepticism about science existed long before Internet chat groups. But the noticeable rise in online hostility has intensified around contentious topics, such as the coronavirus pandemic and climate change, alongside the weakening of gatekeepers to moderate content.For Ndior, dubious claims about pandemic countermeasures and vaccine safety highlighted the struggle between "balancing freedom of expression and the protection of public health and order." Efforts to remove social media content deemed false or risky for public health triggered accusations of censorship and the silencing of legitimate discussion, and in some cases, legal challenges.Concerns about attacks on the credibility of scientists and researchers have generated a flurry of academic papers in recent years and prompted professional communities to address ways to counter online attacks. Such unease extends to the generation that has grown up with digital media. Ndior points out that his students, some of whom research artificial intelligence regulation, hate speech, online surveillance and other controversial topics, hesitate to share their work or viewpoints online. "Many of them don't want to expose themselves too much because they don't feel ready to be faced with the backlash of other people criticising their research," he explains.Safer harbours for engagementLegal challenges against false or defamatory posts are not necessarily a solution — especially given the trans-jurisdictional nature of the Internet. Lawsuits also take time and money, and if they fail, can be counterproductive. Instead, Ndior advises scientists and researchers to be selective in how and where they communicate. Professional online platforms and moderated discussion spaces offer a better environment for engagement. He also highlights the value of more traditional formats, such as radio interviews and in-person discussions at schools, libraries and science fairs, which encourage dialogue with the public. "Personal contact makes a big difference," he says, while acknowledging that these approaches lack the global reach of social media.Ndior’s interest in digital law and online misinformation stems from his time as a doctoral student in international law in the early days of social media. He began analysing posts from governments and heads of state to determine if they could be considered official and thus used as evidence in litigation. His work attracted media attention and led to conference and speaking invitations. "This was not part of my plan," he says, "but this is what happens as a researcher — your interest is sparked by a certain event or phenomenon, and it suddenly becomes your focus."Ndior believes SCTT provides "a double benefit" for researchers, enabling them to connect with peers and interact with the public. "There is a wide community of people who are not in academia who are critical thinkers looking for opportunities to ask questions, to get information and contribute their ideas,” he says. "I really value that kind of feedback."Science|Business, which connects policymakers, academics and industry through its news reporting, analysis and events, is an SCTT partner.
Google Translate Gets Gemini AI for Smarter Translations and Real-Time Headphone Translation
Google is improving its translation features with Gemini integration, adding AI in search and the Google Translate app. Users can expect smarter and more natural text translations, with improvements to phrases with nuanced meanings. Idioms, local expressions, and slang will be translated with Gemini for improved accuracy, and Gemini will parse context instead of giving a literal word-for-word translation.
Q&A: An AI-Powered Way to Help Detect Brain Bleeds Faster
Timing and accuracy are vitally important when it comes to a brain bleed. Viz.ai, the company behind an artificial intelligence platform for disease detection and workflow optimization used in about 2,000 U.S. hospitals, wanted to streamline the process of identifying hemorrhages. Their solution,