Giving Perplexity less weight in AI search tracking can prevent distorted visibility scores without erasing useful competitive signals.
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In one of the most comprehensive investigations of its kind, researchers have taken a major step toward untangling the biology underlying suicide risk, moving the field beyond long-standing debates about whether suicidal behavior is simply a consequence of psychiatric illness. By integrating large-scale genetic data with cutting-edge approaches that map how genes act in specific brain cells and circuits, an international team has identified shared genetic liability that cuts across psychiatric diagnoses while also pinpointing druggable biological pathways that could one day inform prevention strategies. The study, published in Translational Psychiatry, offers a sweeping synthesis of how inherited risk factors may translate into molecular vulnerabilities, and it does so with a level of resolution that previous candidate-gene approaches could never achieve.
Suicide remains one of the leading causes of death worldwide, claiming more than 700,000 lives each year according to global health estimates. Despite decades of research, clinical prediction remains stubbornly imprecise, and biological models of suicidal behavior have historically been hampered by small sample sizes, inconsistent findings, and a tendency to conflate suicide risk with the psychiatric disorders with which it so often co-occurs. Major depression, bipolar disorder, schizophrenia, and substance use disorders all carry elevated suicide risk, yet most people with these conditions never attempt suicide, while some individuals who die by suicide have no diagnosed psychiatric illness at all. This paradox has long suggested to researchers that suicidal behavior may possess partially distinct biological underpinnings, and the new study set out to test that hypothesis with unprecedented statistical power.
At the heart of the work lies genome-wide association study methodology, or GWAS, which scans hundreds of thousands to millions of genetic variants spread across the entire genome in very large groups of people, comparing individuals who have experienced suicidal thoughts or behaviors with those who have not. Each variant is tested for statistical association with the outcome, and the results can be combined into polygenic risk scores, numerical summaries of a person’s inherited liability based on the cumulative small effects of thousands of variants. Because no single common variant exerts a large effect, the field depends on aggregating data across massive biobanks and cohorts, a strategy that has only become feasible in the past decade as datasets containing hundreds of thousands of individuals became available.
The research team took particular care to address a central confound: psychiatric disorders themselves are highly heritable, and any genetic signal linked to suicide could simply reflect inherited liability to depression or schizophrenia rather than to suicide specifically. To disentangle this, the investigators applied sophisticated statistical techniques, including genetic correlation analysis and conditional modeling, which allow researchers to ask how much genetic overlap remains between suicidal behavior and psychiatric conditions after accounting for their shared heritability. Their findings revealed a complex architecture. Substantial genetic correlations confirmed that suicide risk shares a meaningful fraction of its inherited basis with major psychiatric disorders, supporting the idea of a common pool of genetic liability. Yet the analyses also uncovered signals that persisted beyond what could be explained by those disorders, pointing to biological mechanisms that may be more specific to the emergence of suicidal thoughts and actions.
A key conceptual innovation of the study is its framing of “shared genetic liability” as a bridge between diagnoses. Rather than treating suicide as an epiphenomenon of any single disorder, the researchers modeled it as a transdiagnostic phenomenon, one whose risk emerges partly from inherited vulnerabilities that cut across diagnostic categories. This perspective aligns with a growing movement in biological psychiatry that emphasizes symptom dimensions and mechanisms over traditional diagnostic boundaries. From this vantage point, the genetic variants that raise risk for depression, PTSD, and schizophrenia may do so partly because they converge on shared molecular pathways, and those same pathways may govern an individual’s threshold for suicidal crises regardless of which diagnosis they carry.
Identifying genetic variants, however, is only the first step. A GWAS signal typically points to a genomic region rather than a specific causal gene or mechanism, since many associated variants fall in non-coding stretches of DNA that regulate gene activity from a distance. To translate statistical associations into biological insight, the team deployed a battery of functional genomics approaches. Transcriptome-wide association studies, known as TWAS, integrate gene expression data to identify genes whose genetically predicted expression levels are linked to suicide risk. Tissue and cell-type enrichment analyses then ask whether the implicated genes are unusually active in particular organs or cell populations. The results converged strikingly on brain tissue, and more specifically on neuronal populations, providing support for the idea that inherited suicide risk is mediated largely through gene regulation within the central nervous system.
The study’s most clinically provocative findings concern druggable biology. Using databases that catalog the protein targets of approved and investigational medications, the researchers examined whether genes implicated in suicide risk were over-represented among known drug targets. The analysis identified several classes of candidate targets, including components of neurotransmitter signaling systems, ion channels, and intracellular signaling cascades that are already the focus of pharmacological development for other conditions. This “druggable genome” approach, which has gained traction in genetics research across many diseases, offers a pragmatic shortcut for translational science: rather than waiting decades for entirely new therapeutic classes to be invented, researchers can prioritize existing or in-development compounds whose molecular targets intersect with genetically implicated pathways.
Among the pathways receiving attention in the analysis were those involving glutamatergic and GABAergic neurotransmission, the primary excitatory and inhibitory systems of the brain, which have repeatedly surfaced in studies of mood disorders and stress response. The endocannabinoid system and inflammatory signaling pathways also appeared in the convergent analyses, consistent with a broader literature linking immune dysregulation and allostatic load to suicidal ideation. Importantly, the authors emphasize that these findings identify hypotheses rather than ready-made treatments. Genetic association with a drug target does not guarantee that modulating that target will reduce suicide risk, and any therapeutic application would require extensive preclinical validation and carefully designed clinical trials. Nevertheless, the work provides a principled, data-driven map for where to look, replacing decades of largely failed candidate-gene speculation with a genome-wide evidence base.
The methodological rigor underpinning the study deserves emphasis, because the genetics of suicidal behavior has a troubled history. Early candidate-gene studies, which typically examined a handful of variants in genes related to serotonin signaling, produced findings that largely failed to replicate and in some cases were later shown to reflect statistical artifacts, small samples, and publication bias. The field has since undergone a reckoning, and modern efforts adhere to far stricter standards: preregistered analyses, very large and ancestrally diverse cohorts, replication in independent samples, and conservative multiple-testing corrections that demand genome-wide significance thresholds on the order of five times ten to the negative eighth power. The new study reflects these lessons, applying sensitivity analyses to guard against confounding from psychiatric comorbidity and examining whether findings generalize across ancestral groups, an issue of considerable importance given that most genetic discovery to date has been conducted in populations of European ancestry.
The implications for clinical practice, while still distant, are worth considering. Polygenic risk scores for suicide-related outcomes currently explain only a small fraction of individual differences in risk, far too little to be used as stand-alone screening tools, and their predictive performance is degraded in populations not represented in the discovery data. Researchers in the field are careful to stress that genetic risk is not destiny: suicidal behavior arises from an intricate interplay of inherited vulnerability, mental illness, trauma, substance use, social isolation, access to lethal means, and the availability of support. What genetics offers is not a crystal ball but a window into mechanism, and mechanistic knowledge is the raw material from which better biomarkers, risk stratification tools, and ultimately targeted interventions are built. In the near term, the druggable pathway findings are most valuable as a guide for laboratory science, directing neurobiologists toward molecular systems whose manipulation might alter suicide-relevant phenotypes in animal models and cellular assays.
Looking forward, the study signals where the field must go next. Sample sizes for suicide-specific genetic analyses remain smaller than those for major psychiatric disorders, and expanding representation across ancestries is essential if the benefits of this research are to be shared equitably. Integrating genetic data with longitudinal measures of life stress, neural imaging, and digital phenotyping may eventually allow researchers to model how inherited liability interacts with environmental triggers across the lifespan. Functional experiments in induced pluripotent stem cell-derived neurons and brain organoids could test whether the implicated genes genuinely alter stress-response circuitry or resilience. And randomized trials in patients carrying high genetic liability, though ethically and logistically complex, represent the logical endpoint of the druggable-target approach.
What the study ultimately delivers is a reframe. Suicide risk, in this analysis, is not an amorphous clinical outcome but a measurable, partly heritable trait whose genetic architecture is now being resolved with the same tools that have transformed our understanding of heart disease, diabetes, and schizophrenia. By demonstrating that shared genetic liability links suicide risk across diagnostic boundaries and by mapping that liability onto specific, pharmacologically tractable biology, the researchers have converted a long-standing clinical puzzle into a concrete research agenda. The path from genetic correlation to effective prevention is long, and no genetic finding will soon replace the human work of crisis intervention, connection, and care. But for a problem that has defied biological explanation for generations, the identification of shared, druggable mechanisms represents a genuine and hard-won advance, one that may shape suicide research for years to come.
Article Title: From shared genetic liability to druggable biology in suicide risk
Article References: Yao, K., Zhuo, C., Chen, X., Li, C., Song, H., Zou, J., & Tian, H. (2026). From shared genetic liability to druggable biology in suicide risk. Translational Psychiatry. https://doi.org/10.1038/s41398-026-04428-1
Image Credits: AI Generated
DOI: 10.1038/s41398-026-04428-1
Keywords: suicide risk, genetic liability, genome-wide association study, polygenic risk score, druggable genome, transdiagnostic psychiatry, TWAS, brain gene expression, glutamatergic signaling, psychiatric genetics, Translational Psychiatry
AI visibility tools track brand mentions and citations, but marketers need outcome data to know what those movements mean and what to do next.
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Modeled data now fills the gaps left by signal loss, and mistaking estimates for measurements can lead to costly budget misallocation.
The post When Attribution Looks More Precise Than The Data Behind It appeared first on Search Engine Journal.
Google is adding a pre-trained foundation model for tabular data to BigQuery, allowing enterprise teams to generate predictions from structured data without having to train and deploy a machine learning model from scratch.
The TabFM model, which has been developed by Google Research and is now available in preview, can perform classification and regression, two common types of predictive machine learning.
While classification is used to predict a category or outcome, for example, whether a customer is likely to churn or whether a transaction may be fraudulent, regression is typically used to predict a numerical value, such as the expected value of a customer or the amount of a future claim.
Traditionally, enterprise teams in a typical machine learning workflow would need to train and deploy a separate ML model for each use case they want to pursue. In contrast, TabFM uses historical data as in-context examples to make predictions, the company wrote in a blog post.
That means teams can provide labeled historical data, where the outcome is already known, along with the new data they want to make predictions about, and use BigQuery’s AI.PREDICT function to generate those predictions.
The function uses the historical data as examples to identify patterns and then applies what it has learned to the new data, without requiring teams to separately train and deploy a machine learning model.
TabFM, according to Google, can also automatically work with data expressed as categories, such as customer type or product category, as well as datasets with missing values, reducing the preparation required before making predictions.
Further, the company is providing an AI.EVALUATE function to assess the model’s performance on the prediction task. Teams can use it to compare TabFM’s predictions with the known outcomes in their data and measure how accurately the model performed, Google wrote, adding that this gives teams a way to evaluate whether the predictions are useful for a particular use case.
For enterprises, the addition of TabFM could make predictive analytics more accessible to teams that do not have extensive ML expertise, while reducing some of the engineering and infrastructure work involved in developing and maintaining conventional models, said Amit Kumar Jena, AI development head at IT consulting firm Kanerika.
“Someone who knows SQL can now generate a classification or regression prediction directly against tables already in BigQuery, using the two new functions, without training, tuning, or deploying a model first, and without moving data out to a separate ML platform, which also simplifies governance and cuts the infrastructure cost of running a parallel platform,” Jena explained.
That, according to Manoj Chandra Jha, principal analyst at Nord-IQ Research, translates to TabFM collapsing the traditional multi-persona workflow, stretching from an analyst, a data scientist, and a ML engineer, into a single SQL call, thereby potentially reducing headcount requirements.
However, the new model has limitations that enterprises should be aware of before replacing conventional ML workflows.
Google’s own guidance and documentation still point to XGBoost, which is a widely used machine-learning algorithm for making predictions from structured or tabular data, and custom models for very large datasets, datasets with more features than TabFM’s current limit of 20 feature columns.
“Use traditional models like XGBoost when you have very large historical datasets, require complete control over custom hyperparameter tuning, have a high number of features that exceed current limits of TabFM, or need feature-importance explainability, i.e., which of the input features contributed most to the prediction,” the company wrote.
More so because, in large datasets and high-frequency workloads, the cost advantage can diminish, analysts pointed out.
While TabFM can reduce the cost of setting up a model-training pipeline, Google’s plans to move TabFM to token-based pricing on top of standard BigQuery charges starting October 30, 2026, could result in higher costs for workloads that generate predictions at scale compared with a trained and cached model, Jena said.
Enterprises should therefore pilot TabFM on their own workloads, run the numbers based on their actual query volumes, and compare the costs with their existing ML infrastructure before shifting significant parts of their ML budgets to the service, Jha advised.
In other words, that makes TabFM more of a workload-specific tool than a wholesale replacement for traditional ML models, Jha said, adding that it is better suited to workloads such as ad hoc analysis, experimentation and lower-frequency business use cases.
An analysis of dozens of strange objects captured in official U.S. government videos has revealed new insights—and challenges—involving the study ofunidentified anomalous phenomena (UAP).
Since May 8, 2026, the U.S. Department of War has released more than 100 videos that reportedly capture evidence of unknown objects in U.S. airspace and near American military operations around the world.
Now, a preprint paper by researchers Jacob Haqq-Misra of Blue Marble Space and Ravi Kopparapu of NASA’s Goddard Space Flight Center examined 112 videos released this year under the Presidential Unsealing and Reporting System for UAP Encounters (PURSUE).
The researchers, both of whom serve on the UAP Science Advisory Council to the U.S. government, say their study highlights significant limitations on analysis of the purported UAP videos due to missing or redacted sensor data, which prevents civilian researchers from having enough of the tools necessary to determine whether seemingly fast-moving objects in the videos actually display anomalous velocities.
Kopparapu and Haqq-Misra’s new study, which has not yet undergone peer review, highlights a fundamental problem that has impeded analytical efforts involving UAP videos collected by U.S. military personnel: that the apparent speed of an object suggested by its movement across a camera’s field of view is not necessarily an accurate indication of its real physical velocity.
In an email to The Debrief, Haqq-Misra explained that while the new study is among the first to explore the limitations on what can be discerned about these objects based on analysis of the current batch of PURSUE releases, the underlying problem is not a new one for researchers tackling the UAP problem.
“I think our conclusion will come as no surprise to people in the aviation, aerospace, and intelligence communities who have already been closely following UAP disclosure,” Haqq-Misra told The Debrief, though expressing that the conclusions he and Kopparapu present “may not be fully appreciated in the broader public discussion.”
“I get lots of questions from friends, family, and other scientists about my opinion of the government ‘UAP disclosure’ data,” Haqq-Misra said. “A lot of people haven’t even looked at the data and may assume that there is more available than we really are getting.”
In terms of what data has been made available to the public since May, Haqq-Misra says the limitations are obvious.
“Most of the useful quantities are redacted,” he said. “It is also not immediately obvious that what appears to be a fast-moving object in an infrared sensor video might be a mundane object (like a bird) much closer to the camera.”
The videos Haqq-Misra and Kopparapu analyzed, all of which are currently available at the U.S. Department of War website, were acquired by targeting pods and other turret-mounted camera systems aboard military aircraft or similar government platforms. The collection includes infrared and electro-optical imagery, much of it showing unidentified objects either crossing the camera’s field of view or being actively tracked by the sensor.
Haqq-Misra and Kopparapu reviewed all 112 videos to determine what kinematic information could be extracted from them. At its simplest, analysts can measure how quickly an object moves across a video in pixels per frame. However, the problem arises when it comes to converting that apparent motion into an actual velocity—a process that requires several additional pieces of information.
In an email to The Debrief, Kopparapu, who expressed that his views are based on his own independent analysis and do not reflect those of NASA, said there was one primary form of data that, if it were to accompany future video releases, could make them far more useful to scientists working to understand the performance characteristics of the objects they convey.
“Based on our preliminary mathematical analysis, perhaps the single most important missing quantity is the range; the distance between the object and the observing platform, ideally measured over time,” Kopparapu told The Debrief. “Without range, something moving rapidly across the image could be a small nearby object moving relatively slowly or a much larger, distant object moving extremely fast.”
Additional information Kopparapu says would be very useful would include details about the camera’s field of view, as well as “the motion of the observing platform and viewing geometry.”
In their recent paper, Kopparapu and Haqq-Misra detail the specific measurements that would be required to determine an object’s velocity. However, Kopparapu expressed to The Debrief that “Some apparent anomalies could even arise from sensor or imaging effects,” adding that without the additional data outlined above, “there is simply not much more that can be concluded scientifically.”
Haqq-Misra told The Debrief that one of the goals of the UAP Science Advisory Council is to make recommendations about what kinds of data future PURSUE releases might be able to include that would enable independent scientists to conduct more accurate and meaningful kinematic analyses.
“We are hopeful that our recommendations based on analyses of sensor videos like these will lead to additional release of data that may even help us resolve some cases,” Haqq-Misra told The Debrief, echoing Kopparapu’s sentiments about the importance of having access to information about the object’s range from the camera system at the time the video was captured.
“Knowing the distance from the camera to the object is essential,” Haqq-Misra said. “This information might be under the redacted black boxes of some of the videos, so unredacted videos would be best.”
The question of whether information currently redacted from government UAP videos can be safely released to scientists without compromising U.S. national security—as well as how much of that data should be released—remains a sticking point between Pentagon officials and the broader scientific community.
For researchers like Haqq-Misra, one solution might involve having some form of verifiable U.S. government record that certifies certain portions of the redacted information, without necessarily conveying that in the videos themselves in ways that could compromise sensitive information about the systems used to obtain the footage.
“Even just telling us the distance on an official government document would help!” Haqq-Misra told The Debrief.
“The flight trajectory of the aircraft with the observing camera is another important piece of data,” Haqq-Misra added. “In some cases, this data may simply be lost, but if flight logs exist for any of these cases, that would also help.
Although Haqq-Misra adds that technical information about the specific cameras used to capture a given video would “make the data even more useful,” the U.S. Department of War regulates much of this technical data under strict security classification, export control, and Controlled Unclassified Information (CUI) policies. Such restrictions are imposed to protect critical capabilities like sensor resolution, focal lengths, tracking algorithms, and other capabilities or technical properties that might potentially be exploited by foreign adversaries if they were revealed publicly.
While U.S. military concerns over the release of such information is often warranted, without access to it, the nature of how some military systems operate can make it difficult—if not impossible—to make meaningful determinations about the objects detected.
One example includes objects that are being actively tracked while they are filmed. By its design, a targeting system can move its camera to keep an object near the center of the frame, making the subject’s apparent movement approach zero, even if it is traveling rapidly through the environment. The resulting imagery can create the illusion of a stationary object and becomes unhelpful without access to additional information about the object’s movement.
Another example where the appearance of an object’s movement on video can differ significantly from its actual speed appears in the well-known Navy “GOFAST” video. Haqq-Misra and Kopparapu note that this footage was once widely interpreted as evidence of an object moving at extraordinary speed near the ocean surface. However, subsequent analysis using available telemetry showed that much of its apparent motion resulted from parallax generated by the observing aircraft, with its calculated motion ultimately being consistent with wind-borne drift—in other words, the object was most likely a balloon.
The important difference between the GOFAST video and those included in recent PURSUE releases, the researchers note, comes down to the amount of additional information that was available.
“In the GOFAST case, sufficient information was available to constrain the object’s motion relative to the observer and assess it in relation to reported wind conditions,” Haqq-Misra and Kopparapu write in their recent study. Notably, following its initial unauthorized release in 2017 along with two other historical U.S. Navy videos of alleged UAP, the Pentagon eventually determined that GOFAST did not reveal any sensitive information that compromised U.S. national security.
“After a thorough review, the department has determined that the authorized release of these unclassified videos does not reveal any sensitive capabilities or systems, and does not impinge on any subsequent investigations of military air space incursions by unidentified aerial phenomena,” an April 2020 Pentagon statement read. Seemingly, this case would appear to demonstrate that in at least some cases, enough additional information could remain unredacted in future UAP video releases to allow similar technical determinations to be made.
Another example Kopparapu and Haqq-Misra look at in their paper involves a video in the recent PURSUE releases designated DOW-UAP-PR113, which features an infrared video reportedly recorded over the western United States in 1996.
The footage depicts what appears to be a compact dark object (or possibly two moving in very close proximity) rapidly crossing the camera’s field of view. Based on Kopparapu and Haqq-Misra’s measurements, the object travels approximately 142 pixels per frame, equivalent to roughly 2.2 frame widths per second, and remains directly visible for only about one-tenth of a second.
One thing that makes the video unique among the PURSUE releases is the presence of markings in the imagery, which the researchers say allows them at least a limited ability to reconstruct an approximate angular scale and horizontal field of view. However, like in other cases, too little information is present to be able to make a complete determination about the object’s motion and capabilities.
For Kopparapu, circumstances like these demonstrate why he advises against drawing conclusions based on what can often appear to be remarkable speed or other capabilities in the PURSUE videos, yet without ruling them out necessarily as being mundane.
“With such limited data, I would urge people to avoid both extraordinary conclusions and outright dismissal,” Kopparapu told The Debrief. “Something can look extremely anomalous when we are seeing only a small part of the information needed to understand it.”
Fundamentally, Haqq-Misra and Kopparapu believe that the videos currently made available under the PURSUE initiative cannot demonstrate—nor can they necessarily be used to exclude—the presence of anomalous kinematics displayed by the alleged UAP they contain.
This issue extends beyond just the PURSUE releases, however, since similar limitations can arise even with imagery captured by conventional cameras, whose technical capabilities are easily discernible. For instance, a single camera primarily measures angles, meaning some aspects of an object’s three-dimensional motion simply cannot be reconstructed based on that imagery alone.
To overcome such problems, Haqq-Misra and Kopparapu point to the use of multi-sensor approaches by independent research efforts.
In terms of what kinds of scientific observations might constitute a “genuine” UAP in future detections, Haqq-Misra says he and his colleagues with the UAP Science Advisory Council are already providing recommendations for the kinds of sensor platforms that could be leveraged to capture better UAP imagery and other data in the future.
“We have already provided recommendations for sensors that could be deployed to collect better quality data for understanding these objects,” Haqq-Misra told The Debrief. “An ideal observatory would be a multi-modal and multi-site observatory, leveraging video observations from infrared to visible and including other modalities such as radio, radar, and audio data collection.”
“The Galileo Project at Harvard is an example of a privately funded effort following this experimental design,” he adds.
Overall, the researchers say that while based on their analysis “the PURSUE sensor videos in their present form cannot fully resolve unidentified cases or conclusively indicate anomalous velocities,” the new research presents a meaningful early step toward identifying future strategies that may help researchers obtain better, more useful data.
“Reality takes time,” Kopparapu told The Debrief, “but fallacy does not.”
“Careful scientific investigation, accompanied by access to the underlying data, is the only reliable way to make progress on these cases,” he adds.
Kopparapu and Haqq-Misra’s recent paper, “Limits on Velocity Recovery from the PURSUE Sensor Videos,” appeared on the arXiv.org preprint server.
Micah Hanks is the Editor-in-Chief and Co-Founder of The Debrief. A longtime reporter on science, defense, and technology with a focus on space and astronomy, he can be reached at micah@thedebrief.org. Follow him on X @MicahHanks, and at micahhanks.com.
The Met Office has quietly axed its dedicated wind and storms chapter from its annual climate report – just as decades of data show UK wind speeds are falling. Funny that, says the Daily Sceptic's Environment Editor.
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Clarity now splits AI citation queries into branded and non-branded. The two mean different things, and only one counts as discovery.
The post Microsoft Clarity Now Separates Branded And Non-Branded AI Citations appeared first on Search Engine Journal.
Apple Health data can now move between the Health app and Google Health on an iPhone. The connection lets Google Health import supported information recorded by an iPhone, Apple Watch, and compatible apps. The latest Google Health update can also send supported Fitbit information to Apple Health.
This two-way connection is useful for people who wear a Fitbit but prefer Apple’s Health app as the central place for viewing activity, sleep, steps, and other wellness information. It also reduces the need for third-party synchronization apps.
The feature requires a recent version of Google Health. Google began introducing support for sending data back to Apple Health with Google Health version 5.05. Because app updates can roll out gradually, the new sharing options may not appear until the latest version reaches your account.
The Fitbit app became Google Health in 2026. Existing Fitbit users should find that the installed app has changed its name and design after updating. Fitbit accounts must also be moved to an adult Google Account to use the redesigned service.
Google says the Apple Health connection requires iOS 16.4 or later. The iPhone must have internet access while the connection is being created.
Settings > General > About > iOS Version
Install the latest compatible iOS update if the phone is running an older release.
Settings > General > Software Update
Next, confirm that Google Health is current. Version 5.05 or later is required for the newer option that sends supported Google Health and Fitbit information to Apple Health.
App Store > Profile Picture > Available Updates > Google Health > Update
If Google Health does not appear under Available Updates, search for the app manually. The App Store page may show an Update button if a newer release is available.
App Store > Search > Enter Google Health > Google Health (Fitbit)
People who previously used Fitbit should sign in with the Google Account connected to their Fitbit information. Creating a different account may produce an empty Google Health profile.
Connecting the apps allows Google Health to read supported information from Apple Health. This can include data collected by the iPhone, Apple Watch, and other apps that already share information with Apple Health.
Google Health > Connections > Apps and Services > Apple Health > Get Started
Depending on the Google Health version and interface shown on the iPhone, Connections may appear behind the profile icon.
Google Health > Profile Icon > Partner Apps > Apple Health
Google Health will display a consent screen explaining the connection. Read the information before continuing.
Sync Google Health With Apple Health > Agree
The iPhone will then display Apple’s health permission screen. This screen lets you decide which information Google Health can read.
Apple Health Permission Screen > Turn All Categories On > Allow
You do not have to share every available category. Selecting individual permissions provides more control over sensitive health information.
Apple Health Permission Screen > Select Each Health Category > Allow
Google Health can import supported information such as steps, distance, active energy, exercise records, routes, sleep sessions, sleep stages, heart rate, resting heart rate, heart rate variability, blood oxygen, respiratory rate, body temperature, weight, body fat percentage, water, nutrition information, and some cycle-tracking data.
Available categories depend on the devices and apps connected to Apple Health. An iPhone may provide steps and walking information, while an Apple Watch can contribute heart rate, workouts, sleep, and other measurements supported by the model.
After the permissions are approved, Google Health should confirm that the connection is complete.
Google Health Confirmation Screen > Done
New Apple Health information should begin appearing automatically in Google Health. Google has said that imported data can be used across relevant Google Health metrics, although not every external data type is used for every calculation.
Google Health > Health > Select a Metric
Google Health may display information from several connected sources together. Its value for a metric can differ slightly from the number shown by Apple Health because the apps may process available data differently.
Google Health version 5.05 adds the reverse direction that many iPhone and Fitbit users previously lacked. Supported information recorded through Fitbit can now be written into Apple Health.
Before enabling this option, make sure the Fitbit is properly connected to Google Health. Fitbit devices normally synchronize automatically throughout the day when Bluetooth is available and the wearable is near the iPhone.
Google Health > Profile Icon > Devices > Select Your Fitbit
The device page should show the wearable, its battery level, and its most recent synchronization time. Opening Google Health can also prompt a nearby Fitbit to transfer recently collected information.
Once the wearable is updating Google Health correctly, open the Apple Health connection settings.
Google Health > Profile Icon > Partner Apps > Apple Health
The interface may use Apps and Services instead of Partner Apps on some versions.
Google Health > Connections > Apps and Services > Apple Health
Follow the prompts for sharing Google Health information with Apple Health. The iPhone may display separate permissions for data that Google Health can read and data that it can write.
Apple Health Permission Screen > Allow Google Health to Write > Select Health Categories > Allow
Google says supported information can include exercise records, steps, sleep, vitals, and other health data. The exact selection can vary according to the Fitbit model, region, Google Health version, and information stored in the account.
Selecting every category is convenient, but it can create overlapping sources when an iPhone, Apple Watch, and Fitbit record the same type of activity. Choose only the categories you want Google Health to add.
A person who uses a Fitbit mainly for sleep, for example, can allow sleep information while leaving steps disabled. Someone who wears a Fitbit instead of an Apple Watch may prefer to enable steps, workouts, heart rate, sleep, and other supported categories.
Apple Health keeps permission controls on the iPhone. You can change them after the first connection without deleting either app.
Health > Profile Picture > Privacy > Apps > Google Health
The page shows the categories Google Health is permitted to read or update. Turn off any category you no longer want to share.
Some iOS versions provide the same controls through the Sharing tab.
Health > Sharing > Apps and Services > Google Health
Turning off a permission stops future sharing for that category. It does not necessarily remove information that has already been written to Google Health or Apple Health.
The Google Health app also provides controls for the connection.
Google Health > Connections > Apps and Services > Connected > Apple Health
If Google Health displays a message that new data types are available, open the connection and approve the additional categories you want to use. Newly supported information is not automatically shared without permission.
An iPhone, Apple Watch, and Fitbit can record similar information at the same time. Apple Health keeps each source attached to its data and uses a priority order when presenting combined measurements.
You can inspect the sources contributing to a category and choose which one Apple Health should prefer.
Health > Search > Activity > Steps > Data Sources and Access
The screen shows the apps and devices that provide step records. If Google Health, an Apple Watch, and the iPhone all appear, you can adjust their priority.
Health > Search > Activity > Steps > Data Sources and Access > Edit > Drag Sources Into the Preferred Order > Done
Move the source you trust most toward the top. Apple Health generally gives higher priority to sources placed above others when similar records overlap.
Repeat the same process for other categories if their totals do not look right. Exercise, sleep, active energy, and distance can each have their own sources and permissions.
Synchronization is not always immediate. A Fitbit first sends its information to Google Health, and Google Health then writes supported records to Apple Health. Bluetooth availability, Background App Refresh, internet access, and battery-saving behavior can affect this process.
Begin by opening Google Health with the Fitbit close to the iPhone. Wait for the device to complete its normal synchronization before checking Apple Health.
If Apple Health information is missing from Google Health, confirm that the connection remains active.
Google Health > Connections > Apps and Services > Apple Health
If Google Health displays an error or a request for new permissions, follow the message shown on the screen.
Background App Refresh can help Google Health update when the app is not open.
Settings > General > Background App Refresh > Google Health > On
Bluetooth must remain available for a Fitbit to communicate with the iPhone.
Settings > Bluetooth > Bluetooth On
If the connection continues to fail, disconnecting and reconnecting Apple Health may restore the permission request.
Google Health > Connections > Apps and Services > Apple Health > Disconnect > Confirm Disconnect
Disconnecting the service does not automatically remove every permission already stored by Apple Health. Review the Google Health entry in the Health app if you want to remove those permissions as well.
Health > Profile Picture > Privacy > Apps > Google Health > Turn Off All
After reconnecting the apps, allow time for new records to appear. Previously stored information may not transfer in exactly the same way as activity recorded after the connection was enabled.
You can inspect an individual entry to confirm whether it came from Google Health, a Fitbit, an Apple Watch, or the iPhone.
Health > Search > Select a Health Category > Show All Data > Select the Latest Entry
The post How to Sync Apple Health Data With Google Health on Your iPhone appeared first on AppleMagazine.
Apple lawsuit against OpenAI has reached its first major procedural challenge, with the ChatGPT developer asking a US federal judge to dismiss allegations that it acquired or used trade secrets taken by former Apple employees.
OpenAI submitted its 31-page dismissal motion on Wednesday in the US District Court for the Northern District of California. The filing argues that Apple has not adequately identified protectable trade secrets or alleged facts showing that OpenAI misappropriated confidential information.
Apple filed the case on July 10 against OpenAI, its io Products hardware operation, former Apple Vice President Tang Yew Tan, and former electrical engineer Chang Liu. The complaint accuses the defendants of obtaining information connected to Apple’s product development, manufacturing, components, testing, and supply chain operations.
The allegations have not been proven, and OpenAI denies possessing or seeking Apple’s trade secrets. US District Judge Edward Davila must now decide whether Apple’s complaint contains enough factual detail for the litigation to continue.
OpenAI’s motion focuses initially on how Apple described the information it claims was stolen. According to OpenAI, the complaint relies on generalized product-development categories without identifying the protected information with enough precision.
Trade secret plaintiffs face a difficult balance at the beginning of litigation. Apple must describe the protected material sufficiently to establish a plausible legal claim, but revealing the material publicly could expose the same information it is trying to protect.
Sensitive details can later be handled under seal or through a protective order. At the dismissal stage, however, Apple must still provide enough information for the court to distinguish genuine trade secrets from general engineering knowledge, employee experience, or skills acquired through years of work.
OpenAI argues that Apple has not crossed that threshold. Its motion says the complaint fails to establish ownership of a protectable trade secret and does not plausibly allege that OpenAI acquired, disclosed, or used such information.
The filing also disputes the commercial premise behind Apple’s accusations. OpenAI says it is developing products that are different from Apple’s hardware and has no need or desire to use the iPhone maker’s confidential material.
That position does not settle whether information was taken. A company can potentially misappropriate trade secrets even when its eventual product has a different design. The court’s immediate task is narrower: determining whether Apple has alleged facts that could support its claims if those facts are later proven.
Apple’s complaint centers partly on Chang Liu, who spent eight years working as a senior system electrical engineer at the company before joining OpenAI in January 2026.
Apple alleges that Liu retained an Apple-issued computer and accessed confidential technical information after leaving. It claims he exploited residual authentication access on multiple occasions to obtain internal documents while already working for OpenAI.
OpenAI presents a different explanation. It says Apple employees contacted Liu after his departure and asked him to help locate files needed for ongoing Apple work. The company published redacted messages that it says support this account.
OpenAI argues that Apple’s systems and policies allowed personal and corporate information to become intermingled through iCloud accounts. Former employees, it says, could retain access to Apple files without requesting it or realizing that access remained active.
The motion maintains that Apple cannot treat the foreseeable effects of its own access-management practices as proof that former employees intentionally stole data. Apple, in contrast, alleges that its records document repeated access to restricted information rather than an accidental discovery of lingering permissions.
These competing accounts are likely to become central if the case enters discovery. Authentication logs, downloaded files, device records, internal communications, and employment timelines could establish whether access was deliberate, what information was obtained, and whether it reached anyone working on OpenAI products.
A dismissal motion normally tests whether the complaint states a legally sufficient claim. It is not a trial, and the judge is not expected to resolve every factual disagreement using competing evidence at this point.
Apple separately accuses Tang Tan, OpenAI’s chief hardware officer, of seeking confidential information from Apple employees during recruiting conversations.
Tan spent more than 24 years at Apple and worked on products including the iPhone, Apple Watch, AirPods, and iPod. He later helped establish io Products with former Apple design chief Jony Ive and other collaborators. OpenAI acquired the hardware company as part of its effort to create a family of devices built around artificial intelligence.
Apple alleges that Tan used confidential project names, retained internal materials, and encouraged job candidates to discuss unreleased components or bring physical parts to interviews. OpenAI rejects the claim that these interactions formed a coordinated effort to collect Apple secrets.
OpenAI says Tan repeatedly instructed employees that confidential information from previous employers could not be brought into its organization. It also offers an alternative explanation for an Apple offboarding document found in his possession, arguing that Tan retained it to help incoming employees comply with Apple’s departure procedures.
Apple’s position is that the document and recruiting communications must be examined alongside the other alleged conduct. It describes the events as part of a connected campaign rather than isolated employment or administrative issues.
The dispute comes during intense competition for engineers capable of developing consumer AI hardware. Apple alleges that more than 400 former employees have moved to OpenAI, although hiring people from a competitor does not by itself establish trade secret misappropriation.
A company must generally connect recruitment to the acquisition, disclosure, or use of protected information. OpenAI is relying heavily on that distinction, presenting Apple’s lawsuit as an attempt to respond to employee departures and its difficulties competing for AI talent.
OpenAI’s dismissal request arrives shortly after Apple asked the court for a preliminary injunction. Apple wants restrictions imposed while the lawsuit proceeds, arguing that it could suffer harm if its confidential information is used during OpenAI’s hardware development.
The requested relief would prevent OpenAI and the individual defendants from acquiring, accessing, using, or disclosing the alleged trade secrets. Apple has also requested expedited discovery and forensic examination of relevant devices and accounts.
OpenAI says those restrictions are unnecessary because it does not possess or want Apple’s protected information. It has described Apple’s request as based on false information and has offered to cooperate in addressing specific concerns.
The two companies also disagree over their communications before the lawsuit. Apple initially said its concerns had been raised with OpenAI months earlier. OpenAI responded that Apple’s outside lawyers contacted the wrong person after confusing names and incorrectly described a conversation with OpenAI’s general counsel.
OpenAI says Apple did not present the specific accusations before filing the case and that the companies had previously discussed resolving any access issues. Apple has since provided additional information in support of its injunction request, including allegations involving other former employees.
These disputes have moved beyond ordinary court filings. OpenAI published messages and emails on its website to support its version of events, calling Apple’s lawsuit aggressive and poorly investigated. Apple has primarily presented its account through formal legal submissions.
Judge Davila could dismiss some or all of Apple’s claims, deny the motion, or allow Apple to amend its complaint with more specific allegations. Even a dismissal would not necessarily end the case if permission to revise the complaint is granted.
The next scheduled public test is an October 1 hearing covering Apple’s preliminary injunction and request for early discovery. That proceeding may determine whether OpenAI faces immediate limits on its hardware work before the underlying trade secret claims receive a full evidentiary review.
The post Apple Lawsuit: OpenAI Seeks Dismissal of Trade Secrets Case appeared first on AppleMagazine.
Apple Intelligence files can occupy several gigabytes of Mac storage because locally downloaded models power text generation, summaries, image creation, Siri requests, and other on-device features. Removing these files can recover valuable space when Apple Intelligence is no longer needed.
Apple lists 7GB of available device storage among the requirements for Apple Intelligence, but the amount displayed on a Mac can be higher. Additional languages, image-generation resources, updated models, and temporary assets can increase the total over time.
The files are managed by macOS and do not appear as ordinary documents that can be moved to the Trash. Apple provides a supported removal method through System Settings: turning off Apple Intelligence causes macOS to remove the downloaded on-device models.
This also disables features that depend on Apple Intelligence. Writing Tools, summaries, Image Playground, Genmoji, intelligent Shortcuts actions, and Apple Intelligence functions inside Siri may become unavailable. Regular Mac applications and the rest of macOS continue working normally.
Recording the available storage before removing the files makes it easier to confirm how much space macOS recovered afterward.
Apple menu > System Settings > General > Storage
Allow the Storage page to finish calculating its categories. The process may take a moment when the Mac contains many applications, local files, connected volumes, or several user accounts.
Apple Intelligence may appear as a separate item within the macOS storage category. Depending on the installed macOS version, some related assets may instead be counted under macOS or System Data.
Note the amount of available storage displayed near the storage graph. Comparing the total free space before and after the procedure is usually more reliable than watching one category because macOS can reclassify system-managed files while deleting them.
Apple menu > System Settings > General > Storage > All Volumes
This view is useful when the internal drive contains several APFS volumes sharing the same physical storage. The available capacity belongs to the shared container rather than remaining permanently assigned to one volume.
The storage used by Apple Intelligence is different from the memory, or RAM, used while features are running. Closing applications may reduce RAM consumption, but it does not delete the models stored on the internal SSD.
Apple Intelligence is available on compatible Macs with Apple silicon. Intel-based Macs do not support the complete Apple Intelligence system and should not contain the same downloadable model collection.
Apple menu > System Settings > Apple Intelligence & Siri > Turn off Apple Intelligence > Turn Off Apple Intelligence
The second Turn Off Apple Intelligence command confirms the change. The exact appearance may vary between macOS versions, with the first control presented as either a switch or a button.
Apple says that turning off Apple Intelligence removes the on-device models. There is no separate model manager, Delete Models button, or supported Finder location from which users must remove the files manually.
Keep the Mac connected to power while macOS completes its background storage work. The Apple Intelligence setting changes immediately, but the Storage page may not display the recovered capacity at once.
macOS manages these models through its system asset service. The operating system must identify the associated packages, release them, and recalculate the storage categories. The process can therefore continue after the Apple Intelligence switch has already moved to the off position.
Turning off only individual features does not remove the complete model collection. Disabling notification summaries, Writing Tools, ChatGPT access, or Siri suggestions leaves Apple Intelligence itself active.
A Screen Time restriction also serves a different purpose. It can prevent access to image generation, Writing Tools, and other functions without turning off the underlying intelligence system. Use the Apple Intelligence control inside Apple Intelligence & Siri when the objective is recovering storage.
After macOS has had time to process the removal, return to the Storage page.
Apple menu > System Settings > General > Storage
Wait for the storage graph and categories to finish updating. Compare the new amount of available space with the figure recorded before Apple Intelligence was turned off.
The Apple Intelligence entry may disappear completely, display a smaller amount, or become part of an adjusted macOS or System Data category. The increase in total available storage provides the better indication that the local models were removed.
If the figures have not changed, restart the Mac to refresh its system services and storage calculation.
Apple menu > Restart > Restart
Sign back in, allow the Mac to remain idle for a while, and open the Storage page again.
Apple menu > System Settings > General > Storage
Restarting does not delete the models by itself. Apple Intelligence must remain disabled while macOS completes the removal.
Emptying the Trash is not part of this procedure because macOS does not place Apple Intelligence models there. The Trash affects only files that a user or application has already marked for deletion.
First, verify that the complete Apple Intelligence system is off rather than one related feature.
Apple menu > System Settings > Apple Intelligence & Siri > Apple Intelligence
The control should display Apple Intelligence as disabled. If macOS presents another confirmation request, complete it and leave the feature off.
Storage information can take time to settle because macOS also manages caches, local snapshots, temporary update packages, and purgeable files. These categories can change while the Mac is idle, making an immediate before-and-after comparison misleading.
Installing the latest stable macOS update can correct storage reporting or asset-management problems.
Apple menu > System Settings > General > Software Update
Save current work before installing an update. The Mac may need to restart, and the installation temporarily requires additional free storage.
Beta versions of macOS require extra caution. Development releases may download experimental models, retain previous model revisions, or temporarily change the Apple Intelligence controls. A procedure confirmed for the public version of macOS may not produce the same storage behavior in a beta.
Avoid following online instructions that require starting in macOS Recovery, disabling System Integrity Protection, mounting protected volumes, and deleting folders with names referring to MobileAsset or generative models.
System Integrity Protection prevents applications and administrator accounts from modifying protected parts of macOS. Disabling it weakens that protection and creates the possibility of deleting the wrong asset, leaving macOS with an incomplete model installation, or causing the system database to disagree with the files present on the drive.
A storage utility can help identify how much space system files occupy, but it should not require disabling macOS security to remove Apple Intelligence. If the documented switch leaves a large allocation after a restart and sufficient processing time, Apple Support can investigate the asset database without exposing the Mac to an unsupported system modification.
Turning Apple Intelligence on again reverses the storage saving.
Apple menu > System Settings > Apple Intelligence & Siri > Turn On Apple Intelligence
The Mac will download the required local models again. Apple recommends leaving the device connected to Wi-Fi and power while the model download completes.
The post How to Remove Apple Intelligence Files From Your Mac appeared first on AppleMagazine.
If you run CRO or SEO, the oldest mistake in your discipline just came back wearing a new referrer string. Here's the fix.
The post AI Referrals Are Recreating The Oldest Mistake In Conversion Optimization appeared first on Search Engine Journal.
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