TransUnion's survey of 100 marketing leaders shows AI search blind spots mirror walled garden measurement gaps. Frame GEO as a company-wide problem to get funded.
The post What TransUnion’s AI Research Reveals About How Brands Actually Get Cited appeared first on Search Engine Journal.
AI's impact outpaced our ability to measure it in H1 2026. Indig's citation data, agent share shifts, and the intelligence-versus-agency split explain what changes next.
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A Canadian hacker has admitted being part of a group responsible for several major cyberattacks. Connor Riley Moucka pleaded guilty to being part of a coterie of hackers that hit 165 organizations, resulting in the theft of customer records and the extortion of millions of dollars.
Industry sources have identified Moucka as one of the main players in attacks on data hosted by cloud data warehouse Snowflake. Companies affected by the hacks include the likes of AT&T, Ticketmaster and the Neiman Marcus Group.
He worked with two other hackers: John Edward Binns and Cameron John Wagenius. Binns was not in US custody as of April 2026, while Wagenius, going by the name of Kiberphant0m, was arrested in January 2025 and pleaded guilty in July that year
Moucka and other members of the group used stolen login credentials to compromise data belonging to at least 165 customers of a US-based software-as-a-service company. This unauthorized access was used to steal billions of sensitive customer records and download terabytes of information,
“Connor Moucka hacked over 150 companies and organizations, obtained extremely sensitive information, and extorted the victims for millions of dollars. Today’s guilty plea serves as a reminder to all cybercriminals, regardless of where they live, that they cannot hide behind a wall of anonymity. You will be found and brought to justice,” said assistant attorney general A. Tysen Duva of the Justice Department’s Criminal Division
The trial is the result of a coordinated worldwide action against the Snowflake group. The investigation was led by the FBI but benefited from contributions from the Royal Canadian Mounted Police, the Australian Federal Police, Spain’s Guardia Civil, the Security Service of Ukraine and the Turkish National Police.
This article first appeared on CSO.
Citations, sentiment, and LLM referral traffic make poor KPIs. Here are the AI visibility metrics that actually connect to leads, pipeline, and revenue.
The post AI Visibility Measurement: What To Track & What To Ignore appeared first on Search Engine Journal.
Databricks is adding an agentic code conversion capability to its Lakebridge toolkit, using Genie Code to provide its new customers another way to move from rival data warehouses to its lakehouse built atop Databricks SQL.
The new capability, called the agentic code converter, uses AI subagents to analyze source code, convert files in parallel, validate the translated SQL, and iteratively retry failed sections within a migration project, while allowing developers to define reusable migration rules for enterprise-specific SQL constructs.
The Lakebridge migration tooling already includes multiple transpilation engines, including the pattern-based BladeBridge technology Databricks acquired in 2025, the compiler-based Morpheus, and the LLM-powered Switch transpiler.
The key difference between the transpilers and the new agentic code converter, according to analysts, lies in how each of them executes conversion and how that could benefit enterprises.
“Deterministic transpilers (Morpheus) handle syntax mapping well: date functions, join syntax, straightforward pattern-to-pattern translation. They get brittle fast on anything requiring control-flow reasoning, cursors, dynamic SQL assembled at runtime, recursive CTEs implemented differently across T-SQL, Teradata, and Oracle,” said Amit Chandak, chief analytics officer at IT consulting firm Kanerika.
“That’s the same 10% to 15% of a legacy codebase that eats most of the manual remediation time on every migration I’ve worked on,” Chandak noted.
In contrast, agentic code conversion, said Pareekh Jain, principal analyst at Pareekh Consulting, is better suited for stored procedures, dynamic SQL, proprietary functions, and complex business logic that require contextual understanding.
“It should improve first-pass conversion quality and reduce manual remediation, especially for the most complex portions of a migration,” Jain noted.
For CIOs, that translates into shorter timelines and lower costs to complete migration projects, especially those that involve thousands of SQL scripts, Jain added.
Chandak, however, said enterprises should temper expectations, mostly because the agentic code converter capability continues to be in beta.
The current beta limits conversion jobs to batches of 300 files, meaning migrations involving thousands of stored procedures still require multiple runs and manual checkpoints, Chandak pointed out.
That limitation could be somewhat offset by the new custom migration skills feature, included with the capability, which allows developers to codify one-off fixes as reusable conversion rules, although whether the agentic system can perform consistently remains an open question till the product matures, Chandak added.
Even if the capability delivers on that promise, Chandak further pointed out that enterprises should not expect it to dramatically shorten end-to-end migration projects because SQL conversion is only one part of the overall modernization effort, as enterprises still need to reconcile migrated data against production systems, sequence dependencies, and obtain business sign-off before workloads can be cut over.
“Because Databricks’ roadmap places data migration and reconciliation in a future phase, the new capability is likely to compress the SQL rewrite effort rather than the overall migration timeline,” Chandak noted.
Databricks isn’t the only data platform vendor using AI to lower the barriers to migration.
Rival Snowflake already has a similar tool, named SnowCovert, that uses agents to help enterprises move their data, data warehouses, business intelligence (BI) reports, and code to Snowflake’s platform.
Google Cloud, too, enables automated migration workflow through Gemini-powered schema and code conversion capabilities in its Database Migration Service.
A new Bayesian system called ALADYNOULLI is designed to forecast how multiple diseases unfold over time from longitudinal electronic health record (EHR) histories. The work targets a central challenge in longitudinal modelling: evaluating prediction without “information leakage,” so that future diagnoses never influence training for past timepoints. By explicitly simulating real clinical follow-up, the researchers aim to make risk estimates more trustworthy for decision-making.
In the primary assessment, ALADYNOULLI produced dynamic 1-year forecasts at 10 fixed moments during follow-up, starting at enrollment and extending to years 1 through 9. At each timepoint, models were retrained using only data available up to that moment, then tested on 1-year outcomes. The key metric was the median area under the receiver operating characteristic curve (AUC) across these dynamic evaluations, summarizing how predictive accuracy changes as new diagnoses accumulate.
The study spans 28 diseases and reports robust discrimination for several clinically important endpoints. Performance was strong for atherosclerotic cardiovascular disease (ASCVD), breast cancer, atrial fibrillation, heart failure, and Parkinson’s disease, with AUC values of 0.879, 0.867, 0.801, 0.811, and 0.796, respectively. Importantly, the evaluation excluded people with prevalent disease at prediction time, aligning the comparisons with prospective risk.
ALADYNOULLI was also benchmarked against established clinical risk tools where feasible. At enrollment, static models trained only on baseline information were compared for both 1-year and 10-year horizons. While longer-term predictions naturally degraded, ALADYNOULLI retained meaningful power—for example, for ASCVD at 10 years—highlighting the advantage of learning from longitudinal EHR trajectories rather than relying solely on cross-sectional inputs.
Calibration analyses further supported reliability: predicted and observed event rates matched closely across millions of at-risk patient-time observations. The researchers report a mean squared error on calibration of 4.67 × 10⁻⁷, with mean predicted rates (5.55 × 10⁻⁴) closely tracking mean observed rates (5.45 × 10⁻⁴). This suggests not only ranking accuracy, but also quantitative risk realism.
To test whether results were driven by subtle temporal artifacts, the team ran sensitivity analyses that excluded events occurring shortly before prediction and varied washout windows. They also examined alternative horizons and a dynamic 10-year rolling interpolation strategy, which updates risk estimates as annual information becomes available. Together, these checks help confirm that temporal patterns are being learned in a causally aligned manner.
Overall, the findings position ALADYNOULLI as a clinically relevant framework for multi-disease risk forecasting, combining longitudinal EHR modelling with predictive discrimination and calibration. By rigorously preventing leakage and validating dynamic follow-up scenarios, the approach offers a pathway toward more actionable, time-aware risk prediction in real-world healthcare settings.
Subject of Research: Longitudinal EHR-based multi-disease risk prediction using Bayesian modelling and genetic/EHR integration
Article Title: A Bayesian framework for longitudinal EHR and genetic discovery.
Article References: Urbut, S.M., Ding, Y., Nakao, T. et al. A Bayesian framework for longitudinal EHR and genetic discovery. Nature (2026). https://doi.org/10.1038/s41586-026-10780-5
Image Credits: AI Generated
DOI: https://doi.org/10.1038/s41586-026-10780-5
Keywords: longitudinal EHR, Bayesian framework, disease risk prediction, dynamic forecasting, temporal validation, calibration, AUC, ASCVD
As enterprises rush to build AI agents that can reason over business data and take action, Databricks argues that the long-standing practice of separating operational and analytical data systems is turning into a liability.
That separation, the cloud-based data warehouse provider says, is becoming increasingly strained as AI agents require simultaneous access to live operational data and historical context to make decisions and take actions in real time, unlike humans, who traditionally can work with data that is minutes or hours old.
At its annual Data + AI Summit, the data warehouse provider introduced Lake Transactional and Analytical Processing (LTAP), a new architecture designed to unify transactional and analytical data on a single storage layer.
The new approach, according to Databricks, differs from traditional online transaction processing (OLTP) and online analytical processing (OLAP) architectures, which typically store operational and analytical data in separate systems.
Traditionally, OLTP databases are optimized for running day-to-day business operations such as order processing, payments, and inventory updates, while OLAP systems are designed for large-scale analytical queries and reporting.
As a result, enterprises often need to rely on ETL pipelines, data replication, and separate infrastructure to move information between the two environments.
LTAP, Databricks said, seeks to eliminate the reliance on ETL pipelines, replicated databases or separate data copies by storing data once in a shared lakehouse layer while allowing dedicated compute engines to handle transactional and analytical workloads independently.
This approach, the company argued, provides AI-driven agents and applications access to both live operational data and historical analytical context without requiring data movement or duplicate copies.
Analysts, too, agree with Databricks’ contention that AI agents place new demands on enterprise data architectures.
“Agents don’t behave like people, or even like the apps we built for people. They read for context, loop, try things, then write something back, thousands of times over in ways you can’t fully predict. At that volume, the constant bouncing between production and analytics systems starts becoming the bottleneck. The pressure to collapse that gap is real, and LTAP is one way to approach it,” said Michael Leone, principal analyst at Moor Insights and Strategy.
Bhupendra Chopra, cofounder and CRO at IT consulting firm Kanerika, pointed out that an autonomous agent’s data access pattern makes the traditional architectures brittle: “We’re seeing this directly with clients deploying multi-agent systems, the pipeline layer becomes the ceiling almost immediately as an agent runs hundreds of times per task.”
The analysts also pointed out that the ability to collapse the gap between OLAP and OLTP is likely to help developers design more robust AI agents or applications that enterprises are currently targeting to deploy.
“The most interesting workflow or application patterns are real-time, context-aware applications that combine transactions, analytics, and AI in one flow,” said Stephanie Walter, practice leader of AI stack at HyperFRAME Research.
“Examples include AI agents that update customer workflows while seeing historical account context and fraud systems that act on live transactions and long-term behavioral patterns,” Walter added.
Designing such applications today, however, according to Leone, would require developers to pull together data from transactional systems, data warehouses, vector databases, and other sources through custom integrations, creating significant engineering complexity and maintenance overhead.
For CIOs, LTAP’s ability to reduce that engineering complexity, according to Ashish Chaturvedi, leader of executive research at HFS Research, will result in operational simplicity as well as cost savings.
“Most prominent advantage would be fewer data pipelines and everything that cascades from eliminating them. Most enterprises don’t realize how much of their data engineering budget is pure plumbing maintenance,” Chaturvedi said.
Kanerika’s Chopra pointed out that a substantial portion of data engineering capacity in mid-to-large enterprises today is consumed by maintaining synchronization between transactional and analytical systems.
The implications, however, Chaturvedi noted, are not limited to developer productivity, architectural simplicity, or cost savings: “The strategic prize is simplified governance. When you have one copy of data under one governance model instead of the same data scattered across operational stores, replicas, warehouses, and vector databases, you’ve solved the governance fragmentation problem.”
That simplification, according to Chopra, will matter operationally for enterprises deploying multiple AI agents, as these workflows can amplify governance gaps at a speed and scale that no human workflow ever did.
Despite all its benefits, though, LTAP isn’t the first effort to unify operational and analytical workloads under a single architecture and for years.
The industry has pursued a similar goal through Hybrid Transactional and Analytical Processing (HTAP) architecture, which sought to combine operational and analytical workloads on tightly coupled infrastructure to serve both workload types from the same system.
LTAP, in contrast, separates storage from compute, allowing different engines to access a common data layer while remaining independently scalable, Databricks said.
That separation of compute engines is why analysts think that LTAP might be a better bet than HTAP.
“HTAP never took off because asking one tightly bound system to be great at transactions and great at analytics usually left it mediocre at both, so customers ended up paying a premium for that compromise,” Leone said.
“I think separating storage from compute is the right instinct, and it’s the same move that made the modern cloud data world work in the first place. It matters because the thing that sank HTAP was one workload starving the other, and giving each side its own dedicated engine is exactly how you keep that from happening,” Leone added.
Another reason for HTAP’s failure, according to David Menninger, executive director of software research at ISG, was its requirement for enterprises to replace existing data platforms with a new architecture.
LTAP, by contrast, builds on the now-common practice of separating compute and storage, making the addition of an operational layer less of an architectural transformation and potentially lowering the barrier to adoption, Menninger added.
However, despite the enthusiasm around LTAP, analysts warned CIOs against viewing this as the inevitable successor to existing data architectures.
“CIOs will still need to choose their data architecture based on latency, reliability, ecosystem fit, cost, compliance, and developer experience,” Walter said.
Echoing Walter, Chaturvedi pointed out that for LTAP to become the de facto standard for the industry, Databricks will need more than architectural elegance: “The architecture looks sound on paper. The proof will be in the commit-to-query latency numbers under real load.”
LTAP, Databricks said, is expected to be released soon as part of Lakebase, without providing any specific timelines.
Urban Congestion Relief Through Intelligent Routing-App Interventions: A Paradigm Shift in City Traffic Management
The relentless growth of urban environments across the globe has precipitated a parallel surge in vehicular congestion—a complex phenomenon undermining mobility, elevating pollution levels, and eroding the quality of life for city dwellers. Conventional traffic management strategies, often reliant on static infrastructure modifications such as road expansions and traffic signal adjustments, have reached their practical limits. An emerging frontier in mitigating urban congestion lies within the harnessing of advanced digital technologies, particularly the integration of real-time data analytics with routing applications used ubiquitously by commuters. Recent groundbreaking research unveiled by Arora, Bayen, Cabannes, and their colleagues in Nature Cities embarks on an ambitious exploration of this very premise, investigating the potential of dynamic routing-app interventions as tactical levers to alleviate urban congestion.
At the core of this research is the hypothesis that leveraging the behavioral responsiveness of drivers to app-generated routing directives can induce systemic traffic flow improvements, transcending the limitations of localized traffic optimizations. Unlike traditional traffic control mechanisms that operate with fixed or pre-programmed rules, modern routing applications provide an interactive, real-time communication channel that dynamically responds to evolving congestion patterns. This interplay between user behavior and algorithmic routing creates a feedback loop, offering fertile ground for experimentation in large-scale urban ecosystems.
The research team implemented a series of carefully controlled field experiments in multiple metropolitan areas, characterized by chronic congestion issues, to test the efficacy of app-driven routing interventions. The methodology involved modulating routing recommendations provided to a subset of drivers using popular navigation platforms while continuously monitoring traffic density, speed metrics, and emission profiles through a network of sensors and GPS data streams. This data-driven approach enabled the researchers to assess how incentivizing alternative route adoption or staggered travel times among participants influenced overall traffic distribution and congestion hotspots.
One of the standout features of the study was its commitment to preserving user autonomy while gently nudging route selection behaviors. Instead of mandating predetermined paths, the deployed interventions focused on transparent communication of traffic conditions alongside personalized routing suggestions crafted using machine learning models. These models factored in predicted congestion levels, expected travel times, and the spatial-temporal dynamics of urban traffic, ensuring that suggested alternatives were not only theoretically optimal but practically attractive to users.
The observed outcomes painted a compelling narrative. Across multiple urban contexts, the introduction of routing-app interventions led to statistically significant reduction in peak-period congestion. Key arterial roads, historically burdened with stop-and-go traffic, experienced improved throughput, while lesser-used parallel corridors saw increased but manageable load distributions. Such traffic load balancing effectively attenuated bottlenecks that conventional infrastructure modifications had failed to resolve. Moreover, the dynamic routing strategies contributed to smoother traffic flow patterns, reducing unnecessary idling times and thus curbing vehicular emissions—a critical co-benefit aligned with urban sustainability goals.
Delving into the technical underpinnings, the researchers employed reinforcement learning frameworks to optimize routing recommendations adaptively. These algorithms continuously integrated real-time traffic data, user compliance rates, and historical congestion trends to refine their decision-making processes. This adaptive learning mechanism was pivotal in accommodating the stochastic nature of urban traffic, including unanticipated events like accidents or sudden weather changes, enhancing the robustness of intervention efficacy.
Critically, the researchers also explored the social dimensions of their interventions. Recognizing that user acceptance and trust are fundamental to the success of app-driven traffic management, the study encompassed surveys and behavioral analyses to understand motivational drivers behind compliance. Findings indicated that transparency in communication, coupled with clear demonstrable benefits such as time savings, significantly boosted user engagement with routing suggestions, highlighting the importance of human-centric design in such digital systems.
The scalability of these experiments further positions routing-app interventions as transformational tools for urban planners and policymakers. Unlike physical infrastructure projects entailing substantial capital expenditures and prolonged timelines, digital routing modifications are inherently flexible and can be deployed rapidly across heterogeneous city landscapes. The research advocates for the incorporation of such intelligent systems into broader smart city frameworks, facilitating integrative traffic management that synergizes infrastructure, data analytics, and user behavior.
Despite the encouraging results, the study also foregrounds challenges warranting ongoing inquiry. Among these, the risk of shifting congestion rather than alleviating it entirely necessitates sophisticated algorithmic balancing to avoid creating secondary bottlenecks. Furthermore, the prevalence of privacy concerns regarding data collection and user tracking underscores the need for ethical governance and secure data handling protocols to maintain public trust.
The implications of this research extend into future urban mobility paradigms. As autonomous vehicles and connected infrastructure become more entrenched, routing-app frameworks will likely evolve into coordinated multi-agent systems capable of orchestrating complex traffic ecosystems with minimal human intervention. The demonstrated capacity of current routing-app interventions to modulate and improve urban congestion lays critical groundwork for such advancements, heralding a new era of intelligent traffic management.
Integrating these insights, city administrators are now empowered to design pilot programs that leverage existing navigation apps as low-barrier platforms for peripheral traffic optimization. By fostering collaboration between municipal authorities, app developers, and the commuting public, cities stand on the cusp of realizing adaptive, data-rich environments where urban congestion is actively managed through intelligent digital interventions rather than reactive measures.
In conclusion, the pioneering work by Arora et al. delineates a paradigm shift away from solely infrastructure-centric congestion mitigation towards a more holistic model that incorporates human behavior, artificial intelligence, and real-time data streams. As cities grapple with escalating mobility demands and environmental imperatives, routing-app interventions emerge as a scalable, effective, and user-friendly strategy with the potential to reshape the urban transportation landscape fundamentally. Continuing research and practical deployment in diverse urban contexts will be essential in refining these approaches to achieve sustainable, equitable, and resilient urban mobility ecosystems globally.
Subject of Research:
Article Title:
Article References:
Arora, N., Bayen, A., Cabannes, T. et al. Urban congestion relief experiments through routing-app interventions. Nat Cities (2026). https://doi.org/10.1038/s44284-026-00443-x
Image Credits: AI Generated
DOI: https://doi.org/10.1038/s44284-026-00443-x
Keywords: Urban congestion, routing applications, traffic management, real-time data analytics, adaptive algorithms, reinforcement learning, smart cities, digital interventions, traffic flow optimization
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