With continued developments in image generation technology, what can be easily accomplished now? This post and accompanying video shares both humorous and useful examples, but also discusses some of the risks involved with technology that can produce convincing words within images.
Blog
-
Beyond Prompt Engineering – What Do Students Really Need to Learn About AI?
What do students really need to know about AI? Does this go beyond prompt engineering. I argue that, although prompt engineering is currently a necessary skill, students will need to interface with AI systems in different ways. Read the post to discover more – and also to find out how I developed and refined the ideas stated. -
Creating Vibrant and Fresh Image Styles With ChatGPT And Dall-E 3
Here’s how you can use ChatGPT to create a variety of new image styles, then pick and choose the one you like the best. Let your creativity run wild. -
Capturing Movement and Thought with Neon Motion Blur Photography
Want to create images using a new simulated image style? Check out the Neon Blue Photography style, for which images are easily produced using ChatGPT. Even better, ChatGPT itself inspired this creative looking style. -
How to sign PDFs easily online with a PDF signer
Signing PDFs has become an important task for businesses and individuals alike. Whether you’re handling contracts, legal agreements, or forms, the ability to quickly and securely sign PDFs online is essential. Fortunately, with the rise of online PDF signers, signing PDFs has never been easier.
Common challenges in signing PDFs
Signing PDFs might seem straightforward, but several challenges often arise during the process. Some of the most common issues include:
- File compatibility: Not all PDF editors or viewers allow you to easily add a signature, especially if the document is encrypted or password-protected.
- Document security: Ensuring that your signature is secure and not vulnerable to tampering is critical, particularly in sensitive legal documents.
- Legal compliance: Making sure your electronic signature is legally valid is crucial, especially for contracts and formal agreements.
By understanding these challenges, you can better prepare for a smooth and secure PDF signing experience.
Choosing the right PDF signer
Selecting the right PDF signer is essential for a hassle-free experience. With many options available, it’s important to choose a tool that meets your needs and ensures your documents are signed efficiently and securely.
Key features to look for
When evaluating PDF signers, here are some important features to consider:
- Ease of use: The interface should be intuitive, allowing you to quickly upload documents, sign them, and download the signed copy.
- Security: Look for a PDF signer that offers encryption and complies with e-signature laws, like the ESIGN Act and UETA.
- Integration with other tools: A good PDF signer should integrate with cloud storage solutions like Google Drive, Dropbox, or OneDrive, allowing easy access to documents.
- Multi-signature support: If you need multiple parties to sign a document, choose a signer that supports multi-party signatures.
- Audit trail: Ensure the signer provides an audit trail for legal purposes, documenting who signed the document and when.
Comparing popular PDF signers
There are several options to choose from when it comes to PDF signing tools. Here’s a quick comparison of popular PDF signers:
- Lumin: A comprehensive solution for signing and collaborating on PDFs. Lumin’s easy-to-use interface and robust security features make it a top choice for professionals.
- DocuSign: A well-known and trusted electronic signature platform with enterprise-level features and extensive legal compliance.
- Adobe Acrobat Sign: Adobe’s offering integrates with other Adobe tools and provides a reliable, secure way to sign PDFs.
- HelloSign: Known for its user-friendly interface and ease of use, HelloSign is a great option for small businesses and individuals looking to sign documents online.
Each of these tools offers different features, but the best option will depend on your specific needs.
Step-by-step guide to signing PDFs online
Now that you know how to choose a PDF signer, let’s walk through the process of signing a PDF online, whether you’re using Lumin or another tool.
Preparing your document
Before you sign your document, make sure it’s ready:
- Ensure the document is complete: Double-check that the content of the document is final and there are no further edits needed before signing.
- Check for any required fields: Some PDFs, especially forms, may have fields that need to be filled out before you sign.
- Ensure document compatibility: Make sure the PDF is not encrypted or password-protected, as this could prevent you from adding a signature.
Using a PDF signer tool
Here’s a simple guide to signing PDFs online using a PDF signer:
- Upload the PDF: Open the PDF signer of your choice and upload the PDF document you need to sign.
- Choose Signature Type: Most PDF signers will allow you to either type your name, draw your signature, or upload an image of your signature.
- Place the Signature: Drag and drop your signature to the appropriate spot on the document. You may also be able to resize or adjust its placement.
- Add Initials or Date: If required, you can add your initials or the date of signing.
- Save and Download: After signing the document, save it and download the signed copy for your records or to share with others.
Verifying your signature
Once you’ve signed your PDF, it’s important to verify that the signature is correctly applied and that the document is secure. Many PDF signers, including Lumin, automatically ensure that your signature is encrypted and legally binding. Always check the signature’s status before finalizing any legal agreement.
Benefits of using an online PDF signer
There are numerous advantages to using an online PDF signer, especially when compared to traditional methods like printing and scanning.
Time and cost efficiency
One of the primary benefits of signing PDFs online is the time saved by eliminating the need to print and scan documents. You can sign documents from anywhere, at any time, which is particularly helpful for remote work and teams spread in multiple locations. And, many online PDF signers offer free versions or affordable subscription plans, which saves you the cost of ink and postage.
Enhanced security measures
Using an online PDF signer often offers enhanced security features, including encryption and compliance with legal e-signature regulations. Tools like Lumin ensure that the signed document is protected from tampering and provide an audit trail that verifies who signed the document and when. This is especially important for businesses that deal with sensitive contracts or legal agreements.
Tips for a seamless PDF signing experience
Here are a few tips for a seamless signing experience in order to get the most out of your online PDF signer:
Ensuring compatibility
Make sure that the PDF signer you choose is compatible with your operating system and the devices you use regularly. Many tools work in multiple platforms, including Windows, macOS, and mobile devices, but it’s always best to confirm before starting.
Maintaining signature legality
Ensure that the tool you’re using complies with the necessary electronic signature laws, like the ESIGN Act in the US or eIDAS in the EU. These laws ensure that your digital signature is legally binding, so you can confidently sign contracts and agreements online.
Final thoughts
Signing PDFs online has never been easier, and with the right PDF signer, you can streamline your workflow and ensure that your documents are signed securely and efficiently. Whether you’re using Lumin, DocuSign, or another tool, taking the time to choose the right PDF signer for your needs will help you save time, reduce costs, and improve document security.
The post How to sign PDFs easily online with a PDF signer appeared first on AI News.
-
Autonomous AI Data Loss in DevOps: Building Efficient Defenses
Autonomous AI agents are altering the speed at which software is shipped. Unfortunately, they are also shrinking the time it takes for a mistake to become a catastrophe, creating a dangerous blind spot in many security strategies.
The threat no longer comes just from external ransomware or malicious insiders. It comes from authorized, internal tools. To make matters worse, these tools cause damage faster, across more systems, and with fewer chances for your security team to notice in time.
In 2025 alone, major DevOps platforms experienced 68 distinct AI-related security incidents, ranging from prompt injections to credential exfiltrations. But even more concerning is the trajectory, incidents accelerated significantly in the latter half of the year, as the DevOps Threats Unwrapped 2026 Report shows.
Organizations must accept that access controls alone cannot stop an authorized agent from making a destructive mistake. Once an agent is authenticated, access controls assume its actions are intentional, leaving you defenseless if the AI misinterprets a prompt or hallucinates.
The pivotal question for your security strategy now is no longer how you control these agents, but how fast your business can recover when they execute a destructive command.
The Threat from Within: How AI Data Loss Emerges and Scales
Traditional data loss scenarios revolve around predictable adversaries—a developer accidentally deleting a repository or a ransomware group extorting your infrastructure. AI introduces a completely different threat vector.
The fundamental problem with AI-driven data loss is that the call is coming from inside the house. This means you must protect your production environment from the tools you explicitly authorized to modify it.
Traditional security defenses fall flat against AI-driven data loss for two main reasons:
- AI agents do not hack their way in; they interact with your environment using the API keys, tokens, and permissions you provide them, executing commands as trusted insiders.
- An agent can hallucinate, encounter an error, or fall victim to an injected prompt, triggering destructive actions in milliseconds.
This isn’t just theoretical. When an autonomous tool goes off the rails with elevated access, the fallout is immediate and severe.
In the 2026 PocketOS incident, during a standard workflow, an AI agent tasked with a routine operation stumbled upon a credential mismatch. Instead of halting, it used an unrelated, highly permissive API key left in the environment to erase the production database volume permanently, alongside the provider’s native backups stored in the same blast radius.
An entire live production database vanished in exactly nine seconds…
This incident proves that when an autonomous agent makes a mistake, the damage outpaces any human ability to detect and intervene, leaving your database exposed to a hyper-accelerated blast radius.
And if your recovery strategy relies on human intervention to stop such an agent, it might already be too late.
Just as the PocketOS agent had permissive access to database volumes, CI/CD AI agents hold the keys to your version control platforms. If an authorized agent goes rogue, your source code and intellectual property can vanish in seconds, instantly paralyzing development.
Ensuring business continuity and operational resilience means fundamentally re-evaluating where your data safety net lives, because your current infrastructure might be a trap.
AI Data Loss in DevOps: The Native Infrastructure Trap
Assuming that native platform protections will save you from such an AI-driven wipe ignores the fundamental mechanics of the shared responsibility model, where you are responsible for the data.
What is more, native platform protection often does not cover deletion and corruption when it is executed by an authorized account. Therefore, relying on your version control platform as your primary backup strategy leaves a massive gap in your disaster recovery plan.
Another major engineering flaw seen in DevOps pipelines is the overlapping authorization perimeters. If your backups are stored inside the same platform as your active codebase, they share the same blast radius, as in the PocketOS case.
The lesson here is straightforward: You cannot use the same environment to build your code and back it up. Surviving AI-speed threats requires stepping outside the native ecosystem and architecting a truly decoupled backup and DR infrastructure.
How to Survive: Architecting a Decoupled Recovery Layer
If your native infrastructure is a trap, the only viable survival strategy is physical decoupling. To ensure that machine-speed destruction is met with machine-speed recovery, you must deploy an independent, immutable recovery layer.
True resilience against AI data loss requires you to neutralize the AI threat vector across four specific fronts:
#1 Blast Radius Isolation
AI data loss becomes catastrophic only when an agent’s permissions reach your backups. Physically separate this blast radius by routing your DevOps backups to a completely decoupled storage destination of your choice, such as an independent AWS S3 bucket, Azure, or an on-premise NAS. If an AI agent completely wipes the primary Git environment, the isolated backups remain 100% untouched.
#2 Encryption and Immutability
An autonomous agent with elevated privileges can easily overwrite business-critical backup storage. Enforcing AES-GCM encryption secures your data against unauthorized access, while WORM (Write Once, Read Many) storage protocols make it systemically impossible for a rogue agent to modify or delete the archive.
#3 Complete Context Recovery
AI data loss reaches far beyond deletion. It involves subtle corruption, such as when an agent introduces flawed code or poisons a context window. Because source code alone does not restore the full delivery context, you must secure the entire ecosystem, including workflows, pull requests, issues, and pipeline metadata. This allows your team to roll back the entire operational state to a known-good baseline.
#4 Granular Restore
When AI wipes a repository in nine seconds, time is the deciding factor. Point-in-time granular restore allows DevOps teams to surgically target and recover the exact repositories, branches, or variables the AI agent destroyed, neutralizing the business impact instantly.
Securing your source code on these four fronts builds a resilient disaster recovery strategy for your company’s intellectual property. A tested, isolated backup and DR is your secret weapon to maintain business continuity after an AI agent wipes out your repositories.
Precaution is Better Than Cure
As you integrate more autonomous AI agents into your pipeline, your security strategy must evolve to survive their speed. The only way to act faster than autonomous AI is to act in advance and back up your repositories with a dedicated DevOps backup solution before an AI agent reaches them.
GitProtect delivers on all four fronts of AI data loss resilience by enabling you to enforce strict precautionary measures:
- strict blast radius isolation through BYOS,
- mathematically unbreakable immutability with AES-GCM encryption and WORM,
- complete context recovery (both code and metadata),
- and granular restores.
All that secured by robust access controls like RBAC, SSO, and MFA to give you an impenetrable, automated disaster recovery engine.
When an agent can erase your environment in seconds, waiting for an alert is no longer a viable strategy. Architectural precaution is the only measure that guarantees your business can recover faster than an AI can destroy it.
The post Autonomous AI Data Loss in DevOps: Building Efficient Defenses appeared first on AI News.
-
Aviva deploys AI to stop £230M in sophisticated insurance fraud
Aviva has uncovered a record £230 million in insurance fraud claims and is using AI tools to counter the growing problem.
The battleground has changed, and the culprits are also coming armed with a new generation of tools. We’re now in an environment where AI is being used not just to defend against fraud, but to perpetrate it.
The insurance industry has long dealt with opportunistic dishonesty. A bumped car suddenly needs four new doors, or a minor slip becomes a life-altering injury. However, according to Aviva’s data, the nature of the deception is getting deeper, more sophisticated, and harder for the human eye to catch.
Aviva is fighting fire with fire, deploying its own AI to uncover these elaborate schemes.
Countering the AI-powered insurance fraud factories
Aviva reports that scammers are now using AI to generate convincing fakes of car accident scenes. These aren’t clumsy photoshop jobs; they’re detailed, plausible images that can easily fool a claims handler working through a heavy caseload.
The same generative AI tools are being used to create fake documents, from invoices for repairs that were never done, to medical reports that have no basis in fact. Fraudsters don’t need access to a network of corrupt garages or medical professionals to back up their story. They just need a subscription to an AI service and a bit of imagination. The AI handles the rest, producing official-looking documents that can pass a cursory inspection.
An individual or small group can now generate the supporting evidence for dozens of high-value claims without ever leaving their desk. How do you validate reality when reality itself can be so easily and cheaply faked?
Aviva’s response has been to build an AI-powered defence system that can operate at the same scale and speed as the threat. While the company is understandably tight-lipped about the exact architecture, you can piece together what a system like this needs to do.
At its core, the AI detective carries out pattern recognition at scale. The AI sifts through millions of data points from current and past claims, learning what a legitimate claim looks like—and, more importantly, what it doesn’t.
When a new claim comes in, the system is cross-referencing everything. Does the damage in the photo match the physics of the described accident? Do the timestamps on the documents make sense? Has this vehicle registration number appeared in other suspicious claims? Are the repair costs quoted on the invoice out of line with the thousands of other similar repairs in the database? It’s a level of forensic analysis that would be impossible to perform manually on every one of the thousands of claims filed each day.
From organised crime to exaggerated claims
It’s important to note that this isn’t all about organised criminal gangs. A portion of that £230 million figure comes from what the industry calls “claims inflation.”
Claims inflation is the more common fraud where policyholders or service providers pad the bill. For instance, a garage might add unnecessary repairs to a quote, or an individual might exaggerate the value of items stolen in a burglary.
Here, too, AI is proving to be a heavy-duty tool. By analysing vast datasets of repair costs and market values, the system can instantly flag when a quoted price is an outlier. It can compare the cost of a replacement part from one garage against the average from hundreds of others in the same region for the same make and model.
The goal of Aviva’s AI isn’t to outright deny claims, it’s an augmentation tool for their human investigators. The AI acts as a filter, sifting through the noise to surface the most likely instances of fraud. This human-in-the-loop approach is essential for ensuring fairness and preventing the system from becoming a black box that makes decisions without oversight.
What Aviva is doing provides a potential route for any customer-facing enterprise in the age of generative AI. The same technology that creates these threats is also the most effective way to combat them.
As it becomes easier to fake everything from identities to invoices, the only viable defence is an intelligent system that can learn, adapt, and spot deception at a scale that humans alone can’t match.
See also: Weis Markets adds Instacart AI-powered shopping carts to stores
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
The post Aviva deploys AI to stop £230M in sophisticated insurance fraud appeared first on AI News.
-
Weis Markets adds Instacart AI-powered shopping carts to stores
Weis Markets is adding Instacart’s AI-powered shopping carts, Caper Carts, to select stores in Pennsylvania, bringing digital coupons, loyalty features, and repeat-purchase recommendations into the grocery aisle.
The Pennsylvania-based grocery chain is working with Instacart to deploy the smart carts, which include cameras, certified scales, location systems, and a touchscreen.
According to Instacart, Caper Carts use basket-facing camera sensors, outward-facing cameras, certified scales, and location-tracking systems to support item recognition and checkout functions. The system combines edge computing on the carts with cloud AI trained on more than 1.6 billion online grocery orders.
Shoppers can use the cart screen to monitor spending during their trip. They can also access location-based digital coupons directly from the cart.
Weis customers can sign up for a Weis Rewards account through the cart and redeem loyalty benefits while shopping. Customers who link their accounts can also use a Buy It Again feature, which shows items they have previously purchased.
Weis and Instacart already work together on online grocery services. In 2023, Weis partnered with Instacart to offer same-day delivery from 133 locations in Pennsylvania, New York, and Delaware.
Instacart expands Caper Cart rollout
The Weis rollout adds to Instacart’s wider Caper Cart deployment. The company says the carts now span more than 100 cities across 15 states.
Caper Carts are available across more than a dozen retail banners, including Kroger, Schnucks, and Wakefern banners such as ShopRite and Fairway Market.
Earlier deployments have produced some store-level usage data. Retail Dive reported that Schnucks data showed Caper Carts handled more than 10% of sales on busy days at one store. That store had 10 Caper Carts and around 160 traditional carts, according to the report.
Greg Zeh, senior vice president and chief information officer at Weis Markets, described the carts as part of the company’s effort to improve the shopping process. He pointed to real-time spend tracking and on-cart coupons as key features.
Instacart described the partnership as an extension of Weis Markets’ use of digital tools inside stores. David McIntosh, Instacart’s chief connected stores officer, said Caper Carts bring together in-store and online data.
Weis adds AI to checkout operations
Weis has also been adding AI to self-checkout. Toshiba Global Commerce Solutions said Weis completed a chainwide deployment of its ELERA Security Suite across self-checkout lanes.
The system includes produce recognition and loss prevention tools. Toshiba says the technology uses edge AI for on-device processing.
At the time of Toshiba’s December 2025 announcement, the system was operational across self-checkout lanes in all 199 Weis locations. Weis also reported that more than 94% of customers selected the produce recognition feature at self-checkout.
Grocers test AI beyond checkout
Albertsons Companies has also introduced an AI-based quality control tool for produce inspection. The system is designed to help identify moldy or damaged fruit before it reaches store shelves.
The tool initially focuses on strawberries and red and green grapes. Albertsons says it is intended to improve quality rating consistency and support faster decision-making.
The company also says the tool expands quality data and helps align inspections with company standards.
Albertsons operates more than 2,000 stores, including Safeway, Jewel-Osco, and ACME. The system supports quality inspectors working in its distribution centres.
The quality control system uses computer vision to support produce inspections across Albertsons’ store brands. It was developed in-house by the company’s technology and supply chain teams.
Albertsons built the tool on Google Cloud’s Gemini Enterprise platform, including Vision AI and Gemini models. Google Cloud said it advised on the AI component used in the supply chain process.
(Photo by Franki Chamaki)
See also: Amazon brings AI shopping assistant to retailers with Kate Spade
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
The post Weis Markets adds Instacart AI-powered shopping carts to stores appeared first on AI News.
-
How C3 AI agents will automate predictive maintenance for Shell
Shell will use agents from C3 AI to shift from basic anomaly detection towards fully-automated predictive maintenance.
The global energy giant is building on their current use of the C3 AI Reliability Suite, which already keeps tabs on more than 30,000 crucial pieces of equipment across upstream and downstream operations. Shell now intends to lean heavily into autonomous AI agents, putting them in charge of the entire maintenance lifecycle.
Going from that first warning sign all the way to a completed repair, this level of automation strips away the need for constant human oversight and makes sure the company’s resources are pointed exactly where they are needed most.
“This expanded partnership with Shell proves what’s possible when enterprise AI is fully operationalised at global scale for predictive maintenance—reducing unplanned downtime and delivering hundreds of millions of dollars in economic value,” said Stephen Ehikian, President of C3 AI.
“Shell has built mature AI predictive maintenance programs on our platform, and together we’re now pushing into agentic AI, advancing how this technology can further transform reliability, safety, efficiency, and operational performance.”
C3’s AI agents help Shell move past basic anomaly detection
In the beginning, Shell used machine learning simply to spot odd patterns in sensor data, giving engineers an early heads-up before things broke. To pull this off, the system ingests a massive amount of real-time operational technology (OT) data and mixes it with business context from ERP platforms such as SAP.
The next step introduces AI agents built for actual reasoning and independent action. While older systems stopped at pinging an engineer when things looked unusual, this next-generation framework independently investigates why an alert fired in the first place.
Once it pinpoints the root cause, the agent steps up to draft precise work orders, confirm part availability in the inventory, and generate procurement requests.
C3 AI’s platform handles the heavy lifting, providing a model-driven space to easily integrate high-frequency sensor feeds with structured financial and maintenance logs. These AI capabilities are trained to learn the normal operating baselines for specific gear, like pumps, turbines, and compressors.
The agentic layer sits on top of this foundation. Operators configure an individual agent for a given piece of equipment by defining its objectives and permitted responses. If the core machine learning models detect a deviation from normal operations, this agent activates, gathering extensive contextual data to build a complete picture of the situation. This context usually includes recent maintenance history, environmental conditions, and upstream process variables.
Using all that information, it suggests a fix backed by solid evidence. Human operators can then easily approve or override the plan. As the system proves itself over time, Shell can fully automate its responses to certain types of alerts. Connecting straight into systems like SAP is critical here, allowing the agent to work inside the exact same workflows that human planners already use.
The real impact of agentic AI for predictive maintenance
Putting agentic AI to work at this scale tackles the classic “last mile” headache in predictive maintenance. Many industrial companies can predict failures just fine, but turning those insights into fast, efficient action remains a challenge. Usually, engineers still have to manually dig through alerts, investigate the causes, and write up the work orders themselves.
Shell wants to shrink that timeline. By letting AI handle root cause analysis and work orders, the delay between a predicted failure and the actual fix drops. That directly improves equipment uptime and protects production.
Moving to a model where repairs only happen when the equipment condition actually demands it naturally saves money, simply because nobody is wasting time tinkering with perfectly fine machinery. Leaving healthy hardware alone also means it lasts much longer.
On top of the cost savings, stepping in before a catastrophe hits makes the whole operation much safer and cuts down on environmental risks, which is always top of mind in the energy sector.
“What Shell and C3 AI have built on Azure over the past several years is exactly what enterprise AI should look like—real applications, running in production, delivering measurable value at global scale,” commented Sandy Gupta, VP GISV, Software Development Companies at Microsoft.
This expanded rollout shows that we are finally talking about practical industrial AI production workflows instead of just algorithms. Rather than just the prediction itself, the real value comes from the system’s ability to act on it with barely any human oversight.
See also: Meta Business Agent drives AI-powered conversational commerce
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
The post How C3 AI agents will automate predictive maintenance for Shell appeared first on AI News.