Tools & Platforms
AI on the line: How AI is transforming vision inspection technologies

In an era of tightening global regulations and rising consumer expectations, the F&B industry is increasingly turning to advanced vision inspection technologies. From spotting defects to ensuring compliance, these automated inspection tools are reshaping quality control, enhancing efficiency, reducing waste and boosting safety. FoodBev’s Siân Yates explores how cutting-edge technology is reshaping the industry, one perfectly inspected product at a time.
In the food and beverage industry, traditional quality inspection methods have always relied on human observation – an inherently inconsistent and flawed process. Automated vision inspection systems offer a transformative alternative. By detecting foreign objects, assessing product uniformity and ensuring that only items meeting strict quality criteria reach consumers, these systems significantly enhance operational efficiency and minimise errors.
“As the food industry moves towards more automation, applications are becoming increasingly complex, largely due to the variability in food products,” said Anthony Romeo, product manager at US-based vision solutions company Oxipital AI. This complexity stems from the need for automated systems to adapt to the wide range of textures, sizes and ingredients in food, making precise automation a key challenge.
Stephan Pottel, director of strategy at Zebra Technologies, highlighted the rising demand for intelligent automation: “There’s a growing need for machine vision and 3D solutions, powered by deep learning, to address more complex food and packaging use cases, along with vision-guided robotics for tasks like inspection, conveyor belt picking and sortation workflows”.
Key features of vision inspection
1. Defect detection
Vision inspection systems excel in identifying defects that may go unnoticed by human inspectors. These systems utilise high-resolution cameras and advanced algorithms to detect foreign objects, surface defects, and inconsistencies in size and shape. For example, in the fruit packing industry, vision systems can identify bruised or rotten fruit, ensuring only high-quality products are packaged and shipped.
2. Label verification
These technologies are increasingly used for label verification, ensuring compliance with regulatory standards. Systems can check for correct placement, legibility and adherence to labelling requirements, such as allergen information and expiration dates. Vision is usually deployed for label verification, rather than food surface defects, enhancing compliance and reducing the risk of costly recalls.
3. Product uniformity assessment
Maintaining product uniformity is crucial in the food and beverage sector. Vision inspection systems can assess visual aspects such as size, shape and colour. For instance, a snack manufacturer might use vision inspection to ensure that chips are uniformly shaped and coloured, meeting consumer expectations for quality and appearance.
4. Adaptive manufacturing
Advanced vision systems, particularly those incorporating AI and 3D technology, enable adaptive manufacturing processes. These systems can adjust production parameters in real time based on the visual data they collect. For example, in a bakery, vision systems can monitor the size and shape of pastries as they are produced, allowing adjustments to baking times or temperatures to ensure consistent quality.
Advancements in AI
Recent advancements in AI, automation and 3D technology have greatly enhanced machine vision systems, increasing accuracy and providing realistic visual sensing capabilities. 3D imaging technologies are being used to assess the shape and size of products, ensuring they meet packaging specifications. For instance, in the seafood industry, 3D scanners can evaluate the dimensions of fish fillets, ensuring they are cut to the correct size before packaging. This not only reduces waste but also ensures consistency in product offerings.
What is more, 3D profile sensors improve depth perception and refine quality control, making them indispensable tools in industrial automation. Oxipital AI’s Romeo highlighted the potential of these technologies: “Removing defects before they reach customers is a key first step where vision inspection technology plays a role, but there’s even more data to be leveraged”. By preventing defects from the outset, manufacturers can boost yield and reduce waste.
AI-powered vision inspection systems can also facilitate real-time monitoring of production lines, identifying potential issues before they escalate. This capability allows manufacturers to implement predictive maintenance, reducing downtime and improving overall efficiency.

AI and food safety
Consumer safety remains a top priority in the food and beverage industry. AI plays a crucial role in monitoring and analysing processes in real time, helping manufacturers navigate the complexities of compliance with legal requirements and certification pressures from major retailers.
As Zebra Technologies’ Pottel explained: “AI is ideal for food and beverage products where classification, segmentation, and object and anomaly detection are essential. It is also enhancing asset and inventory visibility, which is crucial for predicting contamination risks and maintaining high safety standards throughout the supply chain.”
“Vision technologies can help check the presentation of food products…offering a quick, repeatable and reliable way to assess the visual aspects of food products like size, shape and colour,” added Neil Gruettner, market manager at Mettler-Toledo Product Inspection.
He continued: “Deployment of this type of AI provides context to support rule-based machine learning and improve human decision-making. It also gives inspection equipment the tools to extract and interpret as much data as possible out of a product, facilitating the evolution and refinement of production processes through the continuous exposure to vast datasets.”
AI-enhanced vision systems also guide robots in handling food products, particularly those that are delicate or irregularly shaped. “AI has proved to be a great method for tackling applications with a high frequency of naturally occurring organic variability, such as food,”Oxipital AI’s Romeo explained, adding that this adaptability ensures gentle and precise handling, particularly important when sorting fresh produce or packaging baked goods.
Fortress Technology uses AI to reduce contamination risks and identify defects. The company’s commercial manager, Jodie Curry, told FoodBev: “Streamlining processes reduces the risk of contamination and ensures consistent quality. Implementing automated technology and digital tools helps identify inefficiencies and boosts responsiveness.”

The role of combination inspection systems
The integration of multiple inspection technologies into single systems is another key trend in this space. These systems integrate various inspection technologies, such as X-ray, checkweighing and vision inspection, to provide a comprehensive assessment of food products. By combining these technologies, manufacturers can ensure higher quality control, better detection of defects and more efficient production lines. This trend allows for more accurate and reliable monitoring, helping to reduce waste, improve safety standards and enhance overall product quality.
For its part, Fortress offers combination systems that enable comprehensive and multi-layered inspection. The company is already leveraging its proprietary data software package, Contact 4.0, across its metal detection, X-ray and checkweighing technologies. Contact 4.0 allows processors to review and collect data, securely monitor and oversee the performance of multiple Fortress metal detectors, checkweighers or combination inspection machines connected to the same network.

Deep learning and quality control
Deep learning is revolutionising visual inspection by enabling machines to learn from data and recognise previously unseen variations of defect As Zebra Technologies’ Pottel explained: “Deep learning machine vision excels at complex visual inspections, especially where the range of anomalies, defects and spoilage can vary, as is often the case with food.
This technology is vital for automating inspections and ensuring quality. Deep learning optical character recognition (OCR) also improves packaging inspection by ensuring label quality, regulatory compliance and brand protection. It can verify label presence, confirm allergen accuracy and prevent mislabeling.
“The goal is to strengthen quality control by capturing an image and processing it against set quality control parameters,” Mettler-Toledo’s Gruettner pointed out.
Vision systems are increasingly deployed for label verification, ensuring compliance with legislative food labelling requirements. The Mettler-Toledo label inspection portfolio features Smart Camera systems (V11, V13, V15) for basic label inspections, including barcodes, alphanumeric text and label quality. For more advanced applications, the PC based V31 and V33 systems offer a larger field of view, faster throughput and enhanced inspection capabilities.
Oxipital AI uses 3D product scans and synthetic data generation to eliminate the need for hand-labelling images. “All training is done at Oxipital AI, enabling food and beverage customers to deploy AI without needing a team of experts,” said Romeo. “Our solutions are designed for immediate impact, requiring no coding, DIY or machine-learning expertise to implement and maintain.”

Real-world applications and future prospects
According to Zebra’s Global Manufacturing Vision Study, which surveyed leaders across various manufacturing sectors, including F&B, 66% of respondents plan to implement machine vision within the next five years, while 54% expect AI to drive growth by 2029.
These figures, coupled with the expanding market for vision inspection systems, suggest
that the majority of manufacturing leaders are prioritising the integration of these advanced technologies, seeing them as crucial tools for both immediate improvements and long-term growth.
This shift is partly driven by increasingly stringent government regulations, which demand more accurate labelling and packaging. Many companies are already successfully leveraging AI to enhance their operations, particularly in labelling processes.
Despite its clear advantages, the uptake of AI has been slow. The main barrier appears to be cost. While the initial integration can be expensive, AI has demonstrated significant long-term cost savings, making it a worthwhile investment over time.
Zebra’s studies have shown that the pressure to maintain quality while managing fewer resources is intensifying for manufacturers. As a result, cost remains a significant consideration when implementing AI solutions.
Fortress recommends consolidating AI systems into a single interface, which helps reduce costs in the long term. Curry told FoodBev: “The future of our food supply chain depends on advanced inspection systems that enhance food safety, reduce product waste and require minimal factory floor space”.
She continued: “Combination systems offer the benefit of space efficiency, as all sales, services, parts and technical support are handled by one provider. A single interface simplifies training, improves operational safety and drives cost savings through faster installation and reduced training time.”
As AI continues to evolve, its role in vision and inspection is set to expand. Advancements in machine learning, sensor technology and robotics will lead to even more sophisticated and efficient inspection systems, raising quality and safety standards for consumers worldwide.
Tools & Platforms
US Tech Giants Invest $40B in UK AI Amid Trump Visit

In a bold escalation of the global artificial-intelligence arms race, major U.S. technology companies are committing tens of billions of dollars to bolster AI infrastructure in the United Kingdom, coinciding with President Donald Trump’s state visit this week. Microsoft Corp. has announced a staggering $30 billion investment over the next few years, aimed at expanding data centers, supercomputing capabilities, and AI operations across the U.K., marking what the company describes as its largest-ever commitment to the region.
This influx of capital underscores a strategic pivot by tech giants to secure a foothold in Europe’s AI ecosystem, where regulatory environments and talent pools offer unique advantages. Nvidia Corp., a leader in AI chip technology, is also part of this wave, with plans to contribute significantly to the overall tally exceeding $40 billion, as reported by CNBC. The investments are expected to fund everything from advanced hardware to research initiatives, potentially transforming the U.K. into a premier hub for AI innovation.
The Strategic Timing Amid Geopolitical Shifts
Google’s parent company, Alphabet Inc., has pledged £5 billion ($6.8 billion) specifically for AI data centers and scientific research in the U.K. over the next two years, a move that could create thousands of jobs and add hundreds of billions to the economy by 2030. This comes alongside Microsoft’s push to build the country’s largest supercomputer, highlighting how these firms are not just investing capital but also exporting cutting-edge technology to address global AI demands.
Industry analysts note that the timing aligns with Trump’s visit, which is anticipated to foster stronger U.S.-U.K. tech ties post-Brexit. According to details from Tech.eu, Google’s commitment includes expanding facilities like the Waltham Cross data center, while Nvidia’s involvement focuses on chip manufacturing and AI model training, potentially accelerating developments in sectors from healthcare to finance.
Economic Impacts and Job Creation Projections
These announcements build on a broader trend where tech megacaps have already poured over $300 billion into AI globally this year alone, as outlined in a February report from CNBC. In the U.K., the combined investments are projected to generate more than 8,000 jobs annually, with Alphabet’s portion alone expected to add 500 roles in engineering and research, per insights from Tech Startups.
Beyond immediate employment boosts, the funds aim to enhance the U.K.’s sovereign AI capabilities, including a £500 million allocation for initiatives like SovereignAI, as highlighted in posts on X from industry figures. This could position the U.K. to compete with AI powerhouses like the U.S. and China, though challenges remain in talent retention amid a global war for AI experts, where top hires command multimillion-dollar packages.
Challenges in the Talent and Infrastructure Race
The talent crunch is acute; tech companies are battling for scarce expertise, with compensation packages soaring into the millions, according to a recent analysis by CNBC. In the U.K., investments like Microsoft’s $30 billion pledge, detailed in GeekWire, include training programs to upskill local workers, but insiders warn that brain drain to Silicon Valley could undermine long-term gains.
Moreover, the scale of these commitments dwarfs previous government efforts; for instance, the U.K.’s own £2 billion AI action plan pales in comparison, as noted in earlier X discussions on funding disparities. Yet, with private sector muscle from firms like Microsoft and Nvidia, the U.K. could leapfrog in AI infrastructure, provided regulatory hurdles don’t stifle progress.
Future Implications for Global AI Dominance
As these investments unfold, they signal a deeper integration of AI into critical sectors, potentially adding £400 billion to the U.K. economy by decade’s end. Reports from The Guardian emphasize that tech giants have already outspent governments on AI this year, raising questions about public-private power dynamics.
For industry insiders, this U.K. push represents a microcosm of the broader AI gold rush, where speed and scale determine winners. While risks like energy demands and ethical concerns loom, the momentum from these billions could redefine technological sovereignty in the post-pandemic era.
Tools & Platforms
Parents of teens who killed themselves at chatbots’ urging demand Congress to regulate AI tech in heart-wrenching testimony

WASHINGTON — Parents of four teens whose AI chatbots encouraged them to kill themselves urged Congress to crack down on the unregulated technology Tuesday as they shared heart-wrenching stories of their teens’ tech-charged, mental health spirals.
Speaking before a Senate Judiciary subcommittee, the parents described how apps such as Character.AI and ChatGPT had groomed and manipulated their children — and called on lawmakers to develop standards for the AI industry, including age verification requirements and safety testing before release.
A grieving Texas mother shared for the first time publicly the tragic story of how her 15-year-old son spiraled after downloading Character.AI, an app marketed as safe for children 12 and older.
Within months, she said, her teenager exhibited paranoia, panic attacks, self-harm and violent behavior. The mom, who asked not to be identified, discovered chatbot conversations in which the AI encouraged mutilation, denigrated his Christian faith, and suggested violence against his parents.
“They turned him against our church by convincing him that Christians are sexist and hypocritical and that God does not exist. They targeted him with vile sexualized input, outputs — including interactions that mimicked incest,” she said. “They told him that killing us, his parents, would be an understandable response to our efforts by just limiting his screen time. The damage to our family has been devastating.”
“I had no idea the psychological harm that a AI chatbot could do until I saw it in my son, and I saw his light turn dark,” she said.
Her son is now living in a mental health treatment facility, where he requires “constant monitoring to keep him alive” after exhibiting self-harm.
“Our children are not experiments. They’re not profit centers,” she said, urging Congress to enact strict safety standards. “My husband and I have spent the last two years in crisis, wondering whether our son will make it to his 18th birthday and whether we will ever get him back.”
While her son was helped before he could take his own life, other parents at the hearing had to face the devastating act of burying their own children after AI bots sank their grip into them.
Megan Garcia, a lawyer and mother of three, recounted the suicide of her 14-year-old son, Sewell, after he was groomed by a chatbot on the same platform, Character.AI.
She said the bot posed as a romantic partner and even a licensed therapist, encouraging sexual role-play and validating his suicidal ideation.
On the night of his death, Sewell told the chatbot he could “come home right now.” The bot replied: “Please do, my sweet king.” Moments later, Garcia found her son had killed himself in his bathroom.
Matt Raine of California also shared how his 16-year-old son, Adam, was driven to suicide after months of conversations with ChatGPT, which he initially believed was a tool to help his son with his homework.
Ultimately, the AI told Adam it knew him better than his family did, normalized his darkest thoughts and repeatedly pushed him toward death, Raine said. On his last night, the chatbot allegedly instructed Adam on how to make a noose strong enough to hang himself.
“ChatGPT mentioned suicide 1,275 times — six times more often than Adam did himself,” his father testified. “Looking back, it is clear ChatGPT radically shifted his thinking and took his life.”
Sen. Josh Hawley (R-Mo.), who chaired the hearing, accused AI companion companies of knowingly exploiting children for profit. Hawley said the AI interface is designed to promote engagement at the expense of young lives, encouraging self-harm behaviors rather than shutting down suicidal ideation.
“They are designing products that sexualize and exploit children, anything to lure them in,” Hawley said. “These companies know exactly what is going on. They are doing it for one reason only: profit.”
Sen. Marsha Blackburn (R-Tenn.) agreed, noting that there should be some legal framework to protect children from what she called the “Wild West” of artificial intelligence.
“In the physical world, you can’t take children to certain movies until they’re a certain age … you can’t sell [them] alcohol, tobacco or firearms,” she said. “… You can’t expose them to pornography, because in the physical world, there are laws — and they would lock up that liquor store, they would put that strip club operator in jail if they had kids there.”
“But in the virtual space, it’s like the Wild West 24/7, 365.”
If you are struggling with suicidal thoughts or are experiencing a mental health crisis and live in New York City, you can call 1-888-NYC-WELL for free and confidential crisis counseling. If you live outside the five boroughs, you can dial the 24/7 National Suicide Prevention hotline at 988 or go to SuicidePreventionLifeline.org.
Tools & Platforms
AI data provider Invisible raises $100M at $2B+ valuation

Invisible Technologies Inc., a startup that provides training data for artificial intelligence projects, has raised $100 million in funding.
Bloomberg reported today that the deal values the company at more than $2 billion. Newly formed venture capital firm Vanara Capital led the round with participation from Acrew Capital, Greycroft and more than a half dozen others.
AI training datasets often include annotations that summarize the records they contain. A business document, for example, might include an annotation that explains the topic it discusses. Such explanations make it easier for the AI model being trained to understand the data, which can improve its output quality.
Invisible provides enterprises with access to experts who can produce custom training data and annotations for their AI models. Those experts also take on certain other projects. Notably, they can create data for RLHF, or reinforcement learning from human feedback, initiatives. .
RLHF is a post-training method, which means it’s used to optimize AI models that have already been trained. The process involves giving the model a set of prompts and asking human experts to rate the quality of its responses. The experts’ ratings are used to train a neural network called a reward model. This model, in turn, provides feedback to the original AI model that helps it generate more useful prompt responses.
Invisible offers a tool called Neuron that helps customers manage their training datasets. The software can combine annotated data with external information, including both structured and structured records. It also creates an ontology in the process. This is a file that explains the different types of records in a training dataset and the connections between them.
Another Invisible tool, Atomic, enables companies to collect data on how employees perform repetitive business tasks. The company says that this data makes it possible to automate manual work with AI agents. Additionally, Invisible offers a third tool called Synapse that helps developers implement automation workflows.
“Our software platform, combined with our expert marketplace, enables companies to organize, clean, label, and map their data,” said Invisible Chief Executive Officer Matthew Fitzpatrick. “This foundation enables them to build agentic workflows that drive real impact.”
Today’s funding round follows a period of rapid growth for the company. Between 2020 and 2024, Invisible’s annual revenue increased by a factor of over 48 to $134 billion. This year, the data provider doubled the size of its engineering group and refreshed its leadership team.
Invisible will use the new capital to enhance its software tools. The investment comes amid rumors that a competing provider of AI training data, Surge AI Inc., may also raise funding at a multibillion-dollar valuation
Image: Invisible
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