AI Research
How Artificial Intelligence is Transforming Manufacturing
Summary
According to a study by the World Economic Forum, more than 70% of industrial AI projects are abandoned after the pilot phase. While some companies successfully integrate artificial intelligence (AI) into their operations and achieve significant economic benefits, others face major challenges. However, many examples show that AI can be effectively used in manufacturing and has become a vital element of flexible, efficient production (Figure 1). Today, AI solutions are available that not only integrate smoothly into industrial processes but can also handle complex tasks with high efficiency.
Visual quality control
Quality assurance is a key task in industrial manufacturing. AI-powered image processing systems now make reliable quality inspections possible. One example is Inspekto, a solution for visual quality inspection that enables companies to automate product checks without needing deep AI or image-processing knowledge. The intuitive system can be ready for use in less than an hour and needs only about twenty sample images classified as “good” to deliver accurate results. Basic production knowledge of quality testing is enough—no AI expertise is required (Figure 2).
For example, the mid-sized company MTConnectivity Power2pcb uses Inspekto to inspect connectors to identify minimal deviations and slightly bent contacts. By integrating this AI-based system into its production line, the company ensures continuous quality assurance, improves reliability and shortens delivery times.
Generative AI in manufacturing
The application and implementation of generative AI models are more complex. Siemens’ Industrial Copilots are designed to improve human-machine collaboration and accelerate innovation across the entire value chain—from design, planning and engineering to operations and service. The Industrial Copilot for Operations is currently being piloted at customer sites and Siemens factories to test its reliability. Meanwhile, the Industrial Copilot for Engineering is already available as a finished product (Figure 3).
Thyssenkrupp Automation Engineering, a specialized machinery and equipment manufacturer, has integrated the Siemens Industrial Copilot into its systems for handling round cells used in battery inspections for electric vehicles. The Copilot automates repetitive tasks like data management, sensor configuration and detailed reporting that helps meet strict battery inspection standards. By managing routine tasks, the Copilot allows engineering teams to focus on complex, high-value activities, while solving problems in real time, minimizing downtime and ensuring smooth production.
Predictive maintenance with AI
AI is also revolutionizing predictive maintenance. Instead of relying on fixed maintenance intervals or manual analysis, AI uses continuous machine data monitoring to detect early signs of wear and suggest maintenance actions. Siemens’ Senseye Predictive Maintenance solution identifies deviations in temperature, vibration and torque data to offer early warnings and recommendations (Figure 4).
Mercer Celgar, a producer of pulp and wood products, uses this technology to monitor its machinery in real time. Data from multiple production lines is combined into a central platform that provides a full overview of the manufacturing process and significantly reduces downtime.
Seamless integration of AI models
Even companies that have already adopted AI face challenges when scaling their solutions. Issues like time-consuming updates, poor connectivity or complex maintenance often arise. To address these challenges, the Industrial AI Suite is available. Industrial AI Suite is a platform for the smooth implementation of AI solutions on the shop floor.
These solutions are customized in close collaboration with customers to combine their existing AI expertise with Siemens’ infrastructure for scalable deployment . Depending on the use case, these solutions use edge or cloud computing to integrate services like AWS or Microsoft Azure. AI models can be trained in the cloud and then easily deployed to production floors using the AI Inference Server. The Industrial Edge application enables customers to deploy and run trained AI models in production, directly on the Industrial Edge, even with GPU-accelerated inferencing.
The Industrial AI Suite also manages the full AI model lifecycle, which allows easy updates and automatic detection of performance issues. For example, Siemens helped a food and beverage company integrate AI-based soft sensors into its production. These sensors ensure consistent product quality and taste by analyzing process parameters in real time and dynamically adjusting target values to optimize production and reduce waste.
In electronics manufacturing, Siemens’ electronics factory in Erlangen, Germany, uses machine learning models to detect errors in circuit board assembly, which improves speed and cost-efficiency with the help of the Industrial AI Suite.
Making AI accessible and practical
These real-world examples show that AI plays a crucial role in modern industry. Embedding AI systems into products hides the complexity from users and makes AI accessible and usable for everyone. The key to success lies in flexible infrastructures that allow companies to tailor AI solutions to their specific needs.
Industrial AI is no longer a futuristic vision—it is already delivering real competitive advantages today.
Images courtesy of Siemens.
This feature was originally posted on ISA Interchange blog and also appeared in the June/July issue of Automation.com Monthly.
About The Author
Dr. Matthias Loskyll is the senior director of Software, Virtual Control & Industrial AI at Siemens. He is a leader with a passion for customer-centric innovation and management of interdisciplinary teams of experts. He has more than 16 years of experience and background in AI methods, software development, industrial production, automation systems, Industry 4.0, industrial operations and manufacturing execution systems.
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The Pixel 10 Pro is Google’s best phone that is still a pocketable, easy-to-handle size, taking the excellent Pixel 10 and beefing it up in the camera department.
That makes it a contender for the top smaller phone with Apple’s iPhone 17 Pro, offering the best of Google’s hardware without an enormous screen. It is also the cheapest of three Pixel 10 Pro phones starting at £999 (€1,099/$999/A$1,699) sitting below the bigger 10 Pro XL and the tablet-phone hybrid the 10 Pro Fold.
The 10 Pro looks almost identical to last year’s version and has the same size 6.3in OLED screen as the Pixel 10 but slightly brighter, slicker and crisper. It is one of the best displays on a phone, while the polished aluminium sides and mat glass back look expensive even if the colour choice is rather staid compared with its cheaper sibling.
The 10 Pro is one of the first phones to come with Qi2 wireless charging built into the back, which offers compatibility with a range of magnetic accessories, including those made for Apple’s MagSafe.
Inside is Google’s latest Tensor G5 chip, which is about 35% faster than last year’s model but falls short of the best-in-class performance of Qualcomm’s top Android chip used in rivals. Day to day the 10 Pro feels rapid, and it handled games just fine, though there are better options for those who want the absolute best graphics and frame rates.
The Pixel has solid battery life, managing up to about two days between charges with about seven hours of active screen use on a mix of 5G and wifi. Most people will need to charge it every other day, but on heavy use days out and about in London on 5G it still managed to reach midnight with at least 25% left.
Specifications
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Screen: 6.3in 120Hz QHD+ OLED (495ppi)
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Processor: Google Tensor G5
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RAM: 16GB
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Storage: 128, 256, 512GB or 1TB
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Operating system: Android 16
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Camera: 50MP + 48MP UW + 48MP 5x tele; 42MP selfie
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Connectivity: 5G, nano + e-sim (US: e-sim-only), wifi 7, UWB, NFC, Bluetooth 6 and GNSS
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Water resistance: IP68 (1.5m for 30 minutes)
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Dimensions: 152.8 x 72.0 x 8.6mm
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Weight: 207g
Android 16 with AI everywhere
The phone ships with Android 16 installed, with security and software updates until August 2032, ensuring it stays up to date for the life of the phone. It is the same software as the regular Pixel 10 with a bold, colourful and fun design.
Google has shoved AI in almost every corner of the phone, most of it powered by the latest local Gemini Nano models, which means your data doesn’t have to leave your device to be processed, preserving privacy.
The advanced Gemini chatbot is capable of interacting with your apps, seeing what is on your screen or through your camera, and having live back-and forth-conversations via voice.
But the standout new feature is Magic Cue, which runs in the background and combines information from your Google account with data on your phone to offer help or quick suggestions in a number of Google apps. For instance, when you ring a business, Magic Cue pops up a card directly in the phone app showing your emails with your order confirmation details for one-tap access when you need them.
Magic Cue works locally with about 10 days’ worth of data so it is not keeping a permanent log of everything you do, but has been genuinely useful in testing. It only works in Google’s and a select number of third-party apps, such as eBay, but not WhatsApp, so its utility is limited if you don’t use the right apps.
The 10 Pro also comes with a year’s subscription to Google AI Pro, which usually costs £19 a month, and provides access to the more powerful Gemini Pro, image and video-generating models, plus 2TB of cloud storage for Google Drive, Photos and Gmail.
Camera
The 10 Pro has some of the most powerful cameras on a smartphone with a 42-megapixel selfie, 50MP main, 48MP ultrawide and 48MP 5x telephoto camera capable of an optical zoom quality up to 10x. But it is also the first to feature generative AI image processing directly in the camera, which is impressive but calls into question what a photo really is.
The main camera is one of the best in the business, effortlessly capturing great photos that are rich in detail across a range of lighting conditions. The ultrawide camera is also very good for landscapes and group shots, and is used for the great macrophotography mode for fun closeups. The 5x telephoto is one of the very best on a phone and can shoot photos at 10x, which remain good quality, particularly in bright conditions.
Google excels in difficult lighting conditions such as very bright or contrasting scenes, while in dark environments its night sight produces sharper images with more accurate colours than rivals. The Pixel’s portrait mode is greatly improved this year, too.
Zoom beyond 30x up to 100x and the phone uses a local genAI model to put back into the photo the detail and sharpness lost from digital zoom. Generally it works well but not flawlessly. It can get the perspective wrong or superimposes the wrong details, creating images that are clearly made by AI. But shoot predictable subjects such as buildings, cars or trees, and it firms up the digitally stretched details making the 100x zoom surprisingly usable.
When it detects a person it does not even attempt to use the genAI model, which is probably for the best, and like all genAI systems it can struggle with words, often producing something that looks like an alien script.
The camera app adds C2PA content credentials to all photos that records how the image was captured and whether generative AI was involved, including for the new zoom and popular Add Me feature from last year. Best Take has been made automatic, allowing the camera to capture multiple images when you press the shutter button to try to get one where everyone’s looking at the camera.
The 10 Pro also has the same new AI Camera Coach feature as the regular 10, which teaches you how to get a better shot by analysing the scene through the camera and giving you options for different angles and framing.
The camera also has plenty of fun photography and video modes, shoots great films as well as photo, and cements the 10 Pro as one of the very best on the market.
Sustainability
The battery will last in excess of 1,000 full charge cycles with at least 80% of its original capacity. The phone is repairable by Google, third-party shops or self-repair, with manuals and parts available.
The Pixel 10 Pro contains 30% recycled materials by weight including aluminium, cobalt, copper, glass, gold, plastic, rare-earth elements, tungsten and tin. The company breaks down the phone’s environmental impact in its report and will recycle old devices for free.
Price
The Google Pixel 10 Pro costs from £999 (€1,099/$999/A$1,699) in a choice of four colours.
For comparison, the Pixel 10 starts at £799, the Pixel 10 Pro XL at £1,199, the Pixel 9a costs £399, the Samsung Galaxy S25 costs £799, the Galaxy S25 Ultra costs £1,249 and the iPhone 16 Pro costs £999.
Verdict
The Pixel 10 Pro doesn’t reinvent the wheel or set a new bar in quite the same way as the base-model Pixel 10 managed this year. But it still upgrades its already market-leading camera and AI features.
It is snappy in operation, has decent battery life and still looks good, though hardcore gamers may want to look elsewhere for more powerful graphics. Google’s take on Android is one of the best and comes with long-term support so you can keep using it for years.
Gemini’s various new tools are generally useful and less gimmicky than many. Magic Cue has great potential to be a time-saver without getting in the way, but needs to be expanded to more apps.
Injecting genAI directly into the camera app improves its extended zoom images, but further blurs the line between what is and isn’t a photo – a philosophical debate most will probably gloss over because the tool is useful and avoids doing anything outlandish.
The Pixel 10 Pro is easily one of the best smaller phones available and really hammers home just how much more advanced Google’s AI tools are than Apple’s and other rivals.
Pros: seven years of software updates, great camera with 5x and 10x optical magnification plus AI zoom, Magic Cue and impressive local AI features, Qi2 wireless charging and magnetic accessory support, solid battery life, great screen and size, fast fingerprint and face recognition, 12 months of Google AI Pro included.
Cons: quite expensive, face unlock option not as secure as Face ID, raw performance and battery life short of best-in-class, no physical sim card slot in the US, not a big upgrade from the standard Pixel 10.
Artificial intelligence is transforming our world at an unprecedented rate, but what does this mean for Christians, morality and human flourishing?
In this episode of “The Inside Story,” Billy Hallowell sits down with The Christian Post’s Brandon Showalter to unpack the promises and perils of AI.
From positives like Bible translation to fears over what’s to come, they explore how believers can apply a biblical worldview to emerging technology, the dangers of becoming “subjects” of machines, and why keeping Christ at the center is the only true safeguard.
Plus, learn about The Christian Post’s upcoming “AI for Humanity” event at Colorado Christian University and how you can join the conversation in person or via livestream:
“The Inside Story” takes you behind the headlines of the biggest faith, culture and political headlines of the week. In 15 minutes or less, Christian Post staff writers and editors will help you navigate and understand what’s driving each story, the issues at play — and why it all matters.
Listen to more Christian podcasts today on the Edifi app — and be sure to subscribe to The Inside Story on your favorite platforms:
View a PDF of the paper titled Oyster-I: Beyond Refusal — Constructive Safety Alignment for Responsible Language Models, by Ranjie Duan and 26 other authors
Abstract:Large language models (LLMs) typically deploy safety mechanisms to prevent harmful content generation. Most current approaches focus narrowly on risks posed by malicious actors, often framing risks as adversarial events and relying on defensive refusals. However, in real-world settings, risks also come from non-malicious users seeking help while under psychological distress (e.g., self-harm intentions). In such cases, the model’s response can strongly influence the user’s next actions. Simple refusals may lead them to repeat, escalate, or move to unsafe platforms, creating worse outcomes. We introduce Constructive Safety Alignment (CSA), a human-centric paradigm that protects against malicious misuse while actively guiding vulnerable users toward safe and helpful results. Implemented in Oyster-I (Oy1), CSA combines game-theoretic anticipation of user reactions, fine-grained risk boundary discovery, and interpretable reasoning control, turning safety into a trust-building process. Oy1 achieves state-of-the-art safety among open models while retaining high general capabilities. On our Constructive Benchmark, it shows strong constructive engagement, close to GPT-5, and unmatched robustness on the Strata-Sword jailbreak dataset, nearing GPT-o1 levels. By shifting from refusal-first to guidance-first safety, CSA redefines the model-user relationship, aiming for systems that are not just safe, but meaningfully helpful. We release Oy1, code, and the benchmark to support responsible, user-centered AI.
Submission history
From: Ranjie Duan [view email]
[v1]
Tue, 2 Sep 2025 03:04:27 UTC (5,745 KB)
[v2]
Thu, 4 Sep 2025 11:54:06 UTC (5,745 KB)
[v3]
Mon, 8 Sep 2025 15:18:35 UTC (5,746 KB)
[v4]
Fri, 12 Sep 2025 04:23:22 UTC (5,747 KB)
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