Microsoft has launched a new suite of specialized AI models designed to address specific challenges in manufacturing, agriculture, and financial services. In collaboration with partners such as Siemens, Bayer, Rockwell Automation, and others, the tech giant is aiming to bring advanced AI technologies directly into the heart of industries that have long relied on traditional methods and tools.

These purpose-built models—now available through Microsoft’s Azure AI catalog—represent Microsoft’s most focused effort yet to develop AI tools tailored to the unique needs of different sectors. The company’s initiative reflects a broader strategy to move beyond general-purpose AI and deliver solutions that can provide immediate operational improvements in industries like agriculture and manufacturing, which are increasingly facing pressures to innovate.

“Microsoft is in a unique position to deliver the industry-specific solutions organizations need through the combination of the Microsoft Cloud, our industry expertise, and our global partner ecosystem,” Satish Thomas, Corporate Vice President of Business & Industry Solutions at Microsoft, said in a LinkedIn post announcing the new AI models.

“Through these models,” he added, “we’re addressing top industry use cases, from managing regulatory compliance of financial communications to helping frontline workers with asset troubleshooting on the factory floor — ultimately, enabling organizations to adopt AI at scale across every industry and region… and much more to come in future updates!”

Siemens and Microsoft remake industrial design with AI-powered software

At the center of the initiative is a partnership with Siemens to integrate AI into its NX X software, a widely used platform for industrial design. Siemens’ NX X copilot uses natural language processing to allow engineers to issue commands and ask questions about complex design tasks. This feature could drastically reduce the onboarding time for new users while helping seasoned engineers complete their work faster.

By embedding AI into the design process, Siemens and Microsoft are addressing a critical need in manufacturing: the ability to streamline complex tasks and reduce human error. This partnership also highlights a growing trend in enterprise technology, where companies are looking for AI solutions that can improve day-to-day operations rather than experimental or futuristic applications.

Smaller, faster, smarter: How Microsoft’s compact AI models are transforming factory operations

Microsoft’s new initiative relies heavily on its Phi family of small language models (SLMs), which are designed to perform specific tasks while using less computing power than larger models. This makes them ideal for industries like manufacturing, where computing resources can be limited, and where companies often need AI that can operate efficiently on factory floors.

Perhaps one of the most novel uses of AI in this initiative comes from Sight Machine, a leader in manufacturing data analytics. Sight Machine’s Factory Namespace Manager addresses a long-standing but often overlooked problem: the inconsistent naming conventions used to label machines, processes, and data across different factories. This lack of standardization has made it difficult for manufacturers to analyze data across multiple sites. The Factory Namespace Manager helps by automatically translating these varied naming conventions into standardized formats, allowing manufacturers to better integrate their data and make it more actionable.

While this may seem like a minor technical fix, the implications are far-reaching. Standardizing data across a global manufacturing network could unlock operational efficiencies that have been difficult to achieve.

Early adopters like Swire Coca-Cola USA, which plans to use this technology to streamline its production data, likely see the potential for gains in both efficiency and decision-making. In an industry where even small improvements in process management can translate into substantial cost savings, addressing this kind of foundational issue is a crucial step toward more sophisticated data-driven operations.

Smart farming gets real: Bayer’s AI model tackles modern agriculture challenges

In agriculture, the Bayer E.L.Y. Crop Protection model is poised to become a key tool for farmers navigating the complexities of modern farming. Trained on thousands of real-world questions related to crop protection labels, the model provides farmers with insights into how best to apply pesticides and other crop treatments, factoring in everything from regulatory requirements to environmental conditions.

This model comes at a crucial time for the agricultural industry, which is grappling with the effects of climate change, labor shortages, and the need to improve sustainability. By offering AI-driven recommendations, Bayer’s model could help farmers make more informed decisions that not only improve crop yields but also support more sustainable farming practices.

The initiative also extends into the automotive and financial sectors. Cerence, which develops in-car voice assistants, will use Microsoft’s AI models to enhance in-vehicle systems. Its CaLLM Edge model allows drivers to control various car functions, such as climate control and navigation, even in settings with limited or no cloud connectivity—making the technology more reliable for drivers in remote areas.

In finance, Saifr, a regulatory technology startup within Fidelity Investments, is introducing models aimed at helping financial institutions manage regulatory compliance more effectively. These AI tools can analyze broker-dealer communications to flag potential compliance risks in real-time, significantly speeding up the review process and reducing the risk of regulatory penalties.

Rockwell Automation, meanwhile, is releasing the FT Optix Food & Beverage model, which helps factory workers troubleshoot equipment in real time. By providing recommendations directly on the factory floor, this AI tool can reduce downtime and help maintain production efficiency in a sector where operational disruptions can be costly.

The release of these AI models marks a shift in how businesses can adopt and implement artificial intelligence. Rather than requiring companies to adapt to broad, one-size-fits-all AI systems, Microsoft’s approach allows businesses to use AI models that are custom-built to address their specific operational challenges. This addresses a major pain point for industries that have been hesitant to adopt AI due to concerns about cost, complexity, or relevance to their particular needs.

The focus on practicality also reflects Microsoft’s understanding that many businesses are looking for AI tools that can deliver immediate, measurable results. In sectors like manufacturing and agriculture, where margins are often tight and operational disruptions can be costly, the ability to deploy AI that improves efficiency or reduces downtime is far more appealing than speculative AI projects with uncertain payoffs.

By offering tools that are tailored to industry-specific needs, Microsoft is betting that businesses will prioritize tangible improvements in their operations over more experimental technologies. This strategy could accelerate AI adoption in sectors that have traditionally been slower to embrace new technologies, like manufacturing and agriculture.

Inside Microsoft’s plan to dominate industrial AI and edge computing

Microsoft’s push into industry-specific AI models comes at a time of increasing competition in the cloud and AI space. Rivals like Amazon Web Services and Google Cloud are also investing heavily in AI, but Microsoft’s focus on tailored industry solutions sets it apart. By partnering with established leaders like Siemens, Bayer, and Rockwell Automation, Microsoft is positioning itself to be a key player in the digitization of industries that are under growing pressure to modernize.

The availability of these models through Azure AI Studio and Microsoft Copilot Studio also speaks to Microsoft’s broader vision of making AI accessible not just to tech companies, but to businesses in every sector. By integrating AI into the day-to-day operations of industries like manufacturing, agriculture, and finance, Microsoft is helping to bring AI out of the lab and into the real world.

As global manufacturers, agricultural producers, and financial institutions face increasing pressures from supply chain disruptions, sustainability goals, and regulatory demands, Microsoft’s industry-specific AI offerings could become essential tools in helping them adapt and thrive in a fast-changing world.

Lyten, a Silicon Valley battery startup, announced today that it’s acquiring manufacturing assets from Northvolt, a Swedish battery manufacturer that’s facing a cash crunch.

As part of the deal, Northvolt is selling manufacturing equipment the company inherited in its 2021 acquisition of Cuberg, another battery startup. Lyten will also assume the lease of Cuberg’s old manufacturing facility in San Leandro, California. Lyten will invest $20 million next year to expand facilities in San Leandro and its existing operations in San Jose.

Neither Lyten nor Northvolt immediately replied to questions about the deal’s financial terms.

Unlike many other battery manufacturers, Lyten isn’t relying on nickel, cobalt, manganese, or even iron for its cathode materials. Instead, it’s using cheap and abundant sulfur mixed into a graphene matrix. On the anode side, it doesn’t use any graphite, a material that faces export restrictions from China. The company says the combination results in cells that have greater energy density than nickel-manganese-cobalt flavors but are cheaper to produce than low-cost lithium-iron-phosphate.

Northvolt has been struggling lately. The company has struggled to scale up production of lithium-ion batteries, and it missed delivery of a large order from BMW, which nudged the automaker to nullify a €2 billion contract. 

To conserve cash, the company announced in August that it would shutter research and development at the Cuberg site, laying off nearly 200 employees. Then in September, it said that it was laying off an additional 1,600 employees, about 20% of its workforce, and that it had halted two planned factory expansions.

It’s unclear whether that cost-cutting and deal with Lyten will be enough to help Northvolt get through the coming year. Last week, Bloomberg reported that Northvolt needs to raise nearly $1 billion to give it some breathing room; the company’s operations reportedly burn through about $100 million a month.

While Northvolt is on the skids, Lyten appears ascendent.

The San Jose-based startup is planning to break ground next year on a factory in Nevada with a planned capacity of 10 gigawatt-hours. When complete, the $1 billion facility will produce lithium-sulfur batteries destined for micromobility vehicles like scooters and e-bikes, and defense and space applications like drones and satellites. The company expects it to come online in 2027.

Lyten’s purchase of Northvolt’s Cuberg assets give it the equipment and space to produce up to 200 megawatt-hours of lithium-sulfur batteries in the Bay Area. That should give the company some revenue while it prepares its larger factory in Nevada.

Lyten has raised $476 million to date at a $1.17 billion valuation, according to PitchBook, including a $200 million round that closed last year.

OpenAI is preparing to release an autonomous AI agent that can control computers and perform tasks independently, code-named “Operator.” The company plans to debut it as a research preview and developer tool in January, according to Bloomberg.

This move intensifies the competition among tech giants developing AI agents: Anthropic recently introduced its “computer use” capability, while Google is reportedly preparing its own version for a December release. The timing of Operator’s eventual consumer release remains under wraps, but its development signals a pivotal shift toward AI systems that can actively engage with computer interfaces rather than just process text and images.

All the leading AI companies have promised autonomous AI agents, and OpenAI has hyped up the possibility recently. In a Reddit “Ask Me Anything” forum a few weeks ago, OpenAI CEO Sam Altman said “we will have better and better models,” but “I think the thing that will feel like the next giant breakthrough will be agents.” At an OpenAI press event ahead of the company’s annual Dev Day last month, chief product officer Kevin Weil said: “I think 2025 is going to be the year that agentic systems finally hit the mainstream.”

AI labs face mounting pressure to monetize their costly models, especially as incremental improvements may not justify higher prices for users. The hope is that autonomous agents are the next breakthrough product — a ChatGPT-scale innovation that validates the massive investment in AI development.

Watching old episodes of ER won’t make you a doctor, but watching videos may be all the training a robotic surgeon’s AI brain needs to sew you up after a procedure. Researchers at Johns Hopkins University and Stanford University have published a new paper showing off a surgical robot as capable as a human in carrying out some procedures after simply watching humans do so.

The research team tested their idea with the popular da Vinci Surgical System, which is often used for non-invasive surgery. Programming robots usually requires manually inputting every movement that you want them to make. The researchers bypassed this using imitation learning, a technique that implanted human-level surgical skills in the robots by letting them observe how humans do it.

Strava, a popular app for tracking fitness activities, is expanding its Hatmaps feature to help improve the safety of its users. The update should be especially useful now for users in the Northern Hemisphere, which is heading into winter with reduced daylight.

The new Night and Weekly Heatmaps were announced by the San Francisco-based company on Wednesday and are available to all Strava subscribers. As the name of the feature suggests, the Heatmaps show where Strava users are choosing to exercise, with dark thick lines showing well-used routes, and light thin lines showing less popular ones.

First up, the new Night Heatmaps feature is ideal for those who are doing their activities in the late evening or early morning hours, when there’s less light. They show the most popular areas for outdoor activities from sunset to sunrise, helping athletes to better plan their outdoor activities during this time frame. If it’s a new area for you, you may also want to cross-check the Night Heatmap data with Google Street View images to get a better understanding of the place.

Weekly Heatmaps, on the other hand, show data for recent heat from the last seven days so that users can see which trails and roads are currently active, particularly during seasonal transitions when conditions may be impacted by weather.

“Our global community powers ourHeatmaps and now we’ve made it easier for our community members to build routes with confidence, regardless of the season or time of day,” Matt Salazar, Strava’s chief product officer, said in Wednesday’s announcement about the new features. “We are continually improving our mapping technology to make human-powered movement easier for all skill levels.”

Strava has also shared a useful at-a-glance guide to all four of its Heatmaps, Night, Weekly, Global, and Personal:

Night (new): Discover the most frequented areas between sunset and sunrise; ideal for evening or early morning users.

Weekly (new): Stay updated with the latest data from the past seven days; perfect for adjusting plans around seasonal changes or unexpected closures.

Global (existing): Viewable by anyone regardless of whether you have a Strava account, the Global Heatmap allows you to see what areas are most popular around the world based on community activity uploads.

Personal (existing): A one-of-a-kind illustration showing the record of everywhere you’ve logged a GPS activity. This heatmap is private and only available to you.

Google’s Gemini is useful as an educational tool to help you study for that exam. However, Gemini is sort of the “Everything chatbot” that’s useful for just about everything. Well, Google has a new model for people looking for more of a robust educational tool. Google calls it Learn About, and it could give other tools a run for their money.

Say what you want about Google’s AI, the company has been hard at work making AI tools centered around teaching rather than cheating. For example, it has tools in Android studio that guides programmers and help them learn coding. Also, we can’t forget about NotebookLM. This is the tool that takes your uploaded educational content and helps you digest it. We can’t forget abou the Audio Overviews feature that turns your uploaded media into a live podcast-style educational discussion.

So, Google has a strong focus on education with its AI tools. Let’s just hope that other companies will follow suit.

Google’s new AI tool is called Learn About

This tool is pretty self-explanatory, as it focus on giving you more text-book style explanations for your questions. Rather than simply giving you answers, this tool will go the extra mile to be more descriptive with its explanation. Along with that, Learn About will also provide extra context on the subject and give you other educational material on it.

Google achieved this by using a totally different model to power this tool. Rather than using the Gemini model, Ask About uses a model called LearnLM. At this point, we don’t really know much about this model, but we know that Google steered it more towards providing academic answers.

Gemini’s answer vs. Learn About’s answers

We tested it out by asking what pulsars are, and we compared the answer to what Gemini gave us for the same question. Gemini delivered a pretty fleshed-out explanation in the form of a few paragraphs. It also snagged a few pictures from the internet and pasted the link to a page at the bottom. This is good for a person who’s casually looking up a definition. Maybe that person isn’t looking to learn the ins and outs of what a pulsar is.

There was one issue with Gemini’s answer; one of the images that it pasted was an image of a motorcycle. It pasted an image of the Bajaj Pulsar 150. So, while it technically IS a pulsar, a motorcycle shares very few similarities with massive rapidly spinning balls of superheated plasma billions of miles away from Earth.

What about Learn About?

Learn About also gave an explanation in the form of a few paragraphs;  however, Learn About’s explanation was shorter. It makes up for it by producing more extraneous material. Along with images, it provided three links (one of which was a YouTube video) and chips with commands like Simplify, Go deeper, and Get images (more on the chips below).

Under the chips, you’ll see suggestions of other queries that you can put in for additional context. Lastly, in textbook style, you’ll see colored blocks with additional content. For example, there’s a Why it matters block and a Stop & think block.

Chips

Going back to the aforementioned chips, selecting Simplify and Get images are axiomatic enough. Tapping/clicking on the Go Deeper chip is a bit more interesting. It brought up an Interactive List consisting of a selection of additional queries that will provide extra information about pulsars. Each query you select will bring up even more information.

Textbook blocks

Think about the textbooks you used in school, and you’ll be familiar with these blocks. These blocks come in different colors. The Why it matters block tells you why this information is important. Next, the Stop & think block seems to give you a little bit of tangential information. It asks a question and has a button to reveal the answer. It’s a way to get you to think outside of the box a bit.

There’s a Build your vocab box that introduces you to a relevant term and shows you a dictionary-style definition of it. This is a term that the reader is most likely not familiar with.

The next block we encountered was the Test your knowledge block. This one has a quiz-style question and it gives you two options. Other subject matters might have more choices, but this is what we got in our usage.

We also saw a Common misconception block. This one pretty much explains itself.

Bottom bar

At the very bottom of the screen, you’ll see a bar with some additional chips. One chip should show the title of the current subject, and Tapping/clicking on it will bring up a floating window with additional topic suggestions. In our case, we also saw the interactive list that we saw previously. This one will show the list in a floating window.

One issue

So, do you remember when Gemini gave us the image of the motorcycles? Well, while the majority of Learn About’s images were relevant to the subject, it still retrieved two images of the motorcycles. As comical as it is, it shows that Google’s AI still has a ways to go before it’s perfect. However, barring that little mishap, Learn About runs as smoothly as the motorcycle it’s surfacing pictures of.

Use it today!

You can use Learn About today if you want to try it out. Just go to the Learn About website Learn About website, and you’ll be able to try it out. Just know that, as with most Google services, the availability will depend on your region. We were able to access it in the U.S. in English. Just know that you may not have it in regions that Google typically overlooks.

You can use it regardless of if you’re a free or paid user. Please note that Learn About is technically an experiment. This means that Google only put this on the market for testing. Google could potentially lock this behind a paywall after the beta testing phase. Just know that this feature could disappear down the line. So, you’ll want to get in and use it while you can.