By Jade Martinez-Pogue ( February 5, 2025, 4:50 PM EST) — Special purpose acquisition company K&F Growth Acquisition II began trading publicly Wednesday after raising $250 million in its initial public offering, which will be used to help the SPAC merge with a target in the in-person and mobile experiential entertainment sector…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The last mile problem in generative AI refers to the ability of enterprises to deploy applications to production.  For many companies, the answer lies in marketplaces, which enterprises and developers can browse for applications akin to the Apple app store and download new programs onto their phones. Providers such as AWS Bedrock and Hugging Face have begun building marketplaces, offering ready-built applications from partners that customers can integrate into their stack.  The latest entrant into the AI marketplace space is Lightning AI, the company that runs the open-source Python library PyTorch Lightning. Today it is launching AI Hub, a marketplace for both AI models and applications. What sets it apart from other marketplaces, however, is that Lightning allows allows developers to actually do deployment — and enjoy enterprise security too. Lightning AI CEO William Falcon told VentureBeat in an exclusive interview that AI Hub allows enterprises to find the application they want without having all the other platforms required to run it.  Falcon noted that previously, enterprises had to find hardware providers that could run and host models. The next step was to find a way to deploy that model and make it into something useful.  “But then you need those models to do something, and that’s where the last mile issue is, that’s the end thing enterprises use, and most of that is from standalone companies that offer an app,” he said. “They bought all these tools, did a bunch of experiments, and then couldn’t deploy them or really take them to that last mile.” Falcon added that AI Hub “removes the need for specialized platforms.” Enterprises can find any type of AI application they want in one place. This helps organizations stuck in the prototype phase move faster to deployment.  AI Hub as an app store AI Hub hosts more than 50 APIs at launch, with a mix of foundation models and applications. It hosts many popular models, including DeepSeek-R1.  Enterprises can access AI Hub and find applications built using Lightning’s flagship product, Lightning AI Studio, or by other developers. They can then run these on Lightning’s cloud or private enterprise cloud environments. Organizations can link their AWS or Google Cloud instances and keep data within their company’s virtual private cloud. Falcon said this offers enterprises control over deployment.  Lightning AI’s AI Hub can work with most cloud providers. While it hosts open-source models, Falcon said the apps it hosts are not open-source, meaning users cannot alter their code.  Lightning AI will offer AI Hub free for current customers, with 15 monthly credits to run applications. It will offer different pricing tiers for enterprises that want to connect to their private clouds.   Falcon said AI Hub speeds up the deployment of AI applications within an organization because everything they need is on the platform.  “Ultimately, as a platform, what we offer enterprises is iteration and speed,” he said. “I’ll give you an example: We have a Big Fortune 100 pharma company customer. Within a few days of when DeepSeek came out, they had it in production, already running.” More AI marketplaces  Lightning AI’s AI Hub is not the first AI app marketplace, but its launch indicates how fast the enterprise AI space has moved since the launch of ChatGPT, which powered a generative AI boom in enterprise technology. API marketplaces still offer tons of SaaS applications to enterprises, and more companies are beginning to provide access to AI-powered applications like Apple’s App Store to make them easier to deploy.  AWS, for instance, announced the AWS Bedrock Marketplace for specialized foundation models and Buy with AWS — which features services from AWS partners — during re:Invent in December.  Hugging Face, for its part, has launched Spaces, an AI app directory that allows developers to search and try out new apps, for general availability. Hugging Face CEO Clement Delangue posted on X that Spaces “has quietly become the biggest AI app store, with 400,000 total apps, 2,000 new apps created every day, getting visited 2.5M times every week!” He added that the launch of Spaces shows how “The future of AI will be distributed.” Even OpenAI’s GPT Store on ChatGPT technically functions as a marketplace for people to try out custom GPTs.  This is HUGE The AI App store is here Ask anything you want to do with AI With ~400k Apps, this is the best place to find the AI apps you need developers can build apps, users can try them out and find new apps with AI search pic.twitter.com/oDKDRTZvQP — AK (@_akhaliq) February 4, 2025 Falcon noted that most technologies are offered in a marketplace, especially to reach many potential customers. In fact, this is not the first time Lightning AI has launched an AI marketplace. Lightning AI Studio, first announced in December 2023, lets enterprises create AI platforms using pre-built templates.  “Every technology ends up here,” said Falcon. “Through the evolution of any technology, you’re going to end up in something like this. The iPhone’s a good example. You went from point solutions to calculators. flashlights and notepads. Something like Slack did the same thing where you had an app to send files or photos before, but now it’s all in one. There hasn’t really been that for AI because it’s still kind of new.” Lightning AI, though, faces tough competition especially against Hugging Face. Hugging Face has long been a repository of models and applications and is widely used by developers. Falcon said what makes AI Hub different is that users not only access to state of the art applications with powerful models, but it allows them to begin their AI deployment in the platform and focus on enterprise security. “I can hit deployment here. As an enterprise they can point to their AWS or Google Cloud and the application runs in their private cloud. No data leaks or security issues it’s all within your firewall,” he

By Andrew Karpan ( February 4, 2025, 10:30 PM EST) — A company found using the patent review process to try to extort money from VLSI Technology LLC and Intel Corp. after a $2.18 billion jury verdict against the chipmaking giant is arguing it shouldn’t have to pay legal fees, saying its efforts to revive a meritorious patent challenge is simply part of a “potentially profitable business model.”… Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

On Jan. 30, Microsoft showed off the latest Surface for Business Copilot+ PCs, Surface Laptop 7 and Surface Pro 11. Both will be on store shelves Feb. 18. Just a day earlier, the company announced all users of the Microsoft Copilot AI assistant will be able to toggle on OpenAI’s o1 model for slower, more “thoughtful” responses. Microsoft Copilot resources from TechRepublic Introducing Surface Laptop 7 and Surface Pro 11 Both new PCs run on Intel’s Series 2 Core Ultra processors. Plus, both include AI perks like the natural language Windows Search and returning search queries on images. Surface Laptop 7 for Business Surface Laptop for Business comes in two sizes: 13.8 inches or 15 inches. Microsoft says its battery life extends up to 22 hours, and that the laptop offers 26% faster performance for multi-tasking compared to the Surface Laptop 5. It retails starting at $1,499.99. 5G connectivity will be enabled on Surface Laptop for Business for the first time later this year in applicable areas. More cellular connectivity options for people working on the go was “one of our top customer requests,” Microsoft Surface General Manager Nancie Gaskill wrote in a blog post. The Microsoft Surface Laptop supports 5G connectivity. Image: Microsoft Microsoft said the Surface Laptop 7 is helping reach the company’s sustainability goals, with 100% recycled rare earth metals in the magnets and a battery cell using entirely recycled cobalt. Surface Pro 11 for Business The Surface Pro 11 tablet puts similar AI features to the laptop version in a smaller, more versatile package. It has a 13-inch touch screen that can be used with either touch or a stylus. The optional keyboard can be attached to the Surface Pro 11 or used wirelessly. Image: Microsoft It retails starting at $1,499.99. It pairs with the Surface Pro Flex Keyboard. Both new Surfaces include protection in line with Microsoft’s commitment to the Secure Future Initiative. SEE: Security researchers found a side-channel vulnerability in modern Apple chips, although the vulnerability has not been exploited. AI and other product announcements Microsoft is betting on organizations “accessing and unlocking value through both the cloud and endpoints” with AI, Gaskill wrote. Other announcements from Microsoft at the same time as the Surface refreshes include: A new $199.99 Surface USB4 Dock with two USB-C, one USB-A, Ethernet, and HDMI ports, and 4K support. Microsoft Edge and Miracast support for Microsoft Teams Rooms on the Surface Hub 3 smart board. Security Copilot in the Surface Management Portal during a public preview starting Feb. 24. OpenAI o1 now available in Copilot features On Jan. 29, Microsoft AI CEO Mustafa Suleyman announced users of Microsoft Copilot can now switch the generative AI into Think Deeper mode, which taps into OpenAI’s o1 “reasoning” model. Microsoft has embraced generative AI enthusiastically, from its close ties to OpenAI to quickly adding the buzzy new Chinese model DeepSeek R1 to Azure AI Foundry. source

Wearable devices have become a big part of modern health care, helping track a patient’s heart rate, stress levels and brain activity. These devices rely on electrodes, sensors that touch the skin to pick up electrical signals from the body. Creating these electrodes isn’t as easy as it might seem. Human skin is complex. Its properties, such as how well it conducts electricity, can change depending on how hydrated it is, how old you are or even the weather. These changes can make it hard to test how well a wearable device works. Additionally, testing electrodes often involves human volunteers, which can be tricky and unpredictable. Everyone’s skin is different, meaning results aren’t always consistent. Testing also takes time and money. Plus, there are ethical concerns about asking people to participate in these experiments, including making sure they are informed about the risks and benefits and can voluntarily participate. Scientists have tried to create artificial skin models to avoid some of these problems, but existing ones haven’t been able to fully mimic the way skin behaves when interacting with wearable sensors. To address these limitations, my colleagues and I have developed a tool called a biomimetic skin phantom – a model that mimics the electrical behavior of human skin, making testing wearable sensors easier, cheaper and more reliable. What is a skin phantom? Our biomimetic skin phantom is made of two layers that capture the nuances of both the skin’s surface and deeper tissues. “Biomimetic” means it imitates something from nature – in this case, human skin. “Phantom” refers to a physical model or device made to mimic the properties of something real, like human tissues, so it can be used for research instead of relying on actual people. Your skin is made up of multiple layers of cells. OpenStax, CC BY-SA The bottom layer mimics the deeper tissues under the skin. It is made from a gel-like substance called polyvinyl alcohol cryogel, which can be adjusted to have similar softness and electrical conductivity to real biological tissues. We chose this material because these qualities, along with its durability and wide use in biomedical research, make it a good stand-in for the deeper layers of skin. The top layer mimics the outermost part of the skin, known as the stratum corneum. It is made from a silicone-like material called PDMS, which is mixed with special additives to match the skin’s electrical properties. Also widely used in biomedical research, PDMS is flexible and easy to shape to closely replicate the skin’s outer layer. One unique feature of our skin phantom is its ability to mimic different levels of skin hydration. Hydration affects how well skin conducts electricity. Dry skin has higher resistance, meaning it opposes the flow of electricity. This makes it harder for wearable devices to pick up signals. Hydrated skin conducts electricity more easily because water improves the movement of charged particles, leading to better signal quality. Improving how dry skin is modeled and tested can lead to better electrode designs. To replicate the effects of skin hydration, we introduced adjustable pores into the top PDMS layer of the skin phantom. By precisely changing the size and density of the pores, the model can mimic dry or hydrated skin conditions. Testing the skin phantom My team and I tested our skin phantom in several ways to see whether it could truly replace human skin in experiments. First, we used a method called impedance spectroscopy to study the phantom’s electrical properties. This technique applies alternating electrical signals at different frequencies and measures the material’s resistance to electrical flow, providing a detailed profile of its electrical behavior. Results from the experiments we conducted on five volunteers showed that the phantom’s impedance response closely mirrored that of human skin across both dry and hydrated conditions, with a difference of less than 20% between real skin and the phantom. Moist skin behaves differently from dry skin. Frederic Cirou/PhotoAlto via Getty Images We also tested whether wearable devices could pick up signals from the skin phantom and how signal quality changed with different skin conditions. To do this, we recorded eletrocardiogram signals on phantoms designed to mimic dry and hydrated skin. The results showed clear differences in signal quality: The phantom simulating dry skin had a lower signal-to-noise ratio, while the hydrated skin phantom showed better signal clarity. These findings are consistent with previous studies from other researchers. Together, our skin phantom closely replicates the way human skin responds to wearable sensors across a range of conditions, including dry and hydrated states. This accuracy makes it an optimal stand-in for real skin in the lab. Wearable technology The skin phantom is more than just a testing tool – it’s a step forward for wearable health technology. By removing the unpredictability of human testing, scientists can design and improve wearable devices more quickly and effectively. They can also use it to study how skin interacts with medical devices, such as patches that deliver medicine or advanced diagnostic tools. Our skin phantom is also simple and inexpensive. Each phantom costs less than US$3 and can be made with standard lab materials and tools. It can be reused multiple times within the same day without significant changes in its electrical properties, though extended use over several days may require adjustments, such as rehydration, to maintain stable performance. This affordability and reusability make the phantom more accessible for labs with limited budgets or resources. As wearable technology becomes more common in health care, tools such as the skin phantom can help make devices more reliable, accessible and personalized for everyone. source

00:00  Hi everybody, welcome to DEMO, the show where companies come in and they show us their latest products and services. Today, I’m joined by Sumit Taneja. He is the senior vice president and Global Head of AI consulting and implementation at EXL. Welcome to the show, Sumit. 00:12Thank you very much. 00:14Alright, so what are you going to show us today? Some really cool stuff, I hope. 00:16We’re going to talk about Insurance LLM. The world has been talking about LLMs in the last 18 to 24 months. We also thought we could solve all problems in insurance just using the LLMs like cloud or OpenAI, right? We tested them and we found them not accurate because they’re not trained on insurance data. They have been trained on open Internet data, which is not what our insurance clients want. And this is going to solve that problem. So we have created an Insurance LLM which has been made, curated, trained on insurance data. 00:57So I would imagine insurance companies are going to be interested in this offering, but specifically within those companies, are there specific roles that would really benefit from having this LLM in their in their system? 01:11Right now, we have trained our LLM on the claims data, so the claims adjusters would be the real users of this, but we’re expanding that to underwriters as well as we move forward. 01:23What problems do a lot of these claims adjusters and underwriters have with their current system that they would not get some benefit from from an LLM like this? 01:33If you look at a day in a life of a claimed adjuster, and especially let’s imagine they have to adjudicate a claim which has lot of a patient, which has a lot of medical issues, maybe an auto accident, and now he has to ensure that the claim is passed in a very fair manner. So you have to look at what’s covered in the policy. What are the exclusions and inclusions? What’s the medical history of that patient? What is the doctor saying? What procedures are being done? And this is a lot of manual documentation, so you have to understand the claims adjuster is not the doctor, right? They’re not so close to the patient or the context. They have to just read the documents and find out and do it fairly right. What we have seen in the past is one, there is a chance of errors, because you might miss some inclusion which they might have added to the policy, or they might misinterpret the medical documents, and that’s not good for them right now, some customers were or some clients would probably have stacks and stacks of paper, and the adjuster would have to go through that. But even if those documents were digitized, some of the older systems might not catch a like a keyword search or something like that, that they’d be looking for. So this adds another layer of all of the digital documents that companies have. I think today we do find lot of the documents scanned, but that doesn’t solve the problems, because if I have to look at 80 pages and make a decision, and that too, with so many claims in the pipeline, there are chances of error. 03:22Alright, so let’s get into the demo, and because I know you have a lot of cool features that you want to share. 03:26Let’s get into the demo. But before I get into the demo, I also wanted to share some stats, because we did compare the Insurance LLM, which we trained on medical records. And just to give you a sense of the training data, this is worth eight years of processing, which our teams have done. EXL does all of this historically, manually, and we have now automated it, but we could find the right level of data, and now it’s 30% more accurate than the models, and we have compared against the small models as well as the large models, for example, Mistral 7b or LLama3 70 B. And these are different kinds of models from a capacity perspective, but their accuracy is still far lower. Now, what does this accuracy mean? Because that’s important. It’s not just throwing some benchmarks and people should be happy about but when I am a claim adjuster, I have loads of these documents, so I will get these pages in a PDF, which can be 50 pages, 60 pages, 80 pages. Sometimes it’s a progress note, sometimes it’s a radiology report, sometimes it’s just an accident report. So first, what we do is ensure that we split those documents into logical chunks in the system. Nothing out of the ordinary, but that’s required. Then I need to extract the right information so that I can see what information is coming in these documents. So you’ll get a whole medical summary, whether it’s patient name, what the weight, BMI, simple stats are. So we have extracted all the relevant entities. Again, not rocket science. This was evolving, but this is the bare minimum you had to do. Okay, but most in the most important stuff is when the claims adjuster starts to ask or wants to know. And what we have also done is, if you will, you will be looking at these questions. So for example, what was the procedure that the subject underwent? Right now, these Q and A pairs have been designed for with the experience we have had over the last 8 to 10 years working on these documents, because you don’t want a claims adjuster to again start typing a query, because the claims adjuster may not type the query rightly and may not get the right output. So we have preconfigured the most common questions they might get, and you will automatically get all the answers. So for example, I’m looking what was the procedure based on that medical record. This is a procedure which, which the patient underwent. I need to

By Al Barbarino ( February 4, 2025, 3:27 PM EST) — A contentious bidding battle between KKR and Bain Capital intensified Tuesday as the buyout firms continued their fight to take control of Japan’s Fuji Soft, with KKR disclosing it has once again raised its tender offer price. … Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

Generative AI (genAI) for visual content is the subject of many memes — think pictures of humans with three hands. But while the technology is not yet perfect, it’s gaining traction across industries, from retail and manufacturing to insurance and the public sector. Organizations are leveraging this technology to create immersive and interactive customer experiences such as virtual try-ons and hyper-personalized marketing campaigns, as well as virtual simulation environments and AI designs. While many have heard the buzz around AI-generated images, the real transformation is much broader. GenAI isn’t just generating captivating visuals; it’s reshaping how businesses scale creativity, engage audiences, and develop entirely new products. GenAI Revolutionizes Content Productivity Until recently, creating compelling visual content required large budgets, specialized design and production teams, and long production lead times. Now, advanced AI technologies such as generative adversarial networks, diffusion models, and transformer-based architecture can generate, enhance, or remix images and videos in ways that were unimaginable just a few years ago. This means more agility and a faster path to market for creative assets. Forward-thinking firms are already using genAI to prototype products, localize campaigns for different regions, and customize content at a pace that was not possible with traditional methods. Opportunity Knocks, But So Do The Pitfalls Several converging factors are propelling genAI for visual content into the spotlight: Cost and speed. AI-based tools dramatically reduce design and production timelines, providing near-instant variations of an image or video for different target audiences. This creates the opportunity to produce quality content in a fraction of the time previously required and at a fraction of the cost. Engagement and personalization. Fast and cheap are not the only advantages. GenAI for visual content also helps enterprises meet consumers’ demand for relevance. GenAI enables brands to tailor visuals to individual preferences — whether it’s a try-on avatar mirroring the user’s body type or localizing imagery for numerous global markets. Interactive experiences. Beyond static images and video, companies are exploring AI-powered 3D renderings, augmented-reality demos, and dynamic ads that respond in real time to user behavior. Of course, it’s not all smooth sailing. Even as businesses rush to harness genAI’s visual content potential, concerns about copyright, data privacy, and brand integrity arise. Organizations also face mounting regulatory pressures, especially in regions like the EU, where the EU AI Act imposes stringent guidelines for AI-generated media. At the same time, ethical considerations loom large, from ensuring fair training data to avoiding deepfake misuse, and photorealism or visual quality is still a concern for brands that command a premium in the marketplace and require high-quality visuals to match. From Synthetic Moments To Governance: Create A Balanced GenAI Game Plan Executives need a holistic approach that weaves together technology, business, and culture to maximize the impact of genAI. This means tailoring AI-generated experiences for the right consumer moments — using synthetic visuals for scrolling or gaming while preserving authenticity for live encounters such as in-store shopping. It involves freeing employees from repetitive tasks through automation and reserving creative, strategic work for human insight. Finally, leaders must establish solid governance, aligning with emerging global regulations to ensure responsible innovation and effective risk management. Our newly published report, The State Of Generative AI For Visual Content, 2025, illustrates how genAI for visual content brings the promise of scaled production, content personalization, and immersive consumer experiences to life. Forrester clients can read this report to learn about the trends, use cases, and short- to midterm benefits. Clients can also read Maximize The Magic Of AI Visual Content to better understand the risks/rewards of using visual content tools or The State Of Computer Vision Technology, 2024 to understand how advancements in machine learning, enhanced computing power on devices like smartphones, and increased data availability have accelerated the adoption of computer-vision-powered applications. And of course, clients can also connect with us through an inquiry or guidance session to discuss genAI for visual content solutions or other associated topics. source

By Eric Juergens, Marilyn Lion and Nicholas Potter ( January 31, 2025, 10:49 AM EST) — The insurance industry continued to experience dynamic changes during 2024, as the impact of artificial intelligence, cyber risks, increased catastrophe activity, significant private equity investment and more complex transactions influenced the industry…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source