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Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Stability AI is out today with a major update for its text to image generative AI technology with the debut of Stable Diffusion 3.5. A key goal for the new update is raise the bar and improve upon Stability AI’s last major update, which the company admitted didn’t live up to its own standards. Stable Diffusion 3 was first previewed back in February and the first open model version became generally available in June with the debut of Stable Diffusion 3 Medium. While Stability AI was an early pioneer in the text to image generative AI space, it has increasingly faced stiff competition from numerous rivals including Black Forest Labs’ Flux Pro, OpenAI’s Dall-E, Ideogram and Midjourney. With Stable Diffusion 3.5, Stability AI is looking to reclaim its leadership position. The new models are highly customizable and can generate a wide range of different styles. The new update introduces multiple model variants, each designed to cater to different user needs.Stable Diffusion 3.5 Large is an 8 billion parameter model that offers the highest quality and prompt adherence in the series. Stable Diffusion 3.5 Large Turbo is a distilled version of the large model, providing faster image generation. Rounding out the new models is Stable Diffusion 3.5 Medium, which has 2.6 billion parameters and is optimized for edge computing deployments. All three of the new Stable Diffusion 3.5 models are available under the Stability AI Community License, which is an open license that enables free non-commercial usage and free commercial usage for entities with annual revenue under $1 million. Stability AI has an enterprise license for larger deployments. The models are available via Stability AI’s API as well as Hugging Face. The original release of Stable Diffusion 3 Medium in June, was a less than ideal release. The lessons learned from that experience have helped to inform and improve the new Stable Diffusion 3.5 updates. “We identified that several model and dataset choices that we made for the Stable Diffusion Large 8B model were not optimal for the smaller-sized Medium model,” Hanno Basse, CTO of Stability AI told VentureBeat. “We did thorough analysis of these bottlenecks and innovated further on our architecture and training protocols on the Medium model to provide a better balance between the model size and the output quality.”  How Stability AI is improving text to image generative AI with Stable Diffusion 3.5 As part of building out Stable Diffusion 3.5, Stability AI took advantage of a number of novel techniques to improve quality and performance. A notable addition to Stable Diffusion 3.5 is the integration of Query-Key Normalization into the transformer blocks. This technique facilitates easier fine-tuning and further development of the models by end-users. Query-Key Normalization makes the model more stable for training and fine-tuning. “While we have experimented with QK-normalization in the past, this is our first model release with this normalization,” Basse explained. “It made sense to use it for this new model as we prioritized customization.” Stability AI has also enhanced its Multimodal Diffusion Transformer MMDiT-X architecture, specifically for the medium model. Stability AI first highlighted the MMDiT architecture approach in April, when the Stable Diffusion 3 API became available. MMDiT is noteworthy as it blends diffusion model techniques with transformer model techniques. With the updates as part of Stable Diffusion 3.5, MMDiT-X is now able to help improve image quality as well enhancing multi-resolution generation capabilities Prompt adherence makes Stable Diffusion 3.5 even more powerful Stability AI reports that Stable Diffusion 3.5 Large demonstrates superior prompt adherence compared to other models in the market.  The promise of better prompt adherence is all about the models ability to accurately interpret and render user prompts. “This is achieved with a combination of different things – better dataset curation, captioning and additional innovation in training protocols,” Basse said. Customization will get even better with ControlNets Looking forward, Stability AI is planning on releasing a ControlNets capability for Stable Diffusion 3.5.  The promise of ControlNets is more control for various professional use cases. StabilityAI first introduced ControlNet technology as part of its SDXL 1.0 release in July 2023. “ControlNets give spatial control over different professional applications where users, for example, may want to upscale an image while maintaining the overall colors or create an image that follows a specific depth pattern,” Basse said. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Genmo, an AI company focused on video generation, has announced the release of a research preview for Mochi 1, a new open-source model for generating high-quality videos from text prompts — and claims performance comparable to, or exceeding, leading closed-source/proprietary rivals such as Runway’s Gen-3 Alpha, Luma AI’s Dream Machine, Kuaishou’s Kling, Minimax’s Hailuo, and many others. Available under the permissive Apache 2.0 license, Mochi 1 offers users free access to cutting-edge video generation capabilities — whereas pricing for other models starts at limited free tiers but goes as high as $94.99 per month (for the Hailuo Unlimited tier). Users can download the full weights and model code free on Hugging Face, though it requires “at least 4” Nvidia H100 GPUs to operate on a user’s own machine. In addition to the model release, Genmo is also making available a hosted playground, allowing users to experiment with Mochi 1’s features firsthand. The 480p model is available for use today, and a higher-definition version, Mochi 1 HD, is expected to launch later this year. Initial videos shared with VentureBeat show impressively realistic scenery and motion, particularly with human subjects as seen in the video of an elderly woman below: Advancing the state-of-the-art Mochi 1 brings several significant advancements to the field of video generation, including high-fidelity motion and strong prompt adherence. According to Genmo, Mochi 1 excels at following detailed user instructions, allowing for precise control over characters, settings, and actions in generated videos. Genmo has positioned Mochi 1 as a solution that narrows the gap between open and closed video generation models. “We’re 1% of the way to the generative video future. The real challenge is to create long, high-quality, fluid video. We’re focusing heavily on improving motion quality,” said Paras Jain, CEO and co-founder of Genmo, in an interview with VentureBeat. Jain and his co-founder started Genmo with a mission to make AI technology accessible to everyone. “When it came to video, the next frontier for generative AI, we just thought it was so important to get this into the hands of real people,” Jain emphasized. He added, “We fundamentally believe it’s really important to democratize this technology and put it in the hands of as many people as possible. That’s one reason we’re open sourcing it.” Already, Genmo claims that in internal tests, Mochi 1 bests most other video AI models — including the proprietary competition Runway and Luna — at prompt adherence and motion quality. Series A funding to the tune of $28.4M In tandem with the Mochi 1 preview, Genmo also announced it has raised a $28.4 million Series A funding round, led by NEA, with additional participation from The House Fund, Gold House Ventures, WndrCo, Eastlink Capital Partners, and Essence VC. Several angel investors, including Abhay Parasnis (CEO of Typespace) and Amjad Masad (CEO of Replit), are also backing the company’s vision for advanced video generation. Jain’s perspective on the role of video in AI goes beyond entertainment or content creation. “Video is the ultimate form of communication—30 to 50% of our brain’s cortex is devoted to visual signal processing. It’s how humans operate,” he said. Genmo’s long-term vision extends to building tools that can power the future of robotics and autonomous systems. “The long-term vision is that if we nail video generation, we’ll build the world’s best simulators, which could help solve embodied AI, robotics, and self-driving,” Jain explained. Open for collaboration — but training data is still close to the vest Mochi 1 is built on Genmo’s novel Asymmetric Diffusion Transformer (AsymmDiT) architecture. At 10 billion parameters, it’s the largest open source video generation model ever released. The architecture focuses on visual reasoning, with four times the parameters dedicated to processing video data as compared to text. Efficiency is a key aspect of the model’s design. Mochi 1 leverages a video VAE (Variational Autoencoder) that compresses video data to a fraction of its original size, reducing the memory requirements for end-user devices. This makes it more accessible for the developer community, who can download the model weights from HuggingFace or integrate it via API. Jain believes that the open-source nature of Mochi 1 is key to driving innovation. “Open models are like crude oil. They need to be refined and fine-tuned. That’s what we want to enable for the community—so they can build incredible new things on top of it,” he said. However, when asked about the model’s training dataset — among the most controversial aspects of AI creative tools, as evidence has shown many to have trained on vast swaths of human creative work online without express permission or compensation, and some of it copyrighted works — Jain was coy. “Generally, we use publicly available data and sometimes work with a variety of data partners,” he told VentureBeat, declining to go into specifics due to competitive reasons. “It’s really important to have diverse data, and that’s critical for us.” Limitations and roadmap As a preview, Mochi 1 still has some limitations. The current version supports only 480p resolution, and minor visual distortions can occur in edge cases involving complex motion. Additionally, while the model excels in photorealistic styles, it struggles with animated content. However, Genmo plans to release Mochi 1 HD later this year, which will support 720p resolution and offer even greater motion fidelity. “The only uninteresting video is one that doesn’t move—motion is the heart of video. That’s why we’ve invested heavily in motion quality compared to other models,” said Jain. Looking ahead, Genmo is developing image-to-video synthesis capabilities and plans to improve model controllability, giving users even more precise control over video outputs. Expanding use cases via open source video AI Mochi 1’s release opens up possibilities for various industries. Researchers can push the boundaries of video generation technologies, while developers and product teams may find new applications in entertainment, advertising, and education. Mochi 1 can also be used

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Cohere has added multimodal embeddings to its search model, allowing users to deploy images to RAG-style enterprise search.  Embed 3, which emerged last year, uses embedding models that transform data into numerical representations. Embeddings have become crucial in retrieval augmented generation (RAG) because enterprises can make embeddings of their documents that the model can then compare to get the information requested by the prompt.  Your search can see now. We’re excited to release fully multimodal embeddings for folks to start building with! pic.twitter.com/Zdj70B07zJ — Aidan Gomez (@aidangomez) October 22, 2024 The new multimodal version can generate embeddings in both images and texts. Cohere claims Embed 3 is “now the most generally capable multimodal embedding model on the market.” Aidan Gomez, Cohere co-founder and CEO, posted a graph on X showing performance improvements in image search with Embed 3.  The image-search performance of the model across a range of categories is quite compelling. Substantial lifts across nearly all categories considered. pic.twitter.com/6oZ3M6u0V0 — Aidan Gomez (@aidangomez) October 22, 2024 “This advancement enables enterprises to unlock real value from their vast amount of data stored in images,” Cohere said in a blog post. “Businesses can now build systems that accurately and quickly search important multimodal assets such as complex reports, product catalogs and design files to boost workforce productivity.” Cohere said a more multimodal focus expands the volume of data enterprises can access through an RAG search. Many organizations often limit RAG searches to structured and unstructured text despite having multiple file formats in their data libraries. Customers can now bring in more charts, graphs, product images, and design templates.  Performance improvements Cohere said encoders in Embed 3 “share a unified latent space,” allowing users to include both images and text in a database. Some methods of image embedding often require maintaining a separate database for images and text. The company said this method leads to better-mixed modality searches.  According to the company, “Other models tend to cluster text and image data into separate areas, which leads to weak search results that are biased toward text-only data. Embed 3, on the other hand, prioritizes the meaning behind the data without biasing towards a specific modality.” Embed 3 is available in more than 100 languages.  Cohere said multimodal Embed 3 is now available on its platform and Amazon SageMaker.  Playing catch up Many consumers are fast becoming familiar with multimodal search, thanks to the introduction of image-based search in platforms like Google and chat interfaces like ChatGPT. As individual users get used to looking for information from pictures, it makes sense that they would want to get the same experience in their working life.  Enterprises have begun seeing this benefit, too, as other companies that offer embedding models provide some multimodal options. Some model developers, like Google and OpenAI, offer some type of multimodal embedding. Other open-source models can also facilitate embeddings for images and other modalities. The fight is now on the multimodal embeddings model that can perform at the speed, accuracy and security enterprises demand.  Cohere, which was founded by some of the researchers responsible for the Transformer model (Gomez is one of the writers of the famous “Attention is all you need” paper), has struggled to be top of mind for many in the enterprise space. It updated its APIs in September to allow customers to switch from competitor models to Cohere models easily. At the time, Cohere had said the move was to align itself with industry standards where customers often toggle between models.  source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI, the artificial intelligence powerhouse behind ChatGPT, has taken another step in its quest for ubiquity by releasing a Windows desktop application for its popular AI chatbot. The move, announced Thursday, follows the earlier launch of a macOS client and marks a significant push by OpenAI to embed its technology more deeply into users’ daily workflows. The new Windows app, currently available in preview to ChatGPT Plus, Enterprise, Team, and Edu subscribers, allows users to access the AI assistant via a keyboard shortcut (Alt + Space) from anywhere on their PC. This seamless integration aims to boost productivity by making AI assistance readily available without the need to switch to a web browser. OpenAI’s new ChatGPT desktop application for Windows, showing a user interface with conversation history. (Credit: OpenAI) OpenAI’s desktop strategy: More than just convenience OpenAI’s strategy of platform expansion goes beyond mere convenience. By creating native applications for major operating systems, the company is positioning ChatGPT as an indispensable tool in both personal and professional environments. This move serves multiple purposes: it increases user engagement, facilitates more extensive data collection for model improvement, and creates a sticky ecosystem that could be challenging for competitors to displace. The desktop app approach also reveals OpenAI’s ambition to become the de facto AI assistant for knowledge workers. By integrating ChatGPT more deeply into users’ workflows, OpenAI is not just improving accessibility but potentially reshaping how people interact with computers and process information. Enterprise ambitions: ChatGPT as the new office suite? The Windows release comes at a critical juncture for OpenAI, as the company faces increasing competition in the AI space and scrutiny over its rapid growth and influential position. Recent reports suggest that OpenAI is exploring partnerships beyond its well-known Microsoft alliance, including discussions with Oracle for AI data center infrastructure and pitches to the U.S. military and national security establishment. OpenAI’s aggressive expansion into desktop environments signals a potential shift in the enterprise software landscape. The company appears to be positioning ChatGPT as a fundamental productivity tool for businesses, potentially disrupting traditional enterprise software providers. This move, coupled with the recent partnership expansion with Bain & Company to sell ChatGPT to businesses, suggests OpenAI is not content with being merely an AI research lab but is actively pursuing a dominant position in the commercial AI sector. The implications of this strategy are huge. If successful, ChatGPT could become the new “operating system” for knowledge work, fundamentally changing how businesses operate and potentially displacing or absorbing functions currently served by separate software suites. Balancing Act: Innovation, ethics, and commercialization However, OpenAI’s rapid growth and increasing influence have not been without controversy. The company’s AI models have faced scrutiny over potential biases and the societal implications of widespread AI deployment. Additionally, OpenAI’s dual status as a capped-profit company with significant commercial interests has raised questions about its governance and long-term objectives. As OpenAI continues to expand its reach, the company faces a delicate balancing act. It must navigate the tensions between its stated mission of ensuring artificial general intelligence benefits humanity and its increasingly commercial focus. The Windows app release, while a seemingly straightforward product expansion, represents another step in OpenAI’s complex journey of shaping the future of AI in both consumer and enterprise contexts. The success of this desktop strategy could cement OpenAI’s position as the leading AI company, but it also increases the urgency of addressing ethical concerns and potential monopolistic practices. As ChatGPT becomes more deeply integrated into daily work and life, the stakes for getting AI right — in terms of safety, fairness, and societal impact — have never been higher. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More PlayVS has partnered with Omnic.AI, a self-service platform that helps gamers player smarter with AI feedback. The partnership allows players to receive an analysis of their in-game performance, matches them with pro players that have similar gaming styles, and provides them with detailed match analysis data, said Jon Chapman, PlayVS CEO, in an interview with GamesBeat. The goal for PlayVS and Omnic.AI is to help students develop key transferable skills including critical thinking, adaptability, and effective communication. This is aimed at redefining the next generation of gamers, helping them reach their full potential.  PlayVS is focused on middle school and high school esports leagues, and it is targeting such students with the Omnic.AI analytics tools and insights. Back in September, it launched a digital-first esports competition platform to widen its reach. Omnic.AI helps players perform and communicate better in esports. Focuses on esports player performance data and analytics, Omnic.AI uses AI and machine learning to gather insights and a detailed analysis of gameplay for users. The flagship platform, Omnic Forge, analyzes gaming footage and provides players with feedback and statistics to improve their performance in titles including Valorant, Fortnite, Rocket League, Overwatch2 and soon Madden. Omnic.AI provides two kinds of insights. The first is AI coaching insights, which provides tips on team communication, performance, and on the match. An example is here on the website. The other type is advanced analytics, which can help with things such as aim analysis, match recap and stats to other players, and round by round performance summaries. Omnic.AI also has the AI Chat, which is a personalized chatbot that helps with instant coaching advice, gameplay tips, etc., as well as customized results, based on your most recent analyzed match. About 85% of coaches have seen improvement in communication skills among their players, according to last year’s Esports Impact Report from PlayVS. And 83% of coaches agree that their players have improved their leadership skills as a result of esports. It also helps on a broader academic front. The companies also said 60% of coaches have found an improvement in grades and/or attendance among their players. About 45% of PlayVS students reported feeling more excited to go to school after joining an esports team. And 30% of students said they feel more committed to school and academics. In this way, the partnership is about more than just playing games better. “We made a decision earlier this year that we really wanted to find ways to continue to create a resource, and we’re calling it the PlayVS Collective that helps support our community of users, especially those in our leagues at schools,” Chapman said. “Those resources can be a lot of different things. They could be access to curriculum with the connections that can be made between gaming and STEM education.” The PlayVS Collective refers to a thriving partner community that is focused on providing students, coaches and schools with the resources that enrich their esports and gaming experiences.  It could also mean access to the community and infrastructure resources and the equipment a school needs to set up a competitive esports environment. PlayVS organizes esports leagues at schools. “The other dimension that we thought about was how we provide tools that optimize player performance,” said Chapman. “That’s where I got introduced to Shaun Meredith, the CEO of Omnic.Ai, a few months ago. He was really interested in offering their Omnic Forge product, which is essentially an AI tool that analyzes game performance in a number of titles that kids can play in our platform.” The new partnership will bring this technology to PlayVS’ community of gamers at no cost to high school students, helping them gain a competitive edge in their scholastic leagues.  Through Omnic, Forge players can upload five matches at a time and receive two insights per match. They can also match with pro players who share their gaming style and receive detailed match analysis data. PlayVS will also assist in the initial training of Omnic Forge AI through esports coaches that will consult on the platform’s insights. “We’re excited to collaborate with PlayVS to bring our gaming analysis capabilities to a broader audience,” said Shaun Meredith, Omnic.AI CEO, in a statement. “This partnership aligns perfectly with our mission to help gamers improve their skills, win more games and have fun.”  Omnic.AI was founded in 2021 in Maine by MIT alumnus and former nuclear engineer Meredith and former Apple director Chuck Goldman. I asked what distinguishes Omnic.AI from the competition. The company said it is one of the first AI-driven platforms that provides analytics uniquely to competitive gaming, especially on the B2B level. The technology provides customizable insights for individual player performance as well as team performance, which is very interesting and attractive to scholastic teams. Omnic is also focused on social impact, which is very aligned with PlayVS’ values. The Omnic.AI cofounders actually met while working together on a campaign to put a laptop in the hands of every middle school student and teacher in Maine. Their team is committed to bettering the lives of players, designers, coaches, etc. and furthering their careers and success, which is important to PlayVS as well. PlayVS’ mission is to increase accessibility to the positive benefits of esports for all students, so they are very much aligned in their vision of how they can build the industry further, the company said. PlayVS wants to make esports more accessible to youth, while also providing students with valuable skill building opportunities in science, technology engineering and math (STEM) and leadership. Through its partnership with Omnic.AI, PlayVS aims to enhance the player experience by helping them better understand their in-game performance and integrate real-time feedback. This approach not only improves their gameplay, but also equips them with transferable skills such as critical thinking, adaptability, and effective communication—skills that are essential both in and out of the game. “Teaming up with Omnic.AI represents a significant leap

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Anthropic, the artificial intelligence company behind the popular Claude chatbot, today announced a sweeping update to its Responsible Scaling Policy (RSP), aimed at mitigating the risks of highly capable AI systems. The policy, originally introduced in 2023, has evolved with new protocols to ensure that AI models, as they grow more powerful, are developed and deployed safely. This revised policy sets out specific Capability Thresholds—benchmarks that indicate when an AI model’s abilities have reached a point where additional safeguards are necessary. The thresholds cover high-risk areas such as bioweapons creation and autonomous AI research, reflecting Anthropic’s commitment to prevent misuse of its technology. The update also brings more detailed responsibilities for the Responsible Scaling Officer, a role Anthropic will maintain to oversee compliance and ensure that the appropriate safeguards are in place. Anthropic’s proactive approach signals a growing awareness within the AI industry of the need to balance rapid innovation with robust safety standards. With AI capabilities accelerating, the stakes have never been higher. Why Anthropic’s Responsible Scaling Policy matters for AI risk management Anthropic’s updated Responsible Scaling Policy arrives at a critical juncture for the AI industry, where the line between beneficial and harmful AI applications is becoming increasingly thin. The company’s decision to formalize Capability Thresholds with corresponding Required Safeguards shows a clear intent to prevent AI models from causing large-scale harm, whether through malicious use or unintended consequences. The policy’s focus on Chemical, Biological, Radiological, and Nuclear (CBRN) weapons and Autonomous AI Research and Development (AI R&D) highlights areas where frontier AI models could be exploited by bad actors or inadvertently accelerate dangerous advancements. These thresholds act as early-warning systems, ensuring that once an AI model demonstrates risky capabilities, it triggers a higher level of scrutiny and safety measures before deployment. This approach sets a new standard in AI governance, creating a framework that not only addresses today’s risks but also anticipates future threats as AI systems continue to evolve in both power and complexity. How Anthropic’s capability thresholds could influence AI safety standards industry-wide Anthropic’s policy is more than an internal governance system—it’s designed to be a blueprint for the broader AI industry. The company hopes its policy will be “exportable,” meaning it could inspire other AI developers to adopt similar safety frameworks. By introducing AI Safety Levels (ASLs) modeled after the U.S. government’s biosafety standards, Anthropic is setting a precedent for how AI companies can systematically manage risk. The tiered ASL system, which ranges from ASL-2 (current safety standards) to ASL-3 (stricter protections for riskier models), creates a structured approach to scaling AI development. For example, if a model shows signs of dangerous autonomous capabilities, it would automatically move to ASL-3, requiring more rigorous red-teaming (simulated adversarial testing) and third-party audits before it can be deployed. If adopted industry-wide, this system could create what Anthropic has called a “race to the top” for AI safety, where companies compete not only on the performance of their models but also on the strength of their safeguards. This could be transformative for an industry that has so far been reluctant to self-regulate at this level of detail. The role of the responsible scaling officer in AI risk governance A key feature of Anthropic’s updated policy is the expanded responsibilities of the Responsible Scaling Officer (RSO)—a role that Anthropic will continue to maintain from the original version of the policy. The updated policy now details the RSO’s duties, which include overseeing the company’s AI safety protocols, evaluating when AI models cross Capability Thresholds, and reviewing decisions on model deployment. This internal governance mechanism adds another layer of accountability to Anthropic’s operations, ensuring that the company’s safety commitments are not just theoretical but actively enforced. The RSO has the authority to pause AI training or deployment if the safeguards required at ASL-3 or higher are not in place. In an industry moving at breakneck speed, this level of oversight could become a model for other AI companies, particularly those working on frontier AI systems with the potential to cause significant harm if misused. Why Anthropic’s policy update is a timely response to growing AI regulation Anthropic’s updated policy comes at a time when the AI industry is under increasing pressure from regulators and policymakers. Governments across the U.S. and Europe are debating how to regulate powerful AI systems, and companies like Anthropic are being watched closely for their role in shaping the future of AI governance. The Capability Thresholds introduced in this policy could serve as a prototype for future government regulations, offering a clear framework for when AI models should be subject to stricter controls. By committing to public disclosures of Capability Reports and Safeguard Assessments, Anthropic is positioning itself as a leader in AI transparency—an issue that many critics of the industry have highlighted as lacking. This willingness to share internal safety practices could help bridge the gap between AI developers and regulators, providing a roadmap for what responsible AI governance could look like at scale. Looking ahead: What Anthropic’s Responsible Scaling Policy means for the future of AI development As AI models become more powerful, the risks they pose will inevitably grow. Anthropic’s updated Responsible Scaling Policy is a forward-looking response to these risks, creating a dynamic framework that can evolve alongside AI technology. The company’s focus on iterative safety measures—with regular updates to its Capability Thresholds and Safeguards—ensures that it can adapt to new challenges as they arise. While the policy is currently specific to Anthropic, its broader implications for the AI industry are clear. As more companies follow suit, we could see the emergence of a new standard for AI safety, one that balances innovation with the need for rigorous risk management. In the end, Anthropic’s Responsible Scaling Policy is not just about preventing catastrophe—it’s about ensuring that AI can fulfill its promise of transforming industries and improving lives without leaving destruction in its wake.

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Even as enterprises across sectors explore how to integrate generative AI, it’s clear that biomedical research and the sciences are among the areas that could benefit most — as highlighted in the recent Nobel Prizes in Chemistry and Physics awarded to AI researchers. Now Dotmatics, a leader in research and development scientific software, wants to give drug researchers the power of AI to speed up their development of new, life-saving and improving drugs. Today, the Boston-based company launches Geneious Luma, a powerful new bioinformatics solution for antibody discovery, built on its Luma Scientific Intelligence Platform. Geneious Luma is designed to streamline the process of biologic therapeutic discovery by integrating advanced sequence analysis, data management, and AI-powered automation. This release marks the first of several multimodal solutions aimed at transforming scientific research and accelerating the discovery of new therapies. Thomas Swalla, CEO of Dotmatics, explained the benefit of Geneious Luma in a recent video interview with VentureBeat: “The problem we’re solving is that discovering new molecules and drugs has become increasingly complicated. It takes over a decade to bring a drug to market and costs anywhere from two to six billion dollars.” Screenshot of Geneious Luma dashboard. Credit: Dotmatics With Geneious Luma, Dotmatics addresses the challenges of fragmented workflows and siloed data in biologic research. The platform integrates key tools like Geneious Prime and Geneious Biologics to streamline antibody sequence discovery and protein engineering, from in-silico design to wet-lab experimentation and decision support. As Swalla noted, “Dotmatics is addressing the fragmentation and complexity of drug discovery. The promise of new therapies, like cell and gene therapies, is tremendous, but the science is really complicated. We’re working to pull all these areas together.” As such, it goes up against other AI drug discovery platforms such as VeriSIM Life and Platforma.bio, but can also integrate data from them — with the scientist users’ permission, of course. Antibody research Dotmatics supports over 2 million scientists and 10,000 customers in 180 countries. Its solutions streamline R&D processes by connecting science, data, and decision-making. Dotmatics’ team of over 850 employees operates globally, with its principal office in Boston. Geneious Luma builds upon the capabilities of the Luma platform to provide seamless bioinformatics solutions tailored specifically for antibody and protein engineering. The platform is designed to accelerate workflows across therapeutic modalities, including antibodies, antibody-drug conjugates (ADCs), RNA and gene therapies, and vaccines. t enables researchers to work more efficiently by automating complex data processes and centralizing all relevant data into a unified workspace. By incorporating tools like Geneious Prime, which offers industry-leading cloning and sequence analysis, and Geneious Biologics, which enhances antibody sequence discovery, Geneious Luma ensures that researchers have the advanced capabilities needed to tackle the complexities of antibody engineering. Swalla further emphasized how the platform enhances research efficiency: “With our new product, Luma, you can pull together massive datasets across these fragmented areas of science, putting language models on top of them to speed up the discovery of new molecules and drugs.” Advances in AI The flexibility of Geneious Luma is one of its greatest strengths. It leverages AI and machine learning to automate workflows, enabling scientists to manage complex biological data with greater accuracy and speed. Michael Swartz, Chief Strategy Officer at Dotmatics, told VentureBeat in the same video interview call how the platform adapts to real-time needs: “Our software is able to adapt in near real-time to whatever the scientist decides to do. Luma can call out to external models like [DeepMind’s] AlphaFold to assist, which hasn’t been possible before.” But that’s just one of many external AI tools and resources that users can pipe into Geneious Luma. “Today, we enable AI models through Luma, but our customers can pick whichever model they want and put it closest to the data in their life sciences ecosystem,” Swalla clarified. “We know we have to partner with companies that bring models and help with accelerated compute because that’s what will make AI in drug discovery economically feasible.” In addition to AI-assisted discovery, Geneious Luma incorporates powerful tools like Luma Lab Connect, which automates data ingestion from lab instruments such as flow cytometers and mass spectrometers, allowing researchers to efficiently collect, process, and analyze data from multiple sources. “When you think about instrument integration, it’s not static,” Swartz explained. “We grab the data off the software and deliver it precisely at the right time and in the right organizational framework, in a frictionless way.” Solving for siloed data In the increasingly complex landscape of therapeutic discovery, the ability to manage large and diverse datasets is crucial. Geneious Luma offers a solution to one of the biggest hurdles in life sciences today—siloed and unstructured data. Swalla commented, “The real issue in life sciences isn’t a lack of AI models; it’s that the data isn’t big enough, structured enough, or trusted enough to train those models. That’s the problem we’re trying to solve with Luma.” By integrating all the necessary tools and workflows into a single, cohesive platform, Geneious Luma enables researchers to overcome these data challenges, fostering collaboration across teams and speeding up the discovery process. Beyond antibodies While the initial focus of Geneious Luma is on antibody and protein engineering, Dotmatics plans to extend the platform’s capabilities into other areas of biologic research, such as CAR-T therapies, CRISPR, and RNA-based medicine. Swalla sees this as a tremendous opportunity: “In this industry, people are still using paper and pencil, and there are companies that haven’t moved to the cloud. We’re 15 years behind in terms of tech adoption, which is a huge opportunity for us.” The flexibility of the Geneious Luma platform ensures that it can be adapted for various therapeutic discovery processes, driving efficiency across the industry. Dotmatics is also exploring opportunities to extend the platform into other scientific domains, including material science and agritech. Geneious Luma is available now as part of the Dotmatics Luma

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Microsoft just announced a suite of autonomous AI agents for its Dynamics 365 platform, intensifying the competition with Salesforce in the enterprise AI market. The tech giant will release ten new autonomous agents designed to augment sales, service, finance, and supply chain teams. Available in public preview starting next month, these AI agents aim to automate complex tasks and orchestrate business processes across organizations. They surpass traditional chatbots and Microsoft’s earlier AI offerings by reasoning over intent and context, making judgments based on a broader set of data. “We think of these agents as really the apps of the AI era,” said Bryan Goode, Corporate Vice President at Microsoft, in an interview with VentureBeat. “Every line of business system that exists today is going to get reimagined as an agent that sits on top of a copilot.” AI titans clash: Microsoft’s counterpunch to Salesforce’s Agentforce The move comes just weeks after Salesforce unveiled its Agentforce platform, which CEO Marc Benioff has been aggressively promoting while criticizing Microsoft’s Copilot. Benioff recently called Microsoft Copilot “more like Clippy 2.0,” referring to Microsoft’s much-maligned Office assistant from the 1990s. Microsoft’s new offering appears to be a direct challenge to Salesforce’s Agentforce. While Salesforce’s platform relies on its Atlas reasoning engine, Microsoft’s agents are powered by advanced language models and the company’s vast troves of enterprise data. Goode emphasized that these agents are not meant to replace human workers but to enhance their capabilities. “In many cases, these agents can actually enable people to add capabilities that they wouldn’t have otherwise been able to do,” he explained. Battle for AI dominance: Microsoft and Salesforce lead the charge The tech industry is witnessing a paradigm shift as AI agents move from experimental technology to core business tools. Microsoft and Salesforce are at the forefront, each leveraging their unique strengths to shape the future of enterprise software. Microsoft’s strategy hinges on its ubiquitous presence in office productivity and cloud computing. By integrating AI agents with familiar tools like Microsoft 365 and Azure, the company aims to make AI adoption seamless for its vast user base. Salesforce, on the other hand, is banking on its CRM expertise and the power of its recently developed Data Cloud to create AI agents that understand and optimize customer relationships. The success of these platforms could redefine the future of work and enterprise software. As AI agents become more sophisticated, they may blur the lines between human and machine tasks, potentially reshaping organizational structures and job roles. However, challenges remain. Data privacy concerns, the need for transparent AI decision-making, and the potential for job displacement are issues both companies must navigate carefully. Their ability to address these concerns while delivering tangible business value will likely determine the pace and extent of AI agent adoption. As this AI revolution unfolds, one thing is clear: the enterprise software landscape is on the cusp of a major transformation. Whether it’s Microsoft’s vision of “agents plus copilot plus humans” or Salesforce’s “human at the helm” approach, the future of work is being rewritten — one AI agent at a time. source

Presented by SAP Thanks to the growth of generative AI, a truly autonomous supply chain may be closer than we think. Here’s why. Ask any supply chain professional over the last year and they’ll tell you that their company wants to reap the results of generative AI. Research by EY backs this up, finding that nearly three-quarters (73%) of supply chain and operations executives are planning to deploy GenAI. However, just 7% of them say they have successfully implemented the technology. Making the technical leap from proof-of-concept to GenAI at scale is challenging. This becomes even clearer when you consider that supply chain operations everywhere struggle with data quality, organizational readiness and volatility — both internally and externally. However, organizations who have invested in AI early on have, at least partially, broken through these barriers. A 2023 survey from McKinsey found that supply chain and inventory management were two areas that report meaningful revenue increases through AI. To take advantage of this however, it must become simpler for teams to integrate GenAI in their everyday workflows. Here’s how. Accurate, proactive planning Supply chain success is built on a foundation of smart decision-marking. However, without a base of historical business knowledge, supply chain planning leaders are left to manage important lead times and inventory based off gut feelings rather than accurate supply and demand data. The resulting guess work impacts lead times and ultimately affects customer satisfaction. “Data integrity is one of the most powerful components to consider as we move towards the era of autonomous supply chains — it’s essential to enable a seamless end-to-end process across the entire supply chain,” says Mindy Davis, global vice president, product marketing for digital supply chain at SAP. Many companies on a digitalization journey may have eliminated most of their paper-based systems to gain better control over their supply chain, but they haven’t fully incorporated these types of analytics into their decision-making processes. EY’s research found that even for organizations using GenAI in their supply chain, only 50% have achieved end-to-end visibility. “It may sound antiquated, but quite frankly, digitizing paper-based systems is the first step to establish a digital foundation so you can access comprehensive data that impacts your supply chain,” says Davis. AI also proves to be a powerful tool for planners to get a leg up and bridge this gap. With verified and consolidated data, supply chain teams can train AI models to help accurately predict future lead times or track the status of shipments in real-time. Accurate lead times mean teams can deliver the right products at the right time and keep customers satisfied. As companies move toward an era of autonomous supply chains, an AI solution integrated into their ERP can help supercharge business decisions. At SAP, Davis and her team leverage business and financial data found in the company’s ERP solution, SAP S/4HANA, to help planners using their other applications, like SAP Integrated Business Planning, to make more informed decisions and accurately predict lead times. Then, using SAP’s AI copilot Joule, they can get better insight into the variables or constraints facing their inventory and solicit recommendations to be more proactive in their planning. “We’re envisioning a path characterized by technological, procedural and data enhancements that will propel the supply chain into an autonomous era, where supply chains operate with minimal human intervention,” says Davis. Efficient, error-free manufacturing In today’s supply chain environment, there really is no room for disruption — be it labor shortages, geopolitical strife or malfunctions within manufacturing. To keep up with demand, supply chain teams are focused on continuous improvement and finding ways to remove the burden on expensive manual labor in favor of automated, digital solutions. When faulty products come off the production line, it must be addressed quickly. AI can accelerate the resolution process faster than human labor in many instances — preventing production standstills and even catching errors before they occur. Engineers who are creating a product can lean on these insights too, using AI to assess all the errors that have happened in the past to make sure that they don’t happen in the future. But AI doesn’t just improve error resolution, it can transform the first phases of production as well, such as eliminating redundant tasks like tagging data on product visualizations. It can even make designs more efficient, developing, enhancing and customizing recipes for products while supporting product compliance and sustainability. Powerful, predictive maintenance Manufacturing the millions of products that move throughout the supply chain starts with the machinery used to produce them. This “up” time — hours when equipment is in action — is the backbone of effective operations. When those components fail or reach the end of their lifespan, that has an enormous operational and financial impact, shifting budget, influencing payment terms and mitigating cash flow. Regular maintenance on essential equipment is key to keeping the supply chain moving. But monitoring wear and tear to catch issues before they even happen is even better, and increasingly possible thanks to AI. Through camera footage and visual inspections, AI models can help detect errors, faults or defects in equipment before they happen. If the technology identifies an issue — or predicts the need for maintenance — teams can arrange for a technician to perform repairs. This predictive maintenance minimizes unplanned outages, reduces disruptions across the supply chain and optimizes asset performance. Swiss Federal Railways (SBB) is piloting this capability through SAP to help get passengers where they want to go on time and safely by examining a small but critical piece of railway infrastructure: the pantograph. This component mounted on the roof of an electric train collects power through contact with an overhead line. “What many companies will do is implement generative AI tools for small uses cases,”says Davis. “Implementing use cases that show initial success helps show the potential of this technology for the supply chain, and then you can consider scaling across your organization.” Using AI, SBB can assess the pantograph’s thickness and conductivity to determine when

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More DataStax has been steadily expanding its data platform in recent years to help meet the growing need of enterprise AI developers. Today the company is taking the next step forward with the launch of the DataStax AI Platform, Built with Nvidia AI. The new platform integrates DataStax’s existing database technology including DataStax Astra for cloud native and the DataStax Hyper-Converged Database (HCD) for self-managed deployments. It also includes the company’s Langflow technology which is used to help build out agentic AI workflows. The Nvidia enterprise AI components include technologies that will help to accelerate and improve organization’s ability to rapidly build and deploy models. Among the Nvidia enterprise components in the stack are NeMo Retriever, NeMo Guardrails and NIM Agent Blueprints. According to DataStax the new platform can reduce AI development time by 60% and handle AI workloads 19 times faster than current solutions. “Time to production is one of the things we talk about, building these things takes a bunch of time,” Ed Anuff, Chief Product Officer at DataStax told VentureBeat. “What we’ve seen has been that a lot of folks are stuck in development hell.” How Langflow enables enterprises to benefit from agentic AI Langflow, DataStax’s visual AI orchestration tool, plays a crucial role in the new AI platform.   Langflow allows developers to visually construct AI workflows by dragging and dropping components onto a canvas. These components represent various DataStax and Nvidia capabilities, including data sources, AI models and processing steps. This visual approach significantly simplifies the process of building complex AI applications. “What Langflow allows us to do is surface all of the DataStax capabilities and APIs, as well as all of the Nvidia components and microservices as visual components that can be connected together and run in an interactive way,” Anuff said. Langflow also is the critical technology that enables agentic AI to the new DataStax platform as well. According to Anuff, the platform facilitates the development of three main types of agents: Task-oriented agents: These agents can perform specific tasks on behalf of users. For example, in a travel application, an agent could assemble a vacation package based on user preferences. Automation agents: These agents operate behind the scenes, handling tasks without direct user interaction. They often involve APIs communicating with other APIs and agents, facilitating complex automated workflows. Multi-agent systems: This approach involves breaking down complex tasks into subtasks handled by specialized agents.  What the Nvidia DataStax combination enables for enterprise AI The combination of the Nvidia capabilities with DataStax’s data and Langflow will help enterprise AI users in a number of different ways, according to Anuff. He explained that the Nvidia integration will allow enterprise users to more easily invoke custom language models and embeddings through a standardized NIM microservices architecture. By using Nvidia’s microservices, users can also tap into Nvidia’s hardware and software capabilities to run these models efficiently. Guardrails support is another key addition that will help DataStax users to prevent unsafe content and model outputs. “The guardrails capability is one of the features that I think probably has the most developer and end user impact,”Anuff said. “Guardrails are basically a sidecar model, that is able to recognize and intercept unsafe content that is either coming from the user, ingestion or through, stuff retrieved from databases.” The Nvidia integration also will help to enable continuous model improvement. Anuff explained that the NeMo Curator allows enterprise AI users to  be able to determine additional content that can be used for fine tuning purposes. The overall impact of the integration is to help enterprises benefit from AI faster and in a cost efficient approach. Anuff noted that it’s an approach that doesn’t necessarily have to rely entirely on GPUs either. “The Nvidia enterprise stack actually is able to execute workloads on CPUs as well as GPUs,” Anuff said. “GPUs will be faster and  generally are going to be where you want to put these workloads, but if you want to offload some of the stuff to CPUs for cost savings in areas where, where it doesn’t matter, it lets you do that as well.” source