Key Features of Image-Pro AI:

• Superior outline accuracy: Even the most complicated edges can be traced with high quality. AI Models work similar to the human brain, finding objects undeterred by dim, complex edges.
• User-friendly operation: AI Deep Learning Segmentation requires minimal input to operate efficiently. Simple controls, limited options, and fast prediction results.
• Secure & confidential: No uploading or downloading required. Save time and avoid sharing your data on unsecure company servers. All operations are on-device.
• Protocol-ready: Integrate Deep Learning Segmentation accuracy into an automated workflow by saving an Analysis Protocol with a custom or pre-trained model.

Tailored for both life sciences and materials research and quality inspection, Image-Pro® AI offers robust solutions that cater to specific application needs. Trusted globally, Image-Pro® AI represents a significant leap forward in scientific image analysis, providing a comprehensive, user-friendly tool for researchers and scientists.

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Frazier continues to focus on the strategic expansion of its dedicated Center of Excellence (CoE), providing Fortune 500 talent to our portfolio companies in the areas of Human Capital, Technology & Innovation, Data Analytics & Artificial Intelligence, M&A & Corporate Development, Finance, Operations, Legal, Commercial Excellence, and ESG. Frazier Managing Partner Ben Magnano said, “As the firm continues to invest in the breadth and depth of this team, we are targeting expansion in areas where we see accelerated value creation opportunities as part of our differentiated strategy.”

“Frazier is fortunate to have such a talented, collaborative group of professionals who enjoy supporting our management teams and helping them drive transformation,” said Andy Caine, CoE COO & Partner. “We are thrilled to welcome Sanji, Steve, and Lindsey to our team, each of whom brings a wealth of experience in their areas of expertise.”

About Sanji Fernando: Sanji joined Frazier’s CoE Data & AI team after serving as SVP of AI Products and Platforms at Optum, where he spearheaded the development of next generation AI to transform healthcare. He launched United AI Studio, a unified platform enabling rapid experimentation and deployment of advanced ML across UHC and Optum. The United AI Studio provides access to cutting-edge capabilities like Generative AI while ensuring rigorous monitoring of model performance and evaluating models for bias. Previously, Sanji pioneered the application of deep learning for risk adjustment, automating the identification of hierarchical condition codes from medical charts. This innovation drove efficiencies and positioned UnitedHealth as an AI leader in healthcare administration. Sanji also founded Optum Labs’ Center for Applied Data Science (CADS), delivering innovative AI and Machine Learning solutions to complex healthcare challenges. With his proven track record of leveraging AI to create enterprise value, Sanji was key in positioning UnitedHealth to the forefront of healthcare’s digital transformation. His leadership and technical acumen make him a strategic driver of transformative innovation for the organization. Sanji obtained his BS in Computer Science from Trinity College in Hartford.

About Steve Blackhart: Steve Blackhart is an M&A professional with extensive transaction experience on both the buy-side and sell-side. He joins Frazier from Black Eagle Advisors, an M&A advisory firm he founded in 2015. There, Steve executed transactions across a wide breadth of industries, including healthcare, business services, and consumer products. He also provided strategic guidance in the formation of corporate development functions within newly acquisitive organizations and served as the head of M&A for a number of companies. Most recently, Steve led corporate development for MiX Telematics (NYSE: MIXT), directing the Company’s acquisition of Trimble’s Field Services Management division and its merger with Powerfleet (NASDAQ: PWFL). Steve began his career in the investment banking division of Bank of America Merrill Lynch before becoming a private equity professional at FFL Partners. At FFL, Steve was involved in the firm’s investments in healthcare, consumer products, and financial services businesses. Steve holds a BS in Business Administration from the University of Southern California and an MBA from Harvard Business School.

About Lindsey Murphy: Lindsey has over 12 years of experience in sales operations, and in her role at Frazier, she will support its portfolio companies with sales operations, including improving sales processes, tools, and pipeline management. Her responsibilities include utilizing expertise in data analysis, CRM administration, and sales forecasting to improve sales efficiency and revenue growth. Prior to Frazier, she was the Director of Sales Operations at Parata, a Frazier portfolio company. During her time at Parata, Lindsey provided strategy, design, and execution of sales processes for the commercial organization leading to increasing revenue from $75 million to $275 million. Lindsey received her BS in Business Administration from the University of North Carolina, Wilmington.

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KAYTUS, a leading IT infrastructure provider, has unveiled MotusAI, an AI development platform now accessible for trial worldwide. MotusAI is tailored for deep learning and AI development, integrating GPU and data resources alongside AI development environments to streamline computing resource allocation, task orchestration, and centralized management. It accelerates training data and manages AI model development workflows seamlessly. This platform drastically reduces resource investment, boosts development efficiency, elevates cluster computing power utilization to over 70%, and significantly enhance in large-scale training task scheduling performance.

Streamline AI Development for Cost-Effectiveness and Efficiency

The rapid expansion of enterprise AI business and AI model development brings forth challenges including low computing efficiency, complexity in model development, varied requirements for task orchestration across different scenarios, and unstable computing resources. Ensuring efficient, flexible, and stable operation of AI business is critical for enterprises to consistently derive business insights, generate revenue, and maintain competitiveness.

Optimize Resource Management for Maximum Computing Power

MotusAI efficiently allocates resources and workloads by implementing intelligent and flexible GPU scheduling. It caters to diverse AI workload demands for computing power by dynamically allocating GPU resources based on demand. With multi-dimensional and dynamic GPU resource allocation, including fine-grained GPU scheduling and support for Multi-Instance GPU (MIG), MotusAI effectively meets computing power requirements across various scenarios such as model development, debugging, and training.

Streamline Task Orchestration for Versatile Support of Various Scenario

MotusAI has revolutionized cloud-native scheduling systems. Its scheduler surpasses the community version by dramatically improving the scheduling performance of large-scale POD tasks. MotusAI achieves rapid startup and environment readiness for hundreds of PODs, boasting a five times increase in throughput and a five times decrease in latency compared to the community scheduler. This ensures efficient scheduling and utilization of computing resources for large-scale training. Moreover, MotusAI enables dynamic scaling of AI workloads for both training and inference services, supporting burst tasks and fulfilling diverse scheduling needs across various scenarios.

MotusAI empowers users to maximize computing resources, spanning from fine-grained division of single-card multiple instances to large-scale parallel computing across multiple machines and cards. By integrating features like computing power pooling, dynamic scaling, and GPU single-card reuse, MotusAI significantly enhances computing power utilization, achieving an average utilization rate of over 70%. Furthermore, it enhances computing efficiency by leveraging cluster topology awareness and optimizing network communication.

Data Transfer Acceleration for Three Times Efficiency Improvement

MotusAI excels in data transfer acceleration through innovative features such as supporting local loading and computing of remote data, which eliminates delays caused by network I/O during computation. Utilizing strategies like “zero-copy” data transfer, multi-threaded retrieval, incremental data updates, and affinity scheduling, MotusAI significantly reduces data caching cycles. These enhancements greatly improve AI development and training efficiency, resulting in 2-3 times boost in model efficiency during data training.

Reliable, and Automatically Fault-Tolerant Platform

MotusAI supports performance monitoring and alerts for computing resources, providing real-time status updates for core platform services. It employs sandbox isolation mechanisms for data with higher security levels. In case of resource failures or abnormalities, MotusAI automatically initiates fault tolerance processes to ensure the quickest possible recovery during interrupted training tasks. This approach reduces fault handling time by over 90%, on average.

Comprehensive Management of AI Model Development in One Integrated Solution

MotusAI accelerates AI development and supports every stage of large model development. From managing data samples and software stacks to designing model architectures, debugging code, training models, tuning parameters, and conducting evaluation testing, MotusAI offers a complete platform. It integrates popular development frameworks like PyTorch and TensorFlow, along with distributed training frameworks such as Megatron and DeepSpeed.

Moreover, MotusAI enables comprehensive lifecycle management of AI inferencing services, including offline and online testing, A/B testing, rolling release, service orchestration, and service decommissioning. These features collectively enhance the business value of AI technology, fostering continuous business growth.

Additionally, MotusAI provides an integrated visual management and operation interface that covers computing, networking, storage, and application resources. Operational staff can comprehensively manage, monitor, and evaluate the overall platform operation status through a single interface.

Free Trial Available

MotusAI is now available worldwide for a trial period, offering free remote access for one month, along with testing, training, and support. Users can also opt for local deployment using their own devices and environment, with local deployment testing support from KAYTUS. For more information¹ and to register², please visit Link¹,Link².

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Integrating Sony’s Image Sensor Models into Anyverse’s platform will revolutionize perception system design and validation cycles, allowing developers to evaluate sensor configurations and increase model performance before implementation. Leveraging Anyverse’s hyperspectral rendering technology, the collaboration ensures the generation of highly accurate synthetic data and physically-based sensor simulation, now enhanced by Sony’s Image Sensor Models.

“The collaboration with Sony Semiconductor Solutions represents a tremendous leap forward in addressing one of the most critical challenges in dataset creation – the perception domain gap,” said Víctor González, CEO at Anyverse. “By bridging this gap through the combination of high-fidelity data and physical sensor simulation, we empower developers to create deep learning models that can effectively handle a wide spectrum of real-world scenarios.”

The perception domain gap refers to the variance between the characteristics of the sensors utilized in data acquisition and those deployed in production systems. This variance can significantly impact the performance of deep learning models, hindering their ability to interpret data accurately. With Anyverse’s platform enriched by Sony’s Image Sensor Models, developers can now mitigate this challenge, ensuring seamless integration of new data into production systems.

This collaboration reduces the industry’s reliance on real-world data collection, streamlining go-to-market timelines and minimizing costs associated with physical testing. Developers no longer need to deploy sensors on numerous vehicles/devices for testing purposes; instead, they can access high-quality data and sensor models through Anyverse’s platform, expediting the system’s evaluation process or expanding the perception model’s coverage.

“The collaboration between Anyverse and Sony Semiconductor Solutions will provide an automotive-grade end-to-end spectral simulation pipeline to the ADAS perception system developers,” said Tomoki Seita, General Manager, Automotive Business Division, Sony Semiconductor Solutions Corporation. “Sony has prepared Image Sensor Models based on the internal architecture of the image sensors used in camera systems to achieve automotive-grade fidelity.”

Through this technical collaboration, Anyverse and Sony reaffirm their commitment to driving innovation in autonomous systems and shaping the future of advanced perception. For more information, visit https://anyverse.ai/ and https://www.sony-semicon.com/en/.

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Working in digital and data transformation for more than 25 years, Zutavern joins us with extensive experience successfully leading and advising companies for strategy and growth. She is currently Partner and Managing Director at AlixPartners, a financial advisory and global consulting firm specializing in digital, technology and accelerated business transformation, where she leads the firm’s Global artificial intelligence (AI) business.

Angela has led AI research and development efforts, including breakthroughs in quantum machine learning, deep learning, computer vision, and other key technologies. She also advised federal agencies on cybersecurity vulnerability detection and mitigation, worked with the Department of Homeland Security Cybersecurity and Infrastructure Security Agency to define cybersecurity standards, advised the Homeland Security Investigations Cyber Crimes Center, and helped Fortune 500 and Global 2000 companies assess and address cybersecurity risks and respond to incidents. Previously, she helped establish and lead the Data Science practice and Strategic Innovation Group at Booz Allen Hamilton.

In addition to her professional work experience, Angela is the author of the bestselling book, the Mathematical Corporation: Where Machine Intelligence and Human Ingenuity Achieve the Impossible. A frequent speaker on the power of AI and digital transformation, she has been featured on NBC’s TODAY show and in the New York Times, Wall Street Journal, MIT Sloan Management Review, Stanford Social Innovation Review, and other publications. She has authored numerous articles and the academic paper ‘Building an Artificial Intelligence Explanation Capability’. Angela is also the recipient of the Lifetime Achievement Award for Women in Technology form Consulting Magazine.

Angela holds an MBA from Southern Methodist University and Bachelor of Science in Industrial and Systems Engineering from Virginia Tech.

CDM Smith is a privately owned engineering and construction firm providing legendary client service and smart solutions in water, environment, transportation, energy and facilities. Passionate about our work and invested in each other, we are inspired to think and driven to solve the world’s environmental and infrastructure challenges.

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‘3D Perception Network’ represents STRADVISION’s latest advancement in perception technology. It refines the sensing-perceiving-planning-action sequence, providing more reliable ADAS solutions. The 3D Perception Network achieves better performance by reducing detection errors by up to 10% and improves detection stability by up to 30%, and greatly enhances the scalability and cost efficiency of the ADAS/AD technology in passenger vehicles.

The partnership was founded on a shared vision to provide high-quality and cost-efficient driving solutions and is advancing toward a promising business opportunity. The successful optimization of SVNet 3D Perception Network on Journey 3 marks another technological milestone and establishes a straightforward path for a swift and seamless transition to the upcoming Horizon Journey 6. The Horizon Journey 6 series, equipped with Nash BPU architecture, offers unmatched energy efficiency and openness.

The adaptable and scalable joint solution by STRADVISION and Horizon Robotics continues to lead the market in terms of performance, low system complexity, and cost-effectiveness, paving the way for expanded mass-market production of L2/L2+ ADAS systems, and bringing safe, comfortable, and enjoyable driving experiences to consumers.

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Argo Translation, a Chicago-based language services company, announced today the launch of its latest innovative service offering: AI-driven translation. The solution creates custom AI models with each customer’s propriety content and delivers unparalleled accuracy and fluency, transforming how businesses communicate across global markets.

Created by applying the recent advancements in AI and deep learning, Argo Translation’s semi-custom to fully trained AI-driven translation models offer accurate, context-aware translations, enabling businesses of any size and budget to effortlessly engage with a global audience.

Argo Translation’s 29 years of expertise allow the language service provider to fuse decades-long human translation insights with cutting-edge AI technology to develop highly specialized models that not only understand the nuances of language but also reflect the specific terminology, tone, and stylistic preferences unique to each customer’s domain. The AI-optimized workflow also allows for human post-editing options that can further increase quality while taking a fraction of a human translator’s time.

Peter Argondizzo, co-founder of Argo Translation, shares, “We’ve already created AI models for some of our largest customers, and the results have been outstanding. This translation technology is truly game-changing; the optimal intersection between AI, legacy translation work, and solid editing is finally here.”

This revolutionary approach empowers customers to benefit from a translation solution deeply rooted in the rich linguistic expertise of the past, yet fully equipped to meet the dynamic demands of the future.

Learn more about AI-driven translation at argotrans.com/technology/ai-driven-translation.

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Following the company’s presentation at the NVIDIA GTC AI conference, Lightning AI, the company behind PyTorch Lightning, which has over 100 million downloads, today announced the availability of Thunder, a new and powerful source-to-source compiler for PyTorch designed for training and serving the latest generative AI models across multiple GPUs at maximum efficiency. Thunder is the culmination of two years of research on the next generation of deep learning compilers, built with support from NVIDIA.

Large model training can cost billions of dollars today because of the number of GPUs and the length of time it takes to train these models. Lack of high-performance optimization and profiling tools puts this scale of training out of reach for developers who don’t have the resources of a large technology company. Even at its early stage, Thunder achieves up to a 40% speed-up for training large LLMs, compared to unoptimized code in real-world scenarios. These speed-ups save weeks of training and lower training costs proportionally.

In 2022, Lightning AI hired a group of expert PyTorch developers, with the ambitious goal of creating a next-generation deep learning system for PyTorch that could take advantage of the best-in-class executors and software like torch.compile; nvFuser, Apex, CUDA Deep Neural Network Library (cuDNN) – all three products from NVIDIA; and OpenAI’s Triton. The goal of Thunder was to allow developers to use all executors at once so that each executor could handle the mathematical operations it was best designed for. Lightning AI leveraged support from NVIDIA for the integration of NVIDIA’s best executors into Thunder.

The Thunder team is led by Dr. Thomas Viehmann, a pioneer in the deep learning field best known for his early work on PyTorch, his key contributions to TorchScript, and for making PyTorch work on mobile devices for the first time.

Today, Thunder is starting to be adopted by companies of all sizes to accelerate AI workloads. Thunder aims to make the highly specialized optimizations discovered by prominent programmers at organizations such as Open AI, Meta AI, and NVIDIA more accessible to the rest of the open-source community.

“What we are seeing is that customers aren’t using available GPUs to their full capacity, and are instead throwing more GPUs at the problem,” says Luca Antiga, Lightning AI’s CTO. “Thunder, combined with Lightning Studios and its profiling tools, allows customers to effectively utilize their GPUs as they scale their models to be larger and run faster. As the AI community embarks on training increasingly capable models spanning multiple modalities, getting the best GPU performance is of paramount importance. The combination of Thunder’s code optimization and Lightning Studios’ profiling and hardware management will allow them to fully leverage their compute without having to hire their own systems software and optimization experts.”

“NVIDIA accelerates every deep learning framework,” said Christian Sarofeen, Director of Deep Learning Frameworks at NVIDIA. “Thunder facilitates model development and research. Our collaboration with Lightning AI, to integrate NVIDIA technologies into Thunder, will help the AI community improve training efficiency on NVIDIA GPUs and lead to larger and more capable AI models.”

“I couldn’t be more thrilled for Lightning AI to lead the next wave of performance optimizations to make AI more open source and accessible. I’m especially excited to partner with Thomas, one of the giants in our field, to lead the development of Thunder,” says Lightning AI CEO and founder Will Falcon. “Thomas literally wrote the book on PyTorch. At Lightning AI, he will lead the upcoming performance breakthroughs we will make available to the PyTorch and Lightning AI community.”

Thunder will be made available open source under an Apache 2.0 license. Lightning Studios will offer first-class support for Thunder and native profiling tools that enable researchers and developers to easily pinpoint GPU memory and performance bottlenecks.

“Lightning Studios is quickly becoming the standard for not only new developers to build AI apps and train models but also for deep learning experts to understand their model performance in a way that is not possible on any other ML platform,” says Falcon.

Pricing

Thunder is open source and freely available.

To use Lightning Studios, users can get started free without a credit card. They’ll get 15 free credits per month and can buy more credits as they go. For enterprise users, Lightning AI can be deployed on their cloud accounts and private infrastructure so they can train and deploy models on their private data securely.

There are four pricing tiers in Lightning Studios: a free level for individual developers; a pro level for engineers, researchers, and scientists; a teams level for startups and teams; and finally, for larger organizations in need of enterprise-grade AI, the enterprise level.

To sign up for Studios, go to https://lightning.ai.

PyTorch Lightning, developed by Falcon, has become the standard for training large-scale AI models such as Stable Diffusion, with over 10,000 companies using it to build AI at scale. Using this knowledge, the team at Lightning AI rethought from the ground up what AI development on the cloud should feel like.

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Akamai Technologies (NASDAQ: AKAM), the cloud company that powers and protects life online, and Neural Magic, a developer of software that accelerates artificial intelligence (AI) workloads, today announced a strategic partnership intended to supercharge deep learning capabilities on Akamai’s distributed computing infrastructure. The combined solution gives enterprises a high-performing platform to run deep learning AI software efficiently on CPU-based servers. As an Akamai Qualified Computing Partner, Neural Magic’s software will be made available alongside the products and services that power the world’s most distributed platform for cloud computing, security, and content delivery.

Neural Magic’s solution enables deep learning models to run on cost-efficient CPU-based servers rather than on expensive GPU resources. The software accelerates AI workloads using automated model sparsification technologies, available as a CPU inference engine, complementing Akamai’s ability to scale, protect, and deliver applications at the edge. This allows the companies to deploy the capabilities across Akamai’s globally distributed computing infrastructure, offering organizations lower latency and improved performance for data-intensive AI applications.

Moreover, the partnership can help foster innovation around edge-AI inference across a host of industries. The combined capabilities of Akamai and Neural Magic are particularly well suited for applications in which massive amounts of input data are generated close to the edge, placing affordable processing power and security closer to the data sources. Akamai recently announced a new Generalized Edge Compute (Gecko) initiative to embed cloud computing capabilities into its massive edge network, which will ultimately help support such applications and workloads among many others.

“Delivering AI models efficiently at the edge is a bigger challenge than most people realize,” said John O’Hara, SVP of Engineering and COO at Neural Magic. “Specialized or expensive hardware and associated power and delivery requirements are not always available or feasible, leaving organizations to effectively miss out on leveraging the benefits of running AI inference at the edge.”

“We intend to make AI smarter and faster,” said Ramanath Iyer, Chief Strategist at Akamai. “Scaling Neural Magic’s unique capabilities to run deep learning inference models across Akamai gives organizations access to much-needed cost efficiencies and higher performance as they move swiftly to adopt AI applications.”

Akamai and Neural Magic share a common origin, both having been born out of Massachusetts Institute of Technology (MIT). They continue to maintain their respective corporate headquarters nearby.

The Akamai Qualified Computing Partner Program is designed to make solution-based services that are interoperable with Akamai’s cloud computing services easily accessible to Akamai customers. The services are provided by Akamai technology partners that complete a rigorous qualification process to ensure they are readily available to deploy and scale across the globally distributed Akamai Connected Cloud.

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Limmi utilizes cutting-edge AI and deep learning methodologies on a platform built specifically for healthcare and biotech, offering their customers real-time predictions and insights to patients, radically changing the way their customers manage healthcare operations and patient health management.

“I am impressed by the work that Limmi has done in building a platform to leverage AI and ML in healthcare applications,” states Dr. Moody, and continues, “AI and ML hold the potential of bringing accurate and early information to hundreds of millions of people through inexpensive non-invasive procedures, ultimately improving the quality of care and the efficiency of the healthcare system.“

Dr. Moody currently serves as the Director of Operations Research and Principal Algorithms Architect for Viasat, a leading global satellite communications provider. He has previously led software development in financial services and market analysis.  Dr. Moody holds a Ph.D. in Applied Mathematics from the University of California, San Diego, as well as a B.S. in Computer Science from Carnegie Mellon.

“We are thrilled to have Dr. Moody join our advisory board,” states co-founder and Chief Technology Officer at Limmi, Bryan Ivory and adds, “Dr. Moody’s tremendous expertise in software, artificial intelligence, and advanced mathematics will help to accelerate our efforts to unlock new insights in healthcare and ultimately improve patient outcomes.“

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