Today, everything is connected – from phones to watches to cars – modernizing industries, and cellular connectivity changing the way we live, work, and learn. But, at the same time carriers are building out the infrastructure to support this enhanced connectivity, they are setting aggressive decarbonization goals, including public Net Zero ambitions.

What that means for communications service providers (CSPs) is that they can no longer view performance as the sole criterion for success. The telecommunications network of today must be powerful and nimble – and it needs to be sustainable, too. And, AI is the key to creating and managing energy efficiency, reducing carbon footprint while continuing to deliver the high-quality service customers demand.

RAN Represents the Bulk of Energy Usage – And an Opportunity for Savings

The telecom industry is in a unique position when it comes to decarbonization. Advanced connectivity is key to enabling carbon reduction in other industries, and there is also a tremendous opportunity for the telecom industry to lead by example by cutting its own emissions. 

The Radio Access Network (RAN) equipment accounts for 80% of the energy use for an operator, based on a benchmarking study by the GSMA. Yet the GSMA also reports 62 operators, representing 61% of the industry (by revenue), have committed to a science-based carbon reduction target, pledging to reduce direct and indirect emissions by 2030.

As networks become increasingly complex, managing energy usage can’t be done manually. Software that monitors for periods of low usage to shut down unnecessary equipment is a good first step, but purely reactive solutions won’t be enough to manage traffic demands while cutting emissions.

AI is the missing ingredient that will help CSPs gain insights to inform ongoing energy savings, while also driving real-time efficiencies through operational orchestration.

AI is what will take network energy management from reactive to predictive, making decisions based on the network’s needs.

For example, AI functions can decide, based on data, what resources will be needed in the coming hours and days, and if all the capacity in the frequency bands within the RAN will be needed. They can then turn off or on different frequency bands or other resources according to predicted demand.

Because, AI programs dynamically learn, adapt, and act accordingly, these tools enable operators to control cells dynamically and in turn, serve dynamic traffic patterns instead of just peak traffic.

Taking things to the next level, CSPs will be able to use AI and machine learning predictions to build digital twins of the RAN environment, allowing them to test and develop energy-saving features without any risk to their actual, live network. 

AI Can Identify and Enable Savings Beyond Network Operations

And it’s not just the day-to-day network management where AI tools can help reduce energy usage. They can also help operators diagnose and resolve issues remotely, getting things right the first time to reduce unplanned downtime – as well as achieving carbon reduction through fewer maintenance truck rolls. And, with AI insights, CSPs can identify where new cell sites or other resources should be deployed, creating efficiencies with deployments and equipment buildouts.  

Another area where AI can enable energy savings is in the passive equipment at individual cellular sites, things like climate control or air conditioning.

AI solutions can help companies manage the energy infrastructure on-site more intelligently. They can also help in areas where demand response programs are enabled, or when government regulations or tariffs are in place to control peak demand.

For example, AI-powered applications can switch to battery power during times when tariffs are higher (peak load shifting), or when the grid power usage reaches a certain power grid alternating current (AC) limit.

There is a real urgency behind the need for CSPs to make operations more energy efficient, as the telecom industry works to meet self-declared sustainability targets as well as government mandates. Yet, there can be an understandable hesitancy to enact some energy-saving solutions, for fear of disrupting networks or reducing the quality of service. Luckily, AI can be a solution for both these issues – giving CSPs the tools they need to manage complex network functions in the most efficient manner possible, while also collecting and analyzing data to test and refine new applications in a virtual environment, such as a digital twin. Cellular networks need to get smarter to meet sustainability expectations without affecting customer experience – and AI is the key to making it work.

[To share your insights with us, please write to sghosh@martechseries.com]

The post AI-Enabled Solutions Are the Key to Help Cellular Operators Reach Carbon Reduction Targets appeared first on AiThority.

Google Cloud, D-ID, Cohere Using New Platforms for Wide Range of Generative AI Services Including Chatbots, Text-to-Image Content, AI Video and More

NVIDIA launched four inference platforms optimized for a diverse set of rapidly emerging generative AI applications — helping developers quickly build specialized, AI-powered applications that can deliver new services and insights.

The platforms combine NVIDIA’s full stack of inference software with the latest NVIDIA Ada, Hopper and Grace Hopper processors — including the NVIDIA L4 Tensor Core GPU and the NVIDIA H100 NVL GPU, both launched today. Each platform is optimized for in-demand workloads, including AI video, image generation, large language model deployment and recommender inference.

“The rise of generative AI is requiring more powerful inference computing platforms,” said Jensen Huang, founder and CEO of NVIDIA. “The number of applications for generative AI is infinite, limited only by human imagination. Arming developers with the most powerful and flexible inference computing platform will accelerate the creation of new services that will improve our lives in ways not yet imaginable.”

Accelerating Generative AI’s Diverse Set of Inference Workloads

Each of the platforms contains an NVIDIA GPU optimized for specific generative AI inference workloads as well as specialized software:

• NVIDIA L4 for AI Video can deliver 120x more AI-powered video performance than CPUs, combined with 99% better energy efficiency. Serving as a universal GPU for virtually any workload, it offers enhanced video decoding and transcoding capabilities, video streaming, augmented reality, generative AI video and more.
• NVIDIA L40 for Image Generation is optimized for graphics and AI-enabled 2D, video and 3D image generation. The L40 platform serves as the engine of NVIDIA Omniverse, a platform for building and operating metaverse applications in the data center, delivering 7x the inference performance for Stable Diffusion and 12x Omniverse performance over the previous generation.
• NVIDIA H100 NVL for Large Language Model Deployment is ideal for deploying massive LLMs like ChatGPT at scale. The new H100 NVL with 94GB of memory with Transformer Engine acceleration delivers up to 12x faster inference performance at GPT-3 compared to the prior generation A100 at data center scale.
• NVIDIA Grace Hopper for Recommendation Models is ideal for graph recommendation models, vector databases and graph neural networks. With the 900 GB/s NVLink-C2C connection between CPU and GPU, Grace Hopper can deliver 7x faster data transfers and queries compared to PCIe Gen 5.

The platforms’ software layer features the NVIDIA AI Enterprise software suite, which includes NVIDIA TensorRT, a software development kit for high-performance deep learning inference, and NVIDIA Triton Inference Server, an open-source inference-serving software that helps standardize model deployment.

Latest Insights: What Techniques Will Deliver for Measuring Attention in 2023?

Early Adoption and Support

Google Cloud is a key cloud partner and an early customer of NVIDIA’s inference platforms. It is integrating the L4 platform into its machine learning platform, Vertex AI, and is the first cloud service provider to offer L4 instances, with private preview of its G2 virtual machines launching today.

Two of the first organizations to have early access to L4 on Google Cloud include: Descript, which uses generative AI to help creators produce videos and podcasts, and WOMBO, which offers an AI-powered text to digital art app called Dream.

Another early adopter, Kuaishou provides a content community and social platform that leverages GPUs to decode incoming live streaming video, capture key frames, optimize audio and video. It then uses a transformer-based large-scale model to understand multimodal content and improve click-through rates for hundreds of millions of users globally.

“Kuaishou recommendation system serves a community having over 360 million daily users who contribute millions of UGC videos every day,” said Yue Yu, senior vice president at Kuaishou. “Compared to CPUs under the same total cost of ownership, NVIDIA GPUs have been increasing the system end-to-end throughputs by 11x and reducing latency by 20%.”

D-ID, a leading generative AI technology platform, elevates video content for professionals by using NVIDIA L40 GPUs to generate photorealistic digital humans from text — giving a face to any content while reducing the cost and hassle of video production at scale.

AiThority: How Generative AI is Transforming Audio Content

“L40 performance was simply amazing. With it, we were able to double our inference speed,” said Or Gorodissky, vice president of research and development at D-ID. “D-ID is excited to use this new hardware as part of our offering that enables real-time streaming of AI humans at unprecedented performance and resolution while simultaneously reducing our compute costs.”

Seyhan Lee, a leading AI production studio, uses generative AI to develop immersive experiences and captivating creative content for the film, broadcast and entertainment industries.

“The L40 GPU delivers an incredible boost in performance for our generative AI applications,” said Pinar Demirdag, co-founder of Seyhan Lee. “With the inferencing capability and memory size of the L40, we can deploy state-of-the-art models and deliver innovative services to our customers with incredible speed and accuracy.”

Cohere, a leading pioneer in language AI, runs a platform that empowers developers to build natural language models while keeping data private and secure.

“NVIDIA’s new high-performance H100 inference platform can enable us to provide better and more efficient services to our customers with our state-of-the-art generative models, powering a variety of NLP applications such as conversational AI, multilingual enterprise search and information extraction,” said Aidan Gomez, CEO at Cohere.

Read: How AI NFTs Are Unlocking the Democratization of the Digital Economy

[To share your insights with us, please write to sghosh@martechseries.com]

The post NVIDIA Launches Inference Platforms for Large Language Models and Generative AI Workloads appeared first on AiThority.

OctoML released a major platform expansion to accelerate the development of AI-powered applications by eliminating bottlenecks in machine learning deployment. This latest release enables app developers and IT operations teams to transform trained ML models into agile, portable, production-ready software functions that easily integrate with their existing application stacks and DevOps workflows.

One of the biggest challenges in enterprise software development today is building reliable and performant AI-powered applications. The problem is 47 percent of fully trained ML models never reach production, and the rest take an average of 12 weeks to deploy. Model deployment is hindered by dependencies between ML training framework, model type, and required hardware at each stage of the model lifecycle. To break this cycle, users need a way to abstract out complexity, strip away dependencies, and deliver models as production-ready software functions.

Recommended AI News: HP Unlocks Possibilities for Gamers to Play Anywhere, Longer, and Better

“AI has the potential to change the world, but it first needs to become sustainable and accessible,” said Luis Ceze, CEO, OctoML. “Today’s manual, specialized ML deployment workflows are keeping application developers, DevOps engineers and IT operations teams on the sidelines. Our new solution is enabling them to work with models like the rest of their application stack, using their own DevOps workflows and tools. We aim to do that by giving customers the ability to transform models into performant, portable functions that can run on any hardware.”

Models-as-functions can run at high performance anywhere from cloud to edge, remaining stable and consistent even as hardware infrastructure changes. This DevOps-inclusive approach eliminates redundancy by unifying two parallel deployment streams—one for AI and the other for traditional software. It also maximizes the success of the investments that have already been made in model creation and model operations.

The new OctoML platform release enables customers to work with existing tools and teams. Intelligent functions can be leveraged with each user’s unique combination of model, development environment, developer tools, CI/CD framework, application stack and cloud—all while meeting cost and performance SLAs.

Recommended AI News: VMware Enhances Its Unique Lateral Security for Multi‑Cloud

Key platform expansion features include:

• Machine Learning for Machine Learning capabilities—Automation detects and resolves dependencies, cleans and optimizes model code, accelerates and packages the model for any hardware target.
• OctoML CLI provides a local experience of OctoML’s feature set and integrates with SaaS capabilities to create accelerated hardware-independent models-as-functions.
• Comprehensive fleet of 80+ deployment targets—in the cloud (AWS, Azure and GCP) and at the edge with accelerated computing, including GPUs, CPUs, NPUs from NVIDIA, Intel, AMD, ARM and AWS Graviton—used for automated compatibility testing, performance analysis and optimizations on actual hardware.
• Performance and compatibility insights backed by real-world scenarios (not simulated) to accurately inform deployment decisions and ensure SLAs around performance, cost and user experience are met.
• Expansive software catalog covering all major ML frameworks, acceleration engines such as Apache TVM, and software stacks from chip makers.
• NVIDIA Triton Inference Server is packaged as the integrated inference serving software with any model-as-a-function generated by the OctoML CLI or OctoML platform.

Combining NVIDIA Triton with OctoML enables users to more easily choose, integrate, and deploy Triton-powered inference from any framework on mainstream data center servers.

“NVIDIA Triton is the top choice for AI inference and model deployment for workloads of any size, across all major industries worldwide,” said Shankar Chandrasekaran, Product Marketing Manager, NVIDIA. “Its portability, versatility and flexibility make it an ideal companion for the OctoML platform.”

“NVIDIA Triton enables users to leverage all major deep learning frameworks and acceleration technologies across both GPUs and CPUs,” said Jared Roesch, CTO, OctoML. “The OctoML workflow extends the user value of Triton-based deployments by seamlessl

Recommended AI News: Salesforce Expands Social Commerce Offerings, Connecting Merchants with TikTok

[To share your insights with us, please write to sghosh@martechseries.com]

The post OctoML Unveils New Platform to Deliver DevOps Agility to Machine Learning Deployment appeared first on AiThority.

SupportLogic, the world’s first support experience platform, announced the availability of its full SupportLogic SX platform and applications. This announcement comes as more and more companies identified that a proactive support experience is a critical element of a successful customer experience (CX) strategy.

The SupportLogic SX platform is designed to empower the transformation of support organizations into more intelligent and proactive teams providing an elevated support experience. SupportLogic SX includes a number of value-driving AI-powered applications, solving critical issues facing support organizations today, including:

• SX Prevent: The core of the SX platform, providing AI signal extraction to power myriad predictive and actionable insights gleaned from support interactions to prevent “fire drills” and drive more proactive support.
• SX Predict: A customer escalation prediction and prevention toolset which improves customer satisfaction (CSAT) while reducing costly escalations.
• SX Elevate: Agent management, coaching and quality monitoring to drive improved employee satisfaction and increased retention.
• SX Retain: Customer health scoring and churn prediction to provide support and customer success teams the tools they need to provide proactive, standout experiences.

“Positive customer experiences cannot exist without a great support experience and support employees play a key role in delivering that,” said Krishna Raj Raja, founder and CEO of SupportLogic. “SupportLogic SX elevates both employee and customer experience in a single package. And SupportLogic provides valuable insight that feeds the entire business, from customer success, product teams and even sales and marketing, so businesses can protect and grow customer revenue.”

Recommended AI News: Identity Automation Announces Enhanced RapidIdentity Cloud Support Plans with 24/7 Access to US-Based Specialists

Enhancements and New Features

New additions to the SX portfolio include enhanced case assignment and escalation capabilities, as well as further enriching CRM tools like Salesforce with SX sentiment data to drive deeper customer insights and more informed decisions.

Enhanced Case Assignment

When users of Intelligent Case Assignment in SX Predict are assigning a case to an agent based on our AI-powered recommendations, they now have more choices:

• Recommended: See a stack-ranked list of agents ordered by their suitability to work on this case, powered by SupportLogic’s powerful ML model. Restrict recommendations to just agents working on the queue that the case was assigned to, or expand the list to include multiple queues.
• Agent Search: Managers can now search for specific agents across the support team, even those not under their management, to identify best fit and availability for a given case to ensure a consistent and positive customer support experience.
• Route to a Different Queue: This is used when a case has been mistakenly associated with the wrong queue, or if queues are organized by shifts and the case must be transferred to a different team at the end of the shift.

“SupportLogic has made a real impact on the effectiveness of our support operations, and one major addition has been the ability to immediately identify the best agent to take on any particular case at any time,” said Patrick Martin, VP of Technical Support at Coveo and featured speaker at SX Live. “The impact that intelligent case assignment has had on our efficiency, is a 54% improvement in median time to case resolution.”

Recommended AI News: Platform9 5.5 Release Improves Developer Productivity, Simplifies Cloud-Native Operations

Escalations by Customer Groups

In SX Prevent, cases can be grouped together under a single customer account to better understand account health, and cases in any escalation-related state (Likely to Escalate, Escalation Requested, Escalated, Previously Predicted) are now listed in a single table, making it easy to identify all cases from an account that need your attention.

Sentiment Scores in Salesforce

SX Predict and SX Retain now provide sentiment scores for each case to be updated in your Salesforce instance at regular intervals, throughout the life of a case, to empower case workers with real-time insights to take the right action at the right time.

Today’s announcement marks the latest evolution of the SupportLogic vision: transforming the role of support with AI-powered insights that help companies improve agent engagement while protecting and growing revenue through an elevated support experience. The company has built upon its pioneering signal extraction methodology to create a portfolio of applications tailored to the precise needs of B2B support organizations.

“The true value of the support experience is finally being understood as a critical piece of any CX strategy,” said Gordana Warga, Director, Global Support, Kustomer at Meta and recent keynote speaker at SX Live. “It’s important that we are seeing tools created that not just make support a lower effort experience for the customer, but also make it easier and more rewarding for support professionals themselves. It’s great to see the community around support experience is strong, and growing.”

Recommended AI News: LivePerson Collaborates with UCSC to Build the Future of Natural Language Processing

[To share your insights with us, please write to sghosh@martechseries.com]

The post SupportLogic Launches SX Platform and Applications to Transform Customer Support appeared first on AiThority.

Illuminate, Inc., provider of a suite of AI powered applications that enable the Patient-Centered Clinical Practice, announces Illuminate ActKnowledge for simplified tracking, surveillance, and follow-up treatment of at-risk patients — whether they are suffering from chronic diseases or have just been identified as having an incidental or critical finding. Illuminate ActKnowledge, as well as the full suite of Illuminate applications will be available for demonstration in the Illuminate booth at this month’s Radiological Society of North America (RSNA) live, in-person conference.

Top AiThority.com Insights: “Bitcoin Has No Intrinsic Value”. Then What Gives Bitcoin Value?

Illuminate ActKnowledge uses AI to help healthcare providers improve resource utilization, patient care and department management and is being used today to monitor patients identified through:

• Comprehensive recommendation management programs
• Incidental findings efforts
• IVC filter-removal programs

And evaluation of a dozen other chronic conditions within an organization’s unique patient population.

“ActKnowledge enables our department to actively manage a large number of call reports for abnormal and incidental findings to ensure that patients are followed up on appropriately. This has improved patient outcomes and standardized the workflows for managing patient follow up and documentation.,” notes Dr. Mark Perry, MD, Radiologist, Oncologic Imaging, “This in turn saves me 30 minutes a day managing these patients and saves at least 16 or more hours a day across our large team of radiologists!”

Browse The Complete News About AI: Mitratech Ushers in New Era of Contract Management

Healthcare organizations can use ActKnowledge and the entire suite of AI powered Illuminate enterprise applications to better realize the promise of Healthcare IT digitization. By unlocking the patient record that may be stored across multiple systems, and instantly accessing actionable intelligence, they can leverage this information access to:

• Improve Patient Outcomes
• Mitigate Risk to Doctors and Departments
• Increase Revenue
• Improve Patient Safety
• Streamline Workflow

Other Illuminate applications include:

1. InSight – Search driven access to any clinical report.
2. PatientView – Patient clinical documentation indexing and viewing.
3. Analytics – Fact-based monitoring of scheduling and resource allocation.

“Nationally, less than 35% of radiology recommendations are followed for a host of real reasons,” states Matt McLenon CEO. “Illuminate leverages the power of modern AI to support the care team on their patient care mission to bring this number to 100%.”

Read More About AI News: Xfinite (XET) Partners with Yieldly for a New Staking Pool

[To share your insights with us, please write to sghosh@martechseries.com]

The post Illuminate Introduces New AI Powered Enterprise Patient Follow-up, Tracking, and Surveillance Tool for Improved Management of At-Risk Patients appeared first on AiThority.

DataRobot unveiled DataRobot AI Cloud, a unified environment built for the next generation of intelligent business. DataRobot AI Cloud serves as a single platform to accelerate delivery of AI to production for every organization. DataRobot also announced new features and capabilities for DataRobot AI Cloud to enhance operations in production and easily build AI-powered applications to deliver critical business insights.

DataRobot AI Cloud is designed for today’s challenges and opportunities facing organizations. The end-to-end platform brings together disparate data and users, spanning expert data scientists to IT operators to business analysts, through enhanced collaboration and continuous optimization across the entire AI lifecycle. Built as a multi-cloud platform, DataRobot AI Cloud enables organizations to run on any combination of public clouds, data centers, or at the edge, with governance to protect and secure your business.

DataRobot AI Cloud delivers unique advantages to organizations:

• Single platform for all users, unifying data scientists, analytics experts, IT and the business
• Single view of all data from any source, any type
• Unified, end-to-end platform across the AI lifecycle
• Clear and trusted business outcomes
• Deployed and operated at scale on any cloud, in the data center, and at the edge
• Modular and extensible, building on existing investments in applications, infrastructure, and IT operations systems

Recommended AI News: Beyond Protocol and Beckett Media Team Up at Industry Summit to Record Collectibles on Blockchain, Allowing for Immersive Experiences

“AI will reshape every industry, every business service, every customer interaction,” said Dan Wright, CEO of DataRobot. “DataRobot AI Cloud captures insights and learnings from years of technological innovation and partnering closely with our customers, enabling users of all skill sets to deliver more models, faster, and drive unprecedented business value with AI.”

Organizations are under growing pressure to transform the volumes of data captured by their systems into valuable insights that drive impact across all levels and lines of business. But the reality is that success with AI often falls short: 87% of organizations struggle with long deployment cycles, according to an upcoming report by Algorithmia.

DataRobot is one of the most widely deployed and proven AI platforms in the market today, delivering over a trillion predictions for leading companies around the world. The company works across all industries and verticals, including a third of the Fortune 50. Customers like Anheuser-Busch InBev (AB InBev) Europe — the world’s largest brewer and owner of Budweiser, Stella Artois, and Corona — rely on DataRobot’s integrated platform for strategic planning and operational decision-making.

Recommended AI News: Cortavo Revolutionizes Virtual Private Server Performance With a Data Center Refresh

“DataRobot’s platform allows us to make better insight-driven business decisions on a global scale,” said Renato Piai, Commercial and Consumer Analytics Director, AB InBev Europe. “Understanding and engaging with our customers and forecasting demand as we look ahead is imperative. DataRobot provides cutting-edge AI technology for AB InBev to remain agile in a complex and rapidly-shifting market.”

This launch includes groundbreaking new features and capabilities to manage AI at scale in production and enable AI-driven decisions across all lines of business, all within a single platform:

• First-of-its-kind Decision Intelligence to make AI accessible to front-line decision makers, including pre-built use case solutions accelerators to quickly get started with the most popular AI use cases, and new capabilities to automate and scale predictions with Decision Intelligence Flows
• Powerful new tools for code-first data science experts, including Composable ML, enhanced cloud-hosted notebooks, and code-centric data pipelines
• Continuous AI and bias monitoring for ML operators to optimize model performance after deployment and ensure fair and unbiased models in production, at scale

“The AI market is rapidly evolving, reflecting the changing needs of customers and increased demands for mission-critical intelligent business services,” said Ritu Jyoti, Group Vice President, Worldwide Artificial Intelligence and Automation Research Practice Global AI Research Lead of IDC. “Our methodology for building and running AI has to meet these needs, to deliver a cloud fabric that connects diverse data and users with enhanced foresight into business decisions, deployed to any environment. DataRobot AI Cloud is uniquely positioned in the scope and scale it offers to meet these evolving opportunities and deliver a foundation for AI in the future.”

Recommended AI News: Bay Alarm Medical and Kami Vision Sign Deal to Bring Vision AI to Eldercare Industry

The post DataRobot Propels Intelligent Business Forward with Launch of “DataRobot AI Cloud” appeared first on AiThority.